proceedings book

CONTENTS ___________________________________________________________________________________________________

0007-THE INFLUENCE OF OCCUPANT’S BEHAVIOUR IN A HIGH PERFORMING BUILDING. .......................................................................................................................................................1-9 Valentina Fabi1, Simona D’Oca1 , Tiziana Buso1 and Stefano Corgnati2 0012-AN EXPERIMENTAL INVESTIGATION ON PRESSURE DROP CHARACTERISTICS OF ALUMINUM FLEXIBLE AIR DUCT UNDERCOMPRESSION AND BENDING EFFECTS-II ........................................................................10-18 Atilla Bıyıkoğlu 1, Ümit Arı2 and Betül Başkaya3 0014-CASE STUDY: A NET ZERO ENERGY BUILDING BASED ON HEAT PUMP TECHNOLOGY ............................................................................................................................................19-27 Bart Van Reeth1, Hiroshi Aihara1 0019-CLIMAMED 2013 NET ZERO ENERGY USE IN BUILDINGS ..................................................28-35 Lawrence Chee, -Ahmet Akcakaya, Cevat Erdogan 0020-HYBRID HVAC SYSTEMS AND EQUIPMENT OPTIMIZATION IN NET-ZERO ENERGY BUILDINGS ................................................................................................................................36-48 Birol Kılkış, Fellow ASHRAE 0021-TESTING A SIMPLIFIED BUILDING ENERGY SIMULATION PROGRAM VIA BUILDING ENERGY SIMULATION TEST (BESTEST) ..................................................................... 49-57 Gülden Gökçen Akkurt1, Cem Doğan Şahin1, SavaşTakan1, Zeynep Durmuş Arsan1 0024-INCLUDING THE BUILDING ENERGY PERFORMANCE CONSULTANCY TO THE INTEGRATED BUILDING DESIGN PROCESS: THE INDUSTRIAL BUILDING CASE STUDY IN TURKEY ...................................................................................................58-64 Alpay Akguc1, Gozde Gali1 and A. Zerrin Yilmaz2 0025-USING ARTIFICIAL LIGHTING IN HARMONY WITH NATURAL LIGHTING THROUGH AN OFFICE BUILDING EXAMPLE ...................................................................................65-71 Gözde Gali1, Alpay Akgüç1 and A. Zerrin Yılmaz2 0026-FROM HIGH PERFORMING BUILDINGS TO NEARLY ZERO ENERGY BUILDINGS: POTENTIAL OF AN EXISTING OFFICE BUILDING .................................................72-81 Cristina Becchio1, Stefano Paolo Corgnati1 Valentina Monetti1 and Enrico Fabrizio2 0028-INSTRUCTIONS FOR PREPARING THE FULL PAPER TO CLIMAMED 2013 CONGRESS NET ZERO ENERGY USE IN BUILDINGS (TIMES NEW ROMAN BOLD 14 PT, STYLE: TITLE) EVALUATION, IN TERMS OF SOLAR HEAT GAINS, OF THE EFFECTS OF COURTYARD BUILDING SHAPES ON MICROCLIMATE ACCORDING TO DIFFERENT CLIMATIC REGIONS .....................................82-91 Enes Yaşa1, 0030-IGH PERFORMANCE BUILDING SKIN. FROM LOW-ENERGY TO NET ZERO ENERGY BUILDINGS. ............................................................................................................................ 92-101 Fabiana Cambiaso1, Matteo Varioli Pietrasanta2 0031-ENERGY PERFORMANCE AND SUSTAINABLE DEVELOPMENT OF THE NATIONAL CENTER OF COSTUME SCENE (NCCS) IN MOULINS (FRANCE) -AVERAGE 2007-2011 .........................................................................................................102-106 J.Naveteur #1, A.Rousset*2, V.Foray#3

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CONTENTS ___________________________________________________________________________________________________

0032-MUREAUX ADMINISTRATIVE SITE THE FIRST HQE® CERTIFIED OPERATION IN FRANCE: 7 YEARS LATER.................................................................................... 107-113 J.Naveteur #1, S.Barrois*2, G.Aumont#3 0036-NUMERICAL INVESTIGATION OF FLOW PATTERNS AND THERMAL COMFORT IN AIR-CONDITIONED LECTURE ROOMS USING STEADY AND UNSTEADY TECHNIQUES ......................................................................................................... 114-128 Taher M. Abou-Deif1, Essam E. Khalil2 0038-AIR FLOW REGIMES AND THERMAL COMFORT IN A LIVING ROOM ....................... 129-141 Essam E. Khalil ,EsmailM.El-Bialy,and Taher M.Aboudeif 0042-EFFECT OF CITY VENTILATION ON URBAN HEAT ISLAND IN URBAN AREAS:A PARAMETRIC STUDY ........................................................................................................ 142-150 Ayça Gülten1, U.Teoman Aksoy2 and Hakan F. Öztop3 0043-CFD SIMULATIONS OF CHEVRON TYPE PLATE HEAT EXCHANGERS AND VALIDATION WITH EXPERIMENTAL DATA ................................................................................ 151-158 Ece Ozkaya1, Yasin Genc1, Selin Aradag1 and Sadik Kakac1 0046-ENERGY EFFICIENT DATA CENTERS WITH SPECIALIZED SIMULATION TOOLS............................................................................................................................ 159-167 Can Özcan1 0048-INVESTIGATION OF THERMAL COMFORT INSIDE A FURNISHED OFFICE ROOM ACCORDING TO VELOCITY-TEMPERATURE VARIATIONS ..................... 168-176 Firat Karasahin1, Tamer Calisir2 and Senol Baskaya2 0049-ECONOMICAL ANALYSIS OF HEAT RECOVERY VENTILATION FOR DIFFERENT CLIMATE CONDITIONS IN TURKEY ....................................................................... 177-184 İsmail Hakkı Tavman1, Cihan Çangarlı2 0054-INDOOR AIR CONCENTRATIONS OF SYNTHETIC MUSK COMPOUNDS AND THEIR FRACTIONATION BETWEEN GAS AND PARTICULATE PHASES IN A UNIVERSITY CAFETERIA ......................................................................................... 185-189 Çiğdem Özcan, Aysun Sofuoglu, Sait C. Sofuoglu 0056-NUMERICAL SIMULATION OF NATURAL VENTILATION IN A LIVING SPACE FOR DIFFERENT SPACE ORGANIZATION ....................................................... 190-198 Güven Öğüş1, Murat Çakan2 and Gülten Manioğlu3 0057-MODELING ZERO ENERGY BUILDING: TECHNICAL AND ECONOMICAL OPTIMIZATION ...................................................................................................................................... 199-208 Maria Ferrara1-2, Joseph Virgone1, Enrico Fabrizio3, Frédérik Kuznik1, Marco Filippi2 0060-APPLICABILITY OF ONE-DIMENSIONAL RC-MODELSWITH SHORTTIME-STEPFOR RADIANT SYSTEMS IN ENERGY BUILDING SIMULATION PROGRAMS .................................................................................................................. 209-216 Ismael Rodríguez Maestre, Enrique Ángel Rodríguez Jara, Juan Luis Foncubierta Blázquez, Francisco, José Sánchez De La Flor 0064-THE EFFECT OF USING RELIEF DAMPER IN STAIRCASE PRESSURIZATION AS A PART OF POSITIVE VENTILATION SYSTEMS ................................................................... 217-223 Büşra Hepgüzel1

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CONTENTS ___________________________________________________________________________________________________

0065-THE EFFECTS OF SET-POINTS AND DEAD-BANDS OF THE HVAC SYSTEM ON THE ENERGY CONSUMPTION AND OCCUPANT THERMAL COMFORT ..........................................................................................................................224-232 Ongun Berk Kazanci, Bjarne W. Olesen1 0066-A DIRECT NUMERICAL INTEGRATION (DNI) METHOD TO OBTAIN WALL THERMAL RESPONSE FACTORS ........................................................................................233-241 Fernando Varela1, Santiago Aroca2 0072-APPLYING TEARING TECHNIQUES TO IMPROVE BUILDING SIMULATION CPU TIMES ...................................................................................................................242-249 Juan L. Foncubierta Blázquez1, Ismael Rodríguez Maestre1, Juan Fco. Coronel Toro2, Paloma R. Cubillas Fernández 1 0077-ENERGY EFFICIENT HEATING OF BUILDING ....................................................................250-270 Ahmet CAN1, Selin ENGİN2 and Derviş ÖZKAN3 0078-EXPERIMENTAL PERFORMANCE EVALUATION OF PCM THERMAL STORAGE IN A PANEL RADIATOR ..................................................................................................271-278 Türkan ÜÇOK ERKEK1, Ali GÜNGÖR1 0081-A GREEN MUSEUM FOR OTTOMAN ARTEFACTS .............................................................279-286 Jan G Holmberg1, Bengt Kylsberg2 0082-EXPERIMENTAL INVESTIGATION OF HYDROTHERMAL CHARACTERISTICS AND FLOW MALDISTRIBUTION FOR A GASKETED PLATE HEAT ..........................................................................................................................................287-295 Cagin Gulenoglu1*, Selin Aradag1, Nilay Sezer Uzol1, Sadik Kakac1 0084-APPLYING ENERGY STORAGE IN BUILDINGS OF THE FUTURE .................................296-303 Fariborz HAGHIGHAT, Ph.D., P.Eng., Fellow ASHRAE, Fellow ISIAQ Professor 0087-NUMERICAL INVESTIGATION OF THE PERFORMANCE OF A TYPICAL CONDENSER ............................................................................................................................................304-312 L. Berrin Erbay1, Haluk Yılmaz2 0089-NUMERICAL STUDY OF SMOKE DISTRIBUTION IN A HIGH-RISE BUILDING WITH ATRIUM .................................................................................................................313-321 Feyza Çebi1, Yakup Erhan Böke 2 0092-DEVELOPMENT OF AN ASSESSMENT METHODOLOGY FOR LOW ENERGY RESIDENTIAL BUILDINGS DEVELOPMENT IN NORTH OF MOROCCO ...............................322-331 Romani Zaid1,2, Abdeslam Draoui1,and Francis Allard2 0095-SORPTION TECHNOLOGY IN VENTILATION AND AIR-CONDITIONING SYSTEMS ..................................................................................................................................................332-333 0099-DOMESTIC APPLICATIONS USING PEM TYPE FUEL CELLS .........................................334-340 Semih Kurular1,Melike Gülbahçe2,Mustafa Kemal Sevindir3 and Ahmet Yurtseven4 0100-OPTIMAL CONTROL METHOD FOR A HEAT SOURCE SYSTEM CONSISTING OF CENTRIFUGAL CHILLERS .................................................................................341-349 Satoshi Nikaido1, Kenji Ueda1 and Takaaki Miura1

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CONTENTS ___________________________________________________________________________________________________

0101-THE EFFECT OF CHANGING THE DESIGN PARAMETER AND USING VSD CHILLERS ON THE ENERGY CONSUMPTION AND PROJECT ECONOMIC, CASE STUDY FOR A PROJECT UNDER DESIGN. .......................................................................... 350-357 Eng. Rafik Tharwat1, Eng. M.O. Khalil1 and Eng. Mohamed Alaa1 0103-IMPACT OF MOISTURE BUFFERING ON INDOOR CLIMATE FOR MECHANICALLY VENTILATED OFFICES ..................................................................................... 358-366 Amos Ronzino1, Maximilian Neusser2, Paul Wegerer2, Thomas Bednar2, Vincenzo Corrado1 0109-SEASONAL EFFICIENCY, THE NEW ENERGY LABEL OF AIR CONDITIONERS AND HOW TO COMPARE PRODUCTS..................................................... 367-377 Dr. Andaç YAKUT1 0110-HEAT TRANSFER ANALYSIS OF THE ADSORBENT BED OF A THERMAL WAVE CYCLE USING SILICA GEL.............................................................................. 378-386 Ahmet Çağlar1, Cemil Yamalı2 0111-PERFORMANCE ASSESSMENT OF A SOLAR ASSISTED HVAC SYSTEM FOR A MEDIUM-SIZED OFFICE BUILDING IN LARNACA, NORTH CYPRUS ..................................................................................................................................... 387-396 Özgür Bayer1,Doğan Mert Güldiken2 0114-A SIMPLIFIED HOURLY MODEL FOR ENERGY CHARACTERISATION OF TRANSPARENT ACTIVE FAÇADES. A COMPARISON BETWEEN SIMULATION RESULTS AND MONITORED DATA .................................................................................................. 397-405 Lorenza Bianco, Ylenia Cascone, Alice Gorrino, Vincenzo Corrado, Valentina Serra 0123-A SIMPLIFIED CALCULATION METHODOLOGY FOR CONTROLLED NATURAL VENTILATION ................................................................................................................... 406-414 Tobias Schulze1, Ursula Eicker2 and Zerrin Ayşe Yılmaz 1 0129-THEORETICAL ANALYSIS OF TRANSIENT RESPONSE OF COLD BUILDINGS DURING THE HEATING UP PERIOD ......................................................................... 415-421 Mehmet Emin ARICI1and Birkut GÜLER2 0132-CONTRIBUTION TO THE THERMAL RENOVATION OF OLD BUILDINGS: NUMERICAL AND EXPERIMENTAL APPROACH FOR CHARACTERIZING A DOUBLE WINDOW ...................................................................................... 423-429 Mohamed El Mankibi1, Richard Cantin1, Riccardo Issoglio1, 0133-A SENSITIVITY ANALYSIS OF THE SHADING FACTOR FOR BUILDING ENERGY PERFORMANCE ................................................................................................................... 430-438 Ylenia Cascone1, Alfonso Capozzoli1, Vincenzo Corrado1 and Valentina Serra1 0135-PARAMETRIC ANALYSIS AND ADVANCED EXPERIMENTAL DEVICE TO IMPROVE MOROCCAN PUBLIC BUILDINGS ENERGY EFFICIENCY ............................. 439-448 Nezha Elbied1, Mohamed El Mankibi2, Zineb Benmoussa1, Abdellah Bouhouche1, Abdelouahab Bakadiri1, Nour Eddine Mahfoud1, Amine Elkahhak1, Ricardo Issoglio2 . 0136-THE NEED FOR RENEWABLE ENERGY FOR TURKEY AND THE LATEST DEVELOPMENTS IN HEAT PUMPS .................................................................................................. 449-457 Turgay Yay1, Muhammed Mehdi Taşyüz2

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CONTENTS ___________________________________________________________________________________________________

0137-VALIDATION OF THE STOCHASTIC MODELS TO GENERATE ACTUAL OCCUPANCY BEHAVIOR SCHEDULES FOR ACCURATE PREDICTION OF ENERGY CONSUMPTION ....................................................................................................................458-466 GUERNOUTI Sihem1, LE GUEN Solenn2, El MANKIBI Mohamed3 and HUMBERT Myriam1 0140-COMPUTATIONAL INVESTIGATION OF TURBULENT AIR FLOW IN A VENTILATED ROOM ............................................................................................................................467-474 Hıfzı Arda Ersan1 and Erhan Pulat2 142-ON THE ESTABLISHMENT OF CLIMATIC ZONES IN TURKEY WITH REGARD TO THE ENERGY LABELLING FOR AIR CONDITIONERS ..........................474-485 H. Toros*, A. Deniz*, S. İncecik* and U. Sertan** 0143-THE INFLUENCE OF THERMAL BRIDGES ON THE BUILDING ENERGY PERFORMANCE ...................................................................................................................486-494 Alfonso Capozzoli1, Alice Gorrino1, Vincenzo Corrado1, Nunzio Cotrufo2 and Roberto Sora3 0146-DESIGN AND VERIFICATION OF A ZERO ENERGY BUILDING: EVALUATION OF REAL ENERGY PERFORMANCE AND COMPARISON WITH A TAILORED CALCULATION ................................................................................................495-503 Alfonso Capozzoli1, Luca Berra2, Roberto Russo3 and Vincenzo Corrado1 0152-SECURING THERMAL COMFORT IN MODERN OFFICE BUILDINGS ..........................504-510 Assoc. Prof. Dipl. Ing. Mária Budiaková, PhD. 0153-ENERGY PERFORMANCE OF SCHOOL BUILDINGS: FROM ENERGY CERTIFICATES TO BENCHMARKING ............................................................................................511-519 Luísa Dias-Pereira1, Hermano Bernardo2 and Manuel Gameiro da Silva1 0154-PARAMETRIC ANALYSIS AND OPTIMIZATION OF LOW TEMPERATURE ORGANIC RANKINE CYCLE SCENARIOS ......................................................520-530 Mustafa Öz and Atilla Bıyıkoğlu1 0156-A STUDY ON PERFORMANCE ANALYSIS OF VAPOR COMPRESSION-ADSORPTION HYBRID REFRIGERATION CYCLE ........................................531-539 Gamze Gediz Ilis1, Gizem Arslan2, Moghtada Mobedi2, and Semra Ülkü3 0158-EXERGETIC PERFORMANCE ASSESSMENT OF A BUILDING WITH A SOLAR-ASSISTED HEAT PUMP USING ROOF-INTEGRATED SOLAR COLLECTORS ..........................................................................................................................540-548 Arif Hepbasli1,* and M. Tolga Balta2 0159-HUMIDIFICATION SYSTEMS THAT SAVE ENERGY AND REDUCE CO2 EMISSIONS ......................................................................................................................................549-553 Marc Briers 0160-MODELLING AND DESIGN OF A HYBRID SOLAR + MICRO-COGENERATION SYSTEM FOR WATER HEATING .......................................................................................................554-562 José M. Cejudo López, Francisco Fernández Hernández, Fernando Domínguez Muñoz, Antonio Carrillo Andrés 0161-MODEL OF DESICANT VENTILATED FAÇADE FOR OUTDOOR AIR CONDITIONING VENTILATION ........................................................................................................563-571

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CONTENTS ___________________________________________________________________________________________________

0162-ANALYSIS OF THE OVERHEATING AND STAGNATION PROBLEMS OF SOLAR THERMAL INSTALLATIONS ............................................................................................... 572-580 Francisco J. Aguilar1, Pedro V. Quiles1 and Simón Aledo2 0166-ENHANCEMENT OF FLAT PLATE SOLAR COLLECTOR PERFORMANCE THROUGH THE USE OF WIRE-COIL INSERTS ............................................................................. 581-587 Alberto García, Ruth Herrero Martín and José Pérez García 0167-OPERATION AND ENERGY EFFICIENCY OF A HYBRID AIR CONDITIONER SIMULTANEOUSLY CONNECTED TO THE GRID AND TO PHOTOVOLTAIC PANELS .................................................................................................. 588-596 Pedro V. Quiles1, Francisco J. Aguilar1 and Simón Aledo2 0168-IMPLEMENTATION OF THE EUROPEAN DIRECTIVE ON ENERGY CERTIFICATION IN SPAIN ................................................................................................................. 597-603 Julio Cano Guillamón 0172-RESEARCH ON THE CONDENSATE CARRYOVER PHENOMENA IN FINNED-TUBE EVAPORATOR OF AN AUTONOMOUS UNIT ..................................................... 604-611 Miguel Zamora1, Natividad Molero1, José Miguel Corberán2, Emilio Navarro2 0174-CAPABILITIES OF A SOLAR HEAT PUMP FOR DOMESTIC HOT WATER PRODUCTION IN A MEDITERRANEAN CLIMATE ...................................................... 612-620 José Antonio Fdez. Benítez1, Carlos Corrochano1, Adriana Ortiz1, Javier Muñoz1, Diego Fernández2, Juan Antonio Mardomingo2. 0177-SOLAR PHOTO-HEAT VOLTAIC AND THERMAL (PHVT) SYSTEM IN SLOW CITIES..................................................................................................................................... 621-630 Birol Kılkış and Levent Çolak 0178-IS CO2 A GOOD MEASURE FOR INDOOR AIR QUALITY AND VENTILATION? ......... 631-638 Bjarne W. Olesen 0179-FIELD EVALUATION OF THE PERFORMANCE OF A RADIANT HEATING/COOLING CEILING PANEL SYSTEM .......................................................................... 639-647 Rongling LI1*, Togo Yoshidomi1, Ryozo Ooka2, Bjarne W. Olesen3 0181-STUDY OF CHILDREN EXPOSURE TO PARTICULATE MATTER INDOOR AIR INSCHOOL CLASSROOMS .......................................................................................................... 648-655 J.M.Garcia1, R. Cerdeira1, N. Tavares 1, L.M.R. Coelho 1, M.G. Carvalho 2,3 0182-NEW EUROPEAN ENERGY EFFICIENCY LABELING STANDARDS FOR AIR HANDLING UNITS................................................................................................................ 656-662 Handan Öncül Özgen 0184-AN APPROACH FOR ENERGY EFFICIENCY AND SUSTAINABILITY IN EMERGENCY ARCHITECTURE: EVALUATION OF POST-DISASTER SHELTERS IN TURKEY ........................................................................................................................ 663-670 Santiago Brusadin Viola 0185-BUILDING PERFORMANCE OF THE GREEN CERTIFIED BUILDINGS:A CASE STUDY IN TURKEY AND IN THE NETHERLANDS FOR EVALUATING GREEN BUILDING CERTIFICATION PRACTICES .................................. 671-679 Özden Demir1 (Author)

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0187-FEASIBILITY OF HVAC SYSTEMS IN TERMS OF ENERGY .............................................680-688 Ahmet Arısoy, C. Demirkesen, E. Poyraz, S. Koçtürk and S. Kaçaran

POSTERS 0009-INCREASING THE ENERGY EFFICIENCY OF A 600 BED HOSPITAL BY ADJUSTING BOILER ROOM MANAGEMENT AND CONTROL .................................................690-707 J. San José Alonso1, F. Castro Ruiz1, J.M. Villafruela Espina1 and J.C. Fraile Marinero2 0015-POST OCCUPANCY EVALUATION OF DAYLIGHTING IN UNIVERSITY CLASSROOMS IN CONSTANTINE (ALGERIA): OBJECTIVE AND SUBJECTIVE ASSESSMENT ................................................................................................................708-715 Sarah Benharkat1 and Djamila Rouag-Saffidine2 0017-BENEFITS AND WELL-BEING PERCEIVED IN SHADE BY PEDESTRIAN IN VEGETATED URBAN SPACE IN PERIODS OF HEAT STRESS ..............................................716-724 Samira Louafi ep Bellara1, Saliha Abdou1 0037-DESIGN AND ANLYSIS OF A BANK İN ANTALYA BY SOLAR ASISTED ABSOBTION AIR CONDITIONING ....................................................................................................725-735 Eskişehir Osmangazi University Mechanical Engineering Department Eskişehir 0085-ENERGY EFFICIENCY OF RESIDENTIAL AIR CONDITIONERS / SEASONAL EFFICIENCY ............................................................................................................................................736-742 Ugur Sertan 0097-INVESTIGATION OF HEAT TRANSFER AUGMENTATION IN A TUBE WITH DIFFERENT MODIFIED TWISTED TAPE INSERTS UNDER THE SAME CONDITIONS ...........................................................................................................................................743-752 Gokhan Gurlek*, Necdet Ozbalta 0098-COOLING SYSTEM OF EDDY CURRENT BRAKE SYSTEM AND WASTE HEAT RECOVERY .................................................................................................................................753-759 Melike GÜLBAHÇE1, SemihKURULAR1 ,M.Onur GÜLBAHÇE2 , Mustafa Kemal SEVİNDİR1 0107-ENERGY CONSERVATION MEASURES IN LEED HOSPITAL AIR CONDITIONING SYSTEM ...................................................................................................................760-765 Prof. Hari Sankar Dalal 0119-THEORETICAL STUDY OF THE PERFORMANCES OF A THERMO CHEMICAL HEAT STORAGE BED ....................................................................................................766-774 Syntia Metchueng Kamdem1, 2, Kévyn Johannes2, Frédéric Kuznik2, Hassan Bouia1, Jean Jacques Roux2 0120-A COMPARISON IN TERMS OF COP VALUES OFREFRIGERANTS USED IN HVAC ........................................................................................................................................775-781 Alişan Gönül1,M. Kemal Sevindir2, Esen Öztürk3 0147-THE STUDY OF ALTERNATIVE REFRIGERANT GAS R152A AS MOBILE AIR CONDITIONING REFRIGERANT REPLACEMENTS .....................................................................782-790 Kadir Bilen1, Ahmet Tahir Kalkışım2, Ismail Solmuş1

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0180-A STUDY ON THE OPTIMUM INSULATION THICKNESS AND ENERGY SAVINGS OF A RADIANT HEATING PANEL MOUNTED WALL FOR VARIOUS PARAMETERS......................................................................................................................................... 791-797 B.Burak Kanbur1,2, S.Ozgur Atayilmaz2, Aliihsan Koca1,2, Zafer Gemici1 and Ismail Teke2

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CLIMAMED VII. Mediterranean Congress of Climatization, Istanbul, 3-4 October, 2013 TURKISH SOCIETY OF HVAC & SANITARY ENGINEERS ___________________________________________________________________________________________________

THE INFLUENCE OF OCCUPANT’S BEHAVIOUR IN A HIGH PERFORMING BUILDING. Valentina Fabi1, Simona D’Oca1 , Tiziana Buso1 and Stefano Corgnati2 1

Politecnico di Torino, Italy

Corresponding email: [email protected]

SUMMARY Green buildings are now at the forefront of building research and climate change mitigation scenarios. The successful delivery of green buildings requires balancing energy and resource efficiency while providing a comfortable and healthy environment. Since the success of a high-performance building (HPB) depends on how it is designed, built, and managed, occupant comfort and behaviour can have a significant impact on the green building performance. Individual occupants and the choices they make (opening and closing windows, turning up and down the thermostats, etc.) directly affect the amount of energy used in every type of building. This paper focuses on the possible profiles of occupant behavior and their resulting effects on energy consumption in a high performing building. Stochastic profiles of window opening and indoor temperature preferences were implemented in a dynamic building energy simulation tool. The study mainly addresses at the evaluation of the impact of probabilistic occupants profiles on energy consumption in HPB comparing the obtained results with a regular deterministic use of the building energy simulation tool. INTRODUCTION High-performance buildings are designed to save water and energy, reduce waste, improve air quality, and increase occupant health and productivity. They represent a holistic approach to building design that considers all aspects of the built environment as part of a system. High performance buildings maximize operational energy savings; improve comfort, health, and safety of occupants and visitors; and limit detrimental effects on the environment. Starting with a series of energy efficient projects that are reaping meaningful energy and cost savings along with important associated benefits, buildings like the Federal Courthouse in Denver, a new U.S. Environmental Protection Agency facility in North Carolina, or the Natural Resources Defense Council in New York City, boast numerous value-added features known as ‘green,’ ‘sustainable,’ make them “high performance”. From project outset, these building owners, designers, and contractors actively committed to maximizing operational energy savings, providing healthy interiors, and limiting the detrimental environmental impacts of the buildings’ construction and operation. As a consequence, they have also leveraged some compelling side benefits. The building occupants enjoy an improved sense of health and wellbeing that can be attributed to improved daylighting, quality high-efficiency lighting, and better indoor air. Some of these building owners have reported tangible increases in worker productivity. The most important parts of that system are the building occupants. Without occupants' support of a building's high-performance attributes, even the most well designed

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building can fail to measure up to its high-performance potential. Research shows that if occupants don't act in a way that supports design intent, performance standards can be compromised. Although occupants are critical to the success of a high-performing building, they are often the missing piece of the sustainability puzzle because of the complexity in addressing human behaviour. Building simulation tools are based on heat transfer and thermodynamic equations, and typically model human actions (operation of lights, blinds and windows) basing on predefined fixed schedules or predefined rules (the window always open if the indoor temperature exceeds a certain limit). These tools often reproduce building dynamics using numerical approximations of equations modelling only deterministic (fully predictable and repeatable) behaviours. Models of human behaviour are on the other hand based on statistical algorithms that predict the probability of an action or event. For example, to face this topic, different assumptions to model the occupants’ window-opening behaviour are made in literature: assumptions are the defined schedule window opening based on occupancy or the expectation that window opening to be controlled by temperatures, humidity, wind, rain or to produce an established airflow rate, supposing the occupants use the windows to achieve the design ventilation rates [1]. These assumptions do not necessarily represent the occupants’ actual behaviour and for this reason, it is necessary to use algorithms for users interactions with the building control systems based on field investigations in real buildings. This paper focuses on the a probabilistic simulation of occupant behavior and their resulting effects on energy consumption in a selected high performing building. Stochastic profiles of window opening and indoor temperature preferences were implemented in a dynamic building energy simulation tool for the assessment of the impact of probabilistic occupants profiles on energy consumption in HPB. The evaluation is carried on by comparing the obtained results of the probabilistic occupant profiles with a regular deterministic use of the building energy simulation tool. PROBABILISTIC APPROACH A way to consider the importance of occupant behaviour in the energy simulations is explored in this work. A probabilistic approach is adopted in the simulations in order to investigate how user patterns probabilistically defined, influence energy consumptions of a high performing building, improving accuracy of calculated energy performance in buildings simulation tools. The goal is to determinate how occupant behavioural patterns describing user interaction with the set-points controls and with windows opening affect the building energy performance prediction. A high performing building has been chosen as case study for the evaluation of energy performances: it has been simulated as deterministic first in order to compare the results once the probabilistic profiles has been implemented in the building energy simulation software as well. Based on a previous research [2] the information about occupants’ interactions with controls (windows and thermostatic radiator valves) are set. Through the statistical software R, it was possible to determinate the most window opening behavior and temperature setpoint preferences influencing factors within indoor climate variables and outdoor weather conditions. Occupant profiles were assessed on the base of the frequency of the interactions with controls, and named as active, medium or passive users. The probability of opening or closing the windows and turning up and down the heating set-point was interfered by

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logistical regression. Three different user behavioural patterns according to window opening and closing behavior and three other different user behavioural patterns according to the heating set-point changes were defined. They have been implemented in IDA ICE (Indoor Climate and Energy) [3] was used and the equation describing the probability of user interacting with the control of the indoor environmental quality. The event taking place is integrated in the program. To be able to compare energy calculations results, simulations were conducted maintaining constant: location, building construction and thermal zone heating settings. Moreover a probabilistic distribution, instead that a single value, was preferred as a representation of energy consumptions. To do this, distribution curves were calculated with the use of twenty different lists of random numbers to associate to both the probability of opening and closing the windows. Beside energy consumptions, also indoor climate quality of the built environment needs to be taken into account and air change rates represented. Probabilistic distributions of ventilation losses are evaluated for different user types. The probabilistic model In order to assess the influence of occupant behavior on building energy performances, this work uses the results of a field monitoring campaign in Danish dwelling (Andersen et al., 2011), containing both indoor and outdoor environmental variables and occupants’ control interactions. In this study the results of statistical data analysis carried on in other studies [4; 5] are applied as probabilistic input for the building energy simulations. Window opening and closing and set-point dependency on indoor and outdoor environment were deduced by means of logistic regression with interaction between variables accordingly to the following equation:

= a + b1 · x1 + b2 · x2 + … + bn· xn + c12 ·x1·x2 + c13 ·x1·x3 + …

(1)

A model that predicts the degree of opening and the size of the set-point change was inferred using linear regression. The results provide the possibility of defining behavioural models of window and radiator thermostatic valves use to be implemented in simulation tool for energy simulations. SIMULATIONS The aim of this study is to switch from a deterministic approach of building energy simulation toward a probabilistic one that takes into account the occupants presence and interactions with the building and systems. In particular the attention focuses on the interaction between users and window opening behavior and set-point preferences. Results of the statistical analysis provide the possibility of defining behavioural models to be implemented in simulation tool for energy simulations. The reference building: “The Solaire” In order to investigate the effect of occupants behaviours both on energy consumption and indoor climate quality simulations were ran in a residential high performing building: first it was simulated to evaluate the building energy performance in using schedules and set-points predefined (deterministic reference building). The chose case study is named “The Solaire” (figure 1, left). The Solaire at 20 River Terrace is a 27-story, 293-unit, glass-and-brick residential tower in Battery Park City, a planned

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residential and commercial neighborhood built on landfill bordering the west side of New York City's financial district and directly adjacent to the site of the former World Trade Center. It is the first building designed in accordance with new environmental guidelines instituted in 2000 by the Battery Park City Authority (BPCA), the government entity that has overseen the development of Battery Park City since 1969.

Figure 1. The Solaire building in New York (left) and the simulated thermal zone (right). The Solaire was designed to consume 35% less energy, reduce peak demand for electricity by 65%, and require 50% less potable water than a conventional, residential high-rise building. An integrated array of photovoltaic panels generates 5% of the building's energy at peak loading. The building incorporates an advanced HVAC system, fueled by natural gas and free of ozone-depleting refrigerants. Multi-level humidification and ventilation systems supply filtered fresh air to each residential unit. Daylighting was maximized and balanced with the thermal envelope. High-performance casement windows were used throughout. An on-site black water treatment and reuse system supplies the cooling tower and the building's toilets with water. More details on the case study could be found in [6]. In order to investigate the effect of occupants behaviours both on energy consumption and indoor climate quality, simulations were ran firstly in a typical dwelling of the residential building (figure 1, right). The dwelling area is 85m2, consisting of a bedroom, a living room and a kitchen. The external wall is facing west, the window area is 4.6 m2 both in the bedroom and in the living room. The thermo physical properties of the opaque components are resumed in table 1. The transparent component has argon fill and its solar and thermal characteristics are: U value: 2.5 W/(m2K); solar transmittance, T= 0.376; SHGH, g= 0.72.

4


Table 1. Thermophysical properties of the opaque components Thickness (cm) 2 0.2 10 15 1 12

Thermal conductivity (W/(m K)) 1 0.2 0.06 2 1 1

Density (kg/m3) 1200 20 20 550 1 1800

Specific heat capacity (J/(kg·K)) 900 1000 1030 105 1000 840

1.8

0.02 0.12 0.02

1 0.036 1

1200 1200 1200

900 100 900

0.38

2 4 8.5 31 2

0.22 1.83 0.06 0.13 1

0.25

18 5 12 0.2 25 6 0.2

0.13 1.83 0.06 0.2 1 0.04 1

850 2000 20 600 1200 680 2000 20 20 550 150 900

2400 1000 1030 1000 900 1700 1000 1030 1000 105 1030 900

Material

U-value (W/m2)

External wall

Gypsum plaster Waterproof barrier Mineral wool Concrete Gap Brick

0.6

Internal wall

Gypsum plaster Brick Gypsum plaster

Internal ceiling

Paving material Light concrete Mineral wool Concrete Gypsum plaster

External floor

Plastic covering Concrete Mineral wool Waterproof barrier Concrete Mineral wool Gypsum plaster

As internal heat gain, one person was considered present with a house-living schedule at an activity level of 70W/m2 (metabolic activity of 1.2 met). The lighting schedule is connected to the people presence. Furthermore, the light in the room, with an emitted 50W per unit, is automatically switching on if the minimum work plane illuminance is lower than 100 Lux based on the study of the Lightswitch-2002 (Reinhart, 2004); the light, is automatically switched off at an illuminance level of 500 Lux. The location used for the simulation is New York USA and the meteorological data refers to the New York Meteorological Whether Station. RESULTS The deterministic approach At first, the aim was to calibrate the thermal zone in order to obtain the energy consumption provided by the designers’ case study in terms of energy for heating, cooling, electricity and water for domestic usage. Starting from the Battery Park Guide Line and the ASHRAE 62.2 standard indications the following factors were changed: indoor temperature (from 20°C-24°C to 22°C-25°C), air flow (from 0.35-7 L/sm² to 1.5 - 7 L/sm²), relative humidity (from 30%50% to 20%-40%) and boiler efficiency (from 0.8 to 0.6). These simulations are ran in the deterministic standard way that is the common in nowadays approaches. In doing so, evidently, results are calculated on the base of schedules assumptions decided a priori that describe occupancy presence, lighting or equipment: the obtained results of these deterministic simulations are given in Table 1:

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Table 1. Energy consumption for space heating, cooling, lighting and domestic hot water usage for the simulated thermal zone Calibrated thermal zone

Heating (kWh/m2) 193

Cooling (kWh/m2) 64

Lighting (kWh/m2) 15

DHW (kWh/m2) 28

The probabilistic approach A probabilistic approach is adopted to investigate how probabilistic user patterns influence indoor environmental quality and energy consumptions improving accuracy of calculated energy performance in buildings simulation tools. The goal is to determinate user behavioural pattern describing user’s interaction with the controls and in particular with window opening and thermostatic radiator valves. In the occupancy schedule the occupant is considered as always present but since window and heating control are probabilistic in nature, they don’t follow maximum and/or minimum set-point controller. The probability of interacting with windows and adjusting the temperature set-point was calculated based on the logistic regression previously described. Specifically, three behavioural patterns were simulated (Active, Medium and Passive users both for windows use and thermostat set-point adjustment for a total of 9 implemented models). IDA Indoor Climate and Energy, as most simulation programs, is deterministic in nature. Therefore there is a need to translate the probability of an occurring event in a deterministic signal. A way to solve this problem is to compare the given probability to a random number to determine if the event takes place or not. [7;8]As the given probability is the probability of opening the window and switching up/down the thermostatic radiator valves in the next ten minutes, the comparison was made with a random number that change every ten minutes. Results Switching from the deterministic simulation to a probabilistic approach, high variation in energy consumption could be highlighted. In particular, three probabilistic scenarios have been simulated, to evaluate the influence of probabilistic window opening and thermostat setpoint adjustment separately before to implement both the probabilistic controls in IDA ICE. The main results are here presented. Influence of window opening and closing At first, only the probabilistic control on windows has been implemented, maintaining constant the indoor temperature set point at 20°C. The 3 users’ typologies have been simulated 10 times and the obtained results compared to the deterministic scenario, in terms of space heating and cooling energy demand. When implementing behavioural patterns, a significant difference can be appreciated on energy demands for the three different cases. The results displayed in Table 3, show a big discrepancy in heating energy demand with the active users, reaching an increase of about 36% respect to the deterministic standard model. The gap is less in case of space cooling energy demand, consisting of 2.2% maximum increase of medium user typology implementation.

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Table 3. Energy consumption for space heating and cooling, for the simulated users’ typologies and the deterministic standard model.

Influence of set point thermostat The second scenario is represented by the implementation of the probabilistic control of thermostat set-point, maintaining deterministic the control on windows. The 3 users’ typologies have been simulated 10 times as well and the obtained results compared with the deterministic scenario in terms of space heating energy demand, as well. Energy consumption doesn’t linearly increase accordingly to occupants frequency of interaction with set-point controller (Figure 2). The figure shows the trend of the 10 probabilistic simulations: the maximum variation respect to the standard detrministic model is about 6% with passive users. An high discrepancy has been obtained in terms of interaction with thermostat set-point: active users change it more frequently and it results in a wide temperatures range (from 19°C to 26°C), while the medium and passive user types interact less frequently, in particular with the passive user temperatures ranged between 20°-23°C, and for the most of the time the temperature is 22.5°C.

Figure 2. The simulated thermal zone.

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Figure 3. The simulated heating set-point preferences. Influence of window opening/closing and set point thermostat In the third scenario, the control both on window and on thermostat are implemented. Since windows not necessarily comply in the reality with the same thermostat behavioural pattern profile nine different user models were implemented, according to occupant behavioural patterns. The results of the simulations are represented in Figure 4 in terms of requirements for space heating and cooling. The maximum variation with the deterministic model is about 36%, while the maximum variation between the different implemented models is about 40% between active model for windows opening and active model for the thermostat control. In the case of space cooling energy demand, the gap between the deterministic and the probabilistic control on windows and thermostat fall at 8%.

Figure 4. Influence of window opening and set-point thermostat: heating delivered energy

CONCLUSIONS The main goal of this research was to estimate the impact of user control on high performing buildings based on a probabilistic approach. For this purpose, a simulation study on the effects of occupant interactions with windows and the heating control on energy demands has been conducted in a typical dwelling of a residential high performing building. At first, a deterministic approach used nowadays in simulation programs has been applied. Secondly,

8


the probability of opening and closing the windows and switching up/down the set-point temperature on the TRV has been predicted for three different users models for window opening and three for heating set-point preferences (they are named “active”, “medium” and “passive” for both the controls) and implemented in a dynamic building energy simulation software. Three different simulation scenarios are represented, displaying a progressive augmentation in variability. Findings demonstrated that predefined heating set-point preferences and air change rates used as assumption in building energy simulation are far away from actual occupants preferences in buildings. Results of the study highlight significant influences of occupant behaviour on the building energy demands. Energy consumption in the simulated high performing building in which occupants personal control is performed by probabilistic functions, raised up to 36% in comparison to the high performing building where the occupants’ interaction with the controls is regulated in a deterministic way by fixed schedules. The performed study highlights how not to consider human’s interactions with the building and control systems will necessarily lead designers and modellers to an undestimation of the building energy performances. ACKNOWLEDGEMENTS This study was carried out as a part of an international collaboration within the IEA – ECBCS project Annex 53. Moreover, authors wants to thank the Competence Center of Telecom Italia in Polytechnic of Turin for fully supporting the activity. REFERENCES 1.

2.

3. 4.

5.

6. 7.

8.

Rijal HB, Tuohy P, Humphreys MA, Nicol JF. Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings 39 (2007) 823-836. Andersen RV, Olesen BW, Toftum J. Long term monitoring of occupant behaviour and indoor environment in Danish dwelling. Submitted to “International Journal of Building and Environment, May 2009. IDA ICE 4, Manual version: 4.0. EQUA Simulation AB (September 2009). Fabi V., Andersen RV., Corgnati SP., Olesen BW. “A methodology for modelling energyrelated human behaviour: Application to window opening behaviour in residential buildings.” Building Simulation Journal, 2013, DOI information: 10.1007/s12273-013-0119-6. Fabi, V., Andersen RV., Corgnati SP. “Influence of Occupant’s Heating set-point preferences on Indoor Environmental Quality and Heating Demand in Residential Buildings”, HVAC&R Research Journal DOI information:10.1080/10789669.2013.789372. ASHRAE - High Performing Building Magazine, Issue: Sumer 2008. Newsham G, Mahdavi A, Beausoleil-Morrison I, Lightswitch: a stochastic model for predicting office lighting energy consumption, in: Right Light Three, Proceedings of the Third European Conference on Energy Efficient Lighting, Newcastle-upon-Tyne, UK, (1995), pp. 60–66. Reinhart CF. Lightswitch-2002: a model for manual and automated control of electric lighting and blinds. Solar Energy 77 (2004) 15-28.

9


AN EXPERIMENTAL INVESTIGATION ON PRESSURE DROP CHARACTERISTICS OF ALUMINUM FLEXIBLE AIR DUCT UNDER COMPRESSION AND BENDING EFFECTS-II Atilla Bıyıkoğlu 1, Ümit Arı2 and Betül Başkaya3 1

University of Gazi, Ankara Koza-İpek Tedarik Danışmanlık A.Ş., Ankara 3 AFS Boru Sanayi A.Ş., Ankara 2

Corresponding email: [email protected] SUMMARY In this study, the experiments were conducted for the determination of pressure drop characteristics of the non-insulated aluminum-laminated flexible air duct at 8 inch (203 mm) diameter under different compression and bending conditions. The experimental setup was constructed prior to the instructions in ANSI/ASHRAE standard 120–2008 and pressure loss data were measured based on the methodology in the same standard. The experimental data were processed using Power Law Model to form equations representing the pressure drop behavior of the flexible duct. The local loss coefficients were calculated using regression analysis for each bending condition. The results show that under compressed condition, the pressure loss values through the flexible duct with a diameter of 8 inch could be predicted via the empirical pressure loss equations derived using Power Law Model. The measured values of pressure drop fall into the range of 3 and 168 Pa/m for compressed cases, and 1 and 243 Pa for bended cases. The local loss coefficient varies between 0.10 and 1.6 depending on the bending angle. INTRODUCTION Flexible air ducts have been used for applications of heating, ventilation, air conditioning and refrigeration in commercial and residential buildings for years. Flexible rather than rigid air ducts are preferred in many applications due to easiness of construction, applicability in narrow and curved regions, low cost and similar reasons. A flexible air duct is defined as an elastic structure reinforced with spiral steel wire wrapped with a swathe which is formed by combining both metallic and/or non-metallic micron thin layers. Flexible air ducts of different types have a wide range of applications according to its purpose. A variety of solutions may be produced by adjusting the wire pitch and decreasing the gradient/number of coil windings, formounting noise by wrapping an insulation material around a perforated duct, formounting heat loss by wrapping an insulation material around duct. Flexible ducts are successfully applied on air discharge and circulation by allowing the duct structure to bend and compress due to elastic structure. In this study, it was achieved to derive empirical equations in guessing the pressure drop through several bended and compressed

10


conditions. By this way, it was possible to determine the required compression ratio, number of turns and angle of bending to get minimum operational condition for a flexible duct system. LITERATURE SURVEY As a result of detailed search on the subject, it was observed that there are limited numbers of studies on pressure drop through non-metallic flexible air ducts. These studies are reviewed according to historical development of the matter. The first application of flexible ducts has been on circulation of air in coal mines. The flexible ducts used in these early applications have been designed quite different than the contemporary ones. The primary flexible ducts were produced on a mylar core and galvanized metal helical structure by wrapping a flexible plastic at two turns per foot. The leakage experiments on the early flexible ducts were performed by Harris [1] in 1958. He determined leakage coefficients and porosity of the primary version of flexible ducts as a result of experimental study on 25 different types of flexible ducts. However, he did not present any information about the pressure drops through the ducts. The second application in the open literature was a project [2] initiated by General American Research Division (GARD) of General American Transportation Corporation (GATX) as a result of contract with Stanford Research Institute for the Office of Civil Defense Department of U.S. Army to design, fabricate, and test flexible plastic tubing and fittings which would provide a system for handling ventilation air in fallout shelters when used with the shelter ventilator. In this project, the tests were performed at volumetric flow rates between 1300 (37) and 3100 (88) ft3/s (m3/s) to determine the pressure drop characteristics through the elbows with 90o and polyethylene pipes at 20 inch (508 mm) diameter and 4 mil (0.1016 mm) thickness. Fully inflated 20-inch (508 mm) diameter plastic tubing has about three-quarters of the pressure drop of sheet-metal duct. However, the last 50 feet (~15 m) of a plastic duct system, which is not completely inflated, has 1-1/2 to 3 times the pressure drop per foot of fully inflated plastic tubing. The result is that for duct systems over 100 feet (30.5 m) long the pressure drops for sheet-metal and plastic tubing are approximately the same. The earlier methods [3] for the design of duct systems did not include any procedure due to determination of static pressure losses because it had been taken place to use modern flexible ducts in residential buildings after 1965’s. Besides, there was not any published work on the determination of pressure losses through flexible ducts because there hadn’t been any development in duct design methods between the years of 1965 and 1995. After this stagnant period of duct design methods, Air Conditioning Contractors of America (ACCA) published Manual D [4] which includes a friction chart for flexible, spiral wire, helix core ducts, but there are no references available to determine the source of the data included within the chart. Manual D also includes static pressure loss charts for non-metallic flexible duct, but does not include compression rate.

11


A research on flexible ducts and their elbows was also conducted at Integrated Building and Construction Solutions (IBACOS) for the Burt Hill project [5]. IBACOS researchers measured the static pressure drop through straight run flexible duct, flexible duct elbows, and triangular duct board plenum boxes. The straight run duct lengths of 25 ft were tested in fully stretched condition and 10% compressed configurations for diameters of 6", 8", 10", and 12". All testing was conducted with the duct fully supported. The results from the testing showed an increase of pressure loss of 35% to 40% for the relaxed duct work over the fully stretched, with the sheet metal duct experiencing the lowest pressure loss. It was also concluded that the pressure losses associated with the relaxed flexible ductwork had been much greater than the losses associated with the taut flexible ductwork. Total pressure loss measurements were done in Abushakra and co-worker’s studies published in 2001 [6], 2002 [7] and 2004 [8] for flexible ducts having compression ratio up to 30 percent. The tests were designed for the flexible duct applications in residential and commercial facilities. The measurements were repeated for three different flexible duct sizes and compression ratios. The pressure drop through flexible ducts were experimentally investigated at six different diameters, 6,8,10,12,14 and 16 inches, and five different compression ratios, 0, 4, 15, 30 and 45 percent, for ASHRAE by Culp [9] in 2011. The experiments were performed for the cases where the sag is occurred through the core at joist and board supported conditions. Weaver [10] studied the pressure losses in metallic and non-metallic flexible air ducts in 2011. The experiments were done in blow-through direction, under positive pressure in flexible ducts at 6, 8, and 10 inch diameters. The pressure data obtained in this study had been higher values than the ones in ACCA or ASHRAE. DESCRIPTION OF THE PROBLEM In this study, the experiments were performed in blow-through direction, under positive pressure to determine pressure loss characteristics of non-insulated aluminum layered flexible air duct with 35 mm pitch, 10 inch (254 mm) diameter at four different compression ratios, 5, 15, 30, 45 percent, and in fully stretched condition at five different angles of bending, 15, 30, 45, 60 and 90 degrees. EXPERIMENTAL SET-UP An experimental setup was constructed in the research laboratory as presented in Fig. 1. As shown from the figure, the experimental setup consists of four rigid ducts at 254 mm diameter with 22D, 8D, 10D and 4D lengths, four piezometer rings, a flexible air duct with 25D lengths, and a fan. The fan outlet and rigid duct were mounted by a cone-shaped reduction element and rigid channels by a flange. An orifice is placed between the rigid ducts with 8D and 22D length to measure pressure drop which is used in the calculation of mass flow rate of the system.

12


Figure 1. Schematic view of experimental setup

The piezometer rings having equally spaced four sampling holes are placed before and after the orifice to measure pressure. Besides, two more piezometer rings are attached on the rigid ducts connected to the flexible duct inlet and outlet sections to measure the pressure loss through the flexible duct. The samples taken from the sampling holes are sent to pressure transmitter via a single hose which is merged with two hoses from four sampling points. The velocity and mass flow rate calculations were done based on the standard of ANSI/ASHRAE 120-2008 [11] using the orifice placed from the fan exit at 22D distance on the rigid duct. The detailed information about piezometer ring and its connection types can be found in the same standard. A maximum 4 kW powered, having rotational speed of maximum 3100 revolution per minute (rpm), a trademark of S&P, BDB series, double input centrifugal fan was used in the experiments. The pressure losses were measured by TESTO 350 from four piezometer rings that are placed at the inlet and exit of flexible duct and orifice. An adjustable fan drive was used to control the fan revolution. The total length of the system including a 7.5 m length and 254 mm diameter flexible air duct was reached to 24 m. The length of the flexible duct was selected as at least 25D based on the standard of ANSI/ASHRAE 120-2008 [11]. According to the same standard, the measurements were taken at a distance of minimum 11D from the bending location EXPERIMENTAL PROCEDURE The test system shown in Figure 1 is set up, the sample to be tested is connected to the system and then the fan is started. The rotational velocity of the fan is controlled by variable fan drive to reach the test flow rate. After obtaining the test flow rate, the fan is continued to run at the same frequency for a while to reach the flow in a steady state. The measurements are started to be taken after reaching the steady state. The pressure measurements are done from the four points at the inlet/exit of the sample and the orifice by TESTO 350. The measurements are repeated at the mass flow rate required by varying fan frequency. In addition to pressure

13


measurements, the temperature and humidity in the environment and at the inlet of the orifice are measured and used in the calculations of mass flow rate. The pictures of the experimental set-up and flexible ducts for different bending angles are presented in Figures 2 (a) and (b). The views of flexible duct at fully stretched and bended condition, and the pressure measurement connections are shown in Figure 2 (b) and (c).

Figure 2. (a) Experimental setup at bending angle of 60o for fully stretched condition, (b) view from flexible channel at bending angle of 90o and (c) connections of pressure measurement for fully stretched condition. RESULTS The results of this study are presented in four groups; pressure loss measurements, pressure loss coefficient, pressure drop correction factor and empirical equations. In the first section, the collected data in a logarithmic chart (a) from the experiments are presented (b) (c)which includes all the compressed cases. In the second section, after giving the definition of pressure loss coefficient due to bending, the methodology followed in the calculations is introduced. The advantage of using the pressure drop correction factor is supported with a figure. In the final section, the empirical equations are produced to guess the pressure drop behavior of the ducts. PRESSURE LOSS MEASUREMENTS The pressure difference is measured by sending the sample air from the four holes drilled on the rigid duct with a distance of 90o at the inlet and outlet of the flexible duct to the pressure transmitter. The experiments were done on a non-insulated aluminum flexible duct at 203 mm diameter for four different compression case (5, 15, 30 and 45 %) at fully stretched condition. The variation of pressure loss with mass flow rate is presented in Figure 3. As shown in Figure 3, the pressure loss values obtained for fully stretched condition are observed lower than the ones of compressed cases. As the compression ratio and volumetric flow rate increase, it is observed that the pressure losses increase. Besides, the pressure loss data obtained for the compression of 15 % is higher than the ones for the compression of 30%.

14


1000

Pressure loss (Pa/m)

0% 5%

100

15% 30% 45%

10

1

d=203 mm Ø

0.1 100

1000

10000

Volumetric flow rate (m3/h)

Figure 3. Variation of pressure loss with volumetric flow rate for different compression ratios (203 mm) PRESSURE LOSS COEFFICIENT The pressure losses measured and the pressure loss coefficients calculated for the bending cases at a volumetric flow rate of between 300 and 2000 m3/h are presented in Table 1. The pressure loss coefficient due to bending is defined as the ratio of the bending pressure loss to the velocity pressure at the inlet of flexible duct. The detailed information for the calculation of the pressure loss coefficient can be found in the standard of ANSI/ASHRAE 120-2008 [11]. As the bending angle increases, it is observed from Table 1 that the pressure loss coefficient increases. Table 1. Bending pressure losses and loss coefficient ranges* (203 mm, fully stretched) Bending angle (degree)

Bending pressure loss, ∆Pbend (Pa)

Pressure loss coefficient, C (-)

15 30

0.61-34.4

0.10-0.19

3.32-89.8 5.32-133.1 7.57-195.3

0.53-0.52 0.88-0.79 1.39-1.22

9.07-243.4

1.60-1.53

45 60 90 *

Corresponding values for range of volumetric flow rate of 300 and 2000 m3/h

15


PRESSURE DROP CORRECTION FACTOR (PDCF)

Pressure Drop Correction Factor, PDCF

The pressure loss correction factor is defined as the ratio of the pressure loss through compressed flexible duct to the pressure loss through fully stretched flexible duct and is used to guess the static pressure losses through flexible ducts. The variation of PDCF with the volumetric flow rate is presented for four different compression ratios, 5, 15, 30 and 45 percent, in Figure 4. 9.00 8.00 7.00 5%

6.00

15% 30%

5.00

45%

4.00 3.00 300

400

500

600

700

800

Volumetric flow rate (m3/h)

Figure 4. Variation of PDCF with volumetric flow rate for different compression ratios (203 mm fully stretched) It is clearly shown from Figure 4 that as volumetric flow rate increases PDCF increases for all cases and compression ratio increases PDCF increases except for the compression ratio of 15 percent which has higher pressure drop values for the ones of compression ratio of 30 percent. EMPIRICAL EQUATIONS Power Law model is used for equation fitting based on the experimental data and its structure has the form of y = ax b . The pressure loss per unit length in Pa/m was predicted via Power Law model for volumetric flow rates in unit of SI, m3/h. Power Law coefficients at each compression case are produced and presented in Table 2 for volumetric flow rates in the range of 300 and 2000 m3/h. The error is defined as the ratio of the difference between calculated and measured values to the measured value. As shown in Table 2, the minimum error was obtained for the compressed condition of %30 when compared to the other compressed conditions. The condition when the maximum error has occurred at a value of -10 % was obtained for the compressed condition of %45. It was observed that the accuracy of the empirical equations used in guessing the pressure drop through flexible ducts is dependent on the flow rate of air. When the data are examined

16


for errors occurred in all cases, the maximum error in power law model is found as -10 % in the range of 300 and 600 m3/h; -8 % in the range of 600 and 1500 m3/h; -5 % in the range of 1500 and 2000 m3/h. Table 2. Power Law coefficients and maximum error values corresponding to compression conditions (203 mm) POWER LAW POWER LAW ( y = ax b ) MAXIMUM COMPRESSION PARAMETER ERROR COUPLE, (Y - CONDITION (%) COEFFICIENT, POWER, (±%) B X) A

Q

*

0

1,4258E-05

1,8866

-4 / +4

5

1,8184E-05

2,0547

-9 / +5

15

2,0938E-05

2,1328

-8 / +4

30

3,2391E-05

2,0566

-6 / +5

45

3,2294E-05

2,0792

-10 / +5

Corresponding values for range of volumetric flow rate of 300 and 2000 m3/h

DISCUSSION As a result of experiments performed for non-insulated aluminum-laminated flexible air duct at 203 mm (8 inch) diameter with 35 mm pitch under fully stretched and four compressed conditions, say 5, 15, 30 and 45%, empirical equations were produced for pressure drop characteristics of the flexible duct. It was identified that the error rates of Power Law model varied depending on flow rate of air. The pressure loss in 15% compression condition is higher than the one in the 30% compression condition as shown in Figure 4. Therefore, it was foreseen that flow characteristics in flexible duct would be similar to the one in the rigid duct above a specified compression ratio. In addition to compressed conditions, the pressure loss coefficients were determined in blow through configuration for the flexible duct under fully stretched condition at five different bending angles, say 15, 30, 45, 60 and 90o. It was determined that the pressure loss coefficients were in the range of 0.10 and 1.60 depending on the bending angle. Power Law equations based on experimental data would be able to guess the pressure loss values through flexible duct in the range of 300 and 2000 m3/h with an error of maximum ± 10 %. The form of the equations has the same structure including coefficient and power. The power values are higher than 2.0 for all compression conditions except for the one in the fully stretched condition. ACKNOWLEDGEMENT The experimental study is sponsored by AFS Boru Incorporated Company and conducted by R&D team in the laboratory of company.

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REFERENCES 1. Harris, A. D. 1958. “Examination of Flexible Duct” Colliery Engineering 35 (407), pp. 29-30 Neveril, F. B. and Behls, H. F., “Friction Loss In Flexible Plastic Air Duct”, GARD Report 1278-2, October 1965. 2. Harrison, E. 1965. “Balancing Air Flow in Ventilating Duct Systems” IHVE Journal 33, pp. 201-226. 3. ACCA 1995. Residential Duct Systems - Manual D. Air Conditioning Contractors of America. Washington, DC. 4. Kokayko, M, Jolton, J., Beggs, T, Walthour, S and Dickson, B. 1996. Residential Ductwork and Plenum Box Bench Tests. IBACOS Burt Hill Project 95006-13. Integrated Building and Construction Solutions, Pittsburgh, PA. 5. Abushakra, B., Dickerhoff, D. J., Walker, I. S. and Sherman, M. H. 2001. Laboratory Study of Pressure Losses in Residential Air Distribution Systems. Lawrence Berkeley National Laboratory Report LBNL-49293, Berkeley, CA. 6. Abushakra, B., Walker, I. S., Sherman, M. H. 2002. A Study of Pressure Losses in Residential Air Distribution Systems. Proceedings of the ACEEE Summer Study 2002, American Council for an Energy Efficient Economy, Washington DC, Lawrence Berkeley National Laboratory Report LBNL 49700, Berkeley, CA. 7. Abushakra, B., Walker, I. S., Sherman, M. H. 2004. Compression Effects on Pressure Loss in Flexible HVAC Ducts. International Journal of Heating, Ventilating, Air-Conditioning and Refrigeration Research, 10 (3): 275-289. 8. Culp, C. 2011. HVAC Flexible Duct Pressure Loss Measurements, ASHRAE RP-1333, Final Report. 9. Weaver, K.D., “Determining Pressure Losses For Airflow in Residential Ductwork” MSc. Thesis, Mechanical Eng. Dept., Texas A&M University, December 2011. 10. ANSI/ASHRAE Standard 120-2008, “Method of Testing to Determine Flow Resistance of HVAC Ducts and Fittings” American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.

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CASE STUDY: A NET ZERO ENERGY BUILDING BASED ON HEAT PUMP TECHNOLOGY Bart Van Reeth1, Hiroshi Aihara1 1

Daikin Europe NV, Belgium

Corresponding email: [email protected] SUMMARY In 2010 a recast of the European Energy performance of buildings directive was published, introducing nearly zero energy building for new constructions by 2019 and 2021. Manufactures of buildings HVAC equipment are preparing on this new evolution. Field measurements were conducted on a net zero energy building designed for small to medium enterprises (SMEs). Energy flow and comfort parameters were monitored. The net zero energy concept, built around air source heat pump technology, achieved a positive energy balance of 977 kWh after one year of measurements. The study was conducted in cooperation with five European research institutes. INTRODUCTION Since the publication of the Energy Performance of Buildings Directive recast (2010), much attention has been paid to nearly zero energy buildings (nZEB). Building equipment manufacturers have been closely following this evolution and contributing to this development. High efficiency solutions after all play an important role in the total energy picture. To this end, field measurements at a Net Zero Energy Building were started in March 2010 as part of an nZEB project in cooperation with major research institutions. The aim of the project is to develop an economically feasible Net Zero Energy Building concept using heat pump technology. NZEB CONCEPT Building description The project concerns a newly constructed office building (2009) for a small to medium enterprise that is home to 15 fulltime employees. An 800-m² warehouse is connected to the north side of the 2-story office. Interior views can be seen in Figure 1.

Figure 1. Interior and exterior pictures of the test office nZEB Herten

19


The entire construction is steel frame. The general building characteristics, including the climate data, are shown in Table 1. Table 1. General building characteristics Location Owner Typology

Climate data Number of floors Net floor area Conditioned floor area Conditioned volume Lighting level Indoor temperature Ventilation rate Envelope to volume ratio

Herten. Germany Athoka Gmbh. Zeller Gmbh Office and showroom + warehouse and workplace Heating: Tdesign: -8.6°C Cooling: Tdesign: 30.3°C 2 545 m² 515 m² 1424 m³ >500 lux Winter: 20-23°C, zone depending Summer: 24°C, with individual user control According to EN15251, method B1.3 0.66

Since the idea was to start with an open and flexible architectural approach, the building envelope did not target extreme insulation values, but rather a slight improvement in the German EnEV standard (Table 2), in combination with measures to reduce the loads such as controllable solar shading on the facades and windows, cool roof covering, and a free cooling option in the heat recovery ventilation system. Table 2. Envelope technical data

External walls Roof Windows

Material

U value (W/m²K)

Brickwork (insulation 14cm) + sandwich panels (insulation 10cm) Steel deck (insulation 20cm)

0.23 -0.25

EnEV reference Construction 0.28

0.19

0.2

1.3

1.3

Double glazing + insulated aluminium frames

Office envelope (average)

0.41

Equipment description As a next step, high efficiency systems are used to reduce energy consumption. The primary system for heating the building is an air source heat pump, with a water circuit connected to underfloor heating. Each room has one or more piping zones for which the water volume flow is individually controlled by valves, managed by PI-controlled temperature sensors per zone. This ensures a balance between optimal comfort and energy savings. Since heat pump performance very much depends on the provided water temperature, the leaving water

20


temperature is intelligently controlled in function of the weather. The project revealed that good knowledge of the building and its users can facilitate a perfect match between solutions used. Trying to avoid inefficient back-up heater operation in extreme winter conditions can lead to an over-dimensioned design, which results in lower efficiency at low partial load operation (e.g. warmer temperatures). Therefore for this project, the choice was made to optimise the heating system for partial load efficiency, and use combined operation in extreme winter conditions, i.e. augmenting heating capacity using the – already present – airto-air heat pump (designed for cooling the building, but able to run in heating mode). The building’s control system was designed so the that underfloor heating remains the dominant heating system, and use of the electrical backup heater is minimised. This combined operation resulted in high seasonal efficiency. Both outdoor units of the Air/Water and Air/Air (VRF) system can be seen in Figure 2a.

a) b) Figure 2. a) Air/Air VRF(left) and Air/Water heat pump (right) outdoor unit, b) White coated roof covered with PV As noted above, comfort cooling during the summer period is handled by an air-to-air heat pump in reversed cycle mode (VRV III). Each room has individual control of its indoor unit. Ventilation is provided by two heat-recovery ventilation systems with a temperature exchange efficiency of 75% and an enthalpy exchange efficiency of 60-65%. The control system uses free cooling whenever possible in the summer period. The lighting design makes use of LED and other efficient lighting technology where possible. The desks have personalised lighting to guarantee light comfort and the highest possible energy efficiency. A Net Zero Energy Building concept may be defined as a building that is energy neutral over a period of one year: i.e., it must deliver as much energy to the supply grid as it takes from this grid. The energy saving component was handled with the previous actions; the remaining component is the addition of renewable energy sources. In this project, 27.3 kWp thin-film photovoltaic panels were installed on the roof. This system was chosen for its combination of easy installation and good response to the infrared light of the CIGS solar cells (copper indium gallium selenide). The latter is important since the research included an evaluation of the effect of a durable sun reflective roof coating on the photovoltaic energy production. A picture of the roof can be seen in Figure 2b.

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All equipment and sensors are connected to online measurement and visualisation systems that allow research engineers to remotely monitor performance and comfort at the field test site. MEASUREMENT RESULTS Energy flows The building and its equipment were closely monitored for a 12-month measurement period. An analysis of the energy flows shows an energy surplus for measurement year 2011-2012. The positive outcome of 977 kWh (1.8 kWh/m²) is displayed in Table 3 and Figure 3. This result includes the aspects mentioned in the European Energy Performance of Buildings Directive: heating, cooling, domestic hot water, ventilation and lighting. These measurements were conducted in cooperation with five research institutes [1,2,4,5,6]. Table 3. Measured energy performance. All specific values are per net floor area.

Heating DHW Cooling (incl. server room) Ventilation Lighting PV power generation Total

Delivered and exported energy kWh/(m²a) 14.5 1.7 5.1 4.3 11.1 -38.5 -1.8

Primary energy factor 2.6 2.6 2.6 2.6 2.6 2.6

Primary energy use kWh/(m²a) 37.7 4.4 13.3 11.2 28.9 -100.0 -4.6

Figure 3. Yearly comparison of simulated and measured energy consumption and generation As can be seen in Figure 3, energy consumption was in line with the calculation made according to the German DIN18599 energy standard. The months of February and March deviate the most from the calculation. In March 2011, measurement had just started and the lighting control was not yet fully operational. February 2012 was a month with extreme winter conditions: -15° C as a daily minimum, compared to -8.6° C in the reference year.

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Heat pumps appear to be an excellent solution for zero energy buildings. The results of the project show that the success of a zero energy project is already highly influenced in the first stage of the project: the design phase. The aim was to allow an open and flexible architectural approach in which the goal of achieving zero energy building performance would not create obstacles for the architect in the design and form of the building. This was made possible by the early integration of the technical concept into the architectural, allowing both to converge, resulting in a technically and architecturally superior building. Increased PV performance The researched durable sun reflective coating is designed to reduce the roof temperature and be a passive measure for reducing the building’s cooling demand. In this field test, a part of the roof was treated with this special coating in order to compare it with standard white roof coating. The effect on the photovoltaic energy yield was expected to be twofold: (1) increase the solar reflection on the photovoltaic cells and thus increase the energy produced, and (2) lower the working temperature of the cells and thus increase the potential difference across the field Figure 4a. These two aspects resulted in a measured yield increase of 11% in comparison with the standard white roof coating Figure 4b [1].

Figure 4. a) Principle of the photovoltaic system

Figure 4. b) Results of measurement on yield increase of the photovoltaic system [1]

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Thermal comfort The project’s priorities included not only energy savings but also ensuring the highest comfort levels for building users. To evaluate temperature comfort over a longer time span the method out of CEN EN 15251 was used. The different categories are listed in Table 4. Table 4: Temperature ranges for evaluation of the long-term measurements of indoor temperature in offices (from CEN EN 15251-2007). Category

Winter temperature range (ºC) 21 - 23 20 - 24 19 - 25

I – high level of expectation II – normal level of expectation III – acceptable, moderate level of expectation IV – values outside the criteria for the above categories

Summer temperature range (ºC) 23.5 – 25.5 23 – 26 22 - 27

Temperature occurrence during working hours (08h00 till 18h00) was analysed for the different offices. Winter (a) and summer (b) results are displayed in Figure 5 (one month measurement for each season). The winter results show an excellent outcome. To fully understand the results of the summer period an in-depth look is needed of room L1.03 as example (Figure 6) . In this figure the temperature spread is shown. As the end user can set its own desired room temperature (in this case mostly around 22°C), the comfort should be evaluated according to the users request and not only based on the standards ranges. ‘Overcooling’ would be a wrong conclusion of Figure 5(b) and Figure 6 as the user is requesting this temperature range. The exercise is to find the balance between energy saving and respecting the users comfort requirements.

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Figure 5. Temperature ranges measured according to EN 15251 for (A) winter month and (B) summer month

Figure 6. Spread on temperature range during summer month of room L1.03 ALTERNATIVE COMPARISON A comparison is made between the heat pump concept (HP) for nZEB and a total different approach, i.e. a concept build around a biomass fueled cogeneration unit (CHP). This unit can provide heat at sufficient temperature to do the space heating and the generation of the domestic hot water. At the same time the unit produces electricity. In the summer period the operation time of this unit is prolonged by using the heat to start the absorption process of an absorption chiller. To run the thermodynamic process optimally a cooling tower is added. See Figure 7. Due to the fact that the CHP also generates electricity to contribute to the zero energy target, the PV system can be smaller (14kWp).

Figure 7: Biomass fueled cogeneration unit with an absorption chiller . [2] Figure 8 is showing the comparison of the energy flows in the two cases. Although the CHP solution has more distribution and storage losses than the HP system, the primary energy consumption of heating is remarkably lower. The reason can be found in the fact that biomass is counted with a primary energy factor of 0,2 in Germany.

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The cooling energy, on the other hand, is in the alternative solution quite higher. The reason can be found in the higher auxiliary energy demand: distribution of the cold fluid, re-cooling of the absorption chiller. As the energy to run the lights and the ventilation system stays the same, the total sum of the primary energy consumption is in both cases quite similar.

Figure 8: Comparison of the total primary energy consumption . [2] On the heating system side the cogeneration unit is more expensive than the air to water heat pumps. You have to foresee storage and chimneys. On the cooling side the investment for an absorption chiller will also be higher than a compression system. As mentioned the PV field of the CHP alternative is remarkably smaller, so the investment cost will also decrease. Leading to a slightly cheaper investment cost of the CHP case. The overview can be seen in Table 5. Table 5. Comparison of investment costs [2]

Due to the higher running and maintenance costs the investment difference is paid back after one year, as illustrated in Figure 9. So from economic point of view the Heat Pump concept is the most competitive for nZEB.

26


Figure 9. Cost difference (investment, running and maintenance) between the two alternatives. [2] EXPERIENCE GAINED After more than one year of monitoring building operation, two aspects deserve attention. In low energy buildings, proper product dimensioning and selection is a crucial step. Good interaction with the building owner and users prevented oversizing of equipment. Manual monitoring makes it possible to discover upcoming problems before these would be noticeable to the building user and result in a wasting of energy. This process could be automated. REFERENCES [1] ef.Ruhr GmbH – Technische Universität Dortmund, Effectiveness of Building Eneregy Management Systems of Daikin Test nZEB and Integration into an Intelligent Grid, 2012 [2] Fraunhofer IBP, Validation of Net Zero Energy Building Concepts – Study executed for Daikin Europe NV, 2012 [3] International Centre for Indoor Environment and Energy - Technical University of Denmark, Monitoring of indoor environment quality parameters and occupant perceptions in the Daikin nZEB Building in Herten, Germany, 2012 [4] CETIAT, Analysis of a heat recovery ventilation system in an nZEB building, 2012 [5] Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Net Zero Energy Building test office Herten - Efficient heating and cooling with VRV / DAIKIN Altherma combination, 2012 [6] School of Mechanical Aerospace and Civil Engineering - University of Manchester, The Near Zero Energy Building (NZEB) Project: Report on the University of Manchester contribution to the programme of Research sponsored by Daikin Europe N.V., 2012

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CLIMAMED 2013 NET ZERO ENERGY USE IN BUILDINGS 1-Lawrence Chee Orient Research Consulting, Sustainable Consultant FP7-EEB-HIPIN- Dissemination Manager Corresponding email: [email protected]

2-Ahmet Akcakaya Orient Research Consulting, Senior Mechanical Engineer Corresponding email: [email protected]

3-Cevat Erdogan Orient Research Consulting, Senior Mechanical Engineer Corresponding email: [email protected] SUMMARY Zero energy use in Building is a topic that is becoming more important, due to rising fuel costs, and in some instances causes ‘fuel poverty’ in families and national security. In the last 10 years, there have being significant technology advances in building technology relating to insulation materials, equipment in harnessing renewable energy and not forgetting stricter building codes in relation to energy efficiency. With these vast array of innovations, this paper following the principles of sustainability (3 Rs) to Reduce-Reuse-Renew, to explain how a building can reach the potential of being Zero energy use. It is like a jigsaw puzzle where all the different pieces coming together and every piece are important. The paper will briefly consider case study relating in Passive House Standard and ongoing work in how to adapt the Passive House Standard into moderate temperate climatic conditions. INTRODUCTION It is now commonly recognised that the largest CO2 emitters are from buildings. And increasingly, approximately 38% in EU-wide are from the residential sector (1). Therefore, to combat the world-wide problem of global warming, professionals must look at different ways to reduce the emission of CO2 from buildings. In the last 20 years, there has being significant development in this area but only in the last 5 years, like pieces of a jigsaw puzzle, they are being pieced together. And furthermore, by

28


introducing a new idea, it causes new problems; which then requires a new solution. But the architects, engineers and other construction professionals have developed very innovative solutions which are nearly Zero Energy Building.

Definition Nearly Zero Energy Buildings (2) is defined by energy efficient buildings that emit little or no CO2 into the atmosphere due to heating or cooling; satisfying the thermal comfort of its inhabitants. Generally, it is achieved by reducing the energy requirement and supplementing by use of renewable sources of energy; which are wind, sun, geothermal, hydro or wave.

General principles of sustainability The basic principles of sustainability are Reduce, Recycle and Renew (3). We can apply these same principles to buildings. The first principle of an energy efficient building is reducing waste – heat loss or heat gain; the second principle is recycling energy in heating or cooling and the third is the use of renewable energy.

Reduce – Insulation to the building fabric The development of insulation has significant effect in reducing heat loss and/or heat gain in Mediterranean countries. Different insulation companies in Europe like Knauf (4) and Kingspan (5) has developed products that can achieve very low U-values. Also Building codes like EN 18329 minimizing air filtration. And the emergence of ‘Passive Haus Standard’ (6) (11) has significantly changed the way we think of an energy efficient building. But the development of such high insulated buildings with minimum air filtration introduces a known problem – condensation inside buildings leading to building sickness. This problem is recognised and must be designed out by introducing ventilation. The audience may ask – it makes no sense to have ventilation after introducing such high insulation and air filtration standards – why bother changing in the first place. The ventilation system must have an inbuilt heat recovery system; in Mediterranean countries, is also reversible. Another disadvantage of high insulated buildings is that it increases thickness of the external walls; to 500 - 800 cm (11). In places where land is a premium like Istanbul and South East England, this issue affects the internal space standard of the building or reduces the number buildings that can fit in a parcel of land. Research and Development is currently ongoing to increase the insulating properties of existing building materials. OR is part of a European Consortium of companies called ‘Hipin’, funded by European Commission – FP 7 programme where we are attempting to increase the insulating properties of coatings in plaster, building membrane system and paint by incorporating aerogel. (6) The concept of High Performance Insulation Based on Nanostructured Encapsulation of Air (HIPIN), in this project, is to develop a sustainable and affordable technology to produce a nanostructured thermal insulating coating to improve thermal efficiency in new and retrofitting buildings. The insulating material will have enhanced performance compared with the state of the art products and will contribute to the protection of the environment through the reduction of greenhouse gases. In addition to the insulating effect other functionalities will be sought such us self-cleaning, sound insulation

29


and fire retardant properties. The innovative multi-functional material will be suitable for application in a liquid form on exterior of buildings at a reduced cost and time. What is exciting about this new development is that it will improve the insulation properties of many common building materials like plasters, blocks, roof tiles, other insulation material like XPS and similar products An older solution to condensation is reintroducing ‘lime plaster’ allowing walls to breathe (8). The immediate challenge is sourcing the material and the skilled lime plasterers. Other related developments are in: Better insulating window and doors Improving insulating properties of glass

Recycling On to the next principle of recycling, firstly, heat from fridges, PCs, machinery and human living beings are recycled in a well insulated building. With the use of HVAC-R, heat is recycled back into the building but the quality of the air is warm and fresh. In a reversible HVAC-R system, unwanted heat is dispelled and cool and fresh air is ventilated into the building 9. Dependent on the recovery unit, up to 60% of the waste heat can be recovered. (10)

Renew Renew is translated as use of renewable energy. In technological terms, today, we are very rich in our knowledge and availability of various systems. I will list out the commonly available systems now exist in the marketplace, developed by reputable international companies. a. Solar PV systems which converts sun’s rays into electricity. b. Solar thermal systems which converts warmth of sun or air to provide heating and hot water. c. Wind turbines which converts wind to electricity; mini wind turbines were introduced but were found to be inconsequential to the building’s energy usage. d. Geothermal or ground source heat pump system. e. Air source heat pump system, heat extracted from ambient air temperature to provide heating and hot water. f. Night Air Effect – Free Cool air – Design that allows the building to cool at night. g. Water turbines, currently used in large hydro electric dams. h. Wave turbines, developed large scale off the North Sea of Britain. The above b, d and e which uses the heat pump, will require electricity, so they really offer a cheaper solution to traditional gas or electric fired boilers. However, if such systems are combined with PVs linked to a form of energy storage, it can achieve the nearly Zero carbon building due to non CO2 emissivity. Related to renewable energy like PV and other systems is that energy that is produced must be used or stored. There is also a significant amount of R&D to develop better batteries. For

30


example, rechargeable batteries and rechargeable energy storage system. This area of expertise is an emerging technology.

Passive House Standard Case Study (14)

The table below illustrates the required ‘U’ values of the building elements. (12)

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Building Fabric

‘U’ value W(m2 K)

1 2 3 4 5

Wall Roof Window Door Floor Slab

0.097-0.126 0.095 0.85 0.80 0.09-0.123

6 7

Ventilation Supply per person 3 person house

30 m3/h 90 m3/h

Typical Rockwool Insulation Thickness 335 mm 500 mm

320-400mm

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Building Energy Performance Index (BEPI) & Code for Sustainable Homes We discussed the availability of technologies and related issues relevant to Nearly Zero Energy Buildings, on the same theme, governments are also getting on the act by introducing legislation where new buildings must be built to be energy efficient, to demonstrate this, BEPI certification is introduced. Example for England and Wales, but note EU Directive 2002/91/EC Example of home in UK built to Code for Sustainable Homes, Very Good: Building Performance Indicator (BEPI) class B - 84 (12) Building fabric U-values: Roof Wall Floor Windows

0.11 W/m2K 0.21 Wm2K 0.17 Wm2K 1.50 Wm2K

Current Research Work In temperate weather with a milder winter, can we achieve thermal comfort with zero energy building?

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The Passive House Standard illustrates the possibility of a winter with minimal heating requirement. For hot summers, measures to prevent overheating, for instance night cooling, solar shading measures, will they be adequate to prevent the use of air conditioning systems. Furthermore, the thickness requirements e.g. for walls are significant, can these be reduced by incorporation of Hipin-Aerogel in the building materials? The issues we are working on are ….. a. What are the appropriate U values for the respective building elements? b. What measures to reduce overheating? c. If Hipin-aerogel is introduced into the insulation, plaster, blocks, tiles and paint, will the overall thicknesses of the material be reduced? d. And similarly, reduce the heat gained by reflection. Finally, why is all this important to the typical business developer or building owner. • Energy costs are increasing way beyond national inflation index. It is a cost saving over the medium and long term. Typically, cost of insulation can be recovered in less than 3 years. • Cost of carbon – as defined MNI’s Corporate Sustainability Strategy and Goals • Global Warming – can we afford the price of a warmer planet? A recent World Bank report concluded that world temperatures could warm by 4C by the end of this century if no concerted action is taken. In a recent opinion piece in the Washington Post, Mr Kim wrote: "A world that warm means seas would rise 1.5 to 3 feet, putting at risk hundreds of millions of city dwellers globally. "It would mean that storms once dubbed 'once in a century' would become common, perhaps occurring every year. And it would mean that much of the United States, from Los Angeles to Kansas to the nation's capital, would feel like an unbearable oven in the summer." (15) ACKNOWLEDGEMENT European Commission – FP7, EEB, Hipin Research Consortium Orient Research Consulting Engineers, Istanbul, Turkey REFERENCES 1.

http://www.euractiv.com/energy-efficiency/eu-reaches-agreement-energy-savi-news223090 18 Nov 2009

2.

Nearly zero energy buildings: a chieving the EU 2020 target, European Council for an energy efficient economy 8 Feb 2011

3.

http://sustainability.about.com/od/Going-Green/a/The-3-Rs-Reduce-Reuse-AndRecycle.htm http://www.knauf.com/www/en/ http://www.kingspan.com/ http://www.passivehouse-international.org/

4. 5. 6.

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7. 8. 9. 10. 11. 12. 13. 14. 15.

www.hipin.eu http://www.projectbook.co.uk/article_11.html http://www.commercial.carrier.com/commercial/hvac/carrier/0,,CLI1_DIV12_ETI12110,00 .html The Carbon Trust, 2011,Heat Recovery. Dr Wolgang Feist, 2012 , EnerPHit und EnerPHit, Zertifizerungskriterien fur die Modernisierung mit Passivhaus-Komponent, Dr Paul King, 2010, Kingspan System solutions to meeting Code for Sustainable Homes. Jason Palmer, Ian Cooper, 2011, GB Housing Energy Fact File Dr Wolgang Feist, 2005, Case Study Passive Houses Kronenburg Jim Yong Kim,2013, World Bank, A 40C Warmer World

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HYBRID HVAC SYSTEMS AND EQUIPMENT OPTIMIZATION IN NET-ZERO ENERGY BUILDINGS Birol Kılkış, Fellow ASHRAE

Baskent University, Ankara, TURKEY Corresponding email: [email protected] ABSTRACT Large net-zero energy buildings may have diverse thermal and electrical loads with challenging and non-coincident profiles to be simultaneously satisfied. This paper emphasizes the importance of hybrid HVAC systems in energy savings and reduction of exergy destruction and introduces an optimization algorithm. The core of net-zero buildings is a combined heat and power system (CHP) and the algorithm sizes the optimal CHP system when it is bundled with absorption/adsorption chillers, heat pumps, thermal storage, and sustainable energy systems like wind and solar. The algorithm is based on the Rational Exergy Management Model and searches the optimum size for the CHP and all other equipment in the bundle with the objective of maximum exergy efficiency averaged over a typical design year and based on hourly data. The paper analyses four new building scenarios, one of which is a business as usual type of building and the fourth one is a net-zero energy/exergy building. Results show that optimization search may substantially increase the exergy efficiency depending upon the level of the green building concept in each scenario being analyzed and this achieves fuel savings and CO2 emission reduction up to 50%. 1- INTRODUCTION Net-zero building evaluations are based on the first-law of thermodynamics and on-site measurements and do not take into account of the exergy destructions taking place due to direct and indirect activities of the building. Exergy destructions lead to additional but avoidable CO2 emissions that compound on the first-law emissions. In this analysis the base scenario is a large building complex that has no alternative energy utilization, supplies heat from on-site natural gas boilers and the electric power is supplied from the national grid. Green and large building scenarios include four levels of sustainable energy use, trigeneration with absorption and adsorption cooling machines, on-site power-driven groundsource heat pumps, and thermal storage for peak load shaving. Solar applications and wind energy applications may be somewhat limited due to the fact that land is at premium and wind-turbine noise may be a concern in urban areas. When the analysis is focused on the large building complex including all ancillary buildings within the building, the compounded CO2 emission responsibility of the building, is given by the following expression [1]:

∑CO

2

⎛ cj ⎞ ⎛ c ⎛c ⎞ ⎟(1 −ψ Ri )Q + ⎜ j = ⎜⎜ i ⎟⎟Q + ⎜ ⎜ ⎟ ⎜ η ⋅η ⎝ η Bi ⎠ ⎝ η Pj ⎠ ⎝ j T

⎞ ⎟E ⎟ ⎠

.

(1)

Eq. 1 does not include embedded exergy destructions and related CO2 emissions during post and pre construction phases. The subscript (i) stands for the on-site large building systems and the subscript j stands for the off-site energy and power systems. Here, the first and the last term represent CO2 emissions due to the first-law analysis of thermodynamics, the first one being for heat (Q) and the last term being for the electrical power loads (E). Heat loads

36


include absorption or adsorption-cycle cooling heat loads. Electrical power loads include chiller-operated cooling systems and equipment loads. In a typical large building complex the average annual heat demand to electrical energy demand ratio is about 1,4 [ 2, 3]. (2) Q = 1,4 E = n ⋅ E The coefficient 1,4 in Eq. 2 may be generalized for any large building complex by n. Here, n is the annual-averaged heat to electrical energy demand ratio. E (electrical energy consumed) also includes the electrical energy demand of electric power-driven chillers for space cooling. The second term in Eq. 1 represents the compounding CO2 emissions due to the exergy destruction taking place in large buildings and related operations. The main reasoning in developing this term is that if a building destroys exergy of the fuel or power used prior to useful applications in the large buildings (like comfort heating without utilizing the fuel exergy first for on-site power generation), then a distant power plant must proportionately offset this exergy destruction by spending more fuel. Here, exergy destructions are represented by the Rational Exergy Management Efficiency, ψRi in terms of the ideal Carnot cycle [4].

ψ Ri

⎛ Tref ⎞ ⎟ ⎜1 − ⎜ T ⎟ app ⎠ ⎝ = ⎛ Tref ⎞ ⎟ ⎜1 − ⎟ ⎜ T f ⎠ ⎝

(3)

For example, if natural gas at a combustion temperature, Tf of about 2000 K is consumed in an on-site boiler that is dedicated only for comfort heating at an application temperature Tapp, which in this case is equal to the design indoor comfort temperature, Ta of 20oC (293K), if the environment reference temperature Tref is taken equal to the average ground temperature, Tg of 10oC (283 K), then from Eq. 3 ψRi is only about 0,04. This means that the available useful work potential (exergy) of the natural gas is irreversibly destroyed by 96%. In a more exergyrational application, the natural gas could be used first in on-site power generation like in a Combined Heat and Power (CHP) system. In green building applications, ψRi may be higher up to 0,7 [8]. This means that if all variables in Eq. 3 are kept constant except the second term, which includes ψRi, then CO2 emissions may be reduced by about 39 % in a green building if ψRi is increased from 0,04 (base line) to 0,70 or above: (1 − 0,7) × 1,4 + 1 = 1 − 0,75 = 0,39 . X ≅ 1−

(1 − 0,04 ) × 1,4 + 1

Here, X is the CO2 sequestration potential ratio of the large buildings. This calculation may be generalized in terms of n, ψRi, and by replacing the denominator by a new term c. ⎛n⎞ X = (ψ Ri − 0,04 ) ⋅ ⎜ ⎟ ⎝c⎠

{ψRi ≥ 0,04}

(4)

Eq. 4 shows that the CO2 sequestration potential is linearly proportional to ψRi at a rate of (n/c), where, (5) c = 0,96n + 1 Finally, CO2 emission of a green building, ∑CO2, for a known X becomes:

∑ CO

2G

{X ≤ 1} .

= (1 − X ) ⋅ ∑ CO2 BS

37

(6)


Yet, specific loads that may not be reduced below a limit imposed by a collection of certain functional requirements and regulations. Therefore, it may be a prudent statement that a green building may reduce CO2 emissions by up to 50%. CO2 Sequestration Potential Ratio, X with ψRi

Eq. 4

X

n=1,4

n=1

n=1,2

n=1,6

ψRi

n=2

n: Annual-averaged thermal energy to electrical energy consumption ratio.

Fig. 1. The relationship between X and ψRi for different heat to power demand ratios, n.

2- CO2 EMISSON RESPONSIBILITIES of a LARGE BUILDING COMPLEX One cannot isolate a building from its environment in calculating or predicting its overall CO2 emissions responsibility. Even one excludes the embedded emissions; a building constantly interacts with the environment. For example if its site is not optimized, additional emissions are likely due to traffic congestion or additional trip distances. Another example may be deforestation to clear the land for especially constructing a large building, usually which a land with a green forest landscape is preferred. 2.1. CO2 Emissions In this study two main CO2 emission responsibility factors were recognized, namely: a- Large building complex responsibility during operation. b- Deforestation activity, if there is any In case (a) five scenarios were considered. One of them is a business as usual case. 2.1.1 Responsibility of the Large Buildings Complex: The large building complex may first be identified by the n and the ∑CO2 if Eq. 2 is generalized by the following definition: n=

Q E

(7)

⎡⎛ ⎛ c ⎞ ⎛ c j ⎞ ⎞ ⎛ cj ⎟(1 − ψ Ri )⎟n + ⎜ = ⎢⎜ ⎜⎜ i ⎟⎟ + ⎜ ⎜η ⎟ ⎜ η ⎟ ⎜ η j ⋅ ηT ⎠ ⎝ ⎣⎢⎝ ⎝ Bi ⎠ ⎝ Pj ⎠ Ea = E p ⋅ D ⋅ B ⋅ h ⋅ m

∑ CO

2

⎞⎤ ⎟⎥ Ea ⎟⎥ ⎠⎦

(8) (9)

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2.2. Building Scenarios 2.2.1. Business as Usual Scenario-Base Line (BAU) In this scenario Eq. 1 applies with ψRi = 0,04. There is not any energy green building concept applied. The total CO2 emission responsibility of this scenario is analyzed in three main categories, namely compounded emissions of the building and de-forestation. Let for a given power supply mix of a country cj = 0,5 kg CO2/kW-h with an overall firstlaw efficiency of her power plants, ηPj of 0,35 and power transmission efficiency, ηT of 0,8. Assuming that heat is supplied to the building by on-site natural gas boilers with an average first-law efficiency, ηBi of 0,75. Let ci = 0,2 kg CO2/kW-h for natural gas. Then from Eq. 9,

∑ CO

2

⎤ ⎡⎛ 0,2 ⎞ ⎛ 0,5 ⎞ ⎛ 0,5 ⎞ = ⎢⎜ ⎟ Ea = 0,373Ea + 1,920Ea + 1,785Ea = 4,1Ea ⎟(1 − 0,04 )⎥ ×1,4 Ea + ⎜ ⎟+⎜ ⎝ 0,35 × 0,8 ⎠ ⎦ ⎣⎝ 0,75 ⎠ ⎝ 0,35 ⎠

(10)

2.1.2 Responsibility of Deforestation: Loss of CO2 sequestration potential, LCS in ton CO2/a units due to deforestation of DF number of trees, and the annual CO2 sequestration potential of a single typical local tree per annum, s averaged over a 50 year period, which corresponds to the life time of a typical building, may be based upon yearly-piece wise calculations taking into account natural survival rate, CF in the tree stock. LCS =

(DF ⋅ s ⋅ CF ) × 3,67

(11)

1000

Here, the multiplier 3,67 converts carbon sequestration into CO2 sequestration. If for example, if 70000 mature trees will be cut for land clearing purposes, then the CO2 emissions responsibility amounts from Sections 2.1.1 and 2.1.2 are summed. With the sample values of DF = 150000, s = 35 kg CO2/a, CF = 0,85: LCS =

(150000 × 35 × 0,85) × 3,67 = 16377 ton CO /a 1000

2

TCO2 = LCS + ∑ CO2 = 3·106 ton CO2/a.

(12)

Table 1. Breakdown of CO2 Emissions Responsibility: Base Line (BAU). Ψri = 0,04, n = 1,4. No Green Building Features. CO2 Emission Source De-forestation, LCS Building ∑CO2

Direct Emissions Exergy Destruction Total CO2 emission, TCO2 = LCS + ∑CO2

CO2 Emission ton CO2/a 16377 1,58·106 2,98·106 1,4·106 3·106

Comments Corrected for natural tree loss over a 50-year period. From fuel consumption. large building and tree deforestation.

Table 1 gives the break-down of the total CO2 emissions of the Base Scenario according to de-forestation, and direct CO2 emissions (First-law) and compounding CO2 emissions (Second-Law Analysis: Exergy Destructions). 2.2.2 Green Building Scenarios In this study, four scenarios were considered all common in n = 1,4 and Ep = 150 MW. While the first three scenarios are practically possible, the last scenario is a hypothetical scenario where ψRi approaches one. In this hypothetical scenario however, even if all the heat

39


and power is obtained from renewables, there may be other more value adding applications in the nation-wide energy budget to allocate these resources to some other demand points. Therefore, the power load remains in the demand side of the energy and CO2 emission balance sheets. Therefore, X may not approach unity while Ep apparently approaches zero and remains at 0,57. 1- Green Building Case 1: ψRi = 0,3, X = 0,15 In this scenario, there is a CHP plant with a peak 80 MW electric power capacity and 84 MW peak thermal power capacity at design conditions, after necessary correction factors on an annual average were applied, like outdoor temperature, humidity, and air pressure. Cooling is accomplished by electric chillers (BAU). Due to limited space only 15 MW solar PV power is possible without further deforestation. No wind turbines will be installed and only 20 MW green power will be obtained from third-party green power utility companies. Because only a simple CHP system is considered in this scenario, the primary energy savings equation, given by the EU directive [5], modified by the second-law [6] provides a way of estimating the CO2 sequestration: PES RCHP

⎡ ⎤ ⎢ ⎥ 1 ⎥ ×100 = ⎢1 − ⎢ ⎛ CHPH CHPE ⎞ (2 − Refψ ⎥ RCHP ) ⎜ ⎟ + ⎢ ⎜ ⎥ ⎟ × (2 −ψ ) RefH RefE ⎠ RCHP ⎣⎢ ⎝ ⎦⎥

(13)

The reference values for heat generation by a separate boiler is taken 0,85 and power generation by a separate plant is taken 0,52 [5]. Reference value for ψRCHP is 0,2024 [5]. For a set of 80 MW capacity gas-turbine CHP plant, the following annual-average efficiency values were predicted to be CHPHη = 0,45, CHPEη = 0,43, and because the only difference between this scenario and the base scenario is the cogeneration plant, the following identity is set: ψRCHP = ψRi = 0,3. Then the average fuel savings is calculated from Eq. 13:

PES RCHP

⎡ ⎤ ⎢ ⎥ 1 ⎥ × 100 = 30% = ⎢1 − ⎢ ⎛ 0,45 0,43 ⎞ (2 − 0,2024) ⎥ ⎢ ⎜ 0,85 + 0,52 ⎟ × (2 − 0,3) ⎥ ⎠ ⎣ ⎝ ⎦

This result means that the CHP set is saving 30% (0,3) energy based on every kW-h of electrical energy supplied. In terms of the average power generation efficiency of 0,43, energy savings of 0,3 savings on an annual basis using Eq. 10, with D = 0,8, B = 0,7, h = 24 h/day, m = 360 operating days per year is calculated: ES a = 80000 kW × 0,3 × 0,8 × 0,7 × 24 × 360 / 0,43 = 2,7 ⋅10 8 kW - h/a

If cj is equal to 0,2 kg CO2/kW-h for natural gas, then the above energy saving corresponds to an annual CO2 emission reduction may be deduced from the first term in Eq. 1. (14) ΔCO2 = ES a ⋅ c j /1000 (kg/ton) = 2,7 ⋅ 108 × 0,2 / 1000 = 5400 ton CO2 /a 2- Green Building Case 2: ψRi = 0,5, X = 0,27 In this scenario a 150 MW CHP plant is accompanied with 30 MW-cooling capacity absorption system. A 40 MW-cooling capacity (50 MW in heating) ground-source heat pump (GSHP) system will provide additional heating or cooling. Results are given in Table 2.

40


3- Green Building Case 3: ψRi = 0,7, X = 0,39 This is a building with hybrid electromechanical systems. A biogas plant using solid wastes and liquid waste with a capacity of 20 MW electric power will be installed. PV systems are partially replaced by PVT (Photo-Voltaic Thermal) and PVTC (Photo-Voltaic Thermal and Cooling) systems [7]. The general layout of the green electro-mechanical system developed and analyzed for this scenario is shown in Fig. 2. Results are given in Table 2. In this electromechanically green system there is a combined heat and power (CHP) plant at the core. It has dual fuel capability, namely biogas and natural gas. Biogas reactors receive solid and liquid wastes on-site and process them separately. Part of the electric power output of the CHP system drives the water-to-water, ground-source heat pump (GSHP). In order to increase the rational exergy management efficiency, hydronic heating and cooling is also performed to satisfy most of the sensible loads all over the building space through chilled beams and radiant panels, which are proven green building components also for green buildings [8, 9]. Power supply is augmented by solar PVs, PVTs and PVTCs. Remote Green Power Provider

Radiant Cooling

On-site PV, PVT, PVTC

Natural Gas

Biogas

Power

Reject Heat

HWT1 Power Heat or Cold HE

CWT

Fresh Water

HE

Heat Heat

CHP

HWT3

HWT2 ADSC

Domestic Hot Water

ABSC

Comfort Cooling

Comfort Heating Ice Tank Chillers

AC Chilled Beams and Comfort Cooling Radiant Panels

Fig. 2. Green and Hybrid Electro-Mechanical System Components for Scenario 3 [8]. The latter is capable of sensible cooling in thermal radiation and natural convection modes [10, 11, 12]. Green power is purchased by outside vendors available in the vicinity. Thermal energy from solar panels and the CHP system are stored in thermal energy storage tanks (HWT) for different temperatures. 4- Green Building Case 4: Net Zero Energy and Net- Zero Exergy Building (NZEB), ψRi1, X0,57 This scenario will be an upper bound for the analysis and the base scenario will be the lower bound. According to REHVA, nearly zero energy (nZEB) building is a technically possible building that on an annually net basis consumes almost zero energy [12]. NZEB on the other hand that is the subject of this article is the ideal case, which takes the nZEB concept one step further on a mathematical basis. This hypothetical scenario may not be techno-economically possible especially for buildings.

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Table 2. Total CO2 Emissions, TCO2, in Terms of X, Including Deforestation and Site Selection Effects. n =1,4. SCENARIO BAU 1 2

TCO2 3·106 [1] 2,54·106 [2] 2,19·106

X 0 0,15 0,27

ψRi 0,04 0,3 0,5

3

1,83·106

0,39

0,7

Comments Business as usual construction 80 MWe CHP, PV, Remote Green Wind Power 150 MWe CHP, 30 MWt Absorption Chiller, 40 MWt GSHP, PV, Remote Green Wind Power Scenario 2 optimized, thermal storage systems added, 20 MWe biogas plant, PVT and PVTC. Net-zero energy and net-zero exergy building (NZEB)

4 1,57·106 0,69 1 [1] See Table 1. [2] TCO2 values for all scenarios in this column are calculated based on the ∑CO2 value of BAU in Table 1. TCO2 = 2,98·106 x (1 - X) +16377 = 2,54 ·106 ton/a

3- OPTIMIZING THE CHP PLANT Unless the building in question is a military war hospital or natural disaster-ready large hospital (Mega Hospital) the CHP plant capacity is always selected below the anticipated peak power load, expressed by the ratio c: E (15) c = CHP Ep

In Eq. 15, c is the ratio of the optimized and corrected, annual average CHP plant power capacity, ECHP to the peak power load of the building Ep on an annual basis. The catalog value ECHP of the CHP power plant is de-rated according to the altitude of the site. In a detailed hourly analysis of the plant performance hourly changes of the outdoor relative humidity, outdoor DB air temperature, outdoor air pressure, daily variations of the lower heating value of the fuel are also considered in order to further correct the actual performance. ISO standard gives the standard operating conditions for rating and de-rating purposes [13]: 15oC outdoor air temperature, 101,3 kPa outdoor air pressure, 60 % relative humidity and sea level. Any variation from these standard values needs a capacity correction. On a year-by-year basis the technical depreciation of the CHP plant is important, because the capacity decreases by the number of years operated (n). ECHP(t) = ka · kp(t) · krh(t) · kta(t) · kf(d) · y(n)m · ECHP (16) Then an annual average value ECHP is obtained by integrating Eq. 16 over a time period and then dividing it to the given time period (usually year): t

∫E

CHP

ECHP =

(t )dt

(17)

0

t

∫ dt 0

The ratio c may not be less than the so-called cut-off load, which means that the CHP plant may not be operated below a cut-off capacity value due to operational and economical reasons. This limit is usually specified by the manufacturers to be around 0,4. If for example c is 1, the CHP plant corrected capacity on an annual performance average is selected to be equal to the peak load of the building power load that may be observed in a one-year period. Obviously, there will be several hours and days in a year that the CHP plant has to be operated at partial load, which decreases the plant efficiency. Furthermore, even though the excess power may be provided back to the grid with some restrictions like during a general power failure the building may not supply power to the grid. Even in cases when the excess

42


power may be supplied to the grid heat supplied by the CHP plant may exceed the thermal loads and unless building thermal storage tanks are used the heat may be wasted. These arguments clearly show that the c value is very critical in optimizing a CHP plant.

3.1. The Objective Function In this study an objective function was developed based on the following factors: • First-law efficiency, ηI, • c ratio, • Operating factor, IF, ψRi, CO2 emissions, and the pay-back period. The last two factors may be easily related to the other factors mentioned above. Therefore the following optimization equation is implicit on CO2 emissions and the pay-back period. OF = f(ηI, c, IF, ΨRi, CO2)

{Maximize}

⎡ ⎤ ⎢ ⎥ 3 ⎡ 0,9) ⎤ ⎢ CO2 ref ⎥+ OF = [η I ] − ⎢ + ⎥ ⎢ ⎥ 1,84 × IF −1,3 ⎣ IF × c ⎦ ⎛ ci ⎞ ⎢ ⎜⎜ ⎟⎟(1 −ψ Ri )⎥ ⎣⎢ ⎝ η I ⎠ ⎦⎥

(

(18) (19)

)

Equation 19 is a compilation of four terms. The first term is the first-law efficiency and must be maximized. The second term with a minus sign represents the decrease in the CHP plant life in terms of operation hours until the expected life. The third term is for the decrease of CO2emissions against a reference value indexed to the green building threshold value of 0, 70 for ΨRi [8]. The last term represents the simple payback period term for a reference value of 3 years. The last term is the economy indicator in terms of a reference pay-back period of three years.. At this point the following relationships are encountered [3]: ⎡ 0,6 + j ( IF − 0,6) l ⎤ and (20) η = I

⎢ ⎣

0,9

⎥ ⎦

The term (IF) may be expressed by the following relationship. Then. a single-variable function in terms of c is obtained if ψRi is known. IF = -1,2833c2 + 0,7063c + 0,8756 (21) OF = f**(c) {Maximize} (22) dOF =0 , dc

{subject to: ηI ≥ 0,80, 0,4 ≤ c ≤ 0,90, 0,7 ≤ IF ≤ 1,1 >ΨRi ≥ 0,06}

(23)

3.2 Case Study 1. In this case study the first scenario discussed above in Section 2.2.2 is used with an unknown CHP plant capacity instead of 80 MW. The peak design load of the building electric power load is 150 MW, and the optimum c value is sought by using the algorithm given in Section 3.1 and the following design data: ci = 0,2 kg CO2/kW-h (Natural gas) CO2ref : 0,075 kg CO2/kW-h (Natural gas and ΨRi = 0,7), j:0,8, l: 1,2 The data given above was run on a spreadsheet for all scenarios. The maximum condition is satisfied at c = 0,55. Table 3 provides the numerical data for OF for the Scenarios discussed in Section 2.2.2 and the results are plotted in Fig. 3. Results show that the optimum (c) value stays almost the same at 0,55 for all scenarios. The (OF) value increases sharply when ΨRi increases above 0,40.

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Table 3. Results for (OF) for Different Scenarios. c

IF

BAU OF

ηI

Scenario 1 OF

Scenario 2 OF

Scenario 3 OF

0,352197 0,6124612 -1,56774 1,536775788 -1,49235878 1,430466229 0,85 0,4291708 0,6443083 -1,02824 0,995672718 -0,94894609 0,883835206 0,8 0,499728 0,6815587 -0,64174 -0,60728433 -0,55785622 0,488980995 0,75 0,5638688 0,7204477 -0,35211 0,315695108 -0,26344668 0,190641498 0,7 0,621593 0,7588857 -0,13435 0,095988041 -0,04095201 0,035737535 0,65 0,6729008 0,7954727 0,022704 0,062916292 0,120605689 0,200992554 0,6 0,717792 0,8291921 0,122855 0,164771941 0,224906745 0,308701144 0,55 0,7562668 0,8592757 0,165108 0,208545422 0,270861955 0,357696467 0,5 0,788325 0,8851334 0,144363 0,189107706 0,253299491 0,342747059 0,45 0,8139668 0,9063124 0,050886 0,096701328 0,16242906 0,254016883 0,4 0,833192 0,9224724 -0,13155 -0,08492038 -0,01802069 0,075200198 0,9

Scenario 4 OF

1,066098787 0,500521155 0,083505856 0,237969667 0,487216323 0,674237809 0,802006879 0,868899643 0,869333554 0,79320326 0,624000566

Table 4. Comparison of the Feasible Domain and the Optimum (c) Values for the Scenarios. Scenario BAU 1 2 3 4

Feasible Range of c 0,43 ≤ c ≤ 0,66 0,42 ≤ c ≤ 0,67 0,40 ≤ c ≤ 0,69 0,40 ≤ c ≤ 0,71 0,40 ≤ c ≤ 0,78

Optimum Value of c 0,55 0,55 0,55 0,55 0,55

4- DISCUSSION of RESULTS This study have shown that the capacity selection of a CHP plant expressed by the factor (c) for large building complexes is very important from the efficiency, economy, environment, and operational points of view. The case study have shown that the optimum (c) value is

Fig. 3. Optimum Values of (c) for Different (ΨRi) values corresponding to the Scenarios.

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about 0,55 and does not vary much with the factor (ΨRi). However, the optimal range of the term (c), which satisfies the condition OF > 0, widens with the term (ΨRi), that is the metric for the rational about the management of exergy especially for green buildings where ΨRi approaches or exceeds 0,7. Higher the (ΨRi) term, wider the feasible domain.. This domain is important when the optimum point is not achievable. In a wider feasible domain, the designer is able to choose a more suitable solution under the design-specific constraints. At any rate, Fig. 3 shows that the worst case for the term (c) is when it approaches 0,9 and beyond. In this case the objective function becomes negative, which indicates that the shorter life time makes a feasible solution impossible. In order to avoid this, the building must at the same time approach net-zero or near net zero building status, that is ΨRi > 0,7. Otherwise the only permissible case for (c) to approach 0,9 is military hospitals and/or natural disaster emergency complexes where service completeness in the most diverse condition supersedes other factors. The proposed algorithm provides more comprehensive prediction of the performance of a CHP plant. In conclusion, a detailed analysis is a must especially for green building design and this analysis must seek an optimal (c) value including economical, operational, energy and exergy efficiency, CO2 emissions variables. 5- REFERENCES 1. Kılkış, B., Kılkış, Ş. Energy and Exergy Efficiency Comparison of Poly-Generation and Co-generation Systems, Conference Proceedings, (In Serbian), pp: 474-486, the Fortieth International Congress on Heating, Refrigerating and Air-Conditioning, 2-4 December, Belgrade, 2009. 2. Kilkis, B., Erol, O., and, D. C. Bayram, A Tri-Generation Retrofit Towards Green Buildings, IGEC VI Conference, Eskisehir, 5-9 June, Conference Proceedings, 2011.

3. Kılkış, B. and Kılkış, Ş. Combined Heat and Power with Renewable Energy Systems: A Guide for Optimum Design, Rating, and Enviro-Economic Analysis, 160 p., TTMD: Ankara, 2013 (in print). 4. Kılkış, Ş. A Rational Exergy Management Model to Curb CO2 Emissions in the Exergy-Aware Built Environments of the Future, Doctoral Thesis in Civil and Architectural Engineering, KTH, Stockholm, Sweden, 189 p., 2011. 5. EN, Directive 2004/8/EC of the European Parliament and of the Council of 11 February 2004 on the Promotion of Cogeneration Based on a Useful Heat Demand in the Internal Energy Market and Amending Directive 92/42/EEC, L 52/50 Brussels, 2004. 6. Kılkış, B., Kılkış, Ş. Comparison of Poly-generation Systems for Energy Savings, Exergetic Performance, and Harmful Emissions, Proceedings of ES2007, Energy Sustainability, Paper No: ES 2007-36262, June 27-30, Long Beach, California, 2007. 7. Kılkış, B., A New Building Integrated Solar Facade System for Heating, Cooling, and Power (BIPVTC) in Green Buildings, TTMD Journal (English Edition), No: 7, pp: 2633, Ankara, 2010. 8. Kılkış, B. A Benchmarking and Metrication Study in a High Performance, Green Office Building- Energy Efficiency Road Map for Sustainable District Cooling and Heating, CLIMA 2010 International Conference, Proceedings on CD, ISBN: 978-9756907-14-6, 9-14 May, Antalya, 2010. 9. Kılkış, B. Solar Tri-Generation Module for Heating, Cooling, and Power, Conference Proceedings on CD, Solar Future 2010 Conference, 11-12 February, İstanbul, 2010.

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10. Kılkış, B., 2011. Application of a Novel Solar PV Tri-Generation Heat Pumping Facade to a High Performance Building in Ankara, 10th Int. Energy Agency, Heat Pump Conference 2011, May 16-19, Conference Proceedings on CD, Japan, 2011. 11. Bean, R. and B. Kılkış, The Basics of Panel Heating & Cooling, ASHRAE Learning Institute, Short Course, ASHRAE: Atlanta, 150 p., 2010. 12. Kurnitski, J., Technical Definitions for Nearly Zero Energy Buildings, The REHVA European HVAC Journal, Vol: 50, Issue: 3, pp: 22-28, May 2013. 13. ISO, ISO 26382:2010, Cogeneration systems - Technical Declarations for Planning, Evaluation and Procurement, 27 p., 2010. 6- NOMENCLATURE a B b c

Unit electrical energy price, $/kW-h Base load factor, dimensionless Unit fuel price, $/kW-h (Based on the lover heating value, LHV) c = ECHP / E p , dimensionless

ci cj CF CHPHη CHPEη CHPSη COP CO2ref D d DF E Ea ECHP ECHP(t)

Unit CO2 content of the fuel, kg CO2/kW-h Unit CO2 content of the fuel outside the building boundary, kg CO2/kW-h Natural survival rate factor of a given tree type, dimensionless Partial heat generation efficiency of CHP, dimensionless Partial power generation efficiency of CHP) dimensionless Partial steam generation efficiency, dimensionless Coefficient of performance, dimensionless Reference Emissions, dimensionless Diversity factor, dimensionless Day Number of trees to be subjected to cutting (deforestation), dimensionless Electrical energy demand , kW-h Annual electrical energy demand, kW-h Catalog value of the CHP plant electric power generation, kW, or MW Hourly corrected electric power generation capacity of the CHP plant, kW, or MW Optimized and corrected annual average CHP plant power generation capacity, MW Energy savings per annum according to PESRCHP, kW-h/a Peak electrical power demand, kW-h Supplementary heat generation efficiency, dimensionless Daily building operating hours, h Operating Factor, dimensionless Coefficients in Eq. 20 Capacity correction coefficient for the altitude from sea level, dimensionless Capacity correction coefficient for the lower heating value of the fuel, dimensionless Capacity correction coefficient for atmospheric pressure, dimensionless Capacity correction coefficient for relative humidity, dimensionless Capacity correction coefficient for the outdoor air temperature,

ECHP ESa Ep Hsη h IF j, l ka kf kp krh kta

46


LCS m n OF PER PES PESRCHP Q RefHη REFHsη RefEη RefPER RefSη RefψRCHP s Tapp Ta TCO2 Tf Tg Tref t X Greek Symbols ηBİ ηpj ηT ηI ψRi

dimensionless Loss of CO2 sequestration potential due to deforestation, ton CO2/a Annual full operating days of the building, days/a or the coefficient in Eq. 16 Annual-averaged heat to electrical energy demand ratio, dimensionless or age Objective Function, dimensionless Primary Energy Ratio, dimensionless Primary energy savings ratio, per-cent Rational Exergy Management Efficiency embedded PES, per-cent Heat demand, kW-h Reference value for the partial heat generation efficiency by CHP, dimensionless Reference value for the supplementary heat generation efficiency, dimensionless Reference value for the partial power generation efficiency by CHP, dimensionless Reference PER value, dimensionless Reference value for the partial steam generation efficiency of the CHP system Reference value for ψRi, dimensionless Annual carbon sequestration potential of a tree per annum, kg carbon/a. Application temperature, K Design comfort air temperature in a building, K Total CO2 emissions responsibility, LCS + ∑CO2, ton CO2/a Combustion (or equivalent) temperature of exergy source (fuel), K Annual-averaged ground temperature at approximately 1,5 m, K Reference environment temperature, K time, h CO2 sequestration ratio, dimensionless

On-site boiler efficiency, first-law, dimensionless Remote power plant efficiency, first-law, dimensionless Power transmission efficiency, dimensionless First-law efficiency, dimensionless Rational Exergy Management Efficiency for on-site electromechanical systems Compounded CO2 emission responsibility of the building complex, ton ∑CO2 CO2/a ΔCO2 CO2 sequestration potential, ton CO2/a Abbreviations and Acronyms a Annum (year) ABSC Absorption cooling machine AC Air conditioning ADSC Adsorption cooling machine ASHRAE American Society of Heating, Refrigeration, and Air-conditioning Engineers Inc. BAU Business as usual CHP Combined Heat and Power (aka cogeneration)

47


CWT EU GSHP HE HWT ISO NZEB nZEB PV PVT PVTC REHVA Subscripts BS c G i j

Cold water storage tank European Union Ground-source heat pump Heat exchanger Hot-water storage tank International Standardization Organization Net-zero energy and net-zero exergy building Nearly-zero energy building (According to REHVA) Photo-voltaic Photo-voltaic-thermal Photo-voltaic-thermal-cold Federation of European Heating, Ventilation and Air Conditioning Associations Base scenario (BAU scenario) Cooling Green Within building boundaries (on-site) Off-site location

48


TESTING A SIMPLIFIED BUILDING ENERGY SIMULATION PROGRAM VIA BUILDING ENERGY SIMULATION TEST (BESTEST) Gülden Gökçen Akkurt1, Cem Doğan Şahin1, SavaşTakan1, Zeynep Durmuş Arsan1 1

İzmir Institute of Technology,İzmir


SUMMARY In Europe, residential and service buildings are responsible for more than 40% of primary energy consumption and this ratio is expected to rise. European authorities have undertaken the challange to control domestic energy consumption of buildings to reduce greenhouse gas emissions and the studies on efficient energy use have been accelerated since 1992. Most important outcome of these studies is the European Union Directive on the Energy Performance of Buildings. The Directive underlines the structure of methods which determine the energy performance of buildings for member states. Turkey is revising its legislations on building energy performance as foreseen in Directive on the Energy Performance of buildings through the European Union accession process. “Directive on Energy Performance of Buildings” were introduced in July 2008, urges to develop national building energy simulation methodologies on evaluation of building energy performance. KEP-SDM is one of the simplified methodologies developed based on the regulation. The methodologies are reliable as long as they are validated. The aim of this study is to assess the accuracy of “KEP-SDM” by “BESTEST”. Climatic zone approach and software-based weather data were implemented into KEP-SDM and the results were compared with eachother. It was concluded that sensitivity of the weather data affects the accuracy of building energy simulation methodologies quite significantly.

INTRODUCTION The building industry and the built environment are some of the largest contributors to energy and material use worldwide. In the northern part of the European Union, 41% of total final energy consumption comes from buildings, with 30% being used in residential buildings [1]. Due to importance of a good quality of the indoor environment and problems caused by high energy consumption, governments have enacted a series of policies and regulations aimed at increasing the energy efficiency of residential buildings and ensuring a good indoor environment. An example of such initiatives is the European Union Directive on the Energy Performance of Buildings (EPBD). EPBD obliges all European member states to implement

49


performance-based energy regulations aimed at decreasing energy consumption in buildings in relation to heating, cooling, ventilation, lighting and domestic hot water [1]. Turkey is revising its legislations on building energy performance as foreseen in EPBD, through the European union accession process. TS 825 which is “Thermal insulation requirements for buildings” standard, came into force at 2000, was revised in 2008 [2]. Energy Efficiency Law is released in February 2007; urging industry, transportation and residential sectors to take measures on improvement of energy efficiency [3]. The target of the law is to reduce energy intensity (kJ/$) of Turkey by 10% till 2020. Furthermore in December 2008, the Ministry of Public Works and Settlement introduced a regulation titled as “Directive on Energy Performance of Buildings” [4]. According to this regulation, new buildings and buildings under major renovation are urged to obtain an “Energy Certificate” which includes heating, cooling, domestic hot water and lighting energy consumptions as well as “Greenhouse Gas Emission Sertificate” as a result of energy consumption. In July 2008, Turkey signed Kyoto Protocol and commited to reduce greenhouse gas emmisions by 10% compared to 1998 [5]. Based on the “Directive on Energy Performance of Buildings”, national building energy simulation (BES) methodologies have been developed such as Standard Assessment Method for Energy Performance of Residential Buildings (KEP-SDM) [6] and National Building Energy Performance Calculation Methodology (BEP-TR). The methodologies are reliable providing that they are validated.The purpose of this study is to presenta methodology based on the application of a well-known validation and diagnostics procedure, Building Energy Simulation Test (BESTEST) [7],to assess the accuracy of the simplified calculation method KEP-SDM.This paper also presents comparison ofthe test results of KEP-SDM with state of the art building energy simulation program (DB) [8]. Standard Assessment Method for Energy Performance of Residential Buildings (KEPSDM) The KEP-SDM is a methodology composed for calculating the energy performance and carbondioxide emissions of residential buildings per unit floor area. The methodology is compliant with(TS 825) [2]and the Standard Assessment Procedure (SAP) [9]. The calculation is based on energy balance taking into account of a range of factors below that contribute to energy efficiency:

Materials used for construction of the residential building Thermal insulation of the building fabric Ventilation characteristics of the residential building and ventilation equipment Efficiency and control of the heating systems Solar gains through openings of the residential building The fuel used to provide space and water heating, ventilation and lighting Renewable energy technologies

The calculation does not take into account of the factors stated below:

50


• • • •

Household size and composition Ownership Efficiency of particular domestic electrical appliances Individual heating patterns and temperatures

KEP-SDM is used to calculate the energy performace of residential buildings that have floor area less than 450 m2. The methodology estimates the annual energy consumption of the buildings depending on heating, domestic hot water and lighting demand where cooling is not considered [6].

Fig. 1 Heating degree-day climatic zones in Turkey [5]. KEP-SDM uses degree-day phenomenon in calculations. Degree-day approach is still prefered way for building energy performance calculations although there are other ways. According to American Gas Association, keeping an indoor evironment at 21°C is related to difference between 18°C and daily mean outdoor air temperature. Measuring the amount of fuel to heat up any space on whenever day is calculated using the difference between 18°C and daily mean outdoor air temperature. This difference is called heating degree-day (HDD) for any particular day [10]. Since HDD changes with climatal conditions and regions the degree-day phenomenon creates HDD climatic zones apart from geographical regions. There are four different HDD climatic zones according to TS 825, which are shown on Fig.1. Building Energy Simulation Test (BESTEST) BESTEST is a procedure, which was developed by International Energy Agency (IEA) in 1995, to test and diagnose the building energy simulation programs [11]. The procedure contains several tests assessing the effect of physical properties on the results of building energy simulations. The purpose of this procedure is to create obvious, well-defined test series for software-to-software comparisons and program diagnostics. Not every simulation program requires the same input to do calculations. Hence, test series defined in BESTEST are designed to test different building simulation programs[7].

51


Table 1.Description of BESTEST Cases (Summary). Caseno. Description 8m x 6m x 2.7m ; South facing 12 m2 window, no shading; Case 600 internal gains 200W;infiltration rate 0.5 ACH; low thermal mass; Same as Case 600 with 1 m full-width overhang on south Case 610 facade Same as Case 600 but with a 6 m2 east window and a 6 m2 Case 620 west window, no shading. Same as Case 620 with 1 m overehang over windows only, Case 630 plus 1 m fins on both sides of each window. There are 36 BESTEST cases in all, plus 4 free-floating cases (no heating or cooling) [7]. These cases are classified as either qualification or diagnostic cases. A recommended way to apply the procedure is to run the qualification tests first. The remaining cases are designed for diagnostic purpose. In this study, some BESTEST cases were used to assess the accuracy of the KEP-SDM. The reason of the case selection is based on applicability of the cases to KEPSDM. The cases applied are given in Table 1.

Figure 2. Isometric view of test case 600 [8]. The Case 600 is the base case which takes into consideration of the test construction illustrated on Fig. 2. Other test cases are variations of the base construction. The Case 610 includes 1 m overhang on south facade different from Case 600. The Case 620 consider 6m2 window in the west and east facade. Lastly, the Case 630 includes 1 m overhang extended across the 3 m width of each window and side fins different from Case 620. The BESTEST was designed to assess energy simulation programs which are able to run simulation for any climatic region and location [11]. Nevertheless, the KEP-SDM runs simulations taking into account the climatic zones of Turkey. Therefore, it is required to develop a way to apply BESTEST in different climates than the original Denver, USA data [11]. In this study, the KEP-SDM was tested using the weather data of İzmir-Turkey. The following approach [11] was used to convert the BESTEST results into the weather data of İzmir. State of the art building energy simulation program [8], was selected to apply the approach. The simulation program [8] is based on the calculation methodology, Energy Plus [12], improved with weather database and 3D interface [13].

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First, all the BESTEST cases (600, 610, 620, 630) were run using the meteorological data of Denver-USA to confirm whether the energy simulation results of DB (QDB.Denver) are between the minimum and maximum values stated by the BESTEST (Qmin.Denver and Qmax.Denver). Based on the DB results for Denver and on BESTEST acceptable results, confidence intervals for each BESTEST case were calculated using (1) and (2). CImax= (Qmax.Denver – QDB.Denver) / QDB.Denver

(1)

CImin= (Qmin.Denver – QDB.Denver) / QDB.Denver

(2)

After that, the same BESTEST cases were simulated using DB for the weather data of İzmir (QDB.İzmir).Using the confidence intervals determined before and DB results for İzmir, the new acceptable range (maximum and minimum) for İzmir was determined using (3) and (4). Qmax.İzmir= (1 + CImax) * QDB.İzmir

(3)

Qmin.İzmir= (1 + CImin) * QDB.İzmir

(4)

This straightforward approach is developed using several assumptions. In reality, the new maximum and minimum results for İzmir should be determined based on reference programs in the BESTEST procedure. Nevertheless, using of all BES programs in the BESTEST takes loads of time and requires expertise. Thus, the methodology previously suggested in this study providemeans to construct ranges of acceptable results for the BESTEST cases for any location and weather, requiring miminum sources and knowledge[11]. The new minimum and maximum acceptable values of BESTEST (Qmin.İzmir and Qmax.İzmir) calculated using (1), (2), (3) and (4) are given on Table 2.

Case 600 610 620 630

Table 2. BESTEST acceptance ranges for İzmir. Qmin.İzmir (kWh/year) Qmax.İzmir (kWh/year) 1889,87 2511,46 1916,58 2546,34 2062,04 2657,00 2263,03 2898,91

RESULTS The comparison between KEP-SDM and BESTEST results are presented in Fig.s 3-6. The first and the last columns are the minimum and maximum energy demand values obtained by BESTEST, respectively. The second column represents result for İzmirweather data calculated using DB. The third column indicates result for İzmir determined using the KEPSDM (QKEP-SDM.İzmir). According to the results for Case 600 illustrated in Fig. 3, it can be said that DB is close to minimum acceptable value. In other words, DB result for Case 600 is in the acceptance range. For this case, heating energy demand calculated using the KEP-SDM exceeds the maximum acceptable value by 20%.

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Fig 3. Annual heating energy consumptions for Case 600. Analysing the results for Case 610, it can be noticed that Case 610 requires more energy demand than that of Case 600. The result for the KEP-SDM is outside of the acceptable range, when comparing to DB results. For this case, the result determined using DB is 1935 kWh per annum, which is just above the minimum acceptable value. The Case 610 has the same specifications as the Case 600 except that Case 610 has 1m full-witdh overhang on south facade. The overhang importantly increases the heating energy demand in the KEP-SDM. Similarly, the DB result shows increment for this case, but this increment is fairly lower than that of KEP-SDM. Fig. 4 illustrates the results for the Case 610.

Fig 4. Annual heating energy consumptions for Case 610. For Case 620, it can be observed that the change in window orientation increases the heating energy demand for İzmir. As can be seen from the Fig. 5, heating energy consumption calculated using the DB is less than minimum acceptable value (2062 kWh per annum). Moreover, the KEP-SDM result for this case exceeds maximum acceptable value in 24%.

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Fig 5. Annual heating consumptions for Case 620. The Case 630 includes 1 m overhang extended across the 3 m width of each window and side fins different from Case 620. The overhang and side fins increase the heating energy demand for both DB and KEP-SDM.It can be seen from the Fig. 6 that KEP-SDM presents result over the maximum acceptable value while the result determined using DB is below the minimum acceptance value.

Fig 6. Annual heating consumptions for Case 630. It can be noticed that the results calculated using KEP-SDM exceededmaximum acceptance value for all cases. The reason why all cases for KEP-SDM are above the acceptance range is thought to be weather data of İzmir based on climatic zone approach. Thus, weather data of İzmir based on TS 825 was changed with that of based on DB. Later, all cases were reperformed using weather data of İzmir based on DB, instead. The simulation results obtained using the KEP-SDM with new weather data (KEP-SDM(DB)) were compared with KEPSDM that has weather data of İzmir based on TS 825 (KEP-SDM(TS825)). Fig. 7 shows the results obtained for the new situation.

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Fig. 7 Comparison of simulation results with changed weather data. As can be seen obviously that the results obtained using the KEP-SDM(DB) are in of acceptable range for all cases. It can be noticed that accuracy of weather data used in building energy simulations affects the results significantly. CONCLUSION In this study, a simplified BES methodology (KEP-SDM) was tested using a well-known validation and diagnostic procedure, Building Energy Simulation Test (BESTEST). Furthermore, test results obtained using KEP-SDM were compared with DB. It was oserved that for all BESTEST cases the KEP-SDM results calculated using weather data of TS 825 are out of the acceptance range. For cases 600 and 610 DB results are in of the acceptable value while DB results for cases 620 and 630 are lower than the minimum acceptable value. Moreover, all KEP-SDM results obtained using weather data of İzmir of DB were in the acceptable values. As a consequence, it can be concluded that climatic zone approach of TS 825 causes errors on building energy simulations. Therefore, in BES programs it is significant to use weather data obtained from the meteorological station close to the building simulated. In the literature, Dombaycı focused on HDD and cooling degree day (CDD) numbers for 79 city centers in Turkey, covering a period of 21 years (1985-2005) [14]. Aim of this study is to determine HDD and CDD numbers for the accuracy of building energy simulations. Yılmaz inticated that the walls having the same heat transfer coefficient caused different energy consumptions in the cities having similar degree-day values at TS 825[15]. ACKNOWLEDGEMENT The authors would like to thank to Chamber of Mechanical Engineers,İzmir for their support throughout this research.

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REFERENCES 1. Santin,O G, Itard, L andVisscher , H. 2009. The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy and Buildings.Vol. 41, pp 1223-1232 2. Thermal Insulation Requirements for Buildings. 2008.Turkish Standards Institution. 3. Energy Efficiency Law. 2007. Republic of Turkey Ministry of Energy and Natural Resources. 4. Directive on Energy Performance of Buildings. 2008. Republic of Turkey Ministry of Public Works and Settlement. 5. Yaman,M C. 2009. Energy Efficiency in a University Building: Energy Performance Assessment of IZTECH Administrative Building. İzmir İnstitute of Technology Master Thesis. 6. KEP-SDM. 2008. Standard Assessment Method for Energy Performance of Residential Buildings, Chamber of Mechanical Engineers, İzmir, Turkey. 7. Judkoff,R. and Neymark,J. 1995. International Energy Agency (IEA) building energy simulation test (BESTEST) and diagnostic method. ReportNREL/TP-472-6231, NREL, Golden. 8. DesignBuilder. 2012. DesignBuilder Building Energy Simulation Software. Version 3. 9. SAP 2005. 2008. The Government’s Standard Assessment Procedure for Energy Rating of Dwellings. 2005 Ed., Revision 1, Version9.81, 10. Taylor,B L. 1981. Population-Weighted Heating Degree-Days for Canada. AtmosphereOcean, Vol.19 (3), pp 261 – 268. 11. Melo,A P, Costola,D, Lamberts,R, andHensen,J L M. 2012. Assessing the accuracy of a simplified building energy simulation model using BESTEST. Energy and Buildings. Vol.45, pp 219-228. 12. EnergyPlus. 2012. EnergyPlus Energy Simulation Software, Version 7.0.0.036. 13. Tronchin,L and Fabbri,K. 2008.Energy performance building evaluation in Mediterranean countries: Comparison between simulations and operating rating simulation. Energy and Buildings. Vol.4, pp 1176-1187. 14. Dombaycı, Ö A. 2009. Degree-days maps of Turkey for various base temperatures. Energy.Vol.34, pp 1807-1812. 15. Yılmaz, Z. 2007. Evaluation of energy efficient design strategies for different climatic zones: Comparison of thermal performance of buildings in temperate-humid and hot-dry climate. Energy and Buildings. Vol.39, pp306-316.

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INCLUDING THE BUILDING ENERGY PERFORMANCE CONSULTANCY TO THE INTEGRATED BUILDING DESIGN PROCESS: THE INDUSTRIAL BUILDING CASE STUDY IN TURKEY Alpay Akguc1, Gozde Gali1 and A. Zerrin Yilmaz2 1 2

EKOMIM Ecologic Architectural Consultancy, Istanbul, Turkey Istanbul Technical University, Department of Architecture, Istanbul, Turkey

Corresponding email: [email protected] SUMMARY In the design process of the buildings, there are many industrial services coming from different branches of science take place in different parts of the process. Nowadays, the construction industry has become a booming sector and contributed to improvement of the economy especially in developing countries but the integrated building design process is still a problem in building sector. In addition to this, energy efficient design is one of the most important issues in integrated building design process but necessary attention hasn’t been paid on it yet. In the content of this study, an industrial building that an example of integrated design was investigated. The detailed dynamic method was examined for the building energy performance analyses. The simulations were performed during the design process. As a result of the building energy performance analyses, the proposed building HVAC system became around 80% more energy efficient than the baseline building HVAC system. INTRODUCTION The building quantity rises gradually due to increasing human population so that more energy resources will be needed in the future. However, current energy resources are reducing day by day, and more energy resources mean more CO2 emissions. Buildings produce more than 30% of CO2 emissions in the EU [1, 2]. It is also stated that 40% of the entire energy consumption around the world is linked to the buildings [3]. Therefore energy saving become an important issue especially in the buildings. In order to prevent the increasing of these ratios in the future, the description of energy efficient building design comes into prominence for providing the necessary energy demand and choosing the suitable and effective HVAC systems according to the building typology. Many building parameters are necessary for the energy efficient building design about building physics, energy systems, automation systems and etc. and all these parameters are dependent together. Therefore, architects, civil engineers, mechanical engineers, electric engineers should work together during the design process as design team. Each group should be aware of that, constructing a building is to constitute an interacted system to the environment which it will be stand and it will be affected by seasonal and daily climatic changes [4]. For constructing the energy efficient building, integrated design is a very

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important process and the design teams should work collaboratively from the beginning of the design process to the end. In the beginning of the design, physical properties as building geometry, orientation, façade transparency rates, opaque and transparent components, shading elements, interior layout, thermal zones and obstacles around the building that affect the energy performance of buildings should be determined. Secondly, thermo-physical properties as heat conductivity coefficient, density and specific heat of opaque components of the building envelope and the solar heat gain coefficient, daylight transmittance values and the overall heat transfer coefficient of transparent components of the building envelope and infiltrations that are important parameters for determining the building heating and cooling loads should be decided. Besides, illuminance level, loads and efficiency of lighting equipment are also important for energy efficiency and occupancy comfort. After determining to these passive system parameters, the building HVAC equipment with appropriate capacity and efficiency should be chosen working with building automation system. All these parameters should be tested together in order to ensure the energy efficient design. For that reason, the crucial benefits of building energy performance modelling and simulation tools and consultancy on measurements to increase the building energy efficiency are being considered among building design teams [5]. Building energy performance modelling, as a decision making process on building architecture and system design, includes several segments according to the parameters taken into consideration and scale of assessment. Over the last decade, there is a respectable rise about the involvement of building energy performance simulation (BEPS) tools in building design process through scientifically developed modules by energy demand and consumption calculations, thermal and visual comfort analyses and evaluation of emission rates. Wide ranges of users from different disciplines use BEPS tools related with their specialty. BEPS tools give significant foresight, comparison and performance evaluation with various options during early-design, design and operation phases to the users. To ensure the energy efficient design in buildings, energy performance simulations should be performed in the beginning of the design process and continue until the construction process. In this paper, the industrial building was taken into account in order to consider building energy performance. The energy performance analysis of this building were carried out from beginning of the project with leading of building energy performance consultant and performance simulations continued during the design process. There are basically two types of simulation methods mostly in use; one of them is simple semi-dynamic method and the other one is detailed dynamic method. In this paper, detailed dynamic method is examined, since it guides the building design process. According to the energy performance test results, the energy consumption of proposed building HVAC system was less than baseline building HVAC system. The proposed building HVAC system became around 80% more energy efficient with integrated design. METHODS This study consists of energy performance analyses of the proposed industrial building which will be constructed in Çatalca Organize Sanayi Bölgesi. These performance results were

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compared with baseline building energy performance results which is modelled according to ASHRAE 90.1 Appendix G and energy efficiency of proposed building was determined for green building certification. The building consists of three parts as production, technical and office zones. The modelling image of this industrial building with six floors as illustrated in Figure 1.

Figure 1. The northwest and northeast façades of the building. In building energy modelling, the building is divided into 169 thermal zones and 99 of them are conditioned. The total, conditioned and unconditioned areas was shown in Table 1. Table 1. Building area. Total Building Area Net Conditioned Building Area Unconditioned Building Area

Area [m2] 21,384.62 18,385.93 2,998.69

The building energy performance was performed using detailed dynamic building energy simulation tools. The energy consumption of the building is affected by climatic conditions directly. The detailed dynamic method calculates the dynamic building energy performance using hourly climatic data in a period of one year with all dynamic loads of the building [6]. Firstly, the building annual heating and cooling demand was defined determining physical and thermo-physical properties of the building. During these processes the civil engineers, architectures and energy modelling experts were working together which are aware of the integrated design importance. Building envelope, opaque and transparent components data were determined which overall heat transfer coefficient of them minimized the building energy demand basing on TS 825 standards. Also the internal heat gains from process, occupants, electrical and lighting equipment were decided using current schedules which are very important to determine the building energy loads. The internal heat gain data is determined from ASHRAE Fundamentals 2005: 30 SI. Especially the process heat gain amounts were excessive because of thermal process in this industrial building and wrong data

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changes the performance results extremely. Entering the correct data about process heat gains and their schedules in building energy simulation tool was a serious period in order to get correct results. For that reason, the heat gains from thermal processes equipment were measured using analyser for getting real current data. Besides, the lighting systems were determined from ASHRAE 90.1-2007 basing on the lighting power density defined for all thermal zones of industrial building. Besides, the daylighting control also was carried out in order to minimize the energy consumption. As a result, the building annual energy demand was identified under the long-term climatic data with all these passive system parameters. According to the results of heating and cooling energy demands determined for all thermal zones leaded HVAC system design and automation strategies. The conditioning systems of the proposed building consist of three main parts which are heating, cooling and ventilation system. For heating, cooling and ventilation, the variable refrigerant flow (VRF) systems with fresh air intake unit were used for technical and office zones generally. These systems are controlled according to the zone set-point temperatures. Unlike these thermal zones, the heating of mess hall was ensured by under floor heating system. The control of this system was carried out according to the floor temperature and set-point temperature of this zone. The production zones were only ventilated by outdoor air units integrated heat recovery system. There was no need to heating and cooling for these zones. Because, there are many thermal process equipment in production zones and the heat gains from thermal process are excessive so the heating demand is ensured in heating season. According to the building energy performance analyses, the zone temperature in production zones was not exceeded the cooling set-point temperature taken part in occupational health and safety legislation in Turkey. Moreover, the free cooling strategy was determined for outdoor air unit in production zones that when the outdoor air conditions are suitable to cool the building, the cool outdoor air was transferred into the building using dampers in outdoor air units with temperature and humidity sensors. Besides, exhaust systems served for wet areas and technical areas. The capacities and efficiencies of all these systems were determined according to the building heating and cooling energy demand supplied by detailed dynamic building energy simulation tool and ventilation rates of breathing zones were identified according to ASHRAE Standard 62.12010. As a result of building energy improvement, this industrial building applied for green building certification program and thus the energy performance of the building is determined by comparing the annual energy consumptions of proposed building and baseline building. The baseline building was created based on ASHRAE 90.1 – 2007 Appendix G. Baseline building must be the same shape, geometry, use function, set point values and operation schedules of the proposed building. The thermo-physical properties of the building envelope of the baseline building were defined according to ASHRAE 90.1 2007 Table 5.5-4. Baseline HVAC system is determined according to ASHRAE 90.1 – 2007 Appendix G Table G3.1.1A. This system type refers to the System 8 – VAV with Parallel Fan Powered (PFP) Boxes. For heating, there are electrical heating coils and also chilled water cooling coils are used in VAV air handling units integrated to water cooled chiller and cooling tower. Besides, there are reheat units with fan system integrated to VAV air handling units in all conditioned zones. The air economiser is not used due to climate zone of this building. Minimum outdoor

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air ventilation rates shall be the same for the proposed and baseline building according to ASHRAE 90.1 – 2007 Appendix G, G3.1.2.5. The baseline building is rotated for 0, 90,180 and 270 degrees and the results of the building performance are averaged. RESULTS Passive energy modelling calculates the annual energy demands of the building. After completing all of the necessary passive modelling data that will affect the building energy performance, simulation is performed and annual heating and cooling energy demands are calculated for conditioned thermal zones in “kWh”. The results of proposed and baseline building are shown in Table 2 in kWh/m2. In this part of the analysis “Ideal Loads” method is used which means it is supposed that the heating and cooling system works without any loss. Table 2. Annual heating and cooling demands of conditioned zones of the industrial building. Proposed Building Baseline Building

Annual Heating Demand (kWh/m2) 5.39 7.13

Annual Cooling Demand (kWh/m2) 29.76 33.38

The annual energy demand of the building is necessary in order to define required HVAC system. After modelling the HVAC system with appropriate capacity and efficiency that mechanical engineers, building energy performance consultant and energy modelling experts worked together collaboratively during the energy modelling period, the annual energy consumptions of proposed and baseline building was obtained and shown in Table 3. Table 2. Annual electrical consumption of conditioned zones of the industrial building.

Heating Cooling Interior Lighting Exterior Lighting Interior Equipment Exterior Equipment Fans Pumps Heat Rejection Heat Recovery TOTAL

Proposed Building - Annual Electrical Consumption [kWh/year] 79,635.75 98,416.02 77,508.23 8,074.57 3,776,672.85 1,064,595.55 250,240.35 9,21 0 13,889.20 5,369,041.73

Baseline Building - Annual Electrical Consumption [kWh/year] 709,768.32 435,838.00 212,310.11 8,074.57 3,776,672.85 1,064,595.55 563,211.91 391,798.12 36,639.90 0 7,198,909.32

The annual energy consumptions for proposed and baseline buildings according to the type of energy usage are illustrated in Table 3.

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Table 3. The annual energy consumptions according to type of energy usage. Proposed Building - Electricity Intensity [kWh/m2.y] 4.00 20.03 227.04 251.07

Lighting HVAC Other TOTAL

Baseline Building - Electricity Intensity [kWh/m2y] 10.31 99.94 226.39 336.64

Consequently, the annual energy costs shall be compared between proposed and baseline building in order to obtain energy improvement of proposed building for green building energy certification. For that reason, the annual energy costs were calculated using the unit price of electricity ensured from Trakya Serbest Bölgesi. The results and energy improvement were shown in Table 4. Table 4. The annual energy costs and energy improvement rate. Proposed Building Baseline Building

Annual Electrical Energy Cost [TL/year] 1,353,106.76 1,814,270.28

Energy Improvement (%) 25.4

DISCUSSION In this study, the effects of recommended energy performance improvements to proposed building was investigated. Proposed building energy performance improvement was increased by 25.4% in comparison to baseline building modelled using definitions given in ASHRAE 90.1.2007, Appendix G. Moreover, it is seen that, when the annual energy consumptions of HVAC systems, in accordance with improved architectural design and lighting system, are compared between proposed and baseline building, the energy efficiency improvement of proposed building is around 80% as extremely high rate. It shows that, both of the HVAC systems and the architectural systems in the building are highly energy efficient and quite compatible together. In conclusion, the energy modelling of the building is able to be carried out using all building data by detailed dynamic building energy performance tools so the effects of design determinations on initial investment costs and operation costs are able to be considered. After obtaining detailed information about the building energy consumption trends, various optimization strategies can be developed to reduce the energy consumption values. By means of building energy performance tools which collect the architects, engineers and other design groups, many industrial services coming from different branches of science work together during design period collaboratively. Nowadays, the building energy performance analysis has become a part of the design process and even complementary. In addition, the energy companies that provide energy to the occupants and settlements may predict the future energy demands and constitute the future energy planning by using the energy simulation results.

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ACKNOWLEDGEMENT I would like to thank to design team that, aware of the integrated and ecological design importance, for their precious contribution to this project. REFERENCES 1. 2. 3. 4. 5.

6.

Dijk Dv, Spiekman M. 2004. Energy Performance of Buildings Outline for Harmonized EP Procedures. Final Report. ENPER-TEBUC study. Task B6. N.N Directive 2002/91/EC of The European Parliament and of The Council of 16 December 2002 on The Energy Performance of buildings. 2002. Omer A M. 2008. Energy, Environment and Sustainable Development. Renewable and Sustainable Energy Reviews 2008; 12(9):2265–300. Goulding, J.R., Lewis, J.O., Steemers, T. C., 1993. Energy Conscious Design, A Primer for Architects, London, UK. Yilmaz A, Z, Kalaycioglu E, Akguc A. 2012. The Samples of Energy Modelling for Energy Efficient Green Building Design in Turkey. Building Simulation and Optimization 2012, 10-12 September 2012, Henry Ford College, Loughborough University, UK. Bayraktar, M, Schulze, T, Yilmaz A Z. 2009. Binalarda Enerji Simulasyonları için Veri Toplama Listeleri Aracılığıyla Veri Yönetimi Modelinin Oluşturulmasi. IX. Ulusal Tesisat Mühendisliği Kongresi. Izmir, Turkey.

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USING ARTIFICIAL LIGHTING IN HARMONY WITH NATURAL LIGHTING THROUGH AN OFFICE BUILDING EXAMPLE Gözde Gali1, Alpay Akgüç1 and A. Zerrin Yılmaz2 1 2

Ekomim Ecologic Architectural Consultancy, Istanbul Istanbul Technical University Faculty of Architecture, Istanbul

Corresponding email: [email protected] SUMMARY Building energy performance analyses and increasing the performance level to an optimum level has become crucial correspondingly to the developments in legislations. To this aim, performing annual energy demand and consumption analyses of the buildings happened to be one of the most important part of the construction sector. In this paper, only electricity consumption for lighting and annual heating and cooling energy demands will be analyzed. Over the years, artificial lighting design has been done without considering the effect of natural lighting; the purpose of this study is to consider using both lighting methods together and show the effects of this on annual energy demand. To evaluate the effects of the lighting method selection, an office building is used in the assessments. The evaluations have been done by using detailed dynamic building energy simulation tools. Finally, the significant effect of selecting the right lighting method is defined in detail. INTRODUCTION Due to the gravity of being run out of energy and also the harmful effects of fossil fuels energy conservation has gained great importance; in buildings that cause about 40% of energy consumption, energy efficiency studies picked up. Energy efficiency is consuming less energy to provide required services in buildings. In order to improve the energy efficiency level of the buildings by following standard rules, legislations have been formed that define the optimum levels in national base. To this aim, the European Union published Energy Performance of Buildings Directive (EPBD) in 2002 [1]. Within the adaptation process to EU legislations, in Turkey, Building Energy Performance Regulation was published in 2008 and with this regulation all of the buildings have been required to get energy certificate by the national building energy performance calculation method BEP-TR [2, 3]. After that, an important modification has been done in EU and EPBD-Recast 2010 was published [4]. It basically identifies that during the optimum energy efficiency level studies for buildings, costs/initial investment costs have to be considered. Therefore, the term “nearly zero energy buildings” replaced all the other terms. In other words, to be able to provide maximum energy efficiency level in buildings the costs may be too high, however providing optimum energy efficiency level in buildings would be enough with lower costs. Therefore, in the scope of this paper cost analyses are performed too to identify the optimum level. The other way to analyze the building energy performance level is voluntary green building certification systems, however it is important to have knowledge about the first method explained above. Voluntary certification system is nowadays also a kind of prestige symbol

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and LEED, BREEAM and CASBEE are the most popular ones. The aims of these methods are lower operating costs and increase asset value, reduce waste sent to landfills, conserve energy and water, be healthier and safer for occupants, reduce harmful greenhouse gas emissions, qualify for tax rebates, zoning allowances and other incentives in hundreds of cities [5]. This system calculates the energy performance level of the buildings more detailed, therefore after these kinds of analyses the buildings would be more energy efficient in comparison to the national certification method. Annual energy consumption of the building depends on the energy consumed for heating, cooling, ventilation, domestic hot water, and lighting. All of these indicators have been analyzed by a lot of scientific people and within the content of this paper, only the effects of lighting have been analyzed. There are academic studies that evaluate the effect of lighting on energy consumption and the costs together and also standards based on this subject [6, 7, 8, 9]. After all, energy efficiency improvement studies usually base on improvements in building envelope and HVAC systems. Considering that situation, this paper is to emphasize the importance of lighting design in energy efficiency of buildings. Lighting includes the use of both artificial light sources and natural illumination by capturing daylight. Usually, in architectural design projects after the design process, electric engineers place the artificial lighting fixtures according to the design. This phase may be done only by considering the architectural design concept and placing the most appropriate lighting fixtures under the name of design. In another way, during this phase lighting specialists may calculate the peak values for lighting level of each room and places the appropriate lighting fixtures to provide the calculated maximum level. However, in both methods the effect of daylight is ignored. For example, if the effects of daylight have been added to the calculations there might be no need to artificial lighting provides the maximum level alone. There are international standards that suggest minimum illumination level and allowed lighting power density (LPD) for each type of room and should be used in voluntary green energy certification system analyses [10, 11]. Within the scope of this paper, an office building project is evaluated to analyze the effects of lighting system and the aim of the project is to get “EA Credit 1: Optimize Energy Performance (Option 1)” within LEED green energy certification system. The office building locates in Izmir, Turkey. Izmir is in hot-humid climatic zone, so it has long, very hot and humid summer conditions and winter condition is not severe. The climatic condition is very important for daylight analyses and Izmir is very lucky about visible light. METHODS The office building has three basement floors and seven standard floors. Two external facades of the building are adjacent to the other buildings therefore, there are only two external facades that are directly affected from external climatic conditions. These two external facades are on South and East directions. Figure 1 shows the form of the building. The conditioned building area equals to 1,131.5 m2 and the total building area equals to 1,926.4 m2.

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Figure 1. Office building During the building energy performance analyses detailed dynamic energy simulation tools that LEED recognizes are used. These tools base on detailed-dynamic calculation method that described in EN 13790: Energy Performance of Buildings – Calculation of Energy Use for Space Heating and Cooling [12]. According to the LEED certification system, there is a baseline building definition to compare the existing building energy performance results and find out the performance level. So, the baseline building provides the minimum performance requirements. Baseline building is in the same place and has the same geometry with the existing building, but in terms of thermodynamic properties of the building envelope and mechanical and lighting systems complies with ASHRAE 90.1-2007: Energy Standard for Buildings except Low-Rise Residential Buildings for each climatic zone [11]. The building envelope is also a variable between baseline and existing buildings, in the scope of this paper only the lighting power density (LPD) and illumination level requirements of the baseline building is used. During the analyses all of the other parameters that affect the building energy performance are kept constant and only lighting method is altered. So, physical and thermo-physical properties of the building envelope are same in all of the cases. In addition, there is no detailed HVAC system definition since the analyses are performed for annual energy demand. Instead, Ideal Loads method is defined to the building model that hypothesizes that the heating and cooling systems work without any loss. The first case (Case 01) is the existing building lighting system selection. This system is defined according to the corporate identity of the office building. In this plan, there is an automation system in connection with daylight in ground floor and 1st and 2nd floors. The second case (Case 02) is to use the existing lighting system plan, but with LED lamps. This case is formed to understand if the optimum performance level is possible without changing the whole system, since the planning reflects the corporate identity. The automation system exists in the same floors.

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The third case (Case 03) is formed according to the baseline building LPD values; but these values supposed to be the minimum and better values are aimed in this case. There is automation system in all of the floors except basement floors. The fourth case (Case 04) has the same plan; however for LPD baseline building values have been used. The aim in here does not to model the baseline building, since it is only to compare the lighting systems. This case did not evaluated in the cost part, since it is not possible to apply the exact values that ASHRAE requires for each room. The automation system is same with Case 03. The illumination level for each room is determined according to EN 12464: Light and lighting. Lighting of work places. Indoor work places [10]. According to the standard, the supposed illumination levels are shown in Table 1. Table 1. Illumination levels according to EN 12464. Room Type Offices Lobbies Circulation areas Wet zones Storage rooms

Illumination level (lux) 500 300 150 100 200

The automation system in connection with daylight works during the presence of daylight and for example in an office place, if the daylight provides 300 lux, the dimmable lighting system switches on enough to provide 200 lux. That is an important energy saving method. Usually in building projects this system is not used, fortunately the corporate identity and location of this office building is suitable for this system. RESULTS Input Data Comparison During the analyses Case 01 and Case 02 are compared first, to see the effect of LED lamps. Secondly, Case 04 is compared to them, to see the impact of minimum standard LPD values, and finally Case 03 is compared to all to see the impact of energy efficient LPD level. So, it is important to mention that the existing lighting system plan has higher LPD values than ASHRAE requirements. There is a long list for these values, however a summarize table is shown in the scope of this paper in Table 2. Table 2. Internal gain values in W/m2 according to the cases Room Type Offices Lobbies Circulation areas Wet areas Storage rooms

Case 01 21.86 14.26 15.41 16.87 4.87

Case 02 11.35 7.40 8 8.06 2.84

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Case 03 8.03 6.60 10.67 4.03 3.48

Case 04 12 14 5 10 3


For circulation areas the minimum internal gain value just could be provided with LED lamps in Case 02 because of keeping above of a certain illumination level value and this value could not be provided less than 10 W/m2 if the lamps are not LED lamps. Keeping all other parameters that affect the building energy performance level constant, just changing the internal values as in Table 2 and the automation system as explained in methods detailed simulations have been performed for each case. The illumination levels are also kept constant as in Table 1. According to these, the annual energy demand and electricity consumption for lighting results of each case are shown in Table 3. Table 3. Annual energy load results Cases

Case 01 Case 02 Case 03 Case 04

Annual Heating Demand (kWh/m2.y)

Annual Cooling Demand (kWh/m2.y)

3.62 4.24 4.77 4.69

35.74 32.46 30.65 31.11

Annual Electricity Consumption for Lighting (kWh/m2.y) 18.93 13.48 9.25 9.77

Whole Systems (kWh/m2.y) 74.70 66.59 61.08 61.97

Lighting energy has an important effect on annual heating and cooling energy demands. Since lighting fixtures radiates heat they are also a heating source, for this reason a room with a higher internal heat gain from lighting will need less heating energy, however the cooling energy demand will be higher. For example, as in Table 3, Case 01 has less heating demand and more cooling demand than Case 02. Since Case 02 has less internal heat gain from lighting (5.45 kWh/m2.y lower) it has more heating demand and less cooling demand than Case 01. Because the internal heat gain is lower, cooling demand will be lower correspondingly. However, the increase and decrease ratios are not the same for annual heating and cooling demand and electricity consumption for lighting. Electricity consumption changes in a greater ratio than the annual energy demand results, therefore the effect of this can be seen on whole system results. According to Table 3 the most energy efficient case is Case 03. As mentioned before, the costs are also very important according to the latest arrangements in EU legislations. The annual energy load results are also investigated in annual energy costs level. To be able to define the savings electricity unit value is taken as 0.26458 TL/kWh as Energy Institute offers for commercial buildings in Turkey. The annual energy cost comparison between cases is shown in Table 4. The annual energy cost of the existing building is represented with “A” and the differences of the other cases are shown accordingly. Table 4. Annual electricity cost comparison Cases Case 01 Case 02 Case 03 Case 04

Annual Electricity Cost (TL/y) 38,074.38 33,940.34 31,587.47 31,130.05

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Energy Cost Difference According to the Existing Project (TL/y) A A-4,134.04 A-6,944.33 A-6,486.91


According to Table 4, if the lighting system suggestion in Case 03 is applied each year 6,944.33 TL less will be paid for electricity expenses. According to the latest legislations initial investment cost of energy efficient system suggestions is one of the most important parameters that need to be considered while defining the optimum system. Table 5 shows the initial investment cost comparison and the payback times for each scenario except Case 04 as explained before. The lighting fixture and automation system unit costs are taken from a lighting system firm that the office building works with. The initial investment cost of the existing building is represented with “B” and the other cases are compared to that. Table 5. The effect of energy efficient cases to the initial investment cost Cases Case 01 Case 02 Case 03

Effect of Energy Efficient Lighting System Cases to the Initial Cost (TL) B B+18,148.2 B+7,011.5

Payback Time 4 years and 3 months 1 year

1 year is a short payback time, since the building will exist at least 20 years. Therefore, after 1 year, each year 6,944.33 TL less will be paid for the energy demand expenses. DISCUSSION It is important to keep in mind that, in energy performance analysis studies while changing a parameter it affects all of the other parameters. In this investigation, the aim was only changing the lighting values and it was shown that changing the lighting values have a significant effect on annual energy demands. It may be said that all parameters that affect the building energy performance are connected to each other. Therefore, it is important to consider the annual energy demands while analyzing the effects of lighting and annual energy demands should also be calculated. Case 01 is the existing project and in Case 02 only the lamp type is changed into LED lamps. This change has an important effect on annual electricity consumption for lighting, since LED lamps are known as energy efficient. This modification only has effect on annual energy demand, and it increased the annual heating demand and decreased the annual cooling demand. Afterwards, Case 04 is simulated with ASHRAE 90.1-2007 required LPD values. These values are higher than Case 02 LPD values; however the annual electricity consumption result is less than Case 02. That is because automation system in connection with daylight is used every floor that affected by daylight in Case 04, while in Case 02 this system is applied only to the ground floor and 1st and 2nd floors. Therefore, it is obvious that automation system has an important effect on annual energy loads. Finally, Case 03 is simulated with lower LPD values than Case 02 and Case 04. This case has the same automation system plan with Case 04, only the LPD values are different and the

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annual energy load results are less than Case 04. That is because of the lower LPD values. Also, the annual energy load results are less than Case 02. The reason of this is both lower LPD values and better planned automation system. Case 03 is also more economic in the name of initial investment cost in comparison to the all other cases. Therefore, it can be mentioned that Case 03 is the optimum option for this building. This investigation also shows that it is not important to use energy efficient products, also right application is the most important thing to do. In addition, right application is not always the most expensive one, therefore both energy performance and cost analyses are very important in determining the optimum system. REFERENCES 1.

2. 3.

4.

5. 6. 7.

8.

9. 10. 11.

12.

EPBD, 2003. Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the Energy Performance of Buildings. Official Journal of the European Union L 001, 65–71. TC Bayındırlık ve Iskan Bakanlığı, 2008. Binalarda Enerji Performansı Yönetmeliği. TC REsmi Gazete, 27075, Ankara. TC Bayındırlık ve Iskan Bakanlığı, 2010. Binalarda Enerji Performansı Ulusal Hesaplama Yöntemine Dair Tebliğ (Tebliğ No: YİG/2010-02) - Bina Enerji Performansı Hesaplama Yöntemi, Bina Enerji Performansı – Isıtma ve Soğutma için Net Enerji İhtiyacının Hesaplanması, TC Resmi GAzete, 27778, Ankara. EPBD-Recast, 2010. Directive 2010/31/EU of the European Parliament and of Council of 19 May 2010 on the energy performance of buildings (recast). Official Journal of the European Union, L153/13-35. Url-1, , accessed in 18.05.2013. Mahlia, TMI, Said, MFM, Masjuki, HH and Tamjis, MR. 2005. Cost-benefit analysis and emission reduction of lighting retrofits in residential sector. Energy and Buildings, 37, 573-78. Mahlia, TMI, Razakb, HA, Nursahida, MA. 2011. Life cycle cost analysis and payback period of lighting retrofit at the University of Malaya. Renewable and Sustainable Energy Reviews, 15, 1125-32. CEN.2007. EN 15251, Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics, Brussels-Belgium: European Committee for Standardization. CEN.2007. EN 15193, Energy Performance of Buildings. Energy Requirements for Lighting, Brussels-Belgium: European Committee for Standardization. CEN.2011. EN 12464-1, Light and lighting. Lighting of work places. Indoor work places, Brussels-Belgium: European Committee for Standardization. ASHRAE. 2007. ANSI/ASHRAE/IEASNA Standard 90.1-2007, Energy Standard for Buildings Except Low-Rise Residential Buildings, Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. CEN. 2008. EN ISO 13790, Energy Performance of Buildings – Calculation of Energy Use for Space Heating and Cooling, Brussels-Belgium: European Committee for Standardization.

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FROM HIGH PERFORMING BUILDINGS TO NEARLY ZERO ENERGY BUILDINGS: POTENTIAL OF AN EXISTING OFFICE BUILDING Cristina Becchio1, Stefano Paolo Corgnati1 Valentina Monetti1 and Enrico Fabrizio2 1 2

DENERG, Politecnico di Torino, Corso Duca degli Abbruzzi 24, 10124, Torino, Italia DISAFA, University of Torino, Via Leonardo da Vinci 44, 10095 Grugliasco (TO), Italia

Corresponding email: [email protected] SUMMARY The EPBD recast establishes that by the end of 2020 all new buildings must be nearly zeroenergy buildings (nZEBs), that are defined as buildings with a very high energy performance and that require a nearly zero amount of energy, a very significant extent of which should be covered by renewable sources. As no minimum harmonized requirements are fixed by the EU Directive, on this regards, the US high performing buildings represent a starting point to look at as a reference. The aim of this paper is to some guidance to EU Member State into nZEB roadmap by examining a set of US high performing buildings. Especially the energy performance of an existing high performing office building was analyzed by means of dynamic energy simulation and additional efficiency solutions have been applied to it in order to adapt it to the current day and to make it converge towards a nZEB. INTRODUCTION The energy consumption ascribed to the building stock amounts approximately to 40% of the whole Europe’s energy needs [1], even far more than the transport sector [2]. Thus, it’s urgent to decrease this high energy consumption. With regard to this urgent issue and in connection to the 20-20-20 targets, the European Union set a binding legislation in terms of buildings energy performance, referred to as the European Directive 2010/31/EU or Energy Performance of Buildings Directive (EPBD recast). It especially requires Member States to define proper national path to improve the energy performance of their building stock moving forward to reach nearly zero energy targets. The Directive addresses both new and existing buildings but, due to the noted low efficiency rate of the existing dwellings and the low replacement rate of old dwelling by new buildings, (around 1-3% per year) [3], retrofit actions on the existing stock represent a crucial and major step to deal with. While new buildings can be constructed with high performance levels, existing buildings that represent the vast majority of the European building stock, are predominantly characterized by very poor energy performances and consequently in need of renovation work [4]. The renovation of existing buildings stock offers significant potential for both cost-effective CO2 emissions mitigation and substantial energy consumption reduction, with a minimum energy savings estimable to 60-80 Mtoe/year in final energy consumption by 2020. Therefore existing buildings renovation more than new buildings construction is the Europe’s biggest resource in terms of energy and emissions savings and with its potential to reduce energy consumptions and emissions of greenhouse gas and other pollutants, they can have a crucial role in hitting 2050 targets.

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Nevertheless, it remains unclear which concrete actions and legislative measures are necessary at the EU level to reach these long-term targets. To this end, the EPBD recast on the energy efficiency of buildings [1] requires Member States to improve buildings energy performance aiming to the nearly zero energy target by 2020. The Directive does not give a technical definition of nearly zero-energy buildings (nZEBs) but simply describes nZEBs as buildings that have a very high energy performance. The nearly zero or very low amount of energy should be covered by renewable sources. Since the EPBD does not define clearly minimum harmonized requirements, it will be up to Member States to establish a national framework for the definition of a very high energy performance. On this regard an ad hoc Rehva Task Force was established to support Member States experts providing them with a proper technical nZEB definition [5]. Nearly zero energy buildings were defined as technically and reasonably achievable national energy use of > 0 kWh/(m² a) but no more than a national limit value of non-renewable primary energy achieved with a combination of best practice energy efficiency measures and renewable energy technologies which may or may not be cost optimal. This definition allows to take into account local national conditions and to use the comparative calculation framework as defined by EPBD guidelines. Similar concerns have aroused also in United States, where buildings consumes as well 40% of primary energy and 71% of the US electricity [6]. US Department of Energy (DOE), together with the collaboration of ASHRAE are taking concrete steps towards this issue and aimed the net zero energy buildings (NZEBs) to be the market-viable standard by the 2030, where, NZEBs are buildings that produce as much energy as they use. This plan, referred to as ASHRAE Vision 2020 [6] is a critical milestone to be achieved. However a strong strategic plan together with helpful tools (e.g. Design Guides and Standard) and reference project have been set by ASHRAE to guide the building community. In order to fulfill this target US high performing buildings (HPBs) represent a starting point to look at as a reference. The Energy Independence and Security Act of 2007 [7] defines a high performance building as a building that integrates and optimizes on a life-cycle basis all major high performance attributes, including energy conservation, environment, safety, security, durability, accessibility, cost-benefit, productivity, sustainability, functionality and operational considerations. A high performance building can thus be seen as the first step to be taken towards NZEBs. Moreover, to assist professionals to design high-performance building ASHRAE has developed the Standard 189.1P for high-performance green building and a certification program. A Database on HBPs project has also been developed by the U.S. DOE and the National Renewable Energy Laboratory (NREL), as a shared resource for the building industry [8]. To deal with HPBs in Europe, it is not so clear as in US. First in Europe the high performing building term and definition is not harmonized. Rather than it other terms are used for indicating buildings with low energy consumptions. The most used ones are “low energy house”, “passive house” and “energy saving house” [9]. The main difference between European terms and the US ones is that in Europe they are mostly informal and descriptive. They are not accompanied by benchmarks and not have harmonized calculation methodologies. On this account the US approach represents a good lesson to be learnt by European Member States, also on the way to define their national roadmap, on the basis of the EPBD framework, towards the nZEBs. This paper aims to examine some recognized US high performing buildings and to provide some guidance to European Member States into nZEBs roadmap. In particular, an existing

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high performing office building was analyzed. Since the case study represents a high performing building for its construction period, additional efficiency solutions have been applied in order to make it converge towards an European nZEB. The energy consumptions of the selected building and the impact of the efficiency measures have been assessed by means of dynamic simulation within the EnergyPlus code. U.S. HIGH PERFORMINH BUILDINGS VS EUROPEAN nZEB This study reviewed various office buildings, new and renovated ones, referred to as high performing buildings and published from 2010 up to now in the ASHRAE HPBs magazine. In particular 16 existing buildings, mainly located in United States but also in Northern America and a few European sites, were selected and analyzed in order to provide additional guidance on the energy-saving strategies to be adopted in the European context. All case studies shared similar design intents as they all aimed to design new sustainable office with a low environmental impact and low energy consumptions. Providing a high quality and comfortable office working environment was also one of the main goals pursued in all projects as the workers productivity had not to be comprised but indeed enhanced by allaround applied energy saving and sustainable strategies. A special attention to the environmental issues in the design process was also aroused by the intent of accomplishing good rating scores in well-known certification protocols [10,11] . In fact almost all case studies managed to achieve outstanding rating scores in LEED protocol or similar ones. In regards to that, advanced dynamic simulation tools were used to assess the building energy performance and the related obtainable energy savings. The evaluated energy consumption was compared to real data and when, in some cases, they were not verified, the building operation and system efficiency were checked. The not compliance of real consumptions with simulated data was often due to the building operation, later altered on the basis of the real building occupancy. Moreover the use of advanced simulation programs allowed to optimize the building envelope and form. In general the design strategies adopted to achieve these goals can be outlined as follows: - Design of a high-performing building envelope; - Optimization of lighting system to reduce the related energy consumptions; - Maximize the use of daylight; - Design the HVAC system to minimize the energy use; - Use of control points to operate the building systems to ensure high-level comfort to maximize the systems efficiency and to reduce the energy consumptions; - On-site energy production from renewable sources. Furthermore almost all buildings designs were associated with commissioning to ensure that the operation of all systems works properly. This is the case for example of the simulated energy savings to be verified with the monitored ones. The case studies analyzed are not characterized by energy efficiency measures highly different from the ones usually adopted in the design of an European low energy building. Nevertheless, if a difference has to be point out, in regard to the building envelope performance, the minimum requirements of U-values are usually stricter in the European countries than in US. For example the exterior walls U-value set by Italian regulation [12] is 0.33 W/m2K in climate zone E while the mean value in HPB analyzed is 0.45 W/m2K as shown in Figure 1. For the design of a nZEB the U-values are even lower.

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Furthermore what in particular marks all projects is a special attention to minimize the energy delivered to heating and cooling systems, trying to avoid the use of such systems thanks to correct bio-climatic and passive design strategies. When needed the HVAC and ventilation systems were designed to be simple and high-efficient systems and operating on the basis of the real building occupancy through the use of Building Automation and Control Systems (BACs).

Figure 1. U-value of the main envelope components of the HPB analyzed. The lesson that can be learnt from US HPBs and applied to the European is that a major implementation of BACs and a correct design and sizing of buildings systems are strongly pursued also due to the building commissioning. THE CASE STUDY Project description The case study hereby analyzed, called “Grand View”, is the new headquarters [13] of one the most important Canadian consulting firm dedicated to the design of green buildings. Located in central-east of Canada, in the Ontario province, the office buildings was designed aiming to an healthy and sustainable environment, achieving a triple LEED Platinum certification under the New Construction, Commercial Interiors and Existing Buildings: Operations and Maintenance rating systems in 2011. The office building was completed and occupied since September 2009. First years consumptions were monitored and compared to the estimated one, simulated.

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The three-storey office building has approximately 2000 m2 with a 12 m footprint and a EastWest orientation to optimize the exposures. The building has a rectangular plan with a distribution area in the center and open and cellular offices ones. With regard to the design of the building envelope, insulated concrete forms were used for façades together with triple-glazed, low-emissivity and argon filled fiberglass windows. The mechanical system has been designed to be simple and functional, guaranteeing high energy efficiency. The heating/cooling system and the ventilation one have been designed to be and operate separately. Three air-source heat pumps are located on the roof and serve one floor each. The ventilation system operates with energy recovery ventilators for each different zone. The air handling unit is also connected to a geothermal system to decrease the amount of energy needed to heat the air to be introduced in indoor ambient. Moreover the heating produced by the computer server rooms is used to preheat the building domestic hot water demand. Building automation and control systems are fully adopted to customize the building system operation on the basis of the real and not estimated occupants. Occupancy sensors are thus set for the heating and cooling system control as well as for turning on/off the lights when the space is unoccupied. The artificial lights are dimmed automatically when, due to specific sensors, the daylight is sufficient and they are turned off when the space is unoccupied. Additionally with a roof rainwater collection system, the office building achieved a 82% savings in the use of domestic hot water. Rainwater is thus used as non potable water to flush toilets. Energy Modeling The building energy performance was assessed by means of dynamic energy simulation through the EnergyPlus program (version 8.0). The energy model was calibrated on the basis of the real energy consumptions of the first year of operation in order to have a suitable model to be use for the nZEB implementations. The objective of the energy evaluation was to determine the annual overall energy use in term of delivered energy (divided by sources) and primary energy, which includes energy use for heating, cooling, lighting and equipment. The simulations were run in standard weather conditions with the Typical Meteorological Year (TMY) data of London near Ontario, Canada [14]. The building was modeled in 18 thermal zones overall: 17 conditioned zones and 1 non conditioned basement. The real project does not have a basement since the company philosophy aims its worker to use public transit providing them a discount on it. As this study aimed to compare HPBs to European nZEBs, a basement was added to the original model to customize it to Europe. The ground floor and the first floor are composed of five thermal zones each while the second floor is divided in four ones. The distributive areas, (stairs, entrance and services) are respectively modeled as a unique zone for all three floors. The open office area is modeled separately from the cellular offices, located in the North and South façades. The offices interior partitions were defined as internal mass. In order to carry on accurate dynamic simulations, the influence of the surrounding urban context was taken into account and modeled as shading surfaces with their own reflectance properties. In particular the presence of a grove in the building site was considered especially during the summer season. For the envelope characterization, its components were modeled and defined on the basis of their noted R-values. Due to the lack of information about the real building usage of electrical equipment, the schedules regarding the internal gains were

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defined on the basis of rule of thumbs distinguishing the zones respectively by working and service areas. The occupancy, lighting and equipment schedules and the power densities were extracted from the reference building models database developed by the US Department of Energy laboratories [15]. Lighting power densities were set to 4.6 W/m2 based on the actual metered data, which is quite low compared to the Standard 90-1 requirements but it is due to the usage of daylights sensors. The heating system has been assumed to be operating from the 1st of October to the 30th of April in order to guarantee the occupants comfort. The interior temperature set point was differentiated for office areas (21°C) and service area (20°C). The cooling system was assumed to be operating in the remaining months. The interior temperature set point was set to 24°C from the 1st of May to the 30th of June and during September. From the 1st of July to the 31th of August, due to higher outdoor temperatures, the cooling temperature set point was set to 25°C. The heating and cooling system were assumed to operate depending on the building occupancy. During weekdays, the outdoor air flow rate, always operating, was set at 11 l/s per person.

Figure 2. Axonometric view of the case study with the subdivision in thermal zones.

From HPB to nZEB: energy efficiency measures and results The energy evaluation performed demonstrated the building energy performance could be improved to reach a nearly zero standard. A few energy efficiency measures were studied and applied to the original project, referred to as baseline HPB, in order to point out the real feasibility to make the selected case study an “acceptable” nZEB, as defined in [5]. In particular, as highlighted previously in the second paragraph, envelope performances represent the first issue to point at. The “Grander View” building envelope components are characterized by stricter U-values than the HPBs ones analyzed previously, but they can still be improved. For instance, in Europe the Passive House guidelines [16] suggest all building opaque components should be so well-insulated that their U-values do not exceed 0.15 W/m2K.

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Walls Windows Roof Ground slab

U-value [W/m2K] 0.20 1.36 0.17 0.52

HPB higly insulated

HPB baseline

Table 1. Thermal features of the case study (HPB baseline) and of the case study highly insulated Walls Windows Roof Ground slab

U-value [W/m2K] 0.14 1.2 0.15 0.16

On this account, with regard to Italian context, the optional U-values of Turin city regulation [17] were applied to the building model. Table 1 lists the thermal features (U-values) of the main envelope components of the case study as it is (HPB baseline) and of the case study highly insulated, in compliance with [17]. The only improvement of the thermal insulation hasn’t brought to a drastic reduction on the space heating energy consumptions, as shown in Figure 3. In a country like Canada, where there is a quite cold climate, the heating energy need represents one of the first issue to look at when designing a building. However in the case study, the building typology and the shape did reduce the effect of such thermal insulation improvement on the heating energy needs. The building envelope was modified to be highly insulated but the heating energy consumptions was reduced only by the 15% on the overall. This proved the building was still far from zero goals.

Figure 3. Annual end uses consumptions. In order to reduce the energy consumptions and to drive the building energy performance towards nZEB ones, the next step dealt with the production from renewable sources. In particular, as the HPB baseline produces onsite energy from renewable source with a PV system installed on the flat roof. A PV panel with an efficiency of 21 % was selected. To increase the production of energy from system, additional efficiency configuration of PV panels were studied: - covering of almost the entire roof (the atrium roof was excluded), approximately 580 m2, with PV panels (power: 124 kWp). - covering of one half of the roof, 330 m2, with PV panels (power: 71 kWp); - covering of a quarter of the roof surface, 150 m2, with PV panels (power: 33 kWp).

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In Table 2 the results of the simulation carried on with the different PV system configuration. The total covering of the roof accomplished to cover the electricity needs for the lighting and the equipment system together with the HVAC system. The heating energy consumption was thus not covered, and needtherefore further energy efficiency measures to be reduced. Table 2. Energy production on-site and electric coverage

HPB Baseline+PV10% HPB insulated+PV10% HPB insulated+PV25% HPB insulated+PV50% HPB insulated+PV100%

Net Electricity Coming From Utility [kWh]

Surplus Electricity Coming From Utility [kWh]

Total On-Site Electric Sources [kWh]

Percent coverage electricity

144327 144327 105361 51871 -3440

0 0 5955 28317 68660

4430 4430 43395 96885 152197

3% 3% 27% 65% 100%

CONCLUSION Nowadays high performance buildings, low energy performing building or also the so called green buildings are still few and cutting-edge. Even if the energy policies all-world-wide strive to reach net zero target, these new generations of buildings are still a bit of a handful compared to the whole existing building stock. Among them, the LEED-certified buildings are the most famous ones also due to the market differentiation that has grown around. U.S. and ASHRAE approach in supporting the net-zero goal applied to the building stock, has also been successful thanks to a high money investment. In particular an outstanding example of these design representative and showpiece buildings is the National Renewable Energy Laboratory (NREL) campus in Colorado. It was design to be the first and largest truly netzero energy facility in the United States. The analyses carried on within this study demonstrated that US high performing building are still far from been considered as a first step nZEB o NZEB. Especially, taking in consideration the age of the analyzed HPBs, the energy performance of these buildings nowadays is not as good as estimated. The HPBs studied are in fact 5-6 years old and most of them do not comply with the European standard in terms of energy regulation (e.g. Passive house requirements). To this end, it is thus more difficult to reach a net zero target, taking as a starting point an even though recent high performing building, than considering a new construction or also an existing, but actually “aged” building. Drawing a better picture at the European level, the existing building stock is quite old and a large share of it was built before 1960s [4], when there were no regulations in terms of energy requirements or they were quite low. The insulation levels of the existing building stock are thus inefficient and together with old system leave a great improving margin on the building energy performance to be exploited. The analysis carried on within this study took an existing high-performing building, that achieved a triple LEED Platinum certification, considered a quite good building in terms of energy behavior and compared it to an European nZEB, as defined in [5]. By the way, considering it is now 4 years old, its energy performance cannot be considered as good as before and the efforts carried on to move it towards a nearly zero target proved it difficult to

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be considered like that. Energy efficiency measures were applied to the original project in order to improve the building energy performance. Firstly a higher insulation level was applied to the building to make it comply with the European and in particular the Turin regulation in terms of energy policy. Although the building envelope performance was modified optimally in terms of thermal insulation, this measure proved the building was still far from zero targets due to the low significant reduction on the space heating consumptions. Taking in consideration that automation controls have been applied to the original project (e.g. natural light sensors, occupancy sensors, etc), the next energy efficiency measures intent to improve the production of renewable energy on site to cover the building electrical energy need. It was found that, with the current level of performance and even with the PV systems implementation on the roof, the office building could not accomplish the nZEB goal as the heating energy consumption were still too high. The nZEB target was thus not feasible with these simple analyses. Further nZEB strategies needed to be carried on in order to optimize the building energy performance. Furthermore this study tried to develop a list a lessons, in terms of positives aspects and failures from the office building studied and from the set of HPBs selected too, with the intention of defining a list of recommendations and best practices to be followed for the design of nZEBs. In particular a significant lesson that can be learnt regards the strong motivation that drives the owner to decide pursuing a low energy building. The decisions are usually not driven by cost but mostly motivated to advertise the building as an outstanding example for others. Additionally this kind of HPBs or low energy buildings do not always perform as expected, therefore the monitoring need to have crucial role in the building operation. REFERENCES 1.

2.

3. 4. 5. 6.

7. 8. 9. 10. 11.

European Parliament, Council. 2010. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings. Official Journal of the European Union. ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development 2013. RAEE 2011, Energy Efficiency National Report, Executive Summary, ISBN 978-88-8286-279-4. Roma, Retrieved from www.efficienzaenergetica.enea.it. Ma Z, Cooper P, Daly D, Ledo L 2012. Existing building retrofits: Methodology and state-ofthe-art, Energy and Buildings (55) 889–902, doi: 10.1016/j.enbuild.2012.08.018 Becchio C, 2013. Assessment of energy and cost effectiveness in retrofitting existing buildings, Doctoral dissertation, Politecnico di Torino. REHVA, 2013. REHVA nZEB technical definition and system boundaries for nearly zero energy buildings. REHVA Report No 4. ASHRAE, 2008. ASHRAE Vision 2020, Producing Net Zero Energy Buildings: Providing tools by 2020 that enable the building community to produce market-viable NZEBs by 2030. ASHRAE Report, January 2008. Energy independence and security act of 2007, Public Law 110-140, 110th Congress U.S. Department of Energy. High Performance Buildings Database http://buildingdata.energy.gov/ Erhorn, H and Erhorn-Kluttig, Terms and definitions for high performance buildings, Detailed report. Concerted Action, Energy Perfomance of Building, January 2001. U.S. GBC LEED website http://www.usgbc.org/leed Energy Star web portal http://www.energystar.gov/index.cfm?c=new_homes.hm_index

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Decreto Legislativo n.311 del 29 Dicembre 2006 - "Disposizioni correttive ed integrative al decreto legislativo 19 agosto 2005, n. 192, recante attuazione della direttiva 2002/91/CE, relativa al rendimento energetico nell'edilizia". Carpenter, S. 2011. Simply Grand. In High Performing Buildings, ASHRAE, Spring 2011. U.S. Department of Energy. EnergyPlus energy simulation software, Weather data. http://apps1.eere.energy.gov/buildings/energyplus/weatherdata_about.cfm U.S. Department of Energy. Commercial reference buildings http://www1.eere.energy.gov/buildings/commercial/ref_buildings.html Passive House Guidelines. http://www.passivehouse-international.org/index.php?page_id=80 Agenzia Energia e Ambiente di Torino. Allegato energetico – ambientale al regolamento edilizio della città di Torino. Allegato alla deliberazione n. 2010-08963/38. Agosto 2009. Regione Piemonte.

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INSTRUCTIONS FOR PREPARING THE FULL PAPER TO CLIMAMED 2013 CONGRESS NET ZERO ENERGY USE IN BUILDINGS (TIMES NEW ROMAN BOLD 14 PT, STYLE: TITLE) EVALUATION, IN TERMS OF SOLAR HEAT GAINS, OF THE EFFECTS OF COURTYARD BUILDING SHAPES ON MICROCLIMATE ACCORDING TO DIFFERENT CLIMATIC REGIONS Enes Yaşa1, 1 University of Konya N.E.University, Konya Corresponding email: [email protected]

SUMMARY The courtyard buildings, which we face either as a regulator of inter-building microclimate or as a climatic regulator at urban scale especially in hot climate regions, and which constitute one of the fundamental characteristic building styles of such climatic regions, should be applied in a form compatible with the features of the climatic region it is used. There is the need for a study that allows formation of a model toward determination of the optimum courtyard form and meeting the conditions of comfort by establishing an optimization model taking into consideration the climatic, meteorological differences for each climatic region the specific climatic region requires. The purpose of this study is to examine the energy efficiencies of the courtyard buildings used either as a micro climatic regulator in hot-dry climatic regions, or as a climatic regulator at urban scale, and to determine inter-building and courtyard comfort statuses, besides, to manifest different thermal behaviors of such buildings by estimating the same fully and accurately using real meteorological data under different design and climatic conditions with computer energy simulation on different courtyard form options put forth for different climatic regions, and thus to provide new information to designers at the process of putting forward the optimum courtyard form according to the characteristics and data of the specific climate for different climatic regions. By using the CFD program, this study has analyzed the thermal comfort statuses and energy performances of 7 different courtyard shapes in inter-courtyard and building volumes that are discussed in hot-dry, hot-humid and cold climatic regions as well as the effect of the sunbeams received by the building surface and the daily solar movement on the thermal performance on the building. As a result of the entire analysis made for all building shapes, the obtained values were interpreted and the total energy performances were evaluated for each climatic region. In this study, the courtyard buildings will be assessed in terms of their thermal performances. Keywords: Energy performance in buildings, Courtyard Building, Building shape optimization, Climatic comfort, CFD Fluent.

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INTRODUCTION When we look at the courtyard buildings in general, we see that recently, frequent studies are conducted for examining inter-courtyard thermal performance. Such studies can be examined as inter-courtyard air movements, courtyard-building-sun-shadow relation and buildingcourtyard option and inter-courtyard air movement relation as well as thermal performances of courtyards in different climatic regions. In other study conducted for evaluating the effect of courtyard option on the total energy performance; Prof. Ray Clark and Abdelsalam Aldawoud (2007) examined the energy performance of the central atrium whereby the energy performance of a courtyard building with the same geometry and ratio has been comparatively examined [1]. In an other study, which examined the effects of building shape and form on climatic performance in different climatic regions and which assessed the courtyard option in environmental terms, Carlo Ratti, Dana Raydan and Koen Steemers (2003) conducted numerical analysis studies on the shape and form of courtyard building, the relevant building form and its climatic performance in environmental terms [2]. In a different study that examined the thermal performance of courtyard building, Mohsen MA.(2005) assessed the solar radiation effects of the geometric and physical parameters of the courtyard on the courtyard building façade. The variability of the radiation, which was obtained by changing the courtyard parameters, was examined [3] [4]. On the other hand, conducted many studies on the courtyard shape and courtyard buildings. These especially focus on the effect of courtyard shape on solar radiation gain-loss, and also on the sun-shadow effect. The purpose of the study conducted by this couple in 2006 is to provide enough radiation in order to obtain the heat required by the building during winter and to reduce the required energy for the cooling requirement during summer; or for providing sufficient shadow area, to proportion the courtyard’s inner envelope or to conduct a courtyard shape study. In another study they conducted, they examined the effect of inter-courtyard sun-shadow performance; and developed a mathematic model for calculating the shadowed and sunny areas of courtyard buildings in circular geometry. This model so developed examines, during any period within the year, the interactions between the sun and the courtyard buildings in circular geometry, the latter located on ground in any ratio or dimension. In another study that touched upon the courtyard shape and option, Muhaisen, A S., Gadi M. B. examined the shadowing performances of many angled courtyard shapes such as pentagon, hexagon, heptagon and octagon [5] [6] [7] [8]. Recently, the literature hosts studies that especially examine the thermal performances of CFD and courtyard option. Among these, I.Rajapaksha, H. Nagai, M. Okumiya (2004) examined in CFD the passive cooling potential of courtyard buildings, which have single storey and intense massive envelope and located in hot humid climate regions, and manifested the results obtained from this calculated analysis (CFD) and thus asserted that as far as buildings with single storey and highly dense masses are concerned, courtyard buildings located in hot humid climatic regions are suitable for passive cooling [9]. Another study conducted by the Rajapaksha et al. team (2003), examined the passive cooling potential of the courtyard in a building with single storey and highly dense mass under a hothumid climate. They tested the presence of inter-courtyard in building design, and its potential to increase and optimize the natural ventilation for minimizing the extremely hot conditions in internal volumes[10]. Recent years especially witness an increase in numerical simulation studies. Experimental studies are lesser in comparison. An example of studies conducted in this direction is the study of H. Safarzadeh and M. N. Bahadori named: “Airflow In Buildings With Courtyards”.

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In order to forecast the inter-courtyard airflow ratios by taking into consideration, within the courtyard, the air speeds and air flow directions of courtyard buildings located in the Hot-Dry climatic regions of Iran such as the cities of Ahwaz, Kerman, Mashad, Shiraz, Tabriz and Tehran, comparisons have been made both experimentally in a wind tunnel and through numerical simulations, and findings have thus been reached. It was consequently seen that owing to the wind shadowing effects by the courtyard wall and the trees in the courtyard, the wind pressure coefficients on the walls of the windward buildings have decreased as compared to buildings without courtyard [11]). Another study conducted by Muhaisen, A. S., Gadi M. B, emphasizes the effects of the courtyard building shapes with different ratios, and especially of their sun related gains, on total energy. The study aims at observing the ratio at which the heating and cooling requirements of a courtyard building located in a particular climatic region are effected by the variability of the radiation that is obtained as a result of the increase in the surface area of the courtyard shape [8]. All these studies suggest that the number of advanced studies apart from some acknowledgements and certain methods is quite few. Additionally, studies and examinations on the subject show that concerning the courtyard buildings in question, we are far from a comprehensive strategy and research on what kind of direct effects they have in terms of either energy efficiency or climatic comfort, as well as how they act. For this reason, the purpose of the below study is the formation of a model towards the determination of the optimum courtyard form and meeting the conditions of comfort by establishing an optimization model while taking into consideration the climatic, meteorological differences for each climatic region that is required by the specific climatic region and accordingly, to evaluate the energy performances of all courtyard options examined. STUDY METHODS To represent the 3 distinct climatic regions examined in the study, “Diyarbakır” was chosen for the hot-dry climatic region, “Antalya” for the hot-humid climatic region, and “Erzurum” for cold climatic region, and long-term average meteorological climate data pertaining to such provinces were used. The option of building with 7 yards with yard dimension ratios was developed for use in all regions within the optimization model by increasing 1.5, 2, 25, 3 and 5 fold at east-west direction the sizes of the other yard options developed in proportion with the floor height (H=6) of the reference building with a yard size of x=y=z=H and with a fixed building location. (Figure 1) [12].

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FEATURES OF OPTIMIZATION MODEL DEALT WITH COURTYARD OPTIONS H Option

1.5 H Option

Dimensions of 6.00x6.00x6.00 courtyard Building Roof 160 m2 Area Building exterior 800 m2 surface area (for Total Heat Trans.) Total Volume of Building Courtyard Area Courtyard Volume

Dimensions of courtyard Building Roof Area Building exterior surface area (for Total Heat Trans.) Total Volume of Building Courtyard Area Courtyard Volume

960 m3 36 m2 216 m3

6.00x9.00x6.00 184 m2 920m2

1104 m3 54 m2 324 m3

MEAN FEATURES OF COURTYARD OPTIONS 2H Option

Dimensions of courtyard Building Roof Area

2.5 H Option

6.00x12.00x6.00

Dimensions of 6.00x15.00x6.00 courtyard 208 m2 Building Roof 232 m2 Area Building exterior 1040 m2 Building exterior 1148 m2 surface area (for surface area (for Total Heat Trans.) Total Heat Trans.) Total Volume of 1248 m3 Total Volume of 1392 m3 Building Building Courtyard Area 72 m2 Courtyard Area 90 m2 Courtyard Volume 432 m3 Courtyard 540 m3 Volume Figure 1: Courtyard options and their particularities examined in the study[12].

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CREATION OF THE MODELS AND THE ANALYSIS PHASE IN CFD The geometries of 7 different courtyard models examined were drawn and the digital mesh networks of the models belonging to each defined courtyard option were created, the thermal regions of each model were defined and surfaces of the models were created and restricting conditions were decided upon. Later geographical and climatic data of different climatic regions were entered into the Fluent 6.3 simulation program. Further, data such as permeability and reflectivity of the structure envelope, constructional components and constructional materials were entered[12]. The thermal regions, building surfaces and elements thereof previously decided upon during the Gambit phase were defined. Later, the data comprising the inter-building thermal gains were entered and analysis commenced. As criteria of the optimization studies; on 21st day of the 7th month for the cooling period of the summer months, and 21st day of the 1st month for the heating period, hourly, daily, daytime and nighttime inter-courtyard temperature and average temperature distributions, interbuilding total temperature gain and loss values, inter-courtyard air velocity movements, direction of air, layering of air, air change ratio pertaining to courtyard building thermal zones, inter-building thermal zones of courtyard buildings, for all building surfaces and roof area; overall and average heat transition amount, surface temperatures, pressures, and velocity distributions, inter-building and courtyard 1.60 m, 3.20 m and 6.50 m -level horizontalsection temperature, pressure and wind speed values were analyzed, and taking into consideration such values, internal temperature and average temperature distributions, overall temperature gain, total temperature loss calculations and also sunlight gains on the surface of the courtyard building were determined and calculated[12]. For the purpose of better cooling and ventilation throughout the cooling season, and optimization of inter-building temperature gains and losses throughout the heating season, investigation of architectural solutions, and evaluations to reveal the effects of such results on cooling and ventilation load were made[12]. The numerical and visual reports of all such values were prepared and relying on such values; evaluations and comments were made on internal temperature and average temperature distributions on the courtyard-building surface, overall temperature gain, total temperature loss calculations, investigation of architectural solutions for better cooling and ventilation as well as their effects on cooling and ventilation[12]. In view of all these results; optimum courtyard forms were put forward both for the daylight and night period, in terms of either inter-courtyard or inter-building thermal performance throughout the hottest period and less hot period for each climatic zone. The building chosen as the reference in the study model was considered as having 2 storeys, with a floor height of 3.00 m, with external building dimensions of 14.00 x 14.00 x 6.00 m and courtyard dimensions of 6.00 x 6.00 x 6.00 m (Figure 2) [12].

Figure 2: The Plan and Perspective for the Reference Courtyard-Building (With 2 storeys, a floor height of 3.00 m, with external building dimensions of 14.00 x 14.00 x 6.00 m and courtyard dimensions of 6.00 x 6.00 x 6.00 m[12]

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At the entire courtyard configurations, building envelope that make up the courtyard formed from outside to inside like this: External plaster; 0.030 m, Thermal conductivity coefficient (λn) 1.40 W / mK, dn / λn Rockwool 0.040 m, Thermal conductivity coefficient (λn) 0.040 W / Mk, dn / λn 1.00; autoclaved aerated concrete (appropriate with mortar TS 4916), 0.200 m, Thermal conductivity coefficient (λn) 0.140 W / mk, dn / λn 1429; Internal plaster; 0.020 m, Thermal conductivity coefficient (λn) 0.870 W / Mk, Ud value of the building envelope is 0.378 W/m2K[12]. In the CFD Fluent 6.3 program where the analysis study is performed, information on the building envelope such the thickness, density, specific heat, thermal conductance coefficient, sun radiation absorbency, sun radiation reflectivity, surface roughness and number of layers are defined whereas layers in the floorings together with (if present) separate stratifications are defined in the ground floor, mezzanine floor and roof slab. The thickness, density, specific heat, thermal conductance coefficient, sun radiation absorbency, sun radiation reflectivity, surface roughness and number of layers of the material used in the flooring are examined. On the other hand, the data used in the simulation program are defined by entering the values of volume ambient temperatures, boundary conditions for surfaces and thermal zones, absorbency of surfaces, reflectivity, density, specific heat and thermal conductance[12]. Evaluation of the Findings Obtained From Analysis Results This study has evaluated the results of the CFD-Fluent analysis; the total energy performances of all building options in terms of energy gain-loss in the building’s entirety during both periods of summer-cooling and the winter-heating for three different climatic regions examined. From the raw CFD-Fluent data obtained, total heat transfer amount and solar radiation gain tables are created for the sum of all horizontal and vertical building surfaces for a duration of 24 hours on 21st January and 21st July for each three climatic region. The numerical values obtained from tables have been separately evaluated in terms of both total building surface area and total building volume[12].

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Figure 3: Building's total heat transfer amount for January day hours period[12] Optimum courtyard ratios for different climatic regions During the evaluation of total energy performances of all courtyard options, the solar radiation gains obtained by the courtyard grounds have been manifested with separate tabulation and graphics. Accordingly, when we evaluate the Figure 3, we see that whereas the maximum solar radiation gain of the courtyard grounds in the hot-humid climatic region of Antalya during the less hot period 21st January between the daily hours of 07.12- 17.07 is around 80-100 W/m2; the maximum solar radiation gain in the hot-dry climatic region of Diyarbakır between the daily hours of 06.34- 16.26 is 70-80 W/m2; and the maximum solar radiation gain in the cold climatic region of Erzurum between 06.36-16.17 hours is 70-80 W/m2. In Antalya, which bears hot-humid climate property, the maximum solar radiation value is seen in the 5H option; in Diyarbakır, which bears hot-dry climate property, it is seen in the 5H; and in Erzurum, which bears cold climate property, it is seen in the 3H option (Figure 4,5) [12]

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Figure 4: The unit solar radiation gain for all building options according to courtyard ground’s surface area[12]

Figure 5: The solar radiation gain amount of the courtyard ground on 21st January for all courtyard options[12]. Concerning the maximum gain obtained from the building's total courtyard surface on 21st July, it is seen that Antalya, a hot-humid climatic region, attains maximum solar radiation gain of 600-700 W/m2 between the daily hours of 05.00-19.08; Diyarbakır, a hot-dry climatic region, attains maximum solar radiation gain of 500-600 W/m2 between the daily hours of 04.19-18.31; and Erzurum, a cold climatic region, attains maximum solar radiation gain of 400-450 W/m2 between the daily hours of 04.09-18.33 (Figure 4,5) [12]. When the building and inter-courtyard energy performance is evaluated in total, the least solar radiation gain is desired during the cooling period. The building option in this direction is the optimum choice for this period. On 21st July, Antalya, which bears hot-humid climate property, the least solar radiation gain is seen in the H option; in Diyarbakır, which bears hot-dry climate property, it is seen in the H; and in Erzurum, which bears cold climate property, it is seen in the 3H

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option. During this period, “H” option appeared as the optimum choice for all three regions in terms of inter-courtyard thermal gain according to the criteria of comfort and energy gain[12]. RESULTS The Sun-Shadow analysis study that is a part of Yaşa E's doctorate work dated: 2010 and titled: "A Method Developed in the Optimization of Courtyard Building Shape According to Climatic Performance in Different Climate Regions" concluded that particularly in hot-humid and hot-dry climatic regions, the shadowy surface area within the courtyard generally diminishes as the sun rises. The cooling period (21st July) is particularly important in terms of comfort in the hot-dry and hot-humid climatic regions; and at the same time, the intercourtyard shadowy area ratio, where the energy gain is less, shows an increase as the courtyard form gets longer whereas in winter, the sun admission ratios of the courtyard surface areas increase as the depth in the courtyard decreases. During times when the form depth decreases however, it is due to increase in the ability of sunbeams to enter into the courtyard[12]. The optimum courtyard ratio is a form that allows minimum radiation during summer and maximum radiation during winter. The more radiation received by courtyard building surfaces, the more cooling is required during summer and proportionately less heating is required during winter[12]. Generally, the effect of shadowing on the required heating load during winter is more than its effect on decreasing the cooling load during summer. Thus, the sensitivity of the heating load towards the obtained solar radiation is higher than the sensitivity of the cooling load towards shadowy area. We can therefore conclude that making solar radiation gain during winter is more critical (important) than evading this during summer. It has been observed that the required annual energy demand increases in parallel with the increase in courtyard length. However, this increase ratio is not directly proportional to the increase in courtyard length when considered arithmetically[12]. As the courtyard’s plan come close to square, the intercourtyard shadowy area increases and the required energy amount during the 21st July cooling period decreases; whereas during the 21st January heating period, its effect on the increased energy demand somewhat decreases. Generally, annual energy consumption increases as the courtyard building shape gets longer. The solar radiation received by the courtyard building surfaces, heat gain and therefore its effect on the required cooling and heating loads has once again been revealed[12]. REFERENCES [1] Clark R. and Aldawoud A. 2007. “Comparative analysis of energy performance between courtyard and atrium in buildings”, Energy and Buildings, Volume 40, Issue 3, 2008, Pages 209– 214 [2] Ratti C., Raydan D. and Steemers K. 2003. “Building form and environmental performance: archetypes, analysis and an arid climate” Energy and Buildings, Volume 35, Pages 49–59 [3] Mohsen MA.1979.“Solar radiation and courtyard house forms II: Application of the model”, Building and Environment, Volume 14, Issue 2, 1979, Pages 89–106 [4] Mohsen MA. 1995. “Solar Radiation And Courtyard House Forms II: Application Of The Model”. Building and Environment 1979;14: 185–201. 1995

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[5] Muhaisen, A. S. and Gadi M. 2006. “Effect of courtyard proportions on solar heat gain and energy requirement in the temperate climate of Rome”, Building and Environment, Volume 41, Issue 3, March 2006, Pages 245–253 [6] Muhaisen, A. S. and Gadi M. 2006.” Shading performance of polygonal courtyard forms”, Building and Environment, Volume 41, Issue 8, August 2006, Pages 1050–1059 [7] Muhaisen, S. A., 2005. “Shading Simulation of The Courtyard Form in Different Climatic Regions”, School of the Built Environment, The University of Nottingham, University Park, Nottingham, NG7 2RD, UK Received 13 June 2005; received in revised form 24 June 2005;accepted 12 July 2005 [8] Muhaisen, A S., Gadi M. B., 2005. “Mathematical model for calculating the shaded and sunlit areas in a circular courtyard geometry”, Building and Environment, Volume 40, Page 1619-1625, 2005. [9] Rajapaksha, I., H. Nagai, M. Okumiya., 2004. “A Ventilated Courtyard As A Passive Cooling Strategy in The Warm Humid Tropics” Fuel and Energy, Volume 45, Issue 1, Page 53 January 2004. [10] Rajapaksha, I., H. Nagai, M. Okumiya., 2003. “A ventilated Courtyard As A Passive Cooling Strategy in The Warm Humid Tropics” ,Renewable Energy, ISSN 2003, vol. 28, no11, pp. 17551778., 2003. [11] Safarzadeh, H., Bahadori, M.N., 2005. “Airflow In Buildings With Courtyards” School of Mechanical Engineering, Sharif University of Technology, Tehran, I. R. of Iran. 2005. [12] Yaşa E. 2010. "A Method Developed in the Optimization of Courtyard Building Shape According to Climatic Performance in Different Climate Regions". Institute of Science and Technology, Istanbul Technical University, PhD Thesis, February 2010.

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HIGH PERFORMANCE BUILDING SKIN. FROM LOW-ENERGY TO NET ZERO ENERGY BUILDINGS. Fabiana Cambiaso1, Matteo Varioli Pietrasanta2 1

Urban Planning, Design and Architectural Technology Department, Sapienza University of Rome, Italy 2 Pagano System, Rome, Italy [email protected] [email protected] SUMMARY In the context of re-defining the building skin, that is to say, its transition from monofunctional protective roles to polyvalent control functions, much ado is made of synergic effects, and the expression “intelligent building skin” is frequently mentioned. In addition to a multitude of window systems for the direct utilization of solar energy, including natural ventilation, so-called manipulators for shading and heat protection, and daylight deflection, solar components play an important role in intelligent or innovative building skin. The technology they introduce (and their link to an electronic network system), enables the facade to respond flexibility to changing external conditions. The result is lasting effect on essential room or building characteristics, in other words, on user comfort. The product spectrum is vast and the rate of innovation truly stunning: new or improved building materials have offered great opportunities for innovation in architectural expression and design. Significant case studies show that very high energy efficiency is imperative for a realistic chance of achieving an equalised annual energy balance. This paper documents in detail the results of energy monitoring and the experience gained from the planning and use phases, as well as the individual steps on the way to an equalised annual energy balance. Research has revealed the dynamics with which the field of zero energy is currently being developed. INTRODUCTION Discussion on the appropriate energy policy for the future and the growing concerns about climate change regularly focus on the built environment in particular. On the one hand, the construction, maintenance, and operation of buildings throughout their life cycle consumes large amounts of energy and causes emissions. On the other hand, we are already aware of and have tested measures for all kinds of buildings that can dramatically reduce the level of consumption and emission. However, the net zero energy and plus energy buildings dealt with in this research go further than these concepts. They indicate how an equalised annual energy balance can be achieved by bringing together architectural design, energy efficiency and the local use of renewables. They stand for independence from finite resources and immunity to fluctuating energy prices. A zero-carbon building does not contribute to climate change. In the new version of the buildings guidelines published in 2010 the European Union calls upon member states to introduce the energy standard “Nearly Zero Energy Building” for all new buildings by no lather than the end of 2020. The building technology programme of the United States of America formulates the goals of arriving at marketable zero energy residential buildings by 2020 and non-residential buildings by 2025 (in Europe instead 2018).

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Nevertheless, standards that precisely define the goals in relation to the respective national building practice standards do not yet exist. The Swiss MINERGIE-A Certificate, which was released in March 2010, has become a pioneer in this important area of establishing definitions. A proposal towards a calculation process in the context of German standardization has been formulated and accompanied by a relevant calculation tool. Under extreme conditions, autonomous buildings point the way. Far removed from any kind of energy infrastructure and without a connection to an energy grid, they are generally entirely selfsupplying by means of renewables. But for the broad mass of buildings connected to the grid, this can't represent the model of the future. The long-term storage of energy, in particular of electricity, is a significant technological bottleneck. Equipping buildings to produce their own electricity is not only technologically demanding, the maintenance of such systems is complex and expensive, which means that connecting the building to an electricity grid offers a significant advantage. However, a building can only be described as climate neutral if the electricity grids are based to 100% on renewables. Here photovoltaic systems and combined heat and power plants integrated in the building and run on biomass are suitable. Through the interaction of architecture, building construction, and emerging materials and technologies the studies presented utilise diverse possibilities: from the geometry to the U-values of the parts of the building envelope to the performance of combined heat and power units or photovoltaic arrays. Designing and building a net zero energy building means that from the very start energy demand and energy generation must be consistently kept in balance. If the demand in the annual sum exceeds the possibilities for energy generation, further savings must be implemented [1]. The facade is one of the most significant contributors to the energy budget and the comfort parameters of any building. High-performance building skin can be defined as exterior enclosures that use the least possible amount of energy to maintain a comfortable interior environment, which promotes the health and productivity of the building's occupants. This means that sustainable envelope are not simply barriers between interior and exterior; rather, they are building systems that create comfortable spaces by actively responding to the building's external environment, and significantly reduce buildings' energy consumption [2].

Fig. 1. (on the left) Percentage and breakdown of gas emission as CO2 equivalent. Approximately one third of total annual emission of about one million tonnes is attributable directly or indirectly to the building sector. (on the right) Energy use breakdown for commercial buildings (Adapted from DOE, 2012).

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METHODS For most buildings, the facade affects the building's energy budget and the comfort of its occupants more than any other system. To provide occupants with a comfortable and safe environment, a facade must fulfill many functions, such as providing views to the outside, resisting wind loads, supporting its own dead-load weight, allowing daylight to interior spaces, blocking unwanted solar heat gain, protecting occupants from outside noise and temperature extremes, and resisting air and water penetration. One of the primary tasks of the building skin is to regulate the prevailing conditions in the surrounding external atmosphere in order to ensure comfortable conditions in the interior. In view of the additional energy required for the operation of mechanical building systems, any such installation should be understood as a subsidiary system that acts to support the envelope in order to guarantee sufficient interior comfort. Hence, facade and roof must react to climate conditions in order to regulate how these might effect he internal building climate. The direct link between building skin and room climate calls for a precise definition is the basis from which specifications for the conception of the external walls and roof are derived. The main factors are indoor air temperature and average surface temperatures, air change rates, relative indoor humidity, luminance and lighting intensity. These comfort factors should not be seen in isolation: they are closely related and interdependent. Indoor air temperature that is perceived as comfortable is very much dependent on relative indoor humidity, surface temperatures and on air movement in the room; it also influenced by individual factors such as clothing and physical activity [3].

Fig. 2. (on the left) Comfort parameters. (on the right) Building skin parameters. All comfort related parameters, with the exception of relative indoor humidity, can be directly controlled and regulated through the design of the facade and the roof and this is the principal guiding factor in the conception of the building skin. Thus the indoor air and average surface temperatures are the product of the exchange between internal and external heat gains, on the one hand, and transmission and ventilation heat losses through the building skin, on the other. Air change can be regulated through the number and dimension of ventilation openings. Luminance and lighting density are also influenced by the type, position and size of openings in the building skin. Close observation has demonstrated that a well-

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designed building skin is capable of producing a comfortable internal climate with the help of environmental energies even under less than favourable climate conditions. Designers need to consider the external environment, building orientation, space dimensions, and occupants' comfort expectations. The relative importance of these criteria will affect design decisions, such as the properties of opaque materials (thickness, density, conduction, reflectivity) and transparent (glazing) materials (thickness, number of layers, heat transmission, light absorption, reflection). Moreover, different design strategies are required for different climatic zones. Basic methods for designing high-performance building skin include: orienting and developing geometry and massing of the building to respond to solar position; providing solar shading to control cooling loads and improve thermal comfort; using natural ventilation to reduce cooling loads and enhance air quality; minimizing energy used for artificial lighting and mechanical cooling and heating by optimizing exterior wall insulation and the use of daylighting. In choosing design strategies, we need to consider the conditions of the climate zone to minimize their impacts and reduce energy consumption. Designers need to respond to the specific characteristics of a building site [4].

Fig. 3. Environmental conditions and properties of envelope elements that affect thermal, visual, and acoustic comfort.

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Many energy codes reference ASHRAE 90.1, Energy Standard for Buildings, which provides recommendations for building envelopes. ASHRAE 90.1 is periodically updated based on increasing expectations of building performance [5]. These recommendations are based on building location and climate zone, using the IECC climate classification system of eight zones and three sub-zones. Moreover, ASHRAE identifies four types of exterior walls: mass walls, generally constructed of masonry or concrete materials; metal building wall, consisting of metal members spanning between steel structure members (not including spandrel glass or metal panels in curtain walls); steel-framed, with cavities whose exterior surfaces are separated by steel framing members (including typical steel stud walls and curtain walls); wood-framed and other walls. ASHRAE requirements are prescribed in three ways for all climate zones: minimum allowable thermal resistance (R-value) for the different exterior walls; maximum allowable heat transfer coefficient (U-value) for the facade assembly (including thermal bridging effects of framing members); maximum allowable solar heat gain coefficient (SHGC) for the glazed portions of a facade assembly [6]. Finally, advances in metallurgy and in industry have allowed steel, aluminum and wood to be economical options for building envelope. The combination of lighter-weight materials, new technology, and an ever-increasing emphasis on balancing low construction costs with highperformance has led to the development of the exterior curtain wall as one of the most efficient and affordable cladding solutions [7]. RESULTS Three significant case studies illustrate how the various design strategies discussed have been implemented on various building types located in different climates. Different ways of approaching sustainable facade design are detailed, including proper design and passive strategies based on building orientation; control of solar exposure and self-shading mechanics through tectonic building form; design of external shading elements; selection of facade materials; and design of exterior wall assemblies [8]. A) Endesa Pavilion, Barcelona, Spain

Fig. 4. (on the left) Endesa Pavilion. Physical parameters analysis (in the middle). (on the right) Envelope components and technology. ENDESA Pavilion is a self-sufficient solar prototype installed at the Marina Dock, within the framework of the International BCN Smart City Congress. Over a period of one year it will be

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used as control room for monitoring and testing several projects related to intelligent power management. The pavillion is actually the prototype of a multi-scale construction system. A facade composed by modular components, like solar brick, that respond to photovoltaic gaining, solar protection, insulation, ventilation, lighting etc. The same parametric logic adapt façade geometries to the specific environmental requirements for each point of the building. It is is a single component that integrates all levels of intelligence that the building needs. From “form follows function” (classic XX century statement) to “form follows energy”. The facade opens reacting to the solar path, being active and becoming permeable towards south, while becoming closed and protective towards north. The behavior of this skin makes visible the environmental and climatic processes that surrounds the prototype. Self-sufficiency is a multiscale problem. Against finished solutions we propose an open logic, an adaptative component, a building system capable of scaling, applied equally to suburban or metropolitan contexts. The same skin component addresses and responds to the energy collection, passive solar gains, control of shadows and views, insulation, natural ventilation and forced, natural and artificial lighting, storage. A single constructive system that is capable of solving a single house or an office tower without changing logics, just adapting geometries. That simple rules make the system able to respond to the full gradient different positions or orientations, reacting with a whole range of permeability and energy collection. Form follows energy. The final geometry responds to the energy of the place. Thus, the pavilion becomes permeable and active towards south, where the interaction energy is maximized.; Towards north becomes opaque, closed and protective, minimizing heat transfer. Higher overhangs allow more energy collection and greater protection against the incident radiation during summer. Solar calculation software, connected to the logic of parametric design, allows us to reach an optimized solution. Each module, at each point, responds with mathematical accuracy to the different stresses of the different orientation and position. Solar houses should be built with solar materials. The wood, grown with solar power, is used now to build a self-sufficient photovoltaic pavilion. The current digital manufacture techniques, and the last advances in energy management and distributed production, make technology closer to the user, open and participatory. The Endesa pavilion is an accessible device, technologically soft and easily understandable. Its construction, materials and energy, and its climatic behaviour are transparent to the inhabitant. Digital fabrication techniques are applied to speed up construction times. Each piece is coded. Assembly process is just like solving a real scale 3d puzzle [9]. B) Administration Building in Würzburg

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Fig. 5. (on the left) Administration Building in Würzburg. (in the middle) Axonometric detail and diagram of indoor climate strategy and energy concept in summer and winter, day and night. (on the right) Grid cooling-elements and (below) legend of building structure. The form, construction and skin of the building as well as the plant and the central control technology were coordinated with each other to create a complex with a holistic solar-energy system. Fossil fuels are used to meet only peek demands. The two storeys of the building, each 4.10 meters high, were designed as open-plan spaces, with individual rooms divided off by frameless glazed partitions. A key element of the energy concept is the double skin facade, which acts as a climatic buffer. The external skin is in a post and rail construction with fixed glazing; the internal skin consists of prefabricated elements with manually operated sliding doors and motor-operated bottom-hung lights for ventilation purposes. The fresh-air supply is drawn from the 60 cm intermediate space between the two skins. Flaps in the outer skin – in the plinth zone and in the upstand roof- serve to regulate natural vertical convection, while axial fans at the corners of the building create horizontal currents that distribute the preheated fresh air. Lightweight metal blinds reflect sunlight either away from the building, or into the interior via soffit panels lined with cotton fabric. On the south and west faces of the structure, the lower sections pf the louvres have a dark coating on one side and can be adjusted separately from the upper part. Depending on the angle at which the louvres are set, the degree of thermal absorption can be increased. During the cold months of the year, they serve to preheat the air intake that flows over them. In summer, when the blinds are closed and the air flaps are open, the building is cooled by a process of convection ventilation. Additional nigh-time cooling is achieved via the atrium roof and the bottom-hung windows opened in a tipped position. When cooling is required, a 200-square-meters collector installation produces cold water by means of an adsorption heat pump. The water is then fed into ceiling cooling panels along the glazed facade and around the atrium. If required, the system can also provide energy for the underfloor heating. If the various elements of the system do not cover all needs, a cogeneration plant is also available to supply electricity and thermal energy. Since both the cooling soffits and the underfloor heating are water-fed systems, the two circuits can be used reciprocally. Some 250 sensors serve to measure the relevant data, on the basis of which, the

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control centre can respond to demands via more than 500 activating points. In addition, individual users can modulate the system via their PCs [10]-[11].

C) Kuwait University College of Education, Shadadiyah, Kuwait Fig. 6. (at the top on the left) Kuwait University College of Education. (at the top on the right) Diagram of major facade assembly components. (below on the left) Facade energy concept. (below in the middle) Self-shading facade geometry; (below on the right) Physical model and GFRC panel mockup. A self-shading facade protects the building's interiors from the intense solar radiation while maintaining views for its occupants. Inspired by traditional Kuwaiti patterned screens, the facade was designed using three-dimensional modeling and visualization software. The complex geometry of the facade uses integral shading elements, set at the optimal cutoff angles, to shade the building from intense solar radiation. Several layers form the self-shading exterior skin. The major components of the envelope: a) shading fins are constructed of two layers of tempered laminated glass, with a ceramic frit between the glass layers. These shades reduce direct solar radiation and transmitted solar heat gain, thus reducing cooling loads for the HVAC system. These translucent panels also direct diffuse light into the interior spaces and reduce glare at the exterior wall. b) The opaque parts of the facades are panels made of glass-fiber-reinforced concrete (GFRC), a lighter-weight and thin (less than 1 inch or 2.5 cm thick) material. c) The galvanized steel skeleton provides a lighter-weight, structurally efficient support for the GFRC panels. Its low weight results in low superimposed loads on the building's structural system. d) Low-e insulating glass is used to reflect radiant infrared light and to reduce cooling loads. Operable windows on the east and west facade allow natural ventilation during milder seasons. This diamond-shaped windows, similar in shape to the fixed windows of the facade, use a pivoting mechanism to open and close. e) Insulation behind the GFRC minimizes heat transfer between the exterior and interior environment. Energy modeling performed during design indicated that the facade design, along with other energy-efficient design strategies, would reduce the building's energy consumption by 21%

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compared to a baseline building prescribed by the ASHRAE 90.1-2004 standard. The facade design would also eliminate 82% of the solar heat gain. DISCUSSION Growing interest in development of innovative solutions for enhancement of sustainability in the built environments has been observed in recent year. According to the main constituents of buildings particularly in building envelopes, facades are expected to play a significant role towards the promotion os sustainable design in low energy buildings. This research presents a holistic review towards the analysis of “intelligent facades” according to their types, current implementations, challenges, and ultimate impacts. Smart envelope need to be responsive and conscious to the local climate, outdoor environment, and indoor spaces with view to parameters such as energy performance, thermal comfort, indoor air quality, visual comfort etc. The findings demonstrate that energy modeling and simulations should be performed during the early stage of design process of buildings. In conclusion, the study recommends the intelligent facade to become inherent constituent of green buildings for future development not only of carbon neutral buildings but of a more complex zero energy system [12]. ACKNOWLEDGEMENT I thank the authors whose contributions are of such significant importance for the success of my research. With their buildings, committed clients and designers have created the conditions under which net zero energy buildings can become reality. REFERENCES 1. Voss, K., Musall, E., 2013, Net Zero Energy Buildings, München, Edition Detail Green Books. 2. DOE, 2012, Buildings Energy Data Book 2011, Washington, DC: Department of Energy. Retrieved from http://buildingsdatabook.eren.doe.gov/default.aspx 3. Lang, W., Is it all “just” a facade? The functional, energetic and structural aspects of the building skin, in Schittich, C., 2006, Building skin, München, Edition Detail. 4. Aksamija, A., 2009, Context Based Design of Double Skin Facade: Climatic Consideration During the Design Process, Perkins+Will Research Journal, Vol. 1, No. 1, pp. 54-69. 5. ASHRAE, 2007, BSR/ASHRAE/IESNA 90.1-2007, Energy Standard for Buildings except LowRise Residential Buildings, Atlanta, GA: American Society of Heating, Refrigerating and AirConditioning Engineers, Inc. 6. Lawrence, P., and Chase, T., 2010, Investigating the Climate Impacts of Global Land Cover Change in the Community Climate System Model, International Journal of Climatology, Vol. 30, No. 13, pp. 2066-2087. 7. Spillman, W., Sirkis, J., and Gardiner, P., 1996, Smart Materials and Structures: What are they?, in Smart Materials and Structures, Vo. 5, pp. 247-254. 8. Blomsteberg, A., ed. 2007, BESTFAÇADE: Best Practices for Double Skin Facades, (EIE/04/135/S07). 9. IAAC, Endesa Pavilion, Un Prototipo di Facciata Multiscalare progettata in funzione dell'energia, in LegnoArchitettura, 2013, Edicom Edizioni, Anno IV, No. 11, pp. 88-95, ISSN 2039-0858. 10. Schittich, C., 2003, Solar Architecture: strategies, visions, concepts, München, Edition Detail.

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11. Badinelli, G., 2009, Double Skin Facades for Warm Climate Regions: Analysis os a Solution with an Integrated Movable Shading System, in Building and Environment, Vol. 44, pp. 1107-1118. 12. Poirazis, H., 2006, Double Skin Facades: A Literature Review (IEA SCH Task 34, ECBCS Annex 43 Report).

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ENERGY PERFORMANCE AND SUSTAINABLE DEVELOPMENT OF THE NATIONAL CENTER OF COSTUME SCENE (NCCS) IN MOULINS (FRANCE) -AVERAGE 2007-2011 J.Naveteur #1, A.Rousset*2, V.Foray#3 #1 EDF R&D Energy in Buildings and Territories Department. Site des Renardieres. Ecuelles-77818Moret sur Loing-France. [email protected]; *2 EDF - Commerce Rhône-Alpes Auvergne-Collectivités Locales-64, rue des Pêcheurs03006 MOULINS Cedex. [email protected] #3 NCCS Administrator-Quartier Villars, route de Montilly - 03000 Moulins. [email protected]

SUMMARY The NCCS is equipped with a geothermal heat pump which uses groundwater to ensure the environmental conditioning of the museum and reserves. This is the first museum in France to be equipped with a groundwater (geothermal) heat pump EDF R&D has realised energy monitoring of this site since 2007. The main performance indicators of the heat pump are an average annual COP(1) of 3.78, a cost of 21€ TTC per MWh produced and a reduction in CO2 emissions of 77% when compared to a conventional solution (gas boiler and chiller).

1.

A place in history

Located on the left bank of the Allier opposite the town of Moulins, the Quartier Villars was built in the late eighteenth century to accommodate a cavalry regiment. It is a historical monument recognized for its wonderful pink sandstone stairs. It has been renovated by the architect of historical monuments, Mr. François Voinchet. Interior design and construction of the reserves were undertaken by architects Jean-Michel Wilmotte and Jacques Brudin. Project management was entrusted to the Regional Directorate of Cultural Affairs (DRAC) of Auvergne. Mission of the NCCS: NCCS maintains the most prestigious collections of stage costumes in the world, made by such great institutions as the National Library of France, the French Comedie and the Opera National de Paris.

2.

Descriptions of buildings

NCCS is composed of two buildings (total surface area (SA) = 5745m2), one dedicated to the museum (SA = 3400m2; 18th century building and historical monument) and the other, a modern building called “the Reservations” (SA = 2345m²), which ensures the conservation of 10,000 costumes.

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The museum

The reservations (on the left)

Descriptions of Heating, Ventilation and Air Conditioning (HVAC) system Given the proximity of the Allier, the Regional Directorate of Cultural Affairs and the consultancy firm Choulet sought to capitalize on the potential energy contained in the water. The study made by hydrogeology consultancy firm Antéa showed water resources sufficient for feeding the geothermal heat pump on ground water. This is the first museum in France to be equipped with a groundwater (geothermal) heat pump To conserve the costumes in an optimal environment, it is imperative to maintain conditions of constant temperature (18°C) and humidity (55%). This required the creation of a network of 4 tubes (CF: Hydraulic diagram). The heating and cooling of the buildings is performed by a geothermal heat pump on groundwater system. The system includes the heat pump (344kW hot and 314kW cold) that feeds a 4-pipe system (hot, cold) and 4 boreholes (2 pumping 35m3/hr each, and 2 in injection). The museum is equipped with an air handling unit and fan coil. The reserves are equipped with a special air handling unit (temperature 18°C, with 55% hygrometry). It was installed air handling unit like those used in data center.

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3.

Methodological description of the intervention of EDF R & D.

Given its experience in geothermal heat pump (1) and energy monitoring (2), EDF R & D has been requested by the DRAC to provide assistance for the implementation of this operation. -We analyzed hydrogeological studies -Assist in the design flow pattern. We provided support to the -Definition of control -Definition of metrology-install. What types of measures, number of sensors, types of equipment to install, survey frequency -Commissioning of installations -To monitor (since 2007) in partnership with the operator of the facility management. -Assist in the optimization facilities through the analysis of the measurements.

4.

Description of metrology.

Metrology includes: -Many temperatures measurements such as temperatures networks hot and cold temperatures, températures of air handling unit, ground water tempértaure and tempértature of pumped water back into the aquifer. The outdoor temperature and humidity and ambient temperatures. -A calorie counter -A frigories counter -Two electronic energie meters that measure, for the first consumption of the heat pump and the second consumption of the pumps. These counters are the same type as those used for energy billing by EDF.

5.

The costs (including taxes)

The cost of work was 22M€ (Value 2006, 3849€/m2) with 8.5M € (1500€/m2) for the interior.

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The cost of installation of the HVAC system was 935k€ (165€/m2), and 57k€ for the 4 boreholes (1000€ per meter of drilling).

6.

The measurement results (Average 2007-2011).

EDF R&D has monitored the energy consumption of this site since 2007. The main results are: Average 2007-2011

Consumption

Cost (*)

Total

174kWh/m2

13,34€/m²

Geothermal heat pump

57kWh/m2

4,45€/m²

Pumps

27kWh/m2

2,12€/m²

Others uses (*) including taxes

90kWh/m2

7,06€/m²

5 years of measurements showed that there were significant energy transfer between the hot and cold that well justifies the advantage of supplying geothermal heat pump.

The main performance indicators of the heat pump are: - An Average Annual COP(a) of 3.78, a cost of MWh (heat and cold) produced from 21€/MWh compared to 42.9€/MWh by a gas boiler and cooling by electric chiller(b), - Reduction of CO2 emissions by 77% compared to a traditional solution (gas boiler, and electrical chiller). Compared to a gas boiler and electric chiller solution, the economic savings provided by the heat pump on ground water is 26k€. The additional investment is approximately 60k€; the payback is therefore less than three years.

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On average, the buildings (museum and reservations) need 131kWh heat/m², and 84kWh cold/m². To ensure these needs, the heat pump cycles 21m3/m2 of area at an average flow rate of 15m3/h.

7.

What are the benefits for EDF R & D, EDF and other stakeholders

EDF R & D has developed an expertise in the field of consumption analysis of a museum of its heating and cooling needs and concurrency requirements. This expertise allowed us to intervene in the project MUCEM (Museum of the civilizations of Europe and the Mediterranean) which will be equipped with a Heat Pump in sea water and for which we perform the same type of benefits for CNCS. This has allowed us to achieve a transfer of jurisdiction to the installer who had never done this type of installation as the operator for whom this was a first. For the CNCS, it is strongly interested in the involvement of EDF R & D because it ensures that innovative system is correctly performed and operated

8. Conclusion Monitoring has shown that installation of geothermal heat pump ensures the conservation of costumes, with satisfactory operating costs all the while reducing CO2 emissions. Reférences (1) Member of the editorial board of guides: -The geothermal heat pumps from drilling aquifer: Manual for the design and implementation. Edited by l’ADEME (French agency for energy) and The BRGM ( Bureau of Geological and Mining Research) (2) J.Naveteur- The Lyon CAF: A geothermal thermo frigo pump for 13 years (REHVA journal N°48) (a)

COP= Energy consumption in Hot and Cold by the buildings / Electrical energy consumption by the heat pumps + the well pump.

(b)

Annual efficiency for a gas boiler = 0.9; COP in cold for electric chiller = 3

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MUREAUX ADMINISTRATIVE SITE THE FIRST HQE® CERTIFIED OPERATION IN FRANCE: 7 YEARS LATER... J.Naveteur #1, S.Barrois*2, G.Aumont#3 #1*2 EDF R&D Energy in Buildings and Territories Department. Site des Renardières. Ecuelles -77818 Moret-sur-Loing, France. [email protected]; [email protected] #3 Energies responsable for the mayor of Mureaux - Place de la liberation-BP2053-78135 Les Mureaux [email protected] SUMMARY The city of Mureaux, in the Yvelines (78), constructed a new administrative site in 2004. The municipal staff applied an HQE® method and the building was certified in 2005; it was the first building of its kind in France. The building uses a geothermal heat pump for heating and cooling made in free cooling by ground water. EDF R&D has monitored the energy performance of the site for the past seven years. The average energy consumption is 13kWh/m² and the average cost is 7.77€/m² (min = 6.55€/m²; max = 8.99€/m²). The average COP (hot) = 3.59 and the hot consumption is 66kWh/m². The cost of heating is 0.023€/kWh, compared to 0.041€/kWh using a gas boiler. The average COP (cold) = 14.51, and the cold consumption is 11kWh/m². The cost of cooling is 0.00568€/kWh, compared to 0.0167€/kWh if using an electric chiller. The payback for the geothermal heat pump is approximately 5 years. Overall, the heat pump’s high-quality performance enables a 78% reduction in CO2 emissions compared to a gas boiler and electric chiller.

Introduction The city of Mureaux, in the Yvelines (78), constructed a new administrative site in 2004 to manage the various departments distributed over the territory of the municipality. The municipal staff applied an HQE® method and the building was certified in 2005 and was the first of its kind in France. Construction began in May 2004 and the inauguration took place in May 2005. The project manager – in its proactive HQE approach – may, if desired, enhance its action by an independent third party, the CSTB via its subsidiary Certivéa, in order to be certified. The certification is organized on the basis of 2 references: the Operation Management System (OMS) and the Environmental Quality of the Building (EQB). The OSM is defined as the set of possible elements to define the objectives of the EQB and to organize the operation in order to achieve them. The EQB, structured on 14 defined targets by the HEQ Association, allows the customer to define his or her environmental profile (choice of targets). Three levels of performance are defined: basic (regulations or common practice), effective and very effective. The project manager's responsibility is to define the specific environmental objectives (levels) for his or her operation. Assessment is carried out at several stages: end of programming phase, end of design phase and end of implementation phase. The certifying body, Certivéa, commissioned a team to audit the OSM and to check the EQB which enables timely actions to be carried out if necessary.

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Site description

Site description

The unanimous desire of elected officials to preserve the former city hall influenced the architectural choices and management of useful surfaces. Designed by architects Jean-Luc and Marie-Sylvie Hesters Barlatier, the building has a SHON surface of 4437 m2 which spans two floors. Particular care was given to the harmony and to the integration of the building’s ensemble. The total cost of the operation was €9.05 million, translating into a per-unit-surface area cost of €2,011/m2 (taxes included for both total and by per-unit-surface area). The additional cost of the HQE® part is 3.89% (€2005). The stones of the old deconstructed buildings were recovered to construct the basement of the new building in order to create a uniform connection between the new and the old. Photo of the Building

The roof slates were used to create the atmosphere of the planted patios. The new building includes 149 offices, 8 meeting rooms, 3 patios with gardens (2 of which are interior and feature rocks and plants), 2 planted flat roofs, archives, technical rooms and underground parking for 30 places for the vehicles of the different services.

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The old building includes the reception area and civil status on ground floor and the exceptional council room on the first floor. The assistant to the SPACE Environment project manager, in collaboration with EDF, advocated a comprehensive solution taking into account the idea of comfort in its entirety, energy performance and environmental protection.

A high-performance building The building registers excellent results with an interior insulation of expanded polystyrene (80+10), floor insulation by Schöck (Rutherma), thermal switches for building facades, Climaplus 4S Saint-Gobain Glass 4/16/4 glazing (U = 1.4W/m2.K) and is equipped with sliding louvered shutters or venetian blinds for large surfaces. Heating requirements are 11% less than what French regulation calls for (RT 2000) and consumption forecasts were calculated at 15% below the projected reference consumption. Heating and cooling with ground water Heating and cooling is achieved principally through the floor. A geothermal heat pump (CIAT water/water heat pump) with a heat rating of 290 kW uses the energy drawn from the ground water. The bore-well is equipped with a variable speed pump and the maximum flow rate is 32m3/hr. The HP (heat pump) supplies the floor heating in winter. In summer, the ground water (15°C) is used via a heat exchanger in a "free cooling" mode (without the heat pump) and cools the building. A heating management system is implemented in seven zones according to their exposure and times of presence.

Air renewal Essential for the quality of air, the air is renewed by: An Air Handling Unit "AHU" with a single flow of 8000 m3/hr for offices with extraction in the corridors. An AHU with a double flow of 300m3/hr with heat recuperation for the council room. An AHU with a double flow of 300m3/hr with heat recuperation for the civil status service.

Centralized Technical Management The building management system manages and controls 120 checkpoints and feeds back energy consumption information: • water (tap water, hot water boosts and recovered rainwater, ECS, bore-well, etc.), • electrical use (EDF, well pump, heat pump, ECS boost, etc.), • heating use (heating need, cooling, solar panels supply), • temperature (outdoor, indoor (7 zones), starting circuit floor, bore-well water, etc.), and • 120 control points and technical equipment checks.

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Recovery of rainwater A recovery of rainwater recovery system designed for a roof surface of 933 m2 and a tank of 20,000 liters. This recovered water is used for flushing toilets, giving expected savings of €2,000/year. Two filtering wells improve the retention and infiltration of rainwater. Two planted roof terraces with a total surface area of 296 m2 which, in addition to their qualities of comfort, also help to improve retention (65 liters/m3). They increase the thermal inertia by preventing summer sunlight from overheating the terrace.

Methodological description of the intervention of EDF R & D. Given its experience in geothermal heat pump (1) and energy monitoring (2), EDF R & D has been requested by the city of Mureaux to provide assistance for the implementation of this operation. -We analyzed hydrogeological studies -Assist in the design flow pattern. We provided support to the -Definition of control -Definition of metrology-install. What types of measures, number of sensors, types of equipment to install, survey frequency -Commissioning of installations -To monitor (since 2005) in partnership with the énergies responsable for the mayor of Mureaux.--Assist in the optimization facilities through the analysis of the measurements.

Description of metrology. Metrology includes: -Many temperatures measurements such as temperatures networks hot and cold temperatures, températures of air handling unit, ground water tempértaure and tempértature of pumped water back into the aquifer. The outdoor temperature and humidity and ambient temperatures. -2 calories counters, the first measurement of the heat consumption in buildings, the second measures the energy generated by the solar collectors for hot water -A frigories counter. -14 energie électric meters.One for the heat pump, one for the drilling pumps, one for the other pumps, one for the hot water, nine for the lightingng, one for the air handling unit

Main results Electricity consumption average (May 2005 to April 2012) All costs exclude taxes The average energy consumption is 502,959kWh or 113kWh/m²; the consumption ranged from 119kWh/m2 in 2009/10 to 103kWh/m² in 2006/07. The cost is on average 7.77€/m² (min=6.55€/m²; max=8.99€/m²).

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#% + % +

" $! $ ' +

* +

(& +

$ + #) + Energy performance in heating In winter, the system has an average COP Hot (1) of 3.59, and the hot consumption is 66kWh hot/m². Energy performance in cooling In summer, the system has an average COP Cold (2) (free cooling with ground water) of 4.51, and the cold consumption is 11kWh cold/m². The production cost of the cooling is 0.00568 €/kWh (5.7€/MWh heat) cold compared to 0.0167 €/kWh (16.7€/MWh) cold following with chiller (COP in cold = 3). The consumption of drilling pumps The energy consumption resulting from ground water pumping is on average 2kWh/m2. The pumps circulate 17m3/m2 of area at an average pump flow rate of 9m3/hr. The payback of geothermal heat pump is 5 years The energy savings made by the heat pump is 266MWh /year (61kWh/m²); the economic savings on average are 7661€ (1.73€/m2). The additional cost for the heat pump on ground water is 36k€, the payback is therefore approximately 5 years. A 75% reduction in CO2 emissions The CO2 emissions for heating and cooling are 3.52kg/m2, a reduction of 78% compared when compared to gas boilers for heating and electric chillers for cooling.

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Recovery of rainwater The recovery of rainwater is used to supply the toilets. On average, 202 m3of water has been recovered for a total consumption of 732 m3, representing 38% of coverage. Keys to success The correct integration of the elements in the system (installation of the variable speed pump of the well) and a rigorous commissioning of the installation (law of water on the HP, regulation of the variable speed settings) allows for high quality performance. Conclusion Given the seven years of monitoring data, we find that the energy performance of this building is excellent and stable. The geothermal heat pumps, with geocooling in summer, are economically efficient and respect the environment. (1) COP Hot = Energy consumption in Hot by the building / Electrical energy consumption by the heat pump + the drilling pumps. (2) COP Cold = Energy consumption in Cold by the building / Electrical energy consumption by the drilling pumps. Reférences

(3) Member of the editorial board of guides: -The geothermal heat pumps from drilling aquifer: Manual for the design and implementation. Edited by l’ADEME (French agency for energy) and The BRGM ( Bureau of Geological and Mining Research) (4) J.Naveteur- The Lyon CAF: A geothermal thermo frigo pump for 13 years (REHVA journal N°48)

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The HQE® method on the project First implementation of the HQE method: a charter was established during the programming. Thus, 83% of waste was recovered compared to the total mass of generated waste (the requirement of the HQE® certification reference is 15%). All management targets are at the level "very efficient". Energy target C Ref 15% (thermal bridge breaking, ECO double glazing, etc.) Water/water heat pump on water table - Centralized technical management (120 Control - Command points) Water target Rainwater recovery (20,000 liters tank) Economic valves Flow limiter Activity waste target Layout of the waste building (40m2 with differentiated containers) Selective collection Source separation Increasing awareness (sorting ambassadors) Service and maintenance target Arrival of fluids at a single place Regrouping of technical rooms Alarms & control transfer

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NUMERICAL INVESTIGATION OF FLOW PATTERNS AND THERMAL COMFORT IN AIR-CONDITIONED LECTURE ROOMS USING STEADY AND UNSTEADY TECHNIQUES Taher M. Abou-deif1, Essam E. Khalil2 1 Research Student, 2 Professor of Mechanical Engineering Mechanical Power Engineering Department, Faculty of Engineering, Cairo University,Giza, Egypt E-mail:[email protected] ABSTRACT The present paper was concerned primarily with the analysis, simulation of the air flow and thermal patterns in a lecture room. The paper is devoted to numerically investigate the influence of location and number of ventilation and air conditioning supply and extracts openings on air flow properties in a lecture room. The work focuses on air flow patterns, thermal behavior in lecture room where large number of students; using unsteady technique, and comparing the results to previous work which was using steady technique. The effectiveness of an air flow system is commonly assessed by the successful removal of sensible and latent loads from occupants with additional of attaining air pollutant at a prescribed level to attain the human thermal comfort conditions and to improve the indoor air quality; this is the main target during the present paper. The study is carried out using computational fluid dynamics (CFD) simulation techniques as embedded in the commercially available CFD code (FLUENT 6.2). The CFD modelling techniques solved the continuity, momentum and energy conservation equations in addition to standard k – ε model equations for turbulence closure. Throughout the investigations, numerical validation is carried out by way of comparisons of numerical and experimental results. Good agreement is found among both predictions. 1-INTRODUCTION The present work focuses on air flow patterns, thermal behaviours in air-conditioned lecture room.That is in order to satisfy the student's thermal comfort conditions and improving the indoor air quality, which are the main targets during this work.Air conditioning term can be defined as a process that controls the microclimate of an enclosed space. This process involves the movement of air through a space that has certain characteristics of temperature, humidity, cleanliness, pressure differential and noise level attenuation in order to satisfying a comfortable and healthy environment for the occupants. 1.1Thermal comfort is a condition of mind which expresses satisfaction with the surrounding environment, most important factors influencing thermal comfort are. Environmental factors: • Air temperature, air speed,relative humidity, air quality,and Noise. Other factors: • Activity level, clothing level, and psychological factors: such as mental effort. Achieving thermal comfort for most occupants of buildings or other enclosures is a main goal of HVACdesign engineers.

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1.2- In-door Air Quality Indoor Air Quality (IAQ) deals with the content of interior air that could affect health and comfort of building occupants. The IAQ may be compromised by microbial contaminants (mold, bacteria), chemicals (such as carbon dioxide, radon), allergens, or any mass or energy stressor that can induce health effects. So, using ventilation to dilute contaminants and improve the indoor air quality in most buildings. Carbon is an indoor pollutants emitted by humans and correlates with human metabolic activity. Carbon dioxide concentration at levels that are unusually high indoors may cause occupants to grow drowsy, get headaches, or function at lower activity levels, etc... Table 1 is a listing of carbon dioxide air concentrations and related health effects and standards. Table 1: Carbon dioxide air concentration level standards Carbon Dioxide Level

Health Effects

600 ppm

None

800 ppm

None

1000 ppm

None

5000 ppm

No acute (shortterm) or chronic(long-term) healtheffects

Standards or Use of Concentration

Reference

Most indoor air complaints NIOSH [1] eliminated, used as reference for air exchange for protection of children. Used as an indicator of ventilation inadequacy in schools and public MDPH [2] buildings, used as reference for air exchange for protection of children. Used as an indicator of ventilation ASHRAE inadequacy concerning removal of [3] odors from the interior of building. Permissible Exposure Limit (8-hour ACGIH [4], OSHA [5] workday) / Threshold Limit Value.

1.3 Ventilation Principles Ventilation is the exchange of air, typically between an indoor space and the outside. When people are present, ventilation is especially necessary to evacuate the carbon dioxide produced and renew the oxygen used up. It is also needed to remove other pollutants (smoke, chemicals, etc.) from the space. Ventilation air may be classified into natural or mechanical ventilation. In natural ventilation or gravity ventilation, uses the natural forces caused by the temperature difference inside the space to induce air circulation and removal. 1.4 Air Exchange Rate The most common method to measure the ventilation rate is the air exchange rate; the air exchange rate has units of 1/time. When the time unit is hours, the air exchange rate is also called air changes per hour (ACH).The rate of ACH determines the rateat which the total volume of air in the room is cleanedby an air purification system, which is a major factor inthe degree of air cleaning that can be achieved. Where it is the total volume of air flowing into a space in 1 hour divided by the volume of the space, then ACH can be expressed mathematically as,

ACH = 3600 Q / V Where: Q = volumetric air flow rate through the room, m3/s,

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(1.1)


V = volume of the room, m3 The air exchange rate may be defined for several different situations. For example, the air exchange rate for an entire space served by an air handling unit compares the amount of outside air brought into the space to the total interior volume, this the nominal air exchange rate. 1.5 Air Conditioning Systems Air conditioning systems can be categorized according to the means by which the controllable cooling/heating is accomplished in the conditioned space. There are four basic systems categories:1-All-Air Systems;air is used to carry the energy from indoor to outdoor and vice versa. 2-All-Water Systems; water is used to carry the energy from indoor to outdoor and vice versa, 3-Air-Water Systems; air and water are used to carry the energy from indoor to outdoor and vice versa. 4-In Direct Expansion (DX) Systems;refrigerant is used to carry the energy from indoor to outdooror vice versa[i.e. direct expansion of refrigerant, without the chilled water cooling medium]. 2. ASSESSMENT AND VALIDATION An experimental investigation on a real air-conditioned lecture room was done. This investigation aims to validate the used computational fluid dynamics code, the results from both investigations, experimental and numerical, will be compared. Flow parameters like velocity and temperature have been measured at relatively important places on a plane perpendicular to a grill in the supply duct.The space configuration and the measuring instruments used are described. In addition, the experimental locations are described in details. Furthermore, the experimental procedure and test precautions are discussed briefly. 2.1 Description of the lecture room configuration 2.1.1 Room Geometry The room under investigation is a real lecture room "dissuasion room at building number 17" Faculty of Engineering, Cairo University, which has main dimensions as shown in the following figure 1.Conditioned air is supplied to the room through four air conditioners with outside dimensions as shown in figure 2.

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CLIMAMED VII. Mediterranean Congress of Climatization, Istanbul, 3-4 October, 2013 TURKISH SOCIETY OF HVAC & SANITARY ENGINEERS ___________________________________________________________________________________________________ 1m 10.2m

10.2 m

2

3 4

1

3.6m

8.88 m

8.88 m

3.85 m

Figure 1:lecture room configuration

0.25 m

0.75 m

Air in 0.175 m

Air out

Figure 2:air conditioner configuration 2-2 Measuring Locations One place was chosen to perform measurements a plane passing with a supply grill. A vertical plane perpendicular to the supply grill of air conditioner number 3 to show the decay in inlet air velocity and temperature variation downstream. This plane was taken to pass with a supply grill of air conditioner number 3. Measuring points is selected at each 20 cm on this plane. Temperature and velocity are measured at 110 points in this plane.The layout of these measuring points is shown in figure 3.

False Ceiling

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Air

cond.3

1.8 m

1m

Line 1

Line 11 Ground Figure 3 A:Lines of measurements near supply grill.

Measurements plane

Figure 3B:configuration of measurements plane.

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Figure 3 C:predicted velocity contours in the measurements plane. Table1: Measurements Lines Coordinates at Supply grill Y (m) No. X (m) Z (m) From To 1 2 3 4 5 6 7 8 9 10 11

0.25 0.4 0.55 0.65 0.8 0.95 1.05 1.2 1.4 1.5 1.75

1 1 1 1 1 1 1 1 1 1 1

2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8

-11.5 -11.4 -11.3 -11.2 -11.1 -10.9 -10.75 -10.6 -10.45 -10.3 -10

2.3Assessment of CFD Modelling Validation: The typical validation procedure in CFD, as well as other fields, involves graphical comparisons of computational results and the corresponding availableexperimental data. If the computational results "generally agree" with the experimental data, the computational results are declared "validated". 2.4Results and Discussion Temperature and mean velocity values downstream the supply duct is compared below. 2.4.1Temperature measurements:-

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Figure 4 showscomparisons between measured and predicted air temperature profiles downstream the supply grill at line 5.

Figure 4:Comparisons between measured and predicted air temperature profiles downstream the supply grill at line 5. The assumptions were suggested in the numerical model to represent the air supply grill, gave a good agreement with the measured results. The measured values are not equal the numerical ones due to the limited measuring instrument resolution.

2.4.2Velocity measurements Figure 5 showscomparisons between measured and predicted air velocity downstream the supply grill at line 5.

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Figure 5:Comparisons between measured and predicted air velocity downstream the supply grill at line 5. 2.5 Conclusions: The measured air temperatures and velocities were compared against the predicted results. Fair agreement can be found between the simulated and measured results. For the measuring points, the average velocity and temperature prediction errors were calculated equal to 0.04 m/s and 0.5°C (1.8%), respectively. These values verify the satisfactory performance of the CFD model, taking into account the accuracy of the measurement. And most of the predicted air temperatures and velocities were overestimated.Generally the calculations yielded the same trends as the measurements. Curves of measured temperature is displaced from the predicted ones, this could be due to errors in specifying the boundary conditions. All comparisons carried out and shown in this validation gave a direct conclusion of the numerical model capability to predict the air flow characteristics within acceptable deviation from the measured values.

3-Results And Discussions 3.1 Case Studies Specifications All of the case studies will be developed with utilizing FLUENT 6.2 and GAMBIT 2.2 (as mentioned before) based on a lecture room configuration with 12 ×6 × 4 m (12 m in length (L), 6 m in width (W), and 4 m in height (H)). Three case studies developed as shown in figure 6, 7, and 8 to show the influence of supplyextract positions on the air flow characteristics. Case 1 describes the air flow characteristics at ceiling air supply with 6 square supply air ports distributed uniformly at the ceiling, while 6 extract ports from all side walls. Case 2 describes the air flow characteristics when the air supply ports from the side wall (wall of X-Z plane at X = 6 m,five ports), while the extractions from the opposite wall (wall of X-Z plane at X = 0 m, five ports).

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Case 3 describes the air flow characteristics at ceiling air supply with 6 square supply air ports distributed uniformly at the ceiling, while 6 square extract ports at ceiling also

Figure 6: Case 1 Configuration.

Figure 7: Case 2 Configuration

Figure 8: Case 3 Configuration. effect of students load

Figure 9: Modeling of case 1 with

USING STEADY TECHNIQUE:

Figure10: (Case 1) Velocity magnitude contours (m/s),Figure11: (Case 2) Velocity magnitudecontours (m/s),Vertical plane at Z=1.5 m.Vertical plane at Z=1.5 m.

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Figure 12: (Case 3) Velocity magnitude contours (m/s), Figure 13: (Case 1) Temperature contours (K),vertical plane at Z=1.5 m.Vertical plane at Z=1.5 m.

Figure 14: (Case 2) Temperature contours (K),Figure 15: (Case 3) Temperature contours (K), vertical plane at Z=1.5 m.vertical plane at Z=1.5 m.

A complete air flow properties prediction of case 1 in actual study (student's presence); will lead us to more actually comparison with standard values in order to give a comfortable environment within the occupied zones.

Figure 16: (Case 1) Velocity magnitude contours (m/s),Figure 17: (Case 1) Velocity magnitude contours (m/s), vertical plane at Z=1.5 m.Vertical plane at X=6 m.

Figure 18: (Case 1) Temperature contours (K),Figure at Y=1m.vertical plane at Z=1.5 m.

19: (Case 1) Temperature contours (K), horizontal plane

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Figure 20: (Case 1) Temperature contours (K),Figure 21: (Case 1) Temperature contours (K), vertical plane at X=6 m.Horizontalplane at Y=1m.

Figures from 10 to 12 shows the velocity contours in m/s for cases 1, 2, and 3 respectively at vertical plane at Z = 1.5 m. Figures from 13 to 15 shows the temperature contours in K for cases 1, 2, and 3 respectively at vertical plane at Z = 1.5 m. Figure 16 shows the velocity contours in m/s for case 1 with the effect of student's presence at vertical plane at Z = 1.5 m. Figure 17 shows the velocity contours in m/s for case 1 with the effect of student's presence at vertical plane at X = 6 m. Figure 18 shows the velocity contours in m/s for case 1 with the effect of student's presence at horizontal plane at Y = 1 m. Figure 19 shows the temperature contours in k for case 1 with the effect of student's presence at vertical plane at Z = 1.5 m. Figure 20 shows the temperature contours in k for case 1with the effect of student's presence at vertical plane at X = 6 m. Figure 21 shows the temperature contours in k for case 1with the effect of student's presence at vertical plane at Y = 1 m.

USING UN-STEADY TECHNIQUE:

Figure22: (Case 1) Velocity magnitude contours (m/s), (m/s), at X=6 m.

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Figure23: (Case 1) Velocity magnitudecontours Vertical plane at Z=1.5 m.Vertical plane


Figure24: (Case 1) Temperature magnitude contours (K), Figure25: (Case 1) Temperature magnitudecontours (K), Vertical plane at Z=1.5 m.Vertical plane at X=6 m.

Figure26: (Case 2) Velocity magnitude contours (m/s), (m/s), at X=6 m.

Figure27: (Case 2) Velocity magnitudecontours Vertical plane at Z=1.5 m.Vertical plane

Figure28: (Case 2) Temperature magnitude contours (K), Figure29: (Case 2) Temperature magnitudecontours (K), Vertical plane at Z=1.5 m.Vertical plane at X=6 m.

Figure30: (Case 3) Velocity magnitude contours (m/s), Vertical plane at Z=1.5 m.Vertical plane at X=6 m.

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Figure31: (Case 3) Velocity magnitudecontours (m/s),


Figure32: (Case 2) Temperature magnitude contours (K), Vertical plane at Z=1.5 m.Vertical plane at X=6 m.

Figure33: (Case 2) Temperature magnitudecontours (K),

A complete air flow properties prediction of case 1 in actual study (student's presence); willlead us to more actually comparison with standard values in order to give a comfortableenvironment within the occupied zones.

Figure34: (Case 1) Velocity magnitude contours (m/s), Vertical plane at Z=1.5 m.Vertical plane at X=6 m.

Figure35: (Case 1) Velocity magnitudecontours (m/s),

Figure36: (Case 1) Velocity magnitude contours (m/s), horizontal plane at Y=1 m.Vertical plane at Z=1.5 m

Figure37: (Case 1) Temperature magnitudecontours (K),

Figure38: (Case 1) Temperature magnitude contours (K), magnitudecontours (K), Vertical plane at X=6 m.

Figure39: (Case 1) Temperature Horizontal plane at Y=1 m.

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Figures 22, 26, and30 shows the velocity contours in m/s for cases 1, 2, and 3 respectively at vertical plane at Z = 1.5 m. Figures from 24, 28, and 32 shows the temperature contours in K for cases 1, 2, and 3 respectively at vertical plane at Z = 1.5 m. Figures 23, 27, and31 shows the velocity contours in m/s for cases 1, 2, and 3 respectively at vertical plane at X = 6 m. Figures from 25, 29, and 33 shows the temperature contours in K for cases 1, 2, and 3 respectively at vertical plane at X = 1.5 m. Figure 34 shows the velocity contours in m/s for case 1 with the effect of student's presence at vertical plane at Z = 1.5 m. Figure 35 shows the velocity contours in m/s for case 1 with the effect of student's presence at vertical plane at X = 6 m. Figure 36 shows the velocity contours in m/s for case 1 with the effect of student's presence at horizontal plane at Y = 1 m. Figure 37 shows the temperature contours in k for case 1 with the effect of student's presence at vertical plane at Z = 1.5 m. Figure 38 shows the temperature contours in k for case 1 with the effect of student's presence at vertical plane at X = 6 m. Figure 39 shows the temperature contours in k for case 1 with the effect of student's presence at vertical plane at Y = 1 m. 4-Concluding Remarks From the previous chapters and according to the results obtained using the numerical investigation, the following conclusions can expressed concerning different lecture room configurations: •Cases 2 and 3 are rejected due to their problems of stratification and uncomfortable conditions. •CO2 concentrations and relative humidity magnitudes developed in the present work may be slightly decreased due to modeling of students presence in the room based on the maximum full load design through assumption of no free spaces between audience bodies, which in the same chair rows. •Total fresh air through air supply ports assumed in cases of students presence effect, but in actual mixing between recirculated and fresh air should be designed in order to minimize the total cost of this design. •Increasing number of air extraction ports will lead to more uniform air flow distribution and minimize the stagnant air zones. •There is no significant change in results between steady and unsteady techniques in this case study. REFERENCES [1] NIOSH, "Guidance for Indoor Air Quality Investigations ", National Institute for Occupational Safety and Health, Hazards Evaluations and Technical Assistance Branch, Division of Surveillance, Hazard Evaluation and Field Studies Cincinnati, OH, 1987. [2] MDPH, " Indoor Air Quality Assessment ", Massachusetts Department of Public Health, Bureau of Environmental Health Assessment, Boston, MA, 2000.

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[3]ASHRAE, " ASHRAE Standard 62-2004: ventilation for acceptable indoor air quality ", American Society of Heating, Refrigerating and Air Conditioning Engineers, Atlanta, USA, 2004. [4] ACGIH, "Guide to Occupational Exposures-1999 ", American Conference of Governmental Industrial Hygienists, Cincinnati, OH, 1999. [5] OSHA, "Limits for Air Contaminants ", Occupational Safety and Health Administration, Code of Federal Regulations, 29 C.F.R. 1910.1000 Table Z-1-A, 1997.

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AIR FLOW REGIMES AND THERMAL COMFORT IN A LIVING ROOM Essam E. Khalil ,EsmailM.El-Bialy,and Taher M.Aboudeif Fellow ASHRAE EssamE.Khalilis a professor of Mechanical Engineering ،Cairo University, Esmail M. ElBialy is assistant professor of Mechanical Engineering, Cairo University,TaherM.Aboudeifislecturer assistant of Mechanical Engineering, Cairo University E-mail:[email protected]

SUMMARY A computational fluid dynamics model is developed to examine the air flow characteristics of a room with different supply air diffusers. The paper is devoted to numerically investigate the influence of location, type, and number of air supply and extracts openings on air flow properties in a typical seating room. The work focuses on air flow patterns, thermal behavior in the room where few number of occupants. As an input to the full-scale 3-D room model, a 2-D air supply diffuser model that supplies direction and magnitude of air flow into the room is developed. Air distribution effect on thermal comfort parameters was investigated depending on changing the air supply diffusers type, angels and velocity. Air supply diffusers locations and number were also investigated. The preprocessor Gambit is used to create the geometric model with parametric features. Commercially available simulation software “Fluent 6.3” is incorporated to solve the differential equations governing the conservation of mass, three momentum and energy in the processing of air flow distribution. Turbulence effects of the flow are represented by the well developed two equation turbulence model. In this paper work, the so-called standard k-ε turbulence model, one of the most widespread turbulence models for industrial applications, was utilized. Basic parameters included in this work are air dry bulb temperature, air velocity, relative humidity and turbulence parameters are used for numerical predictions of indoor air distribution and thermal comfort. The thermal comfort predictions through this work were based on the PMV (Predicted Mean Vote) model and the PPD (Percentage People Dissatisfied) model, the PMV and PPD were estimated using Fanger’s model. Throughout the investigations, numerical validation is carried out by way of comparisons of published experimental results whenever available. Good qualitative agreement was generally observed.

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ABBREVIATIONS 2D 3D CFD PMV PPD

= = = = =

Two dimensional configuration Three dimensional configuration Computational Fluid Dynamics Predicted mean vote Predicted percentage dissatisfied

INTRODUCTION The present paper work is concerned with the air flow patterns in a simple room due to changing the supply diffuser shape and angles. The pre-processor Gambit is used to create the geometric model with parametric features. Commercially available simulation software “Fluent 6.3” is incorporated to solve conservation of mass, momentum and energy in the processing of air distribution, and to analyze turbulence affection combined heat transfer on air distribution. In this paper work, the so-called standard k-ε turbulence model, one of the most widespread turbulence models for industrial applications, ASHRAE (2009), was utilized. Basic parameters included in this work are air temperature, air velocity, relative humidity and turbulence parameters are used for numerical prediction of indoor air distribution, Khalil (2000, 2006),Berglund, (1989). The room main dimensions are 4 m width, 5 m length and 3 m height as shown in Figure1. A person setting on a sofa is modeled, and a television is added in front of him. The air is supplied to the room with different conditions as shown later in the boundary conditions section.

Supply Diffuser

Lights

Return Diffuser

Figure 1: Case Geometry

Boundary Conditions

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Walls

The room walls were set in the solver to be a constant temperature surface and its temperature was assumed to be 30 ºC. The walls material was simulated to be gypsum which matches the real configuration walls properties.

Lights heat load

Lighting fixtures are mounted at ceiling; the light heat flux is set to a value of 555 w/m2 for an area of 0.18 m2.

Human body

The skin temperature is set at 32.5°C depending on the metabolic rate of a setting person, and the body is assumed to have zero diffusive flux

Television heat load

Television is placed on a table in the middle of the room, the heat load is assumed to be 200 W which corresponds to a heat flux of 200 w/m2 for an area of 1 m2.

The pre-processor GAMBIT was used in meshing the simulated model. The mesh dependency was examined by solving the flow field for five mesh configurations made of 705, 000,1,500,000and 1,720,000 cells, respectively, and results showed that up to 3.7% difference in the maximum velocity existed between the coarser and finer mesh and less than 0.25% difference existed between the two finer meshes, which indicated that the finer mesh resulted in mesh-independent solutions.

SIMULATION AND DISCUSSION This part shows the parametric studies carried out on the room model presented in the previous section. The first case shows the best results in room comfort, as shown in its PPD histogram (Percentage of People Dissatisfied),Fanger (1972), without paying attention to energy efficiency. In the second case the air flow is changed in order to enlarge PPD differences in other cases. Other cases show different air distribution depending on changing diffuser angels, diffusers type, and location. Table (1) summarizes the difference between cases selected for this paper work. Table (1): Description of simulated parametric study cases Case

Diffuser type

1 2 3 4 5 6 7

Square Diffuser Square Diffuser Square Diffuser Circular Diffuser Swirl Diffuser Circular with low side return Side supply

Tsupply C 12.8 12.8 12.8 12.8 12.8 12.8 12.8

Diffuser angles 15-30-60 15-30-60 30-60-90 30-60-90 45 30-60-90 30

Vsupply m/s 0.9 0.65 0.65 0.65 0.65 0.65 0.65

A. Case (1) Square Diffuser (15-30-60˚) Boundary conditions for this case and diffuser angels were mentioned in the previous section. Temperature contours at selected planes are shown, moreover the PMV (Predicted Mean Vote),Olesen (1998), the velocity vectors at supply diffuser, and the PPD histogram. As

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shown in Figure 2 the temperature is in order of 40°C near the heat sources (Lamp and Television set). The temperature around the seating sofa is in the order of 24°C.Figures 3 and 4 demonstrated the corresponding predictions of mean air temperature and PMV for case I.

θ1θ2 θ3

V1

V2

V3 Figure 2: Supply Diffuser

Figure 3a: Temperature contours at the Centerline plane x=2m

Figure 3b: Temperature contours of a horizontal plane at Y=1m

Figure 4a: PMV contours at the centerlineFigure 4b: PMV contours in a horizontal planeat x=2m plane at Y=1m B.Case (2) Square Diffuser (15-30-60˚)

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Figure 5a: Temperature contours at the Centerline at plane X=2m

Figure 5b: Temperature contours in a horizontal plane at Y=1m

Figure 6a: PMV contours at the centerline plane X=2m

Figure 6b: PMV contours in a horizontal plane at Y=1m

Figures 5and6 show the predicted temperature contours at x=2 m and at Y=1 m for case 2 together with the corresponding contours of PMV at same planes. Although these are in general similar to case I predictions but they differ in details due to utilizing a square diffuser at lower velocity of 0.65 m/s instead of 0.9 m/s, Figure 7 shows the velocity vectors at diffuser exit. The computed results shown in Figures 8 to 9 were obtained at supply velocity of 0.65 m/s but at different diffusing angles of 30/60/90 of case 3. Better values of PMV are shown in all across the room as indicated in Figure 9. C. Case (3) Square Diffuser (30-60-90˚)

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Figure 7: Supplied air angles

Figure 8a: Temperature contours at the centerline plane X=2m


Figure 9a: PMV contours at the centerFigure 9b: PMV contours of a horizontal line plane X=2m plane at Y=1m D. Case (4) Circular Diffusers (30-60-90˚) Air is supplied through three annular areas with equal areas at three different angels 90, 60, and30˚ with discharges 0.025m3/s, 0.6 m3/s, and 0.035 m3/s respectively. The obtained predictions at the same selected planes are shown in Figures 10and 11. An even distribution of mean air temperature was shown for case (4) while the predicted PMV values were not uniform along the room.

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Figure 11a:PMV contours at the centerline plane X=2m


Figure 11b: PMV contours of a horizontal plane at Y=1m

E. Case (5) (Swirl Flow) In this case the air is supplied with two equal components in the axial and tangential directions as shown in Fig. 12. The air is supplied with 0.12 m3/s. The predicted temperatures were shown here in Figure 13 at a vertical

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Figure 12 : Supply air at grill exit plane Plane at X=2 m and at a horizontal plane at Y=1 m.The corresponding contours of PMV are shown in Figures 14a and 14b;these generally indicate uniform values of PMV that reflect more comfort level for occupant in the order of PMV=0.4 that is near neutral. Excessively high values indicate discomfort due to feeling worm or even hot at PMV =3.



Figure 14a: PMV contours at the centerlineFigure 14b: PMV contours of a horizontal plane X=2m plane at Y=1m

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F. Case (6) Side Return In some practical situation ; the air is supplied from the ceiling mounted circular diffuser, and is then extracted through the side rectangular grilles as shown in Figure15.For that particular design configuration air was supplied at 0.12 m3/s as shown here after in Figure 15. The corresponding predictions of air temperatures and predicted Mean vote PMV,are shown in Figures 16 and 17. The distributions indicate unhomogenity in temperature distribution in the room as well as uneven distribution of the PMV,as compared to the case shown in Figure 9 for same room configuration although at different design.

Supply Diffuser

Lights Return Grilles

Figure 15: Room Configuration

Figure 16a: Temperature contours at the centerline plane x=2m

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Figure 17a: PMV contours at the centerline horizontal plane X=2m

Figure 17b: PMV contours of a plane at Y=1m

G. Case (7) Side Supply In this case the air is supplied from a side grill as shown in Figure18. This case represents some of the hotel rooms design. The air is kept at a discharge of 0.12 m3/s. Air is making a 30˚ angle with the negative Z-direction as it enters the room.

Supply Grille Return Grille Light

Figure 18: Room Configuration

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Figure 19a: Temperature contours in a vertical a plane at X=1.2m

Figure 19b: Temperature contours in horizontal plane at Y=1m

Figure20a: PMV contours in a vertical planeFigure 20b: PMV contours of a horizontal at X=1.2m plane at Y=1m Figures 19 and 20 show the corresponding predictions for those particular case .The comparisons between all cases are shown later in Figure 21 in terms of the PPD. Case (1) Square Diffuser

Case (2) Square Diffuser

Case (3) Square Diffuser

Case (4) Circular Diffuser

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Case (5) Swirl Flow

Case (6) Side Return

Case (7) Side Supply

Figure 21: Room PPD Histograms

CONCLUSIONS

• The supplied air flow rate was reduced after the first case to magnify the difference between different air distributions systems used afterwards. • Diffuser air supply angles of 15-30-60° and 30-60-90° were used, and the 30-60-90° showed better results as shown before in the PPD histograms. • Circular diffusers didn’t show significant improvement compared to square diffusers. • Swirl diffusers showed the best results as 90% of the room volume have a PPD of 10%. • Side returns are better than ceiling return as shown in comparing cases 4 and 6, the PPD showed that less than 10% of the people will feel dissatisfied in nearly 70% of the room volume. • Side supply was simulated in case 7 as it is common in practical applications, but its results were relatively worse than ceiling diffusers.

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REFERENCES

ASHRAE Handbook, Fundamentals 2009, ASHRAE, Atlanta, USA. Berglund, L. G., and Cain, W. S., Perceived Air Quality and the Thermal Environment, the Human Equations: Health and Comfort, Proceedings of ASHRAE/SOEH Conference IAQ’89 ATLANTA: ASHRAE, (1989), pp.93-99 Fanger P.O.," Thermal Comfort: Analysis and Application in Environmental Engineering", McGraw-Hill, New York, 1972

FLUENT 6.2 Documentation, © Fluent Inc. Khalil,E.E., 2000, Computer aided design for comfort in healthy air conditioned spaces, Proceedings of Healthy Buildings 2000, Finland, Vol. 2, pp. 461-467. Khalil, E.E.,2006, Preserving The Tombs Of The Pharaohs”, ASHRAE Journal, 2006, pp.3438 Olesen, B. W., "Guidelines for Comfort ", ASHRAE Journal, (2000) pp.41 - 46, August 1998.

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EFFECT OF CITY VENTILATION ON URBAN HEAT ISLAND IN URBAN AREAS:A PARAMETRIC STUDY Ayça Gülten1, U.Teoman Aksoy2 and Hakan F. Öztop3 1

Fırat University, Faculty of Technical Education, Elazığ, Turkey Fırat University, Technology Faculty, Department of Construction Education, Elazığ,Turkey 3 Fırat University, Technology Faculty, Department of Mechanical Engineering, Elazığ,Turkey

2


SUMMARY Urban heat island (UHI) is a remarkable phenomenon that affects a city’s demand for cooling especially in summer season. City ventilation could be a good solution to mitigate the adverse effects of UHI. In this study, the impacts of city structure on city ventilation by a parametric study was investigated numericallythat was held out in Elazığ, Turkey, by the meteorologic factors of 1st 11th And 21st days of summer months in 2012.A comparison is performed for different city structures with different aspect ratios. City structure was modeled for 3D space. Meteorologic data have been obtained from Government Meteorological Office. In the simulations, DTRM radiation model for radiation heat transfer and k-ε turbulence model as turbulent flow were used. Results are presented as comparison of surface and air temperatures of each case. It has been found that city structure and orientation has a remarkable effect on city ventilation and UHI. INTRODUCTION UHI phenomena which could be defined as the temperature differences between urban and rural areas is mainly affected by meteorological factors (e.g.wind velocity and cloudness) and by physical properties of city structure such as building coverage materials, vegetation and building height-street width (aspect) ratio [1]. Many studies that are observational or conducted by simulation methods showed the strong effect of aspect ratio on distribution of surface temperature which is theprimary indicator for UHI [2,3].In a street canyon, surface temperature distribution is also affected by wind direction and wind velocity. So then, combined effect of aspect ratio and wind velocity and direction as a cooling tool could be an important indicator in street canyons in order to measure UHI capacity. In this study, we investigated the dimensions of UHI due to surface temperature distribution of urban surfaces (street, pavement, building façade and roof) in a complex urban area (Gazi Street) in Elazığ. For this purpose simulations made for 12:00 of 1st, 11th and 21th days of summer months by a commercial computational fluid dynamics program.3D model of the study area and meshing is also made by a design modeler and meshing program integrated with commercial cfd program [4].

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METHODS The study has two phases; 1. The aspect ratio which is 1 for the existing conditions has been changed to 0,5 and 2 to evaluate different city structures by wind velocity data obtained from Goverment Meteorological Office. 2. Wind velocity for all cases is raised by adding 2 m/s in order to evaluate the cooling effect of wind velocity in terms of different aspect ratios. It is obviously necessary to validate the simulation results of a CFD study at least with one set of observational data [5]. In this study, model description is constructed by a validation test that was held out for Gazi Street in Elazığ by surface temperature measurements and meteorological data of 21st March of 2007. Results for the validation study are presented in Table 1. Table 1. Surface temperature values for the validation study. North Oriented Pavement on Street (K) Wall (K) North Side (K) Simulated Observed Convergence%

Pavement on South Side (K)

South Oriented Wall (K)

The studied model is used in the validation test consists of two buildings 24 m high and facing another, a set of pavement 3m length on each side (north and south) and a street layout 24m length in the middle. The buildings are oriented to north and south sides while the street is east-west oriented (Figure 1).

Figure1. Model description for studied area

For the other cases simulated in this study, the area constructed by validation test is used containing different aspect ratios. The model which comprises (H) m length buildings is

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centered in a cubic domain of which dimensions are 5H for each direction [6].Wind velocity/direction and air temperature values are listed in Table 2. Table 2. Wind velocity/direction and air temperature values for studied days. Days Wind Velocity/ Air Direction (m/s) Temperature (C°) 1 June 3,8 WNW 27,4 11 June 4,97 SE 29 21 June 5,27 WNW 30 1 July 4,3 NW 29,4 11 July 2,35 SW 35,4 21 July 3,4 NNW 36,7 1 August 1,2 W 36,3 11 August 1,65 SW 35,8 21 August 5 WNW 35,7 Mesh skewness criteria is accepted for mesh quality and 0.83 skewness is provided for all cases. Approximately 280000 tetrahedral mesh cells are modeled in the simulations. The other features and boundary conditions for the method are right as below. • Simulations are made for steady-state regime. • DTRM radiation model for radiation heat transfer and k-ε- RNG turbulence model were used. • The coverage materials of urban surfaces and their thermophysical properties are presented in Table 3. • Simulations are made for 12:00 of 1st, 11th and 21st days of summer months. Meteorological data for 12:00 of these days is obtained from Government Meteorological Office and calculated heat transfer coefficients for each situation are listed in Table 3. • Second order upwind difference scheme(UDS) discretization is used for all equations. Table 3.Thermophysical Properties of Coverage Materials of Urban Elements Cp Thermal Urban Material Density 3 (J/kgK) Conductivity Element (kg/m ) (W/mK) Street Asphalt 2120 920 0,74 0,93 Walls Mortar 1860 780 0,72 0,93 Pavements Concrete - 2000 880 1,2 0,7 block Roof Tile 1458 880 0,52 0,91

0,93 0,23 0,6 0,4

In this study, it is accepted that urban surfaces are affected by radiation and convection. Heat balance equation is generated for each surface to predict the surface temperatures of urban surfaces. It is calculated by Eq. (1) [7] as

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(1) Where, is net solar radiation, is net longvawe radiation and issensible heat flux.

(2) Id, IskyveIref, (W/m2)are the amount of incoming direct, sky and reflected solar radiation respectively. is the absorption coefficient of surface, is incidence angle of direct solar radiation and is sky view factor of the surface. The net long wave radiation is sum of longwave radiation and atmospheric radiation .

(3)

(4)

isemissivity of surface, is sky view factor of surrounding buildings and ground, and represents emissivity and surface temperature of surrounding surfaces respectively. Atmospheric radiation is calculated as below:

(5) Where ” a” and“b”are coefficients[8] and e (pa) represents relative humidity of air. Sensible heat flux that affects surfaces by convection is calculated byEq. (6). (6) Heat transfer coefficient, (W/m2K), is calculated based on a method from ref. [9].

RESULTS AND DISCUSSION In this study, the performance of urban surfaces against UHI by comparing their surface temperatures for different aspect ratios is studied numerically. Fig. 2, Figs. 5 to 8 present area-weighted average temperature of horizontal urban elements(street, pavements and roofs on south and north side) for 12:00 of studied days for both of present and raised wind velocity, respectively.Fig. 3 and 4 present the vertical urban elements’ (north and south oriented walls) surface temperature. In summer season, at 12:00 when the sun is at the top, in a street canyon as we studied in our case, street and pavements are exposed to direct sunlight by the absence of shading effect of surrounding buildings. So then, as shown in the Fig. 2, surface temperature of street decreases while the H/W ratio increases. It is just because of increasing amount ofshaded area by a higher H/W ratio on the street on north side. The lowest surface temperatures are obtained for pavement on north side by reason of shading effect while south side pavement has the highest surface temperature values because of its direct exposition to sunlight. Raising H/W ratiodoesnot have any influence on pavement on north side, but it causes a little bit increasing on south side pavement.

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°

°

The north and south oriented walls which are vertical urban elements are influenced by H/W ratio less than horizontal surfaces. The obtained results from the simulations for walls are nearly same for all H/W ratios. South oriented wall presents much higher surface temperatures comparing to north side as expected. On the other hand, a regular variation is in the case for surface temperatures being parallel to air temperature in all cases.

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(a)

(a)

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(b) Fig. 2.Surface Temperature Values For Street. a) present wind velocity b)increased wind velocity

CLIMAMED VII. Mediterranean Congress of Climatization, Istanbul, 3-4 October, 2013 TURKISH SOCIETY OF HVAC & SANITARY ENGINEERS

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(b) Fig 3. Surface Temperature Values for South Oriented Wall a) present wind velocity b) raised wind velocity

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°

(a)

(b) Fig 4. Surface Temperature Values For North Oriented Wall. a) Present wind velocity b)Increased wind velocity Cooling effect of wind is related to it’s direction as much as it’s velocity and could be remarkable for lower H/W ratios. For August 1 and 11 the lowest wind velocity is in the case while only W oriented wind provides a cooling effect in August 1 especially for 0,5 H/W ratio.The main reason of this is parallel orientation of street due to coming wind direction.Wind coming from SW direction in 11 August has not provided any cooling effect. Otherwise, for other wind directions, more wind velocity is needed because of obstacle

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structure of urban canyon. The highest wind velocity is in the case for 21 June and 21 August with a direction of WNW and nearly 5 m/s velocity. They provide better cooling effect for all studied H/W ratios.

°

°

Wind velocity could be more effective on horizontal surfaces than vertical ones depending on winddirection. By raisingthe wind velocity, horizontal surfaces show lower temperatures while vertical surfaces present nearly same values. Surface temperatures of vertical surfaces could be decreased by leeward wind direction.

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(a)

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(b) Fig 5. Surface Temperature Values For Pavement On South Side. a) present wind velocity b) increased wind velocity

(a)

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CLIMAMED VII. Mediterranean Congress of Climatization, Istanbul, 3-4 October, 2013 TURKISH SOCIETY OF HVAC & SANITARY ENGINEERS

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(b)

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Fig 6. Surface Temperature Values For Pavement On North Side. a) present wind velocityb)increasedwind velocity

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(b) Fig 6. Surface Temperature Values For Roof. a) present wind velocity b)increased wind velocity CONCLUSIONS

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The main findings of the study showed that H/W ratio, wind velocity and direction have an importance on surface temperature distribution in an urban canyon.Lower H/W ratio provides less shaded areas then higher surface temperature values especially on horizontal surfaces. On the other hand, cooling effect of wind could be more sensible for lower H/W ratios. Increasing H/W ratio also increases the need for more velocity winds in city structure. It is not an attainable situation for cities. So then it is an urgent necessary to determineappropriate H/W ratio at the design stage of cities to provide bettercity ventilation in order to mitigate adverse effects of urban heat island. REFERENCES 1. GIVONI, B., “ClimateConsiderations in Buildingand Urban Design”,1998. 2. ALI-TOUDERT, F., MAYER, H., “Numericalstudy on theeffects of aspectratioandorientation of an urban Street canyon on outdoorthermalcomfort in hot anddryclimate”, Buildingand Environment, 41(2006)94-108. 3. TONG, N.Y.O., LEUNG, D.Y.C., “Effects of diurnalheatingscenarioandwindspeed on reactivepollutantdispersion in urban streetcanyons”, Journal of EnvironmentalSciences, 2012, 24(12)2091-2103. 4. AnsysUser’s Guide 5. MIRZAEI, P., A., “Approaches to study Urban Heat Island- Abilities and limitations”, Building and Environment, 45 (2010) 2192-2201. 6. YANG, L., LI, Y., “Thermal conditions and ventilation in an ideal city model of HongKong”,Energy and Buildings, 43 (2011)1139-1148. 7. ASAWA, T., HOYANO, A., KAZUAKI, N., “Thermal design tool for outdoor spaces based on heat balance simulation using a 3D-CAD system”, Building and Environment 43 (2008) 2112-2123. 8. books.google.com/ A Test of Brunt's Formula forPredicting Minimum Temperatures. (Son erişim:26.01.2013) 9. LOVEDAY, D.L., TAKI, A.H. Taki, ‘Convective heat transfer coefficients at a plane surface on a full-scale building façade’, International Journal of Heat and Mass Transfer 39 (8) (1996) 1729–1742.

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CFD SIMULATIONS OF CHEVRON TYPE PLATE HEAT EXCHANGERS AND VALIDATION WITH EXPERIMENTAL DATA Ece Ozkaya1, Yasin Genc1, Selin Aradag1 and Sadik Kakac1 1

TOBB University of Economics and Technology, Department of Mechanical Engineering, Sogutozu cad No: 43, 06560 Ankara / Turkey Corresponding email: [email protected]

SUMMARY Computational Fluid Dynamics (CFD) is a powerful and useful tool to predict thermalhydraulic performance of chevron type plate heat exchangers (CPHE). In this study, threedimensional CFD analyses are conducted to assess the thermal and hydraulic performances of a commercial chevron type PHE by performing the CFD analyses for the heat exchanger and comparing the results with a computer program previously developed based on experimental results. Two separate flow zones, one for each of the hot and cold domains, are used to model the heat transfer. Mass flow inlet and pressure outlet boundary conditions are used for each domain and heat transfer between the domains is modeled with a contact region which is conservative heat flux for a thin material. Temperature and pressure distributions are obtained and the total temperature difference and pressure drop values are compared with experimental data. The thermal properties are in good agreement with experimental data; however pressure predictions deviate from the experimental results and the studies for hydraulic characteristic prediction continues. INTRODUCTION Design of plate heat exchangers which has been used since 1930's in food industry, was developed in 1960's with the help of new geometries and the development related to new materials; therefore the utilization of plate heat exchangers increased. Today, they are used in several applications such as heating, ventilation and air conditioning (HVAC) applications, electronic circuits and chemical industries on account of their compactness, high efficiency, flexible design and easy maintenance [1]. Heat transfer calculations of plate heat exchangers are a wide area of research because of their complex geometry and several design parameters involved in the process. Hence, it is necessary to develop correlations for heat transfer and pressure drops associated with these heat exchangers. Experiments for hydraulic and thermal performance analyses have to be performed for different plate types, in order to develop plate-dependent correlations for Nusselt number and friction factor, which are used in thermal and hydraulic performance predictions, respectively [2]. These experiments, however, are quite expensive and limited. For this reason, CFD can be a powerful tool for performance analyses of plate heat exchangers. It provides realistic

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visualizations of the flow, zone by zone and it can also provide a wide range of test cases in order to predict parameters such as temperature, pressure and velocity in the heat exchanger whereas the experiments cannot. There are studies in literature for CFD simulations of different types of plate heat exchangers. Pelletier et al. [3] investigated whether simulation of brazed plate heat exchanger (BPHE) for determining heat transfer characteristics can be conducted by using FLUENT CFD simulation program. The analyses were performed for three-dimensional volumes which are formed by merging two different plate geometries. CFD simulations, were first practiced with a flat surface plate heat exchanger in order to collect the clues for the necessity for simulating compact corrugated plate heat exchanger. Then, the analysis was performed for complex geometries. The k-ω SST turbulence model was used and Reynolds number was about 3500. Constant heat flow and constant wall temperature conditions were used as boundary conditions. The working fluid is water and its properties are constant. CFD results are compared with the experimental results which are conducted for the plates with the same geometrical features. This study concluded that the experimental results could be more accurate when constant heat flux approximation was used. It is observed that the simulation shows a deviation of 4.2% from the experimental values. O’Halloran et al. [4] studied BPHEs experimentally and numerically. By using a commercial CFD code, FLUENT, numerical analyses were performed for three BPHEs which have different chevron angle values, 60o/60o, 27o/60o and 27o/27o. The results of the CFD simulations show good agreement with experimental results. Kanaris et al. [5] investigated heat transfer enhancement and fluid flow inside the channels of a commercial PHE experimentally and numerically. Pressure drop and temperature difference values are used to validate CFD results. An experimental set-up with IR thermography camera was used. CFD simulations are limited with the Reynolds number range in the experiments and the simulations are steady state. It is presented that limited experimental and theoretical work in literature is in fairly good agreement with the obtained results. It is found out that, PHE simulation with commercial CFD codes is an effective tool for predicting the flow characteristics, heat transfer and pressure drops. Utriainen and Sundén [6] carried out a three-dimensional numerical study to assess the thermal and hydraulic performances of cross wavy (CW) ducts. The effects of secondary flow in cross wave on thermal and hydraulic performance are investigated. The amplitude and length are the variables to define waviness and seven different combinations are used for computations where the hydraulic diameter is constant. A commercial software is used for the CFD analyses for a Reynolds number range of 700-1400. Near wall region flow is disturbed by angled streamwise flow and a secondary flow which increases velocity and temperature gradients. These results in not only enhancement in heat transfer but also an increase in wall friction which means higher overall pressure drop. The study concluded that the performance of CW duct is superior to straight duct up to six times; however the pressure drops are similar. Zhang and Che [7] worked on eight turbulence models for the evaluation of performance. The models are Low Reynolds k-ε (LBKE), standard k-ε, realizable k-ε, RNG k-ε, Reynolds Stress Model (RSM), k-ω, SST and Large Eddy Simulation (LES). The best predictions are made by LBKE and SST both for thermal and hydraulic performance within the whole Reynolds number range. Zhang and Che [8], in another study, investigated the effects of

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corrugation profile and inclination angle on the flow and heat transfer for a Reynolds number range of 1000 to 10000 and an angle range of 60° to 120°. Five different corrugation profiles were studied which are sinusoidal, isosceles triangular, trapezoidal, rectangular and elliptical. The trapezoidal profile provides higher performance for heat transfer and the friction factor gets five times higher than that of the elliptical channel. Freund and Kabelac [9] developed a measurement method for PHE performance. An IR camera was used for measuring temperature. The measurements are used to validate CFD simulations for a unitary cell. SST and Explicit Algebraic Stress Model - Reynolds Stress Model (EARSM - RSM) turbulence models are used for the CFD simulations. The SST model predicted the thermal performance 33% lower than the experimental results while EASM – RSM predictions gave 25% lower results. Although EASM – RSM predicts better than SST, CFD results are lower than measured results which indicates a PHE designed with CFD may perform better than predicted because of the uncertainties in the turbulence models used. The main goal of this study is to perform the CFD analyses for determining thermal performance of plate heat exchangers and to validate the results with obtained experimental data. Temperature and pressure distributions of contact region, temperature difference and pressure drop values are obtained from the CFD simulation results. METHODS The geometrical properties of the plate used in the CFD simulations are shown in Figure 1a where chevron angle (β), the most important parameter for thermal and hydraulic performance, is 30°. Corrugation depth (b) is 2.76 mm and the plate thickness (t) is 0.45 mm.

a) b) Figure 1. The plate and CFD model definitions a) Geometrical properties b) CFD set-up

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The Computer Aided Drawing (CAD) model is created by three-dimensional photogrammetry scanner and is simplified for the CFD model. After the CFD model is ready, grid and set-up are prepared as given in Figure 1b. Boundary conditions are mass flow inlet, pressure outlet and wall condition is no slip wall conservative heat flux with thin material for the contact surface and stationary no slip wall for other walls. Hot side inlet temperature is 65°C and cold side temperature is 20°C. Pressure outlet is 0 Pa relative pressure where the operating condition for pressure is 1 atm. In the study of Gulenoglu et. al. [10], correlations for Nusselt number and friction factor were evaluated from experiments. CFD analysis needs to be verified with experimental data taken from the study of Gulenoglu et. al.[10]. Outlet temperatures and inlet pressures are calculated by CFD simulations and differences between the inlet and outlet are compared to this experimental data. For this verification process, the flow chart in Figure 2 is used. Mesh independent results are obtained in the study. After mesh independency is achieved, the effects of the turbulence model are investigated. In this step, boundary conditions are fixed as mass flow inlet and pressure outlet boundary conditions. As mentioned in Zhang and Che [7], SST turbulence model gives the best predictions when compared to various industrial turbulence models, taking cost into account.

Figure 2. Flow chart for the verification of the CFD analysis

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Two properties of mesh, number of elements and y+ (minimum non dimensional cell height) value, are used to obtain mesh independency. The number of elements in the mesh is determined by doubling for every grid as 2.5 million (M), 5M, 10M and 20M elements. The results are nearly the same for 10M and 20M; hence the 10M mesh is chosen for the following analyses. The effects of y+ on the results are also examined. Five inflation layers are used to resolve the boundary layer on heat transfer plate and to predict thermal and hydraulic performance more accurately. Different y+ values, 0.5 and 1, on contact region are examined for 10M element mesh. The best values are obtained for a y+ values of 0.5. The results of 10M elements with a y+ value of 0.5 is used for the rest of the study.

RESULTS Temperature and pressure distribution on the contact region for the chosen grid and set-up is given in Figure 3 for a mass flow rate 0.03 kg/s and in Figure 4 for a mass flow rate 0.06 kg/s.

a) b) c) d) Figure 3. Temperature distribution for Case 1 (mass flow rate of 0.03 kg/s) a) Cold side, b) Hot side, Pressure distribution c) Cold side, d)Hot side

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a) b) c) d) Figure 4. Temperature distribution for Case 2 (mass flow rate of0.06 kg/s) a) Cold side, b) Hot side, Pressure distribution c) Cold side, d)Hot side Temperature distribution on the contact region is given in Figure 3a and Figure 3b for 0.03 kg/s mass flow rate where Reynolds number is 800. Cold water is heated as seen in Figure 3a while hot water is cooled down and temperature of flow is distributed as expected. The flow arrangement is counter flow; hence the average temperature of cold water outlet can be higher than hot water outlet. For a mass flow rate of 0.03 kg/s, the temperature difference is 27.2°C where experimental data for this case is 23°C. Cold water outlet temperature is 9°C higher than the hot water outlet temperature. Pressure distribution on the contact region is given in Figure 3c and Figure 3d for a mass flow rate of 0.03 kg/s. Temperature distribution is affected by Chevron angle and flow direction; however, pressure distribution is not. Pressure drop is about 0.6 kPa where experimental data gives a pressure difference of 2.7 kPa. In this case, CFD overpredicts the heat transfer rate but under predicts total pressure drop. Temperature distribution on contact region is given in Figure 4a and Figure 4b for 0.06 kg/s of mass flow rate where Reynolds number is 1800. Distribution is similar to the 0.03 kg/s case; however, the temperature difference is 16.6 °C where experimental data is 19°C. Although the flow arrangement is counter again, average cold water outlet temperature is lower than hot water outlet. The cold water outlet temperature is 12°C lower than the hot water outlet temperature. Pressure distribution on contact region is given in Figure 4c and Figure 4d for 0.06 kg/s of mass flow rate. Pressure drop is about 1.5 kPa where experimental data shows a pressure drop of 9.4 kPa. In this case, CFD underpredicts both the heat transfer rate and total pressure drop.

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The results for the mesh independency study are given in Table 1 for the minimum mass flow rate of 0.03kg/s. Table 1. Results of different grids for the mass flow rate of 0.03 kg/s Element Number

y+ value

ΔT [°C]

ΔP [kPa]

2.5 M

6

12.7

0.39

5M

6

14.9

0.40

10M

5

17

0.40

20M

4.2

19.6

0.43

10M

1

17.8

0.46

10M

0.5

27.2

0.6

DISCUSSION Chevron type PHE with corrugation improves heat transfer by helping turbulent flow to develop. It is indicated in literature that when Reynolds number is higher than 800-1000, the flow becomes turbulent. In this study, the minimum Reynolds number is chosen as 800 for a mass flow rate of 0.03kg/s; however, the flow is not fully turbulent. Turbulence modeling may cause extra turbulent kinetic energy to develop numerically, this may be the reason for CFD to overpredict heat transfer parameters. On the other hand, the maximum mass flow rate is 0.06kg/s where Reynolds number is 1800. Higher Reynolds number means turbulent flow and for the case with the maximum mass flow rate, the flow is fully turbulent. However, this time, CFD underpredicts the temperature difference. This can be a result of insufficient inflation layers to resolve the secondary flow in corrugation and boundary layer separations. This can also be the main cause of under prediction of pressure drop. The validation procedure continues and mesh independency study is still a work in progress. The experimental data is taken from a correlation-based computer program. To avoid correlation errors, new experiments are being carried out specifically for CFD validation and the CFD results will have a meaning after the validation experiments for exactly the same cases. ACKNOWLEDGEMENT This project is supported by Turkish Scientific and Research Council (TUBITAK) under grant TUBITAK-112M173 and by Turkish Academy of Sciences Distinguished Young Scientists Award Programme and by TUBITAK BIDEB programme. REFERENCES 1.

Kakac, S., Pramuanjaroenkij, A., Liu, H., 2012, Heat Exchangers: Selection, Rating, and Thermal Design, 3rd Edition, CRC Press.

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2. 3. 4. 5.

6. 7. 8. 9.

10.

Wang, L., Sundén, B., Manglik, R.M., 2007,Plate Heat Exchangers: Design, Applications and Performance, WIT Press. Pelletier, O., Stromer, F., Carlson, A., 2005, CFD Simulation of Heat Transfer in Compact Brazed Plate Heat Exchangers, ASHRAE Transactions. Vol. 111, no. 1, pp. 846-854. O’Halloran, S., Jokar, A., 2011, CFD simulation of single-phase flow in plate heat exchangers, ASHRAE Transactions, LV-11-C018, 147-156. Kanaris, A.G., Mouza, A.A., and Paras, S.V., 2005, Numerical and Experimental Investigation of Flow and Heat Transfer in Narrow Channels with Corrugated Walls, 4th International Conference on Heat Transfer, Fluid Mechanics, and Thermodynamics. Utriainen, E., Sundén, B., 2002, A numerical investigation of primary surface rounded cross wavy ducts, Heat and Mass Transfer, Vol. 38. pp. 537-542. Zhang, L., Che, D., 2011, Turbulence Models for Fluid Flow and Heat Transfer between Cross Corrugated Plates, Numerical Heat Transfer, Part A, Vol 60, pp. 410-440. Zhang, L., Che, D., 2011, Influence of Corrugation Profile on the Thermalhydraulic Performance of Cross Corrugated Plates, Numerical Heat Transfer, Part A, Vol 59, pp. 267-296. Freund, S., Kabelac, S.,2010, Investigation of Local Heat Transfer Coefficients in Plate Heat Exchanger with Temperature Oscillation IR Thermography and CFD, International Journal of Heat and Mass Transfer, Vol.53, pp. 3764-3781. Gulenoglu, C., Aradag, S., Sezer-Uzol, N., Kakac, S., Experimental Comparison of Performances of Three Different Plates for Gasketed Plate Heat Exchangers, International Journal of Thermal Sciences (accepted).

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ENERGY EFFICIENT DATA CENTERS WITH SPECIALIZED SIMULATION TOOLS Can Özcan1 1

Akro Engineering Ltd., Kocaeli, Turkey


SUMMARY (heading style: Times New Roman Bold 12 pt, UPPERCASE) Due to increasing demand on cloud computing, centralized data centers are becoming more important where capacities increase. This in turn, not only increases the initial costs but also the running energy costs. As energy prices rise and concerns about global warming grow there is increasing will to attain efficiency in data center energy systems. The criteria for such efficiency is called PUE(Power Usage Effectiveness) and defined as Total Facility Power/Total IT Power. Cooling is about 75% of the non-IT power consumption and can be improved thru simulations. This paper will address usage of specialized simulation tools to effectively calculate PUE and optimize data center cooling systems according to ASHRAE standards. INTRODUCTION The introduction should present the practical and scientific background for the study or presentation, the hypothesis(es) and a clear statement of the objective(s) of the study/presentation. Data Centers are being built in increased number and capacity with recent trends and advancements in information technologies. Penetration of mobile technologies to large communities, storage of user data in form of data-rich formats and cloud computing technologies are factors for such increase of Data Center investments. Energy consumption has been increasing in the world, with diminishing resources for traditional fossil based fuels. With environmental concerns, renewable energy is a current hot topic. Though much research is in the field of energy generation, the opposite side of the equation is the energy consumption which is a field open for improvement with more efficient mechanical systems. Data shows that the energy consumption in Data Centers is at comparable scale to overall energy consumption [1]. And considerable percentage of data center energy consumption is related to data center cooling, not the IT equipment consumption. Because of this significance, efficient Data Center cooling is important for both financial and environmental concerns.

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Computational Fluid Dynamics is the science of solving fluid dynamics and heat transfer equations on a discretized domain. The technique was developed mainly for aerospace industry in its early implementations and has been employed in different industries with great success over several decades. The governing equation for fluid dynamics is Navier-Stokes and is still impractical to solve numerically using DNS(direct numerical solution) with current computer systems. Therefore a reduced version of Navier-Stokes, that is Reynolds Averaged Navier-Stokes (RANS) equations was introduced by Osborne Reynolds in late 19th century. Coupling RANS equations, with proper turbulence and heat transfer equations lead to solution of a coupled fluid dynamic system which is called CFD. In this study, I propose CFD as a valuable tool for energy efficiency in Data Centers and support our hypothesis with an example. Additional benefits of using such technology for reliability and preventing excess redundancy is also discussed.

METHODS Data Center energy efficiency is calculated using CFD methods in this study. I considered a simple optimization model for energy efficient data center design.

have

(a) Numerical Optimization using Unit Cell Model In this case, a large data center facility is analyzed by modeling only a unit cell portion. This case only 1/16th of the data center is modeled using 2 rack rows with a single air handler being in the model. The geometric unit cell model is shown in Figure 1 below.

Figure 1 CFD Domain For Optimization Study

The design of the server room is chosen to be an “under floor configuration”, where the air vent openings will be distributed on the floor. The modeled unit cell, rack and air handler unit dimensions can be listed as: • •

unit cell dimensions (width)x(length)x(height) : 4.8m x 9.0m x 3.8m rack dimensions (width)x(length)x(height) : 1.2m x 0.6m x 2.1m

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•

air handler dimensions (width)x(length)x(height) : 3.0m x 1.2m x 1.6m

As it can be observed from Figure 1, there are 2 rows of server cabinets in the unit cell model, where each row consists of 8 cabinets of 10kW each. The following boundary conditions are required to define the differential equation to be solved using CFD method: • Air handler units are assigned to work as an outlet • Every cabinet/rack will generate 10kW heat with 0.85 m3/s flow rate [3] • Server environment should be kept between 18 and 23 degrees Celsius • AC unit inlet temperature is between 10 and 17 degrees Celsius • AC unit volumetric flow rate is assumed to be 8 m3/s • Wall conditions are given a standard thermal conductivity value with 23 degrees Celsius • Radiation effects are neglected • Buoyancy model is considered for natural convection type flow • Turbulence model is chosen as Shear Stress Transport (SST) • Floor vent tile openings are modeled as porous domain with directional loss coefficients

Figure 2 Boundary Conditions On Air Handling Unit, Service Racks

Figure 3 Boundary Conditions On Air Handling Unit and Venting Tiles

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With the given conditions, the problem is setup for a numerical optimization study using the following input and output parameter set as outlined in Figure 4 and Figure 5. The numerical optimization loop is set to be fully automated using scripting for geometry creation, meshing, solution and post-processing stages of a single CFD analysis. Optimization study has 4 input variables where 3 of them are related to ceiling mounted flow deflector as shown in Figure 4. The position of the deflection is measured from wall (where air handling unit is placed) towards center of the data center room. The length and height of the deflector are used to set the size of the flow deflector. The last input parameter is chosen to be “Grill Free Area Ratio”, this parameter is set in the CFD solver numerically, by adjusting the porosity value. The aim is to define flow amount through the tiles via this parameter.

Figure 4 Input Parameters for Numerical Optimization

For flow optimization, only 2 output parameters are used; “Rack Temperature” and “Temperature Uniformity”. After post processing of the solution the maximum temperature on the racks is read and recorded as “Rack Temperature” parameter. The temperature uniformity is calculated on the racks, based on standard deviations. Standard deviation provides a measurement of temperature variation from the mean. Temperature uniformity corresponds to minimized standard deviation of rack surface temperatures.

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Figure 5 Output Parameters for Numerical Optimization

A response surface based optimization method is chosen, since a single CFD run takes considerable run time and is not suitable for direct optimization. An initial design of experiments (DOE) is generated by “optimal space filling” algorithm, which is suitable for nonlinear flow problems. Non-parametric regression algorithm is employed for response surface. Multiple objective genetic algorithm (MOGA) is used to perform actual optimization. Optimal design candidates are verified with direct numerical simulation as a follow-up for accuracy of the solution. RESULTS (a) Numerical Optimization using Unit Cell Model Response surface fit for “Rack Temperature” is show in Figure 6. “Deflector Position” and “Grill Free Area Ratio” is found out to be the most important factors affecting the maximum rack temperatures. The shape of the response surface suggests the nonlinear relationship between input and output.

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Figure 6 Response Surface for "Rack Temperature" as a Function of Deflector Position and Grill Free Area Ratio

For data center cooling efficiency, one would like to minimize “Rack Temperature” and achieve “Temperature Uniformity” by minimizing standard deviation. Analyzing the data one can obtain valuable information about design parameters for this specific data center design: • • • •

Air vent tile “free area ratio” has to be around 0.3 to 0.35 [Figure 7a] Deflector position is found out to be optimum between 3m to 4m [Figure 7b] Deflector length should be small (just closing over the aisle) [Figure 8a] Deflector height should be large [Figure 8b]

Figure 7 Effect of (a) Grill Free Area Ration and (b) Deflector Position, on Rack Temperature

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Figure 8 Effect of (a) Deflector Height and (b) Deflector Length, on Rack Temperature

Figure 9 Pareto Chart for All Design Points

Pareto frontier surface is useful to determine for multi-objective optimization. Such chart is generated in Figure 9. Since one would like to minimize both Rack Average Temperatures and Standard Deviation for uniform cooling, one can look for a feasible point on the pareto surface as shown with yellow dashed line. One can observe that there is a trade-off between the two output parameters by looking at this chart. The choice can be dependent on a number of parameters and can be chosen by considering all design constrains. For the sake of application, one pick a feasible design on the pareto surface with equal weight on both output parameters. Then we can compare the temperature profile before and after this optimization study in below images:

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Figure 10 Temperature Distribution in Server Room (a) Initial Design (b) Optimized Design

Figure 11 Rack Temperatures (a) Initial Design (b) Optimized Design

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Figure 12 Temperature Distribution via Volume Rendering (a) Initial Design (b) Optimized Design

DISCUSSION In this paper it has been shown that numerical engineering simulation tools can be utilized for greater energy efficiency in data centers. Improvement of software capabilities and technology allows system designers to consider more possibilities in virtual simulated environments before making important design decisions. Current state of general and customized software has benefits effecting the energy efficiency calculations. In this paper, I have studied the optimization and automatic model generation capabilities of a general purpose code with detailed modeling capabilities of a specific data center energy efficiency tool. I believe, there is still room for improvement in these simulation systems by combining best of both options. In future analysis, I would like to concentrate on optimization of detailed systems and enabling of data transfer from design data. ACKNOWLEDGEMENT I would like to thank Applied Math Modelling, NH and Ozen Engineering, CA for their support on this study. REFERENCES 1. J. Koomey, “A Simple Model for Determining True Total Cost of Ownership for Data Centers”, Uptime Institute 2008 2. U. Singh et.al. “CFD-Based Operational Thermal Efficiency Improvement of a Production Data Center” 3. S.V. Patankar “Computational Modeling of Airflow in Raised-Floor Data Centers” 4. C.D. Patel, R. Sharma, C.E. Bash and A.Beitelmal, “Thermal Consideration in Cooling Large Scale High Compute Density Data Centers”, EICTT Conference 2002 CA USA 5. Handbook of Hydraulic Resistance 3rd Edition I. E. Idelchik, CRC Begell House – 1994

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INVESTIGATION OF THERMAL COMFORT INSIDE A FURNISHED OFFICE ROOM ACCORDING TO VELOCITY-TEMPERATURE VARIATIONS Firat Karasahin1, Tamer Calisir2 and Senol Baskaya2 1 2

Hacettepe University Physics Engineering Dep., 06800 Beytepe-Ankara – TURKEY Gazi University, Dep. of Mechanical Engineering, 06570 Maltepe-Ankara – TURKEY

Corresponding email: [email protected] SUMMARY In this study, airflow profiles for conditioning the space of a furnished office room with a manikin have been numerically investigated. An existing experimental space has turned into a numerical model using PHOENICS CFD code, by using boundary conditions that were applied in the experimental study. Obtained results have been compared with the experimental data and a good agreement was achieved. For two different diffuser locations the diffuser inlet velocity was varied between 0.1-2.0 m/s and temperature for summer and winter conditions was varied between 16-20°C and 25-35°C, respectively. Between the investigated cases, for summer conditions, the optimum comfort conditions for the cooling process were obtained for DL1 with 20°C supply air temperature and 2m/s supply air velocity. For winter conditions, in the case of heating, optimum comfort conditions were obtained for DL2 with 30°C supply air temperature and 1m/s supply air velocity. INTRODUCTION For thousands of years the human being is struggling against climate conditions. Although the struggle in the external environment is greatly won through appropriate clothing, indoor conditions have been changed continuously in order to obtain more comfortable living conditions. The main task of a high-performance ventilation system is to supply fresh and conditioned air in order to get rid of pollutants and to ensure a comfortable and healthy indoor air environment. In order to ensure indoor air quality and thermal comfort a lot of research has been performed in the past and certain standards have been established. Catalina et al. reported a full-scale experimental and a computational fluid dynamics (CFD) study of a radiant cooling ceiling installed in a test room [1]. Olesen and Parsons carried out a study on thermal comfort standards [2]. Abanto et al. concerned about the numerical simulation of airflow and the prediction of comfort properties in a visualization room of a research centre [3]. Mendez et al. analyzed numerically the ventilation airflow pattern in a two-bedded hospital room [4]. Baskaya and Eken investigated numerically the air distribution inside a room containing one person and office furniture under different inlet/outlet and summer/winter configurations [5]. Lin et al. investigated the air movement, air temperature profile and gaseous contaminant transportation in an office with stratum ventilation using numerical and experimental techniques [6]. Sajadi et al. investigated numerically the effect of geometric parameters on the

168


performance of a specific type of swirling air diffuser [7]. Some of the standards created by International organizations define systems which provide appropriate air for the necessary comfort and health conditions indoors. The European Prestandard PrENV 1752 is intended to be a flexible tool for assisting the designer in providing a proper indoor environment for people in ventilated buildings [8]. The ANSI/ASHRAE Standard 55-1992 specifies conditions in which 80% or more of the occupants will find the environment thermally acceptable [9]. The purpose of the ISO 7730 Standard is to present a method for predicting the thermal sensation and the degree of discomfort of people exposed to moderate thermal environments and to specify acceptable thermal environmental conditions for comfort [10]. The scope of the ISO 7726 Standard is to specify the minimum characteristics of instruments for measuring physical quantities characterizing an environment as well as the methods for measuring the physical quantities of this environment [11]. ANSI/ASHRAE Standard 62-2001 specifies minimum ventilation rates and indoor air quality that will be acceptable to human occupants and are intended to minimize the potential for adverse health effects [12]. As can be seen, there have been many investigations on thermal comfort and indoor air quality, but there were very few investigations on idealization of diffuser locations according to velocity – temperature variations in a furnished office room. In this study, thermal comfort conditions were investigated numerically by changing velocity, temperature and diffuser location effects of velocity-temperature distributions and diffuser locations on thermal comfort conditions for summer/winter conditions. NUMERICAL MODEL Airflow behavior and resulting thermal comfort conditions for conditioning the space of a furnished office room with a manikin have been numerically investigated using PHOENICS CFD code. Numerical model was designed using table, shelve, cabinet, chairs and a human manikin, according to the experimental setup. The investigated office room and appropriate diffuser locations (DL) are shown in Figure 1. By changing velocity and temperature conditions several parametric combinations have been produced, and the effects of velocitytemperature distributions on thermal comfort conditions were observed. The office room was modeled using the exact dimensions of the experimental space, which were 5x4x2.55m3. Diffuser dimensions of DL1 and DL2 were 437.5x190 mm2 and 600x600 mm2, respectively.

DL1 DL2

(a) (b) Figure 1. Geometry of investigated office room and diffuser locations (a) DL1, (b) DL2

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Standards for air-conditioning and ventilation were investigated carefully and based on these information a model was developed. According to the experimental setup, 64 temperature locations were used in the analysis of numerical results. Figure 2 shows the temperature locations. For a specified diffuser location the velocity was varied between 0.1-2.0 m/s and the temperature for winter and summer conditions was applied in the range of 25 – 35 °C and 16 – 20 °C, respectively. All walls, floor and ceiling temperatures were modeled as 30 °C and 5 °C for summer and winter conditions, respectively.

Figure 2. Temperature measurement locations Trial solutions were obtained with a wide range of iteration and cell number combinations for iteration and grid independency checks. Final simulations were performed with iteration numbers of 5000 and cell numbers up to 100x90x60 depending on the relevant sizes. In order to check the adequacy of the present numerical approach, the obtained results are compared with experimental measurements which were obtained from the furnished office room. In Figure 3. comparison of experimental and numerical results at the 1st temperature measurement location is shown. It can be seen that there exist just 1-2 °C temperature difference between the numerical and experimental results, which shows that the numerical model is suitable.

Figure 3. Comparison of experimental and numerical temperature values at 1st temperature measurement location Governing Equations The airflow and heat transfer inside the office room is a three-dimensional, turbulent, mixed convection problem formulated with three-dimensional equations of conservation of mass, momentum, energy, turbulent kinetic energy and its dissipation rate. For a steady,

170


incompressible, three-dimensional flow the conservation of mass, turbulent momentum and energy equations can be expressed in the following forms. ∂ (ρ U i ) = 0, ∂xi

(1)

⎛ ∂u ∂u j ⎞⎤ ∂ ∂P ∂ ⎡ ⎟⎥ − g i ρ − ρ ref , (2) ⎢ μ ef ⎜ i + ρui u j = − − ⎜ ∂x j ∂xi ⎟⎥ ∂x j ∂xi ∂x j ⎢ ⎠⎦ ⎝ ⎣

(

∂ ∂ ρu j T = ∂x j ∂x j

(

)

)

⎛ ⎜ Γef ∂T ⎜ ∂x j ⎝

(

)

⎞ ⎟ , (3) ⎟ ⎠

Here, g is the gravitational acceleration, ρref is the reference density, μ ef is the effective dynamic viscosity. In the above equation, the g(ρ- ρref) term is the buoyancy force. Γ shows the diffusion term. The turbulence kinetic energy equation is expressed as: ⎛μ ⎞ ∂ (ρui k ) = ∂ ⎜⎜ ef ∂k ⎟⎟ + G K + G B − ρε , (4) ∂xi ∂xi ⎝ σ k ∂xi ⎠

Here, σ k is a turbulence model constant, G K is the rate of shear production of k and G B is the rate of buoyancy production of k. G K and G B are defined as given below: GK = μt

∂ui ∂x j

⎛ ∂ui ∂u j ⎜ + ⎜ ∂x j ∂xi ⎝

⎞ ⎟, ⎟ ⎠

μ 1 ∂ρ i GB = − g i t , σ t ρ ∂xi

(5)

The dissipation rate of turbulence kinetic energy is given as: ⎛μ ⎞ ∂ (ρui ε ) = ∂ ⎜⎜ ef ∂ε ⎟⎟ + ε (C1ε G K + C3ε G B − ρC 2ε ε ), (6) ∂xi ∂xi ⎝ σ ε ∂xi ⎠ k

where, σ ε , C1ε , C 2ε , C3ε are turbulence model constants. These definitions were made according to the standard k-ε turbulence model. In this model the turbulence model quantities are given as: μ k2 Γef = t , (7) μ t = ρC μ , μ ef = μt + μ , σt ε

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where, μ t is the turbulent viscosity, ρ fluid density, C μ turbulence model constant and Γef is the effective exchange coefficient. Values of the turbulence model constants are σt=1.00, Cµ=0.09, σk=1.00, σε=1.314, C1ε=1.44, C2ε=1.92, C3ε=1.00. Boundary Conditions Velocities at the walls and shelve, cabinet, chairs and human manikin are zero because of the no-slip condition. Shelve, cabinet, chairs and human manikin were assumed to be adiabatic. All walls, floor and ceiling temperatures were modeled as 30 °C and 5 °C for summer and winter conditions, respectively. For a specified diffuser location, the velocity was varied between 0.1-2.0 m/s, and the temperature for winter and summer conditions was applied in the range of 25 – 35 °C and 16 – 20 °C, respectively. At the outlet, the pressure is fixed to the ambient pressure and all variations of temperature, turbulent kinetic energy and its dissipation were taken to be zero. The logarithmic law of the wall was used in regions close to the wall surfaces. Equations used for the turbulent inlet boundary conditions are given below [5]. ⎧

k in = 1.5 I t2 U c2 , I t = ⎪⎨⎡⎢⎛⎜ u ′ 2 + v ′ 2 ⎞⎟ / 2⎤⎥ in in ⎝ ⎪ ⎩⎣

⎠

⎦

1/ 2

⎫ ⎪ 3/ 2/ L , L = d / 2 / U c ⎬ , εin = k in ε ε ⎪ ⎭

(9)

Here, kin is the turbulent kinetic energy at the inlet, Itin turbulence intensity at the inlet, Uc characteristic velocity scale, u ′ and v ′ average fluctuating components of velocity, εin dissipation rate of k at the inlet, Lε characteristic length and d is the jet slot width [5]. RESULTS In this study, outside air was supplied at a specific temperature and velocity range from two different locations to provide the necessary human thermal comfort conditions. For temperature, velocity and vertical temperature variations thermal satisfaction categories were considered together. In Table 1 the design criteria for a single office room is shown [8].

Table 1. Design Criteria for the space Operative Temperature Mean air velocity [m/s] Type of [oC] Category Building/Space Summer Winter Summer Winter A 24,5±1,0 22,0±1,0 0,18 0,15 B 24,5±1,5 22,0±2,0 0,22 0,18 Single Office C 24,5±2,5 22,0±3,0 0,25 0,21 K Discomfort As mentioned before, there have been 16 temperature and velocity locations at which four vertical temperature and velocity data were used in the analysis. Using that data mean velocity and mean temperatures were calculated for all 64 locations. The resulting velocity and temperature values were used to determine the variation of thermal comfort conditions inside

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the office room. As a result, in accordance to the most uncomfortable condition, the ultimate comfort category was determined. Figure 4. shows the representation of comfort categories for each location.

Figure 4. Comfort category representation for each location In Figure 5 the velocity and temperature distribution for DL1 and DL2 at winter conditions is shown for 1.0 m/s and 30°C. It is seen from the temperature contour for DL1 that the right side of the room is colder, and especially the location around the desk is below the comfort conditions. The air is entering from the bottom and changes its direction after impinging on the cabinet, and after this the velocity of air decreases along the room. It is believed that due to the decrease in velocity and temperature air could not heat the office room. Similarly, for DL2 it is seen that when air enters from the top of the office the velocity and consequently the temperature distribution is more uniform. Also, there is little effect from occupants for such a diffuser location. In addition, changes of inlet/outlet locations significantly alter the temperature distributions. If the figures are analyzed jointly, one can see that the temperature distribution develops because of airflow characteristics resulting from the variation of inlet/outlet locations and the occupant’s size and location.

(a)

(b)

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Figure 5. Velocity vectors and temperature contours at heating conditions, 1.0 m/s velocity and 30°C temperature (a) DL1 (b) DL2 In Figure 6 the velocity and temperature distribution for DL1 and DL2 at summer conditions is shown for 2.0 m/s and 20°C. It can be seen from the velocity distribution for DL1 that there is a significant effect of the occupants to the velocity and temperature distributions. Accordingly, too high velocities at the diffuser inlet and very low temperatures were observed. But these values are only near the bottom of the office. In general a uniform temperature and velocity distribution along the office was observed. For DL2 it is observed from the velocity distribution that high velocities are present at diffuser inlet and the conditioned air impinges on the bottom. For this reason a wall jet and high velocities are observed at the bottom surface, which leads to discomfort. On the other hand, a more uniform temperature distribution in the range of 24-25°C is obtained. Table 2. shows the comfort analysis performed at winter and summer conditions for 1.0 m/s and 30°C and 2.0 m/s, 20°C, respectively. From the results obtained for DL1 at winter conditions it is seen that category “A” was achieved for mean velocity and vertical temperature difference. However, as a result of flow velocities the mean office temperature was obtained in the range of 16-17°C, and because of that, although a mean comfort category “B” was achieved, the ultimate comfort category was uncomfortable. Similarly, from the comfort analysis for DL2 “A” and “B” comfort categories were achieved for mean velocity and vertical temperature difference, respectively. The mean temperature distribution of the office room was obtained in the range of 22-23°C and a mean comfort category of “B” or “C” was achieved. Despite this, the ultimate comfort category was achieved as uncomfortable. For temperature measurement locations at the heights of z=0.6m and z=1.1m comfort categories for temperature were acceptable. Nevertheless, at the height of z=0.1m the temperature range was very low, so the ultimate comfort category was uncomfortable. As observed from the velocity and temperature distributions for DL1 at summer conditions, “A” ultimate comfort category has been obtained in general. There have been also some points where discomfort exists. But these points are at height levels of ankle like before observed from the velocity distribution. Approximately 24°C temperature was observed in the upper regions of the office room for DL2 at summer conditions. Hence, comfort category “A” was obtained. However, because of high velocity values at the vertical locations of z=0.1m, ultimate discomfort was obtained for this arrangement.

(a)

174


(b) Figure 6. Velocity vectors and temperature contours at cooling conditions, 2.0 m/s velocity and 20°C temperature (a) DL1 (b) DL2

SUMMER CONDITONS

WINTER CONDITIONS

Table 2. Comfort analysis LOCATION 16 DL1 K C (K, A, B) DL2 K B (K, A, A) LOCATION 12 DL1 K B (K, A, A) DL2 K B (K, A, A) LOCATION 8 DL1 K B (K, A, A) DL2 K K (B, K, A) LOCATION 4 DL1 K C (K, K, A) DL2 K C (B, K, A) LOCATION 16 DL1 A A (A, A, A) DL2 K C (B, K, A) LOCATION 12 DL1 K B (A, K, A) DL2 K C (B, K, A) LOCATION 8 DL1 K C (B, K, A) DL2 K C (B, K, A) LOCATION 4 DL1 A A (A, A, A) DL2 K C (B, K, A)

LOCATION 15 K B (K, A, A) K C (K, K, A) LOCATION 11 K B (K, A, A) K C (K, A, B) LOCATION 7 K B (K, A, A) K C (B, K, A) LOCATION 3 K B (K, A, A) K B (K, A, A) LOCATION 15 B B (B, A, A) K C (B, K, A) LOCATION 11 K K (K, K, C) A A (A, A, A) LOCATION 7 A A (A, A, A) K C (B, K, A) LOCATION 3 A A (A, A, A) K C (B, K, A)

DISCUSSION

175

LOCATION 14 K B (K, A, A) K C (B, K, A) LOCATION 10 K B (K, A, A) K B (A, K, A) LOCATION 6

LOCATION 13 K B (K, A, A) B B (B, A, A) LOCATION 9

K B (K, A, A) K C (B, K, A) LOCATION 2

K B (K, A, A) C B (B, A, A) LOCATION 1

K B (K, A, A) K C (B, K, A) LOCATION 14 A A (A, A, A) K C (C, K, A) LOCATION 10 A A (A, A, A) K C (B, K, A) LOCATION 6

K B (K, A, A) C B (C, A, A) LOCATION 13 C A (A, A, A) K C (C, K, A) LOCATION 9

A A (A, A, A) K C (B, K, A) LOCATION 2

A A (A, A, A) K C (B, K, A) LOCATION 1

A A (A, A, A) K C (C, K, A)

A A (A, A, A) K C (C, K, A)

K B (K, A, A) K C (B, K, A) LOCATION 5

K C (C, K, A) K C (B, K, A) LOCATION 5


Airflow behavior and resulting thermal comfort conditions for conditioning the space of a furnished office room with a manikin have been numerically investigated. An existing experimental space has been turned into a numerical model using PHOENICS CFD code. The numerical results obtained have been compared with the experimental data and a good agreement was achieved. For two different diffuser locations the diffuser inlet velocity was varied between 0.1-2.0 m/s, and temperature for summer and winter conditions was varied between 16-20°C and 25-35°C, respectively. For winter conditions, comfort category “A” was achieved for mean velocity and vertical temperature difference for DL1. Although a mean comfort category “B” was achieved, the ultimate comfort category was uncomfortable, because the mean office temperature was obtained in the range of 16-17°C. Similarly, the comfort analysis for DL2 was achieved as “A” and “B” comfort categories for mean velocity and vertical temperature difference, respectively. The mean temperature distribution of the office room was obtained in the range of 22-23°C and a mean comfort category of “B” or “C” was achieved. Despite this, the ultimate comfort category was achieved as uncomfortable. For summer conditions, there has been an “A” ultimate comfort category obtained in general for DL1. There have been also some points seen where discomfort exists. The comfort analysis for DL2 was also presented and comfort category “A” was obtained. However, because of high velocity values at the vertical locations of z=0.1m, ultimate discomfort was obtained for this arrangement. REFERENCES 1. Catalina,T,Virgone,J, Kuznik, F. 2009. Evaluation of thermal comfort using combined CFD an experimentation study in a test room equipped with a cooling ceiling. Building and Environment. Vol. 44, pp. 1740-1750. 2. Olesen,B W, Parsons, K C. 2002. Introduction to the termal comfort standards and the proposed new version of EN ISO 7730. Energy and Buildings. Vol. 34, pp. 537-548. 3. Abanto, J, Barrero, D, Reggio, M, Ozell, B. 2004. Airflow modelling in a computer room. Building and Environment. Vol. 39, pp. 1393-1402. 4. Méndez, C, San José J F, Villafrulea, J M, Castro, F. 2008. Optimization of a hospital room by means of CFD for more efficient ventilation. Energy and Buildings. Vol. 40, pp. 849-854. 5. Baskaya, S, Eken, E. 2003. Investigation Of Room Air Flow Regarding Effects Of Occupants, Inlet/Outlet Locations, Inlet Velocity, And Winter/Summer Conditions, Int. Comm. Heat Mass Transfer. Vol. 30 (8), 1147-1156.

6. Lin, Z, Tian, L, Yao, T, et al. 2011. Experimental and numerical study of room airflow under stratum ventilation. Building and Environment. Vol. 46, pp. 235-244. 7. Sajadi, B, Saidi, M H, Mohebbian, A. 2011. Numerical Investigation of the swirling diffuser: Parametric study and optimization. Energy and Buildings. Vol. 43, pp. 1329-1333. 8. PrENV 1752:1996, Ventilation for buildings - Design criteria for the indoor environment. 9. ASHRAE. 1992. ANSI/ASHRAE Standard 55 1992, Thermal Environmental Conditions for Human Occupancy, Atlanta: American Society of Heating, Refrigerating, and Airconditioning Engineers, Inc. 10. ISO 7730 – 2005, Ergonomics of the thermal environment – Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. 11. ISO 7726 – 1998, Ergonomics of the thermal environment – Instruments for measuring physical quantities. 12. ANSI /ASHRAE Standart 62-2001, Ventilation for Acceptable Indoor Air Quality.

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ECONOMICAL ANALYSIS OF HEAT RECOVERY VENTILATION FOR DIFFERENT CLIMATE CONDITIONS IN TURKEY İsmail Hakkı Tavman1, Cihan Çangarlı2 1 2

Dokuz Eylül University, Mechanical Engineering Department. İzmir Impelair AS, İzmir

Corresponding email: [email protected] SUMMARY Ventilation increases heating and cooling demand because of the additional fresh air load. With heat recovery ventilators, exhausted air energy is transferred to the supplied fresh air by the help of an air to air heat recovery exchanger. Supply air temperature to the space is not constant and is a function of exhaust air and fresh air temperature. In this study 5 cities located at different regions are considered and fresh air temperatures are received for an hourly basis by Turkish State Meteorological Service. A heat recovery ventilator with crossflow heat recovery exchanger has been provided from a manufacturer with a nominal air flow rate of 500 m3/h. Supply air temperature; recovered total energy and heat recovery efficiency are calculated in an hourly basis for the 5 cities. Return on investment calculation shows that Ankara which is the coldest city examined has the shortest return on investment time. INTRODUCTION The energy crisis in 1970’s led the countries to develop new energy strategies. The increasing consumption in the Carbon based fuels and the limited resources not only increase the interest in renewable energy researches but also encourage the governments to search for more efficient ways to use the Carbon based fuels. Researches made in U.S show that 70% of the total electricity consumption and 50% of the total natural gas consumption takes place in the buildings [1]. Investors, architects, landlords and building operators are looking for modernistic solutions to support marketing activities that can both decrease the investment and operational costs and also create a better indoor environment to increase productivity and human comfort in buildings. Heat recovery ventilation (HRV) units are designed to meet the modern ventilation needs while decreasing the fresh air cooling/heating load. HRV systems are examined for investment and operational costs by many researchers. It is shown that HRV systems are creating energy savings in colder climates and with the increase in temperature efficiency the energy savings also increase [2]. With the use of HRV systems according to climate, it is possible to reduce the total cooling design load up to 29% and the total heating design load up to 37% [3]. To decrease the humidity indoors, HRV systems are more efficient than natural ventilation systems and mechanical exhaust systems [4].

177


METHODS In this research an HRV unit serving a call center with 16 operators is examined. The HRV unit has a nominal air flow rate 500 m3/h and is running 24 hours a day. The unit is equipped with a cross flow plate type heat recovery exchanger without humidity transfer. The unit has an automatic by-pass system; when the temperature is between 21°C and 26°C, a by-pass clamp is opened and return air is not introduced and by-passed through the heat exchanger. These operational periods are not calculated in the energy savings calculation. Indoor air conditions for all the climates examined is fixed and taken into consideration as 22°C, 50%RH during winter and 26°C, 50% RH during summer. Outdoor air relative humidity for the examined cities are also fixed and taken into consideration as 75% in winter and 35% in summer. The heat recovery exchanger used in this research has a plate size of 400 mm and a length of 320 mm. The plate distance is 3,5 mm.

Picture 1. Cross flow Heat Exchanger

Picture 2. Air Passing Ducts in a Heat Exchanger

Outside air temperatures for 5 cities located in different climate zones in Turkey are delivered by Turkish State Meteorological Service on an hourly base for 2005. Energy recovery efficiency, supply air temperature and total recovered energy are calculated for the 5 cities separately by the software supplied by the heat exchanger manufacturer and presented in the charts. The software is a scientific hybrid of heat exchanger calculation and empirical data received from tests conducted according to the standard EN 308 "Heat exchangers - Test procedures for establishing performance of air to air and flue gases heat recovery devices". The software is approved by the independent testing and certification authority Eurovent.

RESULTS Monthly average outdoor air temperatures are calculated and given in Table 1. Hourly based outside air temperatures are illustrated through Chart 1-5. Minimum winter outdoor air temperatures for Antalya and İzmir are over 0°C, below 0°C for Urfa and İstanbul and less than -6°C for Ankara. Maximum summer outdoor air temperatures are over 30°C for Ankara and İstanbul, over 33°C for Antalya and İzmir and over 36°C for Urfa. Seasonal maximum and minimum temperature difference for Ankara, Antalya and Urfa is over 42°C, for İzmir and İstanbul is over 35°C.

178


Table 1. Monthly Average Temperatures for the examined cities. Jan.

Feb.

Mar.

Apr.

May

June

July

Aug. Sep.

Oct.

Nov. Dec.

Ankara

3,3

2,3

6,0

11,2

16,2

18,9

24,5

25,0

18,6

10,8

5,9

2,8

Antalya

10,8

10,1

13,4

16,8

21,1

25,4

28,4

28,9

25,6

19,3

13,9

11,8

İstanbul

7,1

6,0

7,3

12,3

16,3

20,3

24,2

25,1

20,8

14,7

10,5

8,7

İzmir

9,9

8,6

12,0

16,3

21,2

24,7

28,4

28,4

23,9

17,8

12,8

11,2

Urfa

6,6

6,5

11,4

17,4

23,0

27,2

33,0

32,0

26,5

18,6

11,1

9,8

Chart 1. Outdoor Air Temperature for Ankara

Chart 2. Outdoor Air Temperature for Antalya

Chart 3. Outdoor Air Temperature for İstanbul

Chart 4. Outdoor Air Temperature for İzmir

179


Chart 5. Outdoor Air Temperature for Urfa

To evaluate recovered total energy, the year has been divided into three seasons; heating, cooling and by-pass. Heating season is regarded for outdoor air temperatures below 21 °C and cooling season for outdoor air temperatures above 26 °C. Between these values heat recovery exchanger will not operate return air from the call center will be by-passed. Fresh air will be introduced to indoor space without heat treatment. Energy recovered is calculated in kW for heating and cooling seasons and in heating season is denoted in red dots and in cooling season blue dots through Chart 5-10. For Ankara and İstanbul the heat recovery occurred for heating season the most. For Izmir and Antalya the season heat recovery is more balanced but still more for heating season. In Urfa the heat recovery occurred for cooling and heating are in balance. The charts show that in colder climates where outdoor air temperature is lower than 0°C and with warmer summer temperatures slightly above 30°C; the heat recovery develops mainly for heating seasons. Similarly; for climates with warmer conditions also the heat recovery rate in cooling season is increasing whereas for climates with high outdoor air temperatures like Urfa it is more balanced.

Chart 6. Recovered Energy for Ankara

Chart 7. Recovered Energy for Antalya

Chart 8. Recovered Energy for İstanbul

Chart 9. Recovered Energy for İzmir

180


Chart 10. Recovered Energy for Urfa

Thermal effectiveness also known as heat recovery efficiency is defined as;

ε=

Actual Heat Transfer Rate , Theoretical Maximum Heat Transfer Rate

(1)

The maximum theoretical heat transfer rate occurs in counter-flow with infinite heat transfer surface area. It cannot occur in parallel flow because the exit temperature must be between the two inlet temperatures. Figure 1. Temperature change in a heat exchanger Total energy recovery with the formula for constant specific heats with no phase change for heating and cooling seasons;

Qh = (m c p )h (Th 2 − Th1 ) ,

Qc = (m c p )c (Tc 2 − Tc1 ) ,

(2) (3)

The actual heat transfer is given by; c )H (TH ,in − TH ,out ) = (m c )C (TC ,out − TC ,in ), Qmax = (m

(4)

If the hot fluid has the lower thermal capacity, then the efficiency formula is generated for even fluid flows as;

ε=

(m c )h (TH ,in − TH ,out ) (TH ,in − TH ,out ) , = (m c )h (TH ,in − TC ,in ) (TH ,in − TC ,in )

(5)

If the cold fluid has the lower thermal capacity then the efficiency formula is generated for even fluid flows as;

181


ε=

(m c )h (TC ,out − TC ,int ) (TC ,out − TC ,in ) , = (m c )h (TH ,in − TC ,in ) (TH ,in − TC ,in )

(6)

Supply air temperatures for 5 cities are calculated from the software and are shown in Chart 11. The shallow area in the chart shows the by-pass season where no heat recovery has considered for the different climates. Although outdoor air temperatures and energy recovery rates are different it is observed that the supply air temperatures after the heat recovery ventilation unit are close to each other.

Chart 11. Supply air temperature comparison for 5 cities

Although the outdoor air temperatures vary for the examined cities with maximum of 11 °C in heating season as illustrated on Chart 1 through Chart 5, supply air temperatures variation is smaller and is a maximum of 5 °C. As the indoor air temperatures are fixed for the heating season at 22 °C, this change in variation is a function of the efficiency. As shown in Formula 6, when the temperature difference in the two opposite air streams increase, the efficiency of the HRV system also increases. With the evaluated data and the efficiency formula it is shown in Chart 12 that the HRV system efficiency increases with the change in temperature difference and in a colder climate examined, supply air temperature may result to have a smaller margin than a warmer climate because of the higher heat recovery efficiency. For the examined system, heat recovery efficiency is calculated and Chart 12 is developed. Although the outdoor air temperatures vary in summer, the heat recovery efficiencies are similar for the cooling season, this result in a significant diversity in the supply air temperatures as shown in Chart 11.

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Chart 12. Supply air temperature comparison for 5 cities

The payback period of the installed HRV system is calculated for the 5 cities. To calculate return on investment (ROI), HRV system investment and operational cost is compared to a duct fan system (DFS). DFS consist of one supply air fan to introduce fresh air indoors and one return air fan to extract dirty, stale air indoors. To treat incoming air against particles, dust and similar a filter box is also adapted to the system. The power consumption of two systems is compared for the nominal air flow and measured a difference of 18W/hour because of the pressure drop across the heat recovery exchanger and return air filter. This results in an additional electrical consumption of 157 kW. The installation and the maintenance cost for the two systems are identical, that’s why it is neglected in the calculation. To calculate the heating/cooling savings of the HRV system, a natural gas burner is used for comparison for winter and an air source heat pump unit is used for calculation with a with a SEER (Season Energy Efficiency Ratio) of 3,0 for summer. Table 2 shows the input to the ROI calculation. Table 2. Data used for Return on Investment calculation. HRV System Duct Fan System Initial Cost Investment Cost 710.00 € 454.00 € Electrical Consumption 134 W/hour 116 W/hour Same as Same as Service Costs alternative alternative Operational Natural Gas Heating Costs 0.028 €/kW Cost Heat Pump Cooling 0.031 €/kW Cost Table 3. Total heat recovery in 5 cities Ankara Antalya İstanbul İzmir Urfa Heating (kW) 3689 1614 4540 1880 2050 Cooling (kW) 85 265 35 197 604 Total (kW) 3774 1879 4575 2077 2654

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Total recovered heat for heating and cooling season is given in Table 3. For heating season a natural gas burner with 0,028 €/kW heating cost and a heat pump system with 0,031 €/kW is used for comparison. Figure 2 shows the return on investment for the 5 cities.

Figure 2. Return on Investment for the 5 cities. DISCUSSION The results carried out in this research shows that in colder climates due to the higher temperature differences heat recovery efficiency is higher. Although the outdoor air temperatures are lower, because of the high heat recovery efficiency supply air temperatures are similar to warmer climates. In colder climates, ROI is less than warmer climates. Also in climates with hot summer and warm winter, ROI is less than warmer climates. In Ankara and Istanbul ROI is the lowest due to colder climate data but also in Urfa where summers are very hot ROI is lower than the warmer cities, Izmir and Antalya. To make a more appropriate calculation for warmer climates it is important to evaluate the enthalpy exchange with a humidity transfer capable heat recovery exchanger. The results should be effected because of the humidity transfer in the fresh air. REFERENCES 1.

2.

3.

4.

J. L. Niu and L. Z. Zhang, Membrane-based enthalpy exchanger: material considerations and clarification of moisture resistance, Journal of membrane science 189, 179–191, Retrieved November 14,2007 from Science Direct database. Kragh Jesper , Rose Jørgen & Svendsen Svend Mechanical Ventilation with heat recovery in cold climates. Proceedings of the 7th Symposium on Building Physics in the Nordic Countries 1033-1040. Serhan Küçüka, Isı Geri Kazanım Cihazlarının Bazı Şehirlerdeki Yıllık Toplam Isıtma Ve Soğutma Kazançlariı, VII. Ulusal Tesisat Mühendisliği Kongresi TESKON ’05 Bildiriler Kitabı, 39-47, 2005 L. Z. Zhang & J. L. Niu, Effectiveness correlations for heat and moisture transfer processes in an enthalpy exchanger with membrane cores, Journal Of Heat Transfer 124, 922–929, Retrieved November 14,2007 from ASME database.

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INDOOR AIR CONCENTRATIONS OF SYNTHETIC MUSK COMPOUNDS AND THEIR FRACTIONATION BETWEEN GAS AND PARTICULATE PHASES IN A UNIVERSITY CAFETERIA Çiğdem Özcan, Aysun Sofuoglu, Sait C. Sofuoglu İzmir Institute of Technology, İzmir, Turkey Corresponding email: [email protected]; [email protected] SUMMARY In this study, we measured indoor air gas and particulate phase SMC concentrations in a cafeteria at Izmir Institute of Technology. Total Suspended Particles (TSP) samples were collected on glass-fiber filters while gas phase samples were collected using XAD-2 resin sandwiched between polyurethane foam. A total of ten 7-hour samples were collected. After clean-up, extraction, and fractionation the samples were analzed for 11 nitro and polycyclic SMC using a GC-MS. Results showed that SMC are heavily (~87% on average) present in the gas phase. Cashmeran, celestolide, galoxolide, musk ketone, and tonalide were the five compounds detected in all samples followed by phantolide, musk xylene, and tresolide. Total (gas+particle) mean ± standard deviation concentrations ranged from 0.2±0.5 ng/m3 (musk ambrette) to 545±1100 ng/m3 (musk ketone). SMC concentration profile is different than that we previously measured in a women’s sports center and a primary school classroom. INTRODUCTION Synthetic Musk Compounds (SMCs) were synthesized because it was not possible to satisfy the demand for natural musk from musk deer (Moschus moschiferus L.) and some other animals and plants. Mainly two groups of SMCs (nitro and polycyclic musks) have been produced and used in a wide range of personal care and household products such as perfumes, deodorants, antiperspirants, lotions, detergents, softeners, all sorts of cleaning fluids, etc. Nitro musks are Musk Ketone (MK), Musk Xylene (MX), Musk Ambrette (MA), Musk Moskene (MM), and Musk Tibetene (MT). Because MA was determined to be a mutagenic, it was banned by the European Union, and the use of MX was not recommended as it might be carcinogenic [1]. The worldwide production has shifted from nitro musks to polycyclic musks. Cashmeran (DPMI), Phantolide (AHDI), Traseolide (ATII), Tonalide (AHTN), Galaxolide (HHCB), Celestolide (ADBI) are the commonly used polycyclic musk compounds. However, HHCB and AHTN were shown to be Endocrine Disrupting Chemicals [2, 3]. SMCs have been detected since 1980s in biota, fish, mussels, human milk and adipose tissue, waters, wastewaters, and drinking water [4-7]. Because SMC containing products are primarily used indoors, indoor air is the main inhalation exposure pathway. Levels of SMCs indoors have not been characterized well; there still are microenvironments for which no data exists. Investigated microenvironments include a hairdresser shop [8], apartments and kindergartens [9], a primary school classroom and a women’s sports center [10] a cosmetics

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plant [11], for which gas phase average or median levels were ranging from 20 ng/m3 in the hairdresser shop to 267 ng/m3 in the classroom for HHCB, the most frequently detected SMC. Particle phase concentrations were measured only in our previous study [10] and in a cosmetic plant in China [11]. SMCs were found to be dominantly in the gas phase; particle (PM2.5) phase concentrations constituted less than 5% of the total concentrations [10]. This study aimed to measure SMC indoor air concentrations in a university cafeteria, and to determine gas-particle phase partitioning. METHODS Indoor air samples were collected from a cafeteria at İzmir Institue of Technology for ten days in Summer-2012. Gas and particulate phase (Total Suspended Particles, TSP) samples were collected with a sampling system consisted of a Harvard impactor (Air Diagnostics & Engineering Inc., Harrison, ME, USA) and a pump. TSP was collected on 37 mm glass fiber filters. Serially attached two glass cylinders were connected to the impactor to collect the gas phase. While front cylinder consists of PUF-resin-PUF, back cylinder contains only PUF that was used for the detection of breakthrough. Finally, a vacuum pump (Air Diagnostics & Engineering Inc., SP-280) was connected to the PUF cartridges and the air collected left the system. Sampling time was 7 hours with a 20 l/min flow rate. At the begining and end of the sampling the pump was was calibrated using a calibrator (Defender 510-H; BIOS, Buttler, NJ, USA). In this study , the extraction procedure was adapted from Peck and Hornbuckle [12] for the synthetic musk compounds. Each of the gas and particulate phase samples (PUF, resin, GFFs) were extracted by acetone and hexane (1:1) mixture ultrasonically. Then the extracts were evaporated to reduce the volume to 5 ml under the gentle stream of nitrogen gas (100 ml/min). Then, 10 ml hexane was added for three times to exchange solvent into hexane. Finally, the extract was concentrated to 2 ml volume. A Florisil column was used for clean-up of the sample. A piece of glass wool, 0.75 g Florisil and 1 cm sodium anhydrous sulfate were placed in a Pasteur glass pipet to enhance SMCs in the column, respectively. Before column chromotography, florisil was activated at 650 0C and sodium anhydrous sulfate was activated at 450 0C in the oven then 37.5 µl water was added to 0.75 gr Florisil in the column for deactivation. The samples were passed through the columns and discharged. Four ml of ethyl acetate was passed through the column and this solvent phase was collected in a 40 ml amber vial. Volume of the samples was reduced to 1 ml with a gentle nitrogen gas stream. Solvent was changed into the hexane and the extracts were kept in the freezer until GC/MS analysis. A gas chromatograph (Thermo, Trace GC Ultra,Austin, TX, USA) coupled to a mass selective detector (Thermo DSQII) with electron impact ionization operated in selective ion monitoring (SIM) mode was used for the analysis. A 30-m 5% phenyl methyl siloxane capillary column (TR-5MS; 0.25 mm I.D., 0.25 µm film thickness) was used. RESULTS Re-analysis of the samples for ADBI and MK, which were detected in both gas and particulate phases, are in progress due to QA/QC issues. Therefore, here we report concentrations of the remaining compounds. Indoor air average gas phase SMC concentrations in the university cafeteria ranged from 0.27 ng/m3 for MA to 107 ng/m3 for

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HHCB. All SMCs were detected in the gas phase in the order from the highest to the lowest concentration as HHCB > AHTN > ATII > DPMI > MX > AHMI > MM > MT > MA. Three SMCs were detected in the particulate phase samples. Average concentrations were ranked as MX > DPMI > ATII, ranging from 0.07 ng/m3 for ATII to 3.2 ng/m3 for MX. Gas and particulate average concentrations are presented in Figure 1 and 2, respectively.

Concentration (ng/ m3)

Figure 1. Gas phase average SMC concentrations measured in a university cafeteria. (Error bars show one standard deviation) Synthetic Musk Compounds

Figure 2. Particulate phase average SMC concentrations measured in a university cafeteria. (Error bars show one standard deviation) Because AHMI, HHCB, AHTN, MM, MT, and MA were not detected in the particle phase, these compounds were found to be 100% in the gas phase. Partitioning for the remaining SMC were 99%, 98%, and 62% gas phase for ATII, DPMI, and MX, respectively. DISCUSSION Kallenborn and Gatermann [8] measured gas phase concentrations of SMCs in the cafeteria of the Norwegian Institute for Air Research. HHCB, AHTN, and ATII were the detected compounds with concentrations of 35.3 ng/m3, 11.6 ng/m3 and 4.8 ng/m3, respectively. MX

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and MK were not found in the air samples. They also measured SMCs in the rest facilities in the institute. Concentrations reached up to 19 ng/m3 (HHCB). Unlike the cafeteria they detected MX and MK in the rest facilities with concentrations of 0.6 ng/m3 and 0.2 ng/m3, respectively. They also collected indoor air samples from laboratories, a hair dresser, and toilet. The ATII level measured in the hair dresser (5.2 ng/m3) was comparable to that of our study ( 9.54 ng/m3) but in general concentrations were lower than those measured in this study, including HHCB, AHTN, and ATII but with the same order from high to low concentration. We previously measured indoor air gas and particulate phase SMC concentrations in a primary school classroom and a women’s sport center [10]. In the classroom HHCB and AHTN concentrations were reported as the highest particle phase concentrations among the other SMCs while in this study HHCB and AHTN were not found in the particulate phase samples. However, similar to this study all of the SMCs were dominantly found in the gas phase (>96%) in the previous study. Partitioning of MX between gas and particulate phases was low (62% vs. 38%) compared to the other SMCs and the previously measured partitioning (>99% in the gas phase). SMC concentration profile is different than that we previously measured in a women’s sports center and a primary school classroom probabaly due to the use of different cleaning and/or personal care products Indoor air particulate phase polycyclic musks concentrations was measured in a cosmetic plant [11], ranking from high to low as HHCB> AHTN> DPMI> ADBI> AHMI. Based on total gas and particulate phase concentrations, SMCs were dominantly in the gas phase (98%) in a workshop of the cosmetic plant. In conclusion, MX needs to be studied further along with MK and ADBI for verification of the results. In conclusion, it can be speculated that the variation in SMC concentrations are mainly due to the variation in the product use and their SMC content. ACKNOWLEDGEMENT We thank the supervisor of the cafeteria Mr. İbrahim Üçdağ for welcoming our investigation. We also thank Environmental Research Center of Izmir Institute of Technology for GC-MS analysis. REFERENCES 1.

2.

3.

4.

SCHER, 2006. Scientific Comittee on Health and Environmental Risks Opinion on Classification of Musk Ketone. European Comission, Health and Consumer Protection Directorate-General. Seinen, W., Lemmen, J.G., Pieters, R.H., et al., 1999. AHTN and HHCB show weak estrogenic—but no uterotrophic activity. Toxicology Letters. Vol. 111(1), pp. 161168. Schreurs, R.H., Sonneveld, E., Jansen, J.H., et al., 2005. Interaction of polycyclic musks and UV filters with the estrogen receptor (ER), androgen receptor (AR), and progesterone receptor (PR) in reporter gene bioassays. Toxicological Sciences. Vol. 83(2), pp. 264-272. Yamagishi, T., Miyazaki, T., Hori, S., et al., 1981. Identification of Musk Xylene and Musk Ketone in freshwater fish collected from Tama River, Tokyo. Bulletin of Environmental Contamination and Toxicology. Vol. 26, pp. 656-662.

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5.

6.

7.

8.

9.

10.

11. 12.

Kallenborn, R., Gatermann, R., Planting, S., et al., 1999. Gas chromatographic determination of synthetic musk compounds in Norwegian air samples. Journal of Chromatography A. Vol. 846, pp. 295-306. Rimkus, G.G., Gatermann, R., and Hühnerfuss, H., 1999. Musk xylene and musk ketone amino metabolites in the aquatic environment. Toxicology Letters. Vol. 111, pp. 5-15. Benotti, M.J., Trenholm, R.A., Vanderford, B.J., et al., 2009. Pharmaceuticals and endocrine disrupting compounds in U.S. drinking water. Environmental Science and Technology. Vol. 43, pp. 597-603. Kallenborn, R. and Gatermann, R., 2004. Synthetic Musks in Ambient and Indoor Air. Series Anthropogenic Compounds, ed. G. Rimkus. Vol. 3X. Springer Berlin Heidelberg. 85-104. Fromme, H., Lahrz, T., Piloty, M., et al., 2004. Occurrence of phthalates and musk fragrances in indoor air, dust from apartments and kindergartens in Berlin (Germany). Indoor Air. Vol. 14, pp. 188-195. Sofuoglu, A., Kiymet, N., Kavcar, P., et al., 2010. Polycyclic and nitro musks in indoor air: a primary school classroom and a women’s sport center. Indoor Air. Vol. 20(6), pp. 515-522. Chen, D., Zeng, X., Sheng, Y., et al., 2007. The concentrations and distribution of polycyclic musks in a typical cosmetic plant. Chemosphere. Vol. 66(2), pp. 252-258. Peck, A.M. and Hornbuckle, K.C., 2006. Synthetic musk fragrances in urban and rural air of Iowa and the Great Lakes. Atmospheric Environment. Vol. 40(32), pp. 61016111.

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NUMERICAL SIMULATION OF NATURAL VENTILATION IN A LIVING SPACE FOR DIFFERENT SPACE ORGANIZATION Güven Öğüş1, Murat Çakan2 and Gülten Manioğlu3 1

Istanbul Technical University, Energy Institute Istanbul Technical University, Faculty of Mechanical Engineering 3 Istanbul Technical University, Faculty of Architecture 2

Corresponding email: [email protected] SUMMARY Natural ventilation is an essential passive strategy in order to maintain thermal comfort inside buildings and it can be also used as an energy-conserving design strategy to reduce building cooling loads by removing heat stored in the buildings' thermal mass. In Turkey, the temperate-humid climate allows natural ventilation in most of the days in the year. Space organization according to orientation is one of the most important design parameters in order to provide a good natural ventilation strategy in residential buildings. However, most of the residents modified their houses so as to enlarge or divide them and changed the original space organization and consequently the natural ventilation strategy. Modifications such as new walls introduced to divide spaces or removed walls for combining spaces may have a negative effect on natural ventilation. In this study, an apartment with different space organizations is studied numerically for natural ventilation performance. The apartment is north-south oriented and it can provide airflow by means of pressure difference and the modified space organization changes the air velocity and temperature distributions inside the space. Different configuration alternatives are examined with commercial CFD software to simulate the air velocity and temperature distribution in the living space. The aim of the study is to compare different space organization alternatives from the thermal comfort point of view and develop different proposals without disturbing natural ventilation airflow. INTRODUCTION Ataköy Project was one of the first modern housing projects in Istanbul initiated by Emlak Bank comprising 367 villas and multi-storey apartment buildings of various heights. It aimed to form a low-density elite neighborhood as of 1956. The foundation ceremony for Phase-I of this ambitious project was held in 1957, after a number of infrastructural problems were solved, including the construction of the Sirkeci-Florya shore drive (1956-57) and a swimming club on the Ataköy coast (1957). Parallel to these developments, the construction of the E5 (Istanbul-London) motorway and the electrification of the Sirkeci-Halkalı railway supported the project. Phase I (1957-62) was composed of 662 residential units in 52 apartment buildings which were 3 to 13 storeys high. The buildings were set in greenery with selectively designed social, cultural, educational and commercial spaces. Large windows illuminated comfortable spaces and all comfort systems from natural ventilation to elevators and central heating had

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been architecturally designed and inserted, and the wet spaces were planned suitably for the installation of refrigerators and washing machines. The visual characteristics followed modernist ideals in the form of buildings raised on pilotis with flat roofs where elevator towers, chimneys, light shafts and concrete pergolas were visible, full-height “French” windows opened onto balconies, simple cubist façade arrangements introduced bold colored patches inserted in light pastels, and “form followed function”. After almost 50 years, the social structure still reflects a concentration of the upper middle class members according to the evaluation of the 1990 national population survey data and the land rents and real estate values are still in rise, especially after the 1999 earthquake paranoia of “safe” buildings. Although most of the original owners and residents remain in-situ, the needs and ideals of a comfortable life-style are different nowadays. The symbols of a former utopia are transforming rapidly with every apartment that is re-fitted and every building that is renovated. Enclosure of the open and semi-open balconies, a trend that disturbs the original façade compositions visually, are being made to further extend the living areas. Inside, partition walls are added in most cases in order to gain separate rooms; and these affect the original climatic control arrangements [1],[2]. When the design strategies of Phase-I are assessed, it is seen that the climate related design parameters were utilized often at building and settlement scale. Buildings were oriented such that they did not obstruct solar radiation especially in heating period. Buildings were also erected upon pilotis to distribute humidity evenly in spring seasons and to obtain maximum cooling at summer times. Primary volumes of residential units were oriented towards south; taking advantage of the solar radiation when heating demand was high. Besides, window openings on northern and southern façades were designed such that cross-ventilation was maintained in order to benefit from the cooling capacity of the wind through natural convection. The proper integration of these design parameters leads to a decrease in energy expenditures. Due to climate change, the month intervals in which heating or cooling is required have started shifting slightly. Nowadays, building cooling costs approach and sometimes even surpass heating load costs. For this reason, natural ventilation, which contributes to reducing cooling costs, can be considered as one of the most important design parameters. Natural ventilation is directly related to building orientation and volume organization. Rational decisions taken in this respect would clearly improve the performance of natural ventilation schemes and therefore help to eliminate the cooling energy requirement. On the other hand, the originally climate-balanced aspects of Phase-I buildings have started to change due to different requirements and needs of users throughout the years. Tearing down interior walls for the sake of living space expansions and enclosure of open or semi-open balconies led to drastic changes in the initial structures and therefore “initial tasks” of designed spaces. Unconsciously made alterations in internal spaces, not only endangered the structural integrity of buildings but also decreased the efficiency of cross-ventilation schemes. As a natural consequence, users are forced, more and more, to utilize active mechanical ventilation systems which increase management costs of the buildings substantially. The fundamental rule behind natural ventilation in buildings is to create an air movement. Air motion physics depends mainly on the position, the form, the orientation of the building, the

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relative position of the building with respect to other buildings, the size and the position of windows and the organization of its volumes. Natural ventilation rate varies with climatic variables such as wind speed, air temperature and relative humidity.These data are important to determine, evaluate and simulate the thermal comfort and cooling energy loads that can be provided by the building under study or in the planning and design stage [3]. However, the conditions of outdoor air movement will also influence the performance of indoor air movement by the difference of air pressure applied onto a building façade through by relevant passive design parameters [4],[5]. Generally, the natural ventilation in buildings can be classified into two types, which consists of air pressure ventilation or known as wind force, and stack effect ventilation or thermal force [6]. Air pressure ventilation can be summarized as the horizontal air movement where the air flows into the building due to the differences of air pressure between the outdoor and indoor environment. The cool air will flow through the window and door openings or the building claddings into the rooms, as the air temperature indoor is much warmer. The patterns of air flow through openings can be classified into single sided ventilation (where the air flow in and out at the same openings on the same façade), and cross or double side ventilation (where the air flows in and out at different openings on different façades). Stack effect ventilation is the condition of vertical air movement when the cool air has been warmed up by human activities and operations of indoor machinery, and the warm air moves vertically and is discharged out from the buildings through chimney and air well [7]. Researchers have been working for decades to understand the mechanisms of natural ventilation and for developing a suitable reference technique to quantify air movement. Several methods were proposed and investigated, including analytical models based on driving forces or on simple heat and/or mass balance, direct measuring techniques such as anemometers, simulations with computational fluid dynamics (CFD) and multizone modeling, indirect measuring techniques as pressure difference and different tracer gas techniques [8]. Mochida et al. [9] and Lee et al. [10] pointed out that careful consideration of wind flow around a building when deciding the placement of window openings is very important to fully utilize the potential of cross-ventilation and improve thermal comfort. However, different window configurations result in different ventilation effectiveness, indoor air qualities and impact on comfort conditions in the occupied zone [11],[12]. Jiang et al. [13] presented an extensive experimental and computational study of natural ventilation, driven only by wind forces for simple geometries representing cross-ventilation and single-sided ventilation configurations. Schulze et al. [14] developed a simulation methodology to evaluate the functionality of controlled natural ventilation in an office building. The results indicate that analytical equations for the air flow rates are very consistent with simple steady state airflow network simulations. Therefore, in the architectural design phase of natural ventilation, potentials of different opening configurations may be estimated by those equations. Computed ventilation rates under various fixed conditions depend mainly on the opening cross section, but also strongly on the location of the openings and their geometry. Limited studies have been carried out to examine detailed internal aerodynamic information. Effects of compartmentalization due to room partitioning on internal pressures were studied by Liu [15]; and ceiling partitioning effects by Kopp et al.[16] also showed that the effect of opening size

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was of little significance if the leakage area was less than 10% of the windward-dominant opening area. A full-scale boundary layer and separately-performed numerical studies showed the sensitivity of internal pressure characteristics to the size of the dominant openings, as well as the size of the internal volume [17,18,19]. Prianto presented the effect of ceiling, partition and floor design on indoor airflows pattern of traditional dwelling in urban living quarter of tropical humid region. The paper proved that internal division (ceiling, partition, and floor surface) placed in the stream pattern will interrupt and slow down the indoor air speed [20]. Zhang Lin et al. presented a case to investigate the effect of partitions in an office on the performance of under floor air supply ventilation system via computational fluid dynamics. The results indicated that the partitions may significantly affect airflow and performance of an under floor air supply ventilation system [21]. “CIBSE AM10 Natural Ventilation in Non-Domestic Buildings” is the most widely used guide for designing a naturally ventilated space. The design should meet the air quality standards that are provided in ASHRAE 62.1 [22] standard. In this study, two symmetrical residential units: one having an original and the other with an altered plan were compared on the basis of natural ventilation performance through a numerical modelling approach and several space configuration alternatives were assessed in order to improve the ventilation performance of the altered plan. 2. SPACE CONFIGURATION The study has been applied for a residential building selected from Atakoy Phase I, mass housing area in Istanbul. The selected building is 4 storeys high, detached with flat roof; 4 apartments per storey, 2 apartments and entrance hall on ground floor. The building is raised on columns and open at grade level for car parking. The building has four external walls oriented to the main four directions. The building height is 10,4 m. and floor area is 426 m² (Figure1).

Fig. 1 Residential building chosen for the study In both of the residential units (original and modified) openings are on the southern and northern façades. In the modified plan, the northern wing of the living room was added to the kitchen space and the living space was left open only to the south, preventing natural ventilation (Fig. 2). As seen in Fig. 2, since the living room of the original apartment has openings both in the north and south façades, it is possible to ventilate the building by means of cross-ventilation.

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Partition wall

N

K

BR1 B

K

B

wc wc LR

BR2

Original plan

BR1

LR

BR2

Modified plan

Figure 2. Original and modified plan types For all of the configurations, the numerical simulation assumes a window height of 1,5 m and the elevation of the windows measured from the floor is taken as 0,8 m. Northern wall consists of two windows with a total area of 5,92 m2, while the area of the two windows on the southern wall sums up to 6,30 m2. The assessment study was made in two steps. First of all, the original and the modified apartments were compared via modeling and simulations on the basis of average living space temperatures and air velocities. Later on, the ventilation conditions of the modified apartment were tried to be improved by several suggested design alternatives. All of these design suggestions were based on the impact of partition wall openings on cross-ventilation performance. Creating a service passage between the kitchen and the living room and obtaining enough illumination within the living space were two other design criteria taken into account during the study. The alternative configurations that are shown in Figure 3 consisted of 10, 20, 30 and 40 % openings.

OR: %10

OR: %20

OR: %20

OR: %30

OR: %40

Fig. 3. Alternative configurations NUMERICAL STUDY The CFD simulation is made for different opening configurations on the partition wall. For all the configurations, a 3D model has been drawn for the ventilated space. Then the numerical grid has been configured. These configurations were meshed with a commercial CFD mesher software. A grid of about 10.000 hexagonal elements were made (Figure 4). The fluid regions near the walls were meshed denser in order to simulate the boundary flows properly.

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N

N

Figure 4. 3D Model and Numerical Grid The boundary conditions for the windows is the most important parameter. The airflow inside the space is driven by the pressure differences between windows. The real conditions can only be simulated by determining pressure coefficients. As this study focuses on the inside flow, a pressure difference of 5 Pa is assumed between North and South faces. This assumption holds for about 4 m/s of wind speed from north, if the correlations in CIBSE Manual [23] is used. RESULTS and DISCUSSION Table 1 is a summary of the numerical solutions at a plane of 1.5 m height above the floor. Figure (a) in Table 1 shows the numerical solution for standard configuration of the apartment whereas figure (b) reflects the modified conditions in the neighboring residential unit. The other figures show the temperature distribution and velocity vectors of five different partition wall vent configurations. These configurations are selected in order to suppress the overheating effect of the modified partition wall configuration (Figure (b)).When figure (a) and (b) are compared, the temperature readings from the midpoints of the living room show that the configuration with partition wall is 5.7% (1.3) warmer than the standard configuration. This is attributed to the air flow blockage effect of the partition wall in the modified configuration (b) in which natural convection is prevented. In order to overcome this blockage problem, the modified partition wall is altered numerically with different vent configurations which are seen in Figure 3. The effects of these altered configurations are given in Table 1 Figures (c), (d), (e), (f), (g). Clearly, it is seen from the figures that as the vent size to partition wall ratio increases, the temperature distribution becomes more homogeneous which leads to a more comfortable living space. The configurations (d) and (e) have both vent to wall ratio of 20%. Though the ratios are the same, configuration (e) performs better than configuration (d). That is because, instead of using a single vent close to the ceiling, two vents are introduced in the mid region of the partition wall. This shows that not only the ratio of vents to the wall is important but also, the positions of the vents are of importance. Configurations (e) and (f) are the only two configurations that the two spaces: the kitchen and the the living room are partitioned by one or two service windows. With respect to the modified configuration (b), using configurations

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(e) and (f) decreases the average temperature in the living space by 4.33% (1.06°C) and 3.64% (0,89°C) respectively. As a result of this numerical study, it can be concluded that the space organization must be made such that natural ventilation is not prevented. To do so, residents should consult the technical advice of experts in the field. Table 1. CFD Results (Temperature distribution and velocity vectors)

(a)

(b)

(c)

(d)

(e)

(f)

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(a) Standart Configuration (b) Modified Configuration (c) Tested Configuration (OR %10) (d) Tested Configuration (OR %20) (e) Tested Configuration (OR %20) (f) Tested Configuration (OR %30) (g) Tested Configuration (OR %40)

(g) REFERENCES 1. 2. 3. 4. 5.

6.

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8. 9.

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11. 12. 13.

14. 15. 16. 17.

Güvenç, M and Işık, O. 1999. Emlak Bankası: 1926-1998, Istanbul, Turkey. Türkiye Cumhuriyetinin Ellinci Yılında Emlak Kredi Bankası, Ankara, 1973. Chen, Q, Lee, K, Mazumdar, S, et al. 2009. Ventilation performance prediction for buildings: A method overview and recent applications. Building and Environment, 44(4), 848-858. Broderick, C R and Chen, Q. 2001. A simple interface to CFD codes for building environment simulations. In 7th International IBPSA Conference, Rio de Janeiro, 577-584 Ghiaus, C and Allard, F. 2005. The physics of natural ventilation. In Cristian Ghiaus & Francis Allard (Eds). Natural Ventilation in the Urban Environment: Assessment and Design (pp. 3680). London: Earthscan. Ghiaus, C and Roulet, C A. 2005. Strategies for natural ventilation. In Cristian Ghiaus & Francis Allard (Eds). Natural Ventilation in the Urban Environment: Assessment and Design(pp. 136-157). London: Earthscan. Siew, C C, Che-Ani, A I, Tawil, N M, et al. 2011. Classification of Natural Ventilation Strategies in Optimizing Energy Consumption in Malaysian Office Buildings. Procedia Engineering 20- 363 – 371. Ozcan, S E, Vranken, E, Berckmans, D. 2008. Measuring ventilation rate through naturally ventilated air openings by introducing heat flux, Building and Environment 44, 22-33. Mochida, A, Yoshimo, H, Takeda, T, et al. 2005. Methods for controlling airflow in and around a building under cross-ventilation to improve indoor thermal comfort. Journal of Wind Engineering and Industrial Aerodynamics 93, 437-449. Lee, J Y, Kang, J H, Jang, Y G, et al. 2005. PIV analysis of the ventilation flow inside a largescale factory building. In: Choi,C.K., Kim, Y.D., Kwak, H.G. (Eds.), Proceedings of 6th Asian–Pacific Conference on Wind Engineering (APCWE – VI), Seoul, Korea, September 12– 14, pp. 427–436. Heiselberg, P, Svidt, K, and Nielsen, P V. 2001. Characteristics of airflow from open windows. Buildings and Environment 36, 859-869. Heiselberg, P, Bjorn, E, and Nielsen, P V. 2002. Impact of open windows on room air flow and thermal comfort. International Journal of Ventilation 1 (2), 91-100. Jiang, Y, Alexander, H, Jenkins, R, and et al. 2003. Natural ventilation in buildings: measurement in a wind tunnel and numerical simulation with large eddy simulation, Journal of Wind Engineering and Industrial Aerodynamics 91 (3) 331–353. Schulze, T, and Eicker, U. 2012. Controlled natural ventilation for energy efficient buildings, Energy and Building 56, 221-232. Liu H, and Saathoff P J. 1983. Internal pressure of multi-room buildings. J Eng Mech Div.: 109 (EM3):908e19. Kopp, G A, Oh, J H, and Inculet, D R. 2008. Wind-induced internal pressures in houses. J Struct Eng.: 134(7): 1129e38. Ginger, J D, Holmes, J D, and Kopp, G A. 2008. Effect of building volume and opening size on fluctuating internal pressures. Wind Struct.: 11(5):361e76.

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MODELING ZERO ENERGY BUILDING: TECHNICAL AND ECONOMICAL OPTIMIZATION Maria Ferrara1-2, Joseph Virgone1, Enrico Fabrizio3, Frédérik Kuznik1, Marco Filippi2 1

CETHIL, UMR5008, Université Lyon1 – INSA-Lyon, France DENERG, Politecnico di Torino, Italy 3 DISAFA, University of Torino, Italy 2

Corresponding email: [email protected] SUMMARY This study was born in the context of new challenges imposed by the recast of Energy Performance of Buildings. The aim of this work is to provide a useful method to deal with a huge number of simulations corresponding to a large number of single-family house configurations in order to optimize a constructive solution from both technical and economical point of view. The method combines the use of TRNSYS, building energy simulation program, with GenOpt, Generic Optimization program. The reference building is a low-consumption house case-study situated in Amberieu-enBugey, Rhône-Alpes, France. After a short description of the case-study and of TRNSYS model, the link and the configuration files which have been created between the simulation program and the optimization software are illustrated. A first parametric study is performed in order to evaluate the impact of variation of various parameters of envelope and shading elements on the total annual energy consumption for heating and cooling. As a second study, the global cost method is applied to the case-study, and GenOpt is used to determine the cost optimal level of the reference building. Beyond the result, we think this study shows useful method and tools that could support technical and cost optimal level research, providing an easy and fast way to explore various building configuration with a huge number of simulations, as requested by European Standard. INTRODUCTION In the context of the European Union efforts to reduce the growing energy expenditure, it is widely recognized that the building sector has an important role, accounting 40% of the total energy consumption in the European Union [1]. The recast of the Directive on the Energy Performance of Building (EPBD)[2] imposes the adoption of measures to improve energy efficiency in buildings in order to reach the objective of all new buildings to be nearly Zero Energy Building (nZEB) by 2020. This practice could lead to greenhouse gas emission reduction in the building sector of 80-90% by 2050. As the results in term of energy efficiency are evaluated at a global (or at least European) scale, it is remarkable that a good nZEB design is strictly related to the local scale, depending on climatic data, available technologies and materials, population lifestyle. Moreover, as usual, measures related to ecological sustainability could not be pursued without taking into account an economical sustainability. It is obvious that the design of a zero-energy building is not yet profitable in terms of costs, and that this will lead to different results depending on the country, the age of

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the building and its use (commercial buildings, residential, etc.). Consequently, EPBD recast has set out that Member States (MSs) ensure that minimum energy performance requirements are set with a view to achieve the cost optimal level, that is defined as the energy performance level which leads to the lowest cost during the estimated economic lifecycle. Based on Global Cost method, the aim is to define for each MSs the most effective strategies to improve building performance with the lowest global cost. In order to develop general strategies, a huge number of case study should be examined and a common method to compare a large number of simulations has to be established. We have collaborated between France and Italy in order to improve our methodologies and to apply them on the single-family houses, with references buildings, constructive solutions, typical sources of energy accessibility, costs, etc...In the present article we present one carried out with TRNSYS computing environment with a view to establish a procedure for technoeconomic optimization using the tool GenOpt. The reference building. The Reference Building (RB) is a new single-family house situated in Amberieu-en-Bugey, Rhône-Alpes, France. It is representative of new construction of single family house in the region. The gross floor area (GFA) of the two floors is equal to 155 m2 (see plans in figure 2). It’s possible to recognize many design features generally used in passive/low consumption houses: the insulated living space is a cubic compact shape (S/V ratio is equal to about 0,68 m-1, S being the heat losses area and V the heated volume) that minimize the exchange surface between the outside and inside. In order to reduce heat loss due to windows and benefit of solar gains, the maximum of large openings are south-oriented (49% of total glass surface (TGS) on the south external wall, 19% on the south roof slope) while the percentage of openings in east and west orientation is less relevant (respectively 10% and 15% of TGS) and there are only very small north oriented openings (7% of TGS). Window area is approximately 1/5 of the GFA: the minimum imposed by the national regulation [3], which is equal to 1/6 of GFA, is largely exceeded. A roof overhang protects south-oriented windows.

a)

b)

c)

d)

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Figure 1. Facades of reference building. a) South, b) North, c) West, d) East. Thermal insulation is made on the internal side, thereby creating a thermal bridge on the intermediate floor, which has been limited by use of thermal bridge breakers. However, this solution eliminates thermal bridges at the slab and roof levels. At the moment, 20 cm of insulating material are used on external walls, 30 cm on the slab and 40 cm on the roof. RB was modeled using TRNSYS [4], dynamic building simulation program [5]. Each room was modeled as a thermal zone, in order to better evaluate the evolution of temperature and the thermal exchange from one zone to the other, as the HVAC system is considered active only in the main rooms of the house. Set-point temperature for heating (19°C) and cooling (26°C) was set only in the living-room (PP), in the bedrooms (C1, C2, C3) and in the mezzanine (M), while other zones as restrooms (R1, R2), dressing (D) and passages (DGT1, DGT2) are supposed to take heat (or cool) from transmission through internal walls and doors. Garage (G) and laundry(B) are considered non-conditioned zones.

DGT1

G B

R1

C2

DGT2

R2

D PP

M

C1

C3

b)

a)

Figure 2. Plans of reference building, 1:200. a) First floor, b) Second Floor. The standard meteonorm weather file of Amberieu-74820 were used in the simulation. Lighting and occupancy were modeled using schedules related to a standard 4 people family working life, week-ends are taken in account but holidays are not considered. The sum of infiltration and ventilation rate is fixed equal to 0.7 ach as a medium value for all the zones. Based on these settings, heating needs are estimated to be 48 kWh/m2/year, while cooling needs are equal to 12 kWh/m2/year. METHODS – PARAMETRIC STUDY In order to allow multiple simulation and optimal level research, building simulation software was coupled with the general optimization software GenOpt and configuration files were created [6].

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Figure 3. Simulation-optimization framework. Configuration file contains building simulation software configuration, error indicators and start command, the command file contains parameter list and settings and related functions which were inserted in simulation input files to obtain simulation input template. GenOpt run is based on the initialization file, where location of input files and position of the objective function are specified. The more a house is energy efficient, the bigger is the influence of envelope design on the final energy demand. In order to estimate the impact of the variation of each element of building envelope and geometry (wall, roof and slab insulation, window type, window and solar protection dimension) on the final total annual energy consumption, a parametric study on the reference building was done. All set parameters and values referred to the house section are shown in the figure below. Note that minimal window dimension corresponds to the limit imposed by French regulation.

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Figure 4. Parameter list.

Table 1. Parameter settings

Table 2. Window Types

The initial value (Ini) is the fixed value assumed by parameters during the parametric run. Four Ini scenarios were set: one is referred to the low-cossumption RB, the others are respectively representative of a very less-insulated building, a standard insulated building, and the last is an utopic very strong insulation. Geometric parameters are always equal to RB.

ResO

ResR

ResS

0.5 0.75 0.5 Low 2 1.5 Medium 1 1.75 3.5 2.5 RB 3.5 4.5 3 Strong Table 3. Initial value scenarios

s 0.8 0.8 0.8 0.8

Bm 2.4 2.4 2.4 2.4

bpp 4.2 4.2 4.2 4.2

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hr 4.72 4.72 4.72 4.72

MIn 0.4 0.4 0.4 0.4

WT 1 2 4 5

WTR 1 2 4 5

WTS 1 2 4 5


RESULTS – PARAMETRIC STUDY Here below results related to RB scenario are shown. Winter and summer performances are separately evaluated and compared, while percentages are referred to the total annual energy needs. Positive values of percentages indicate energy savings corresponding to decreases in energy demand.

Figure 5. Impact of opaque envelope insulation in term of resistance (m2Kh/kJ) on energy demand gains – RB scenario. It is clear that insulation of opaque envelope takes an important role among energy savings measures. In details, roof insulation accounts the most relevant impact in both summer and winter case. In case of outwall and ground slab an increase of insulation corresponds to an increase of heating energy savings and a decrease of cooling energy savings, while in roof case insulation increase causes energy savings during all the year. This is due to the fact that most of roof surface is south oriented and a major solar absorption is caused by dark color of tiles.

Figure 6. Impact of window type (see table 2) on energy demand gains – RB scenario.

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Results related to window type clearly show the impact of g-value and window orientation on transparent envelope performances. A differentiation of window type based on optimization of these parameters could be desirable taking into account also shading devices geometry and window dimensions (see figure 7). Results based on other scenarios revealed mostly the same curves, but it is clear that the higher performances are set in initial scenarios, the higher is impact of parameter variation in terms of percentage of energy demand.

Figure 7. Impacts of roof window dimension and shading overhang length on total energy demand – RB scenario. METHODS – ECONOMICAL OPTIMIZATION In accordance with the EPBD, global cost calculations result in a net present value of costs incurred during a defined calculation period, taking into account the residual values of components with longer lifetimes. Following the procedure described in the European Standard EN 15459 [7], global cost is directly linked to the duration of the calculation τ and it can be written as: ⎛ n ⎞ CG (τ ) = CI + ∑ ⎜ ∑ Ca,i ( j ) ⋅ Rd (i ) − V f ,τ ( j )⎟ (1) ⎠ j ⎝ i=1 where CG represents the global cost referred to starting year τ0, CI is the initial investment cost, Ca,i (j) is the annual cost for component j at the year i (including running costs and periodic or replacements costs), Rd (i) is the discount rate for year i, Vf,τ is the final value of component j at the end of the calculation period (referred to the starting year τ0). In the context of cost optimal research in this method costs are written as function of parameters p. So the terms of the previous equation become:

()

CI = ∑ f j p j

Ca,i ( j ) = fa, j ( p)

(2)

(3)

Where fj(p) is the cost function of the component j related to parameter p.

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Investment Cost

Parameter

Unit

Unit cost function (€/unit)

ResO

m2

37.639*exp(0.351*ln(ResO))

ResS

m

2

38.115*exp(0.186*ln(ResS))

m

2

43.478*exp(0.309*ln(ResR))

m

2

349.35x+28.17

2

390.85x+29.37

Outwall internal insulation Slab insulation Roof insulation Window Type 1

ResR Area

Window Type 3

Area

m

Window Type 4

Area

m2

454.16x+36.62

Area

2

460.45x+34.45

Window Type 5

m

Table 4. Cost function of parameters. Cost functions were determined combining French price lists [8] and quotations of local construction companies, all costs are comprehensive of human work and installation costs. Cost analysis revealed that insulation cost functions are exponential functions, while window cost were simplified in linear function. A typical all-electrical energy system was considered, whose investment cost is 300 €/kW of maximal power installed, with a replacement time of 15 years. Energy price was assumed equal to current prices of major electricity companies based on difference in tariffs for night and day. In details, costs were fixed equal to 0.07952 €/kWh during the night and 0.11442 €/kWh during the day. Market interest rate was assumed to be 4%, calculation period is 30 years. RESULTS – ECONOMICAL OPTIMIZATION All combination of parameters value performed by the optimization program can be considered as a package of Energy Efficiency Measures, according to the European Guidelines [9]. Note that in (1) only variable cost related to variation of parameters were considered. So the objective function of optimization represents the global cost for each EEM. Particle Swarm Optimization algorithm for discrete variables was used. The first optimization run was performed only with parameters related to opaque and transparent envelope resistance as variables. Geometric parameters (window dimensions and shading overhang length) were fixed equal to RB values. Here below cost values are shown referred to primary energy consumption (primary energy conversion factor for electricity in France is equal to 2.58). Values are normalized to GFA.

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Figure 8. Cost optimal curve for RB geometry Cost Optimal level corresponds to 343 €/m2 for a primary energy consumption of 120 kWhep/m2/year. In details, ResO is 1.75 m2Kh/kJ, ResR is 2.5 m2Kh/kJ, ResS is 2 m2Kh/kJ and window type is 5. The maximal investment cost corresponds to the minimal energy consumption and leads to a global cost of 353 €/m2, while the minimal investment cost corresponds to the maximal energy consumption which leads to a global cost of 431 €/m2. A second optimization run was performed in order to estimate the variation of global cost with variation of window dimension. In this case cost optimal corresponds to 297 €/m2 for a primary energy consumption of 120 kWhep/m2 year. In details, ResO is 1.75 m2Kh/kJ, ResR is 3 m2Kh/kJ, ResS is 3 m2Kh/kJ. South oriented window area is half of RB windows area and WTS is 3. Roof window area is equal to 0 and window type of other windows is 5. Talking about window dimension, internal comfort and natural light should be considered before taking decision to reduce window area.

D

A

C

B O

Figure 9. Cost optimal curve based on variation of all parameters.

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DISCUSSION As cost of electricity is quite high, it is clear that considering global cost for 30 years an initial high investment cost in high-performances of envelope is recommended also from economical point of view. This is only an example, as this work attempted to establish a fast and precise procedure for optimization which could be applied to different case-studies. It is known that the use of more efficient energy system could lead to different solution. Moreover, further studies have to be performed in terms of sensitivity analysis based on variation of financial data and product costs. ACKNOWLEDGEMENT This work has been supported by the Région Rhône Alpes COOPERA-2012 “Modélisation des bâtiments zéro-énergie : optimisation technico-économique”.

project

REFERENCES 1. BPIE (Buildings Performance Institute Europe). Cost Optimality. Discussing methodology and challenges within the recast Energy Performance of Building Directive, 2010. BPIE 2. EPBD recast (2010). Directive 2010/31/EU of the European Parliament and of Council of 19 May 2010 on the energy performance of buildings (recast). Official Journal of the European Union 3. Ministère de l’écologie, de l’énergie, du développement durable et de la mer, Arrêté du 26 octobre 2010 relatif aux caractéristiques thermiques et aux exigences de performance énergétique des bâtiments nouveaux et des parties nouvelles de bâtiments (RT 2012) 4. Solar Energy Laboratory, TRANSSOLAR, Centre Scientifique et Technique du Bâtiment, Thermal Energy System Specialists, TRNSYS 16 Documentation, 2007 5. Enrico FABRIZIO, Marco FILIPPI, Introduzione alla simulazione energetica dinamica degli edifici, Ed.Delfino, Milano, 2011 6. Berkeley National Laboratory, GenOpt-Generic Optimization Program. User Manual, Version 2.1.0, Berkeley, 2008 7. CEN (European Committee for Standardization). Standard EN 15459:2007 8. BATIPRIX-22°edition, vol. 1-2, Gros œuvre Second œuvre, Groupe Moniteur, 2005 9. Guidelines accompanying Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 supplementing Directive 2010/31/EU

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APPLICABILITY OF ONE-DIMENSIONAL RC-MODELSWITH SHORTTIME-STEPFOR RADIANT SYSTEMS IN ENERGY BUILDING SIMULATION PROGRAMS Ismael Rodríguez Maestre, Enrique Ángel Rodríguez Jara, Juan Luis Foncubierta Blázquez, Francisco, José Sánchez De La Flor 1

Thermal Engineering Research Group.University of Cadiz. Spain


SUMMARY Energy building simulation software are evolving towards smaller simulation time steps, improving the coupling of the thermal models of the components of the building with those of the systems HVAC, allowing to evaluate with major precision the effect of the control of the above mentioned systems. For it, it is necessary to check many of the models used for the constructive elements. The heat transfer of radiant systems for heating or refrigeration (radiant floor or ceiling systems), is a transitory multidimensional phenomenon of high thermal inertia. Detailed models based on numerical methods, as finite elements or finite differences have a high degree of precision, but they carry a high computational cost. Nevertheless, the simplified models based on methods very known as functions of transfer, heat interchanger Effectiveness -NTU, or the analytical ones of the type resistance - capacity (RC-Models), always have presented a very acceptable relation among accuracy versus time of computation. In the present article the accuracy of different one-dimensional RC-models applied to radiant systems in transient regime for short simulation time-steps are evaluated. The study was performance for multitude of geometries, property of materials and conditions of operation. The evolution of the superficial temperature and heat flow was compared by the obtained one by using finite. Finally, a correlation to predict the field of application of the above mentioned models is proposed. INTRODUCTION The use in buildings of HVAC systems based on radiant surfaces for heating and cooling, as a way to achieve the thermal comfort of its occupants, has increased widely in recent years. These systems enable a reduction and increase of the set-point temperature for heating andcooling respectively, due to the modificationof the mean radiant temperature within the space. Therefore, this type of HVAC systems result in a significant reduction of the consumption of heating and cooling in comparison with conventional ones [1].Different experimental and theoretical case studies have demonstrated that it is possible to achieve energy savings between 10% to over 30% in comparison with conventional systems by using radiant surface based systems, depending on the building location [2].

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To evaluate the thermal performance of these systems is necessary a sufficiently accurate prediction model which can be integrated into a building energy simulation program (BESprogram). Koschenz and Dorer [3] pointed out the fact that the analysis of this type of system should be based on transient calculations.Detailed and multidimensional models based on numerical methods, such as finite element method (FEM) or finite volume method (MVF), have a high degree of accuracy, but their complexity involves a computational and time consumption too high to be integrated into these programs. Nevertheless, simplified models are suitable because of its simplicity, low time consumption and acceptable accuracy in many situations. There is a wide variety of studies related to simplified thermal models of radiant systems for heating and cooling.Somemodels interestingfrom the point of view of its integration in BESprograms are listed below.Laouadi [4] developed a transient, semi-analytical and 2D model in which the coupling between solid and fluid is performed by an iterative procedure based on the Newton-Raphson method. Different transient and 2D models are based on star RCnetwork method in which the R and C parameters are calibrated by an iterative process.On the other hand, the heat conduction problem within the solid is modeled using an analytical [5] or numerical [6]well-known solution. Néstor Fonseca [7] developed a transient model focused on ε-NTU heat exchanger method and based on a RC-network which parameters are adjusted. This model results in a non-analytical model with an iterative procedure. The study carried out by Odyjas y Górka [8] shows the results of a transient and numerical model based on finite difference method (FDM). Xing Jin, et al. [9] used a numerical model based on finite volume method (FVM) to evaluate the effect of various parameters on the system performance. The EnergyPlus model [10] is based on the well-known method of transfer function in heat conduction.The coupling between radiant system and space is based on ε-NTU heat exchanger approach. One of the simplified models available in TRNSYS [11] is based on a simple capacitance method and ε-NTU heat exchanger approach. TRNSYS also features a detailed three-dimensional model based on FDM [12] which uses an iterative method for solving the resulting equations system. This paper aims to study the degree of accuracy, and therefore, the range of application of a simplified 1D analytical radiant floor model for the integration in BES-programs with a timestep lower than one hour.Firstly, the proposed 1D analytical model is described. Afterwards, a parametric study is carried out to define the different cases to simulate depending on geometric parameters. Then, a detailed numerical model of reference based on FEM is established. Once the analytical and numerical model isdeveloped, the results of the different cases are obtained for both models.Finally, the results of both models are compared in terms of mean heat transfer rate and a correlation of the committed error is obtained by means of a multiple regression method. SIMPLIFIED RC-NETWORK MODEL The proposed 1D analytical model is based on a star RC-network method of 2 degrees of freedom (R: distribution of capacitance nodes along the floor thickness; C: floor volume

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affected by internal heat generation). This model is coupled with the hydraulic network and decoupled with space. Figure 1 shows the electrical analogy of the model.

Figure 1. Electric analogy of the proposed RC-network model. Assuming the existence of heat flux in the transverse direction only, and neglecting the thermal inertia of the pipe, the following expressions for node are obtained from the energy balance:

(1) (2)

Where and are the surface area of the floor and piping respectively, , and are the specific heat capacity, density and thermal conductivity of the floor respectively, and are the volume and temperature of the node affected by internal heat generation and is the internal heat generation in the solid, and are the temperature of the solid capacitive nodes 1 and 2 respectively, and are the thicknesses from the tube center to solid capacitive nodes 1 and 2 respectively, and are the convective heat transfer coefficient inside the pipe and the mean temperature of the fluid respectively. The convective heat transfer coefficient inside the pipe is obtained by applying the empirical correlation given by Dittus-Boelter [13] for fully developed turbulent flow and smooth pipes.

(3)

Where has a value of 0.4 for heating and 0.3 for cooling. , y are the dimensionless numbers of Nusselt, Reynolds and Prandlt respectively.

From an energy balance applied on node

, the following equation is obtained:

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(4)


Where , , , , and are volume, density, specific heat capacity, flow rate, inlet temperature and outlet temperature of water respectively. Similarly for nodes , , y of floor, the corresponding equations are obtained:

(5) (6)

(7)

(8)

(9)

Where and are the floor volume corresponding to capacitive nodes 1 and 2 respectively, and are the thickness of the floor from the pipe center to the upper and lower surface respectively, , , y , and are the temperatures of the upper and lower floor surfaces, the mean air temperature and mean radiant temperature of space corresponding to the upper and lower floor surfaces respectively, , , , , y are the convective-radiant heat transfer coefficients, the convective heat transfer coefficients and the long wave-length radiant heat transfer coefficients corresponding to the upper and lower surface respectively andwhere: and . The described model results in an equation system which can be solved without an iteration procedure.From this equation system,the node temperatures correspondingto the upper and lower surface, the water outlet temperature and the mean heat transfer rate in upper and lower surface can be derived: , , , y respectively. METODOLOGY Firstly, the material properties, boundary conditions and initial conditions for the parametric study have been defined: , , , , , y . Then, the different values of the geometric parameters to vary have been established in order to study the range of applicability. These geometric parameters are:pipe spacingH, floor thicknesseand tube diameterd. For H, 14 different values have been established in a range equal to 5 mm ≤ H ≤ 320 mm. A form factor FF has been defined as FF = e/H. For this parameter a total of 19 values have been establishedand divided into two ranges: from 0.1 to 1, with stepof 0.1 andfrom 1 to 10, with step of 1. Finally, d has also been taken into account within an geometric ratio defined as r = d/min(H,e) and 3 different values: 0.25, 0.50 y 0.75. Therefore, 798 different combinations to simulate are obtained. On the other hand, a reference model has been established in order to have a detailed reference foreach caseabove defined. Different studies related to radiant floor models demonstrate the possibility of neglecting the length of the pipe without significant influence in thermal performance [9], [10], [4], [14].Therefore, a detailed 2D numerical model based on FEM has been built in ANSYS APDL program.

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Finally, the results of the variable of interest have been obtained for both the reference numerical model and theproposed 1D analytical model, that is, the mean heat transfer rate in the upper surface. Then, these results have been compared and the range of applicability in steady-state and transient-state has been analyzed. APPLICABILITY RANGE IN STEADY-STATE From the comparison of the results for both models, the percentage error committed by the 1D analytical model versus the reference numerical model has been obtained for steady-state. In order to consider only common typologies and geometric configurations of radiant floors, the results of the parametric study have been filtered according to the following values of geometric parameters:20 mm ≤ H ≤ 320 mm, d ≥ 5 mm ande≤225 mm[15]. Thus, a total of 383 geometries meet these limits.Figure 2 shows a comparison of results obtained for both models. This figurereveals a large number of cases in which the error committed by the 1D analytical model can be acceptable. Considering a maximum limit of the error of 5% for overestimation or underestimation, 157 different geometric configurations meet this limit.That accounts for 41% of the total cases under consideration.

Figure 2. Mean heat flux in the upper surface: 2D FEM model vs. 1D RC-network model. From these results, a sensitivity analysis has been performed in order to identify which geometric configurations are within of 41% of cases under consideration. To achieve this objective, the geometric configurations have been split into 2 different values of FF:FF ≤ 1 (figure3) andFF≥ 1 (figure4).As can be extracted from figure3a), for r= 0.25 and for a given H value, the higher the FF value the lower the error.Acceptable errors are obtained for values of FF close to 1 and H values less than 125 mm. As shown in Figure 3 b), for d equal to 0.75 times the floor thickness, acceptable errors are obtained for any value of Hand FF valuesbetween 0.4 and 0.5. However, this error is not acceptable for almost all values of FF when H is less than 35 mm. In general, for a given FF and r value, the higher the H value the larger the error, as shown in Figure 3 c). Finally, figure 3 (d) shows that, for a given FF and H value, the higher the rvalue the lower the error. This trend is observed until a certain rvalue close to 0.75, for which the errors are then acceptable. In general, acceptable errors are obtained when H is less than around 35 mm, depending strongly of e and dfor larger H values.

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a)

b)

c)

d)

Figure 3. Mean heat flux error in the upper surface forFF ≥ 1 as a function of:a) FF(r=0.25), b) FF (r=0.75), c) H, d) r. As observed onfigure4a), the floor thickness has not a significant influence on the error. If the H value is less than 35 mm, the errors are acceptable for any d value. For higher H values, this error dependsslightly on the evalue. In particular, geometric configurations withH values less than 50 mm, d values equal to around 5 mm and FF greater significantly than 1 correspond to common typologies of microcapillary systems [15]. Figure4b) showsthat, for a given FF and r value, the higher the H value the greater the error. Finally, in contrast to FF less than 1,figure4c)showsacceptable errorsfor low d values and close to 0.25 times the H value.

a)

214


b)

c)

Figure 4. Mean heat flux error in the upper surface forFF ≤ 1 as a function of:a) FF, b) H, c) r. From the previous results it is possible to extract that, the proposed 1D analytical model, of simple integration in BES-programs, presents acceptable errors in steady-state for a large number of geometric configurations of radiant floor.On the one hand, these errors depend on H, d andeforFF ≤ 1. On the other hand, for FF ≥ 1 these errors depend on H anddprincipally. In general, acceptable errors are obtained with low H values along with FF>> 1, typical configuration of microcapillary systems. However, it is possible to find out other different cases in which the error is also acceptable, depending on the geometric parameters, so that it turns difficult to establish a generalization about the applicability range of the model.Therefore, a correlation of the percentage error committed in mean heat transfer rate in the upper surface has been obtained for FF ≤ 1 (equation 10) and FF ≥ 1 (equation 11) by a multiple regression method. These correlations present aR2value equal to 0.97 and 0.98 respectively for a confidence interval of 95%.

(10) (11)

APPLICABILITY RANGE IN TRANSIENT-STATE For those geometric configurations which meet the limit established for steady-state, the error in transient-state has been quantified by means of the Root Mean Square Error (RMSE). In all cases, a RMSE value in mean heat transfer rateless than 4 W/m2 has been obtained. Therefore, if the calculated error in steady-state is less than 5% by overestimation or underestimation, the model presents an RMSE in transient-state less than 4 W/m2. In this case, the proposed model can be applied with an acceptable error. In general, the corresponding correlation can be used to assess whether the model can be applied for a given geometric configuration of radiant floor. CONCLUSIONS Accuracy in term of surface temperature and heat fluxes of a one-dimensional RC-model applied to radiant systems in transient regime for short simulation time-steps have been showed.Results show a set of cases which committed by the proposed 1D analytical model in

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steady-state is less than or equal to 5%. Correlations for estimate the error by using geometric parameter in order to characterize the problem are presented. Results for microcapillary systems are very satisfied. Furthermore, acceptable results for transient performance are found in those cases with low error for steady-state. ACKNOWLEDGEMENT The authors gratefully acknowledge funding from the Fundación Campus Tecnológico de Algeciras (Spain). REFERENCES 1. 2.

3. 4. 5. 6.

7. 8. 9.

10. 11.

12.

13. 14.

15.

Radiant floor heating in theory and practice. Olensen, B.W. 2002, ASHRAE Journal, Vol. 44, págs. 19-24. Thermal comfort and energy consumption of the radiant ceiling panel system. Comparison with the conventional all-air systym. T. Imanari, T. Omori, K. Bogaki. 1999, Energy and Buildings, Vol. 30, págs. 167-175. Design of air systems with concrete slab cooling. M. Koschenz, V. Dorer. Yokohama, Japan : s.n., 1996. Proceedings of Fifth International Conference Air Distribution in Rooms. Development of a radiant heating and cooling model for building energy simulation software. Laouadi, A. 2004, Building anda Environment, Vol. 39, págs. 421-431. An Optimized RC-network for thermally activated building components. T. Weber, G. Jóhannesson. 2005, Building and Environment, Vol. 40, págs. 1-14. Thermoaktive Bauteilsysteme tabs. M. Koschenz, B. Lehmann. CH-8600 Dübendorf : s.n., 2000. EMPA Dübendorf Zentrum für Energie und Nachhaltigkeit, überlandstr 129. ISBN 3905594-19-6. Experimental analysis and modeling of hydronic radiant ceiling panels using transient-state analysis. Fonseca, N. 2011, International journal of refrigeration, Vol. 34, págs. 958-967. Simulations of floor cooling system capacity. Odyjas, A. Górka. 2012, Applied Thermal Engineering. doi:10.1016/j.applthermaleng.2012.08.029. Numerical simulation of radiant floor cooling system: the effects of thermal resistance of pipe and water velocity on the performance. X. Jin, X. Zhang, Y. Luo, R. Cao. 2010 : s.n., Building and Environment, Vol. 45 (11), págs. 2545-2552. The Ernest Orlando Lawrence Berkeley Laboratory, USA.EnergyPlus Engineering Document, The Reference to EnergyPlus Calculations. 2008. Klein, et al.Type 653: Simple floor heating system - TRNSYS: A Transient System Simulation Program User Manual. [ed.] The Solar Energy Laboratory University of Wisconsin. Madison : s.n., 2009. Klein, et al.Type 713: Radiant slab with embedded pipes (interfaces with zone air temperature) TRNSYS: A Transient System Simulation Program User Manual. [ed.] The Solar Energy Laboratory University of Wisconsin. Madison : s.n., 2009. F. W. Dittus, L. K. Boelter.Publications on Enginnering. [ed.] Berkeley University of California. 1930. pág. 443. Vol. 2. Establishment and validation of modified star-type RC-network model for concrete core cooling slab. K. Liu, Z. Tian, C. Zhang, Y. Ding, W. Wang. 2011, Energy and Buildings, Vol. 43, págs. 2378-2384. REHVA, Federation of European Heating and Air-conditioning Associations.Low Temperature Heating and High Temperature Cooling. [ed.] REHVA. Segunda Edición. Finlandia : s.n., 2009.

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THE EFFECT OF USING RELIEF DAMPER IN STAIRCASE PRESSURIZATION AS A PART OF POSITIVE VENTILATION SYSTEMS Büşra Hepgüzel1 1

Istanbul Technical University, Istanbul


SUMMARY Stairwell pressurization is an important part of ventilation system design, because it is associated with life safety by means of preventing fire and smoke spread into the staircases in case of fire. Fire codes require that the pressure difference between the staircase and the building to be kept in a certain level. This can be achieved in combination of two methods, which have to be applied simultaneously, first adjusting the rotation speed of the pressurization fan by frequency convertors and second implementing relief dampers to discharge the excessive amount of air. In this study, the importance of the use of relief damper is investigated through both experimental and numerical studies. It was found that the relief damper is essential to keep the pressure difference levels at permissible level and also the location of the relief damper is crucial in order to properly discharge the excessive amount of air out of the staircases.

INTRODUCTION During a fire event in a high-rise building, the environment inside the stair enclosure is an important determining factor for the ability of the building occupants to safely evacuate the building. Fire stairs in high-rise buildings are to provide a fire-rated and smoke-proof enclosure, using stair pressurization systems. In a high-rise building, the stairs typically represent the single means of egress during a fire[1]. According to Turkish Fire Code, except for residential use, when the structural height of the building exceeds 30.5 m, the fire stairs shall be pressurized[2] Stair pressurization systems typically consist of a variable speed fan with a frequency converter, an overpressure relief damper, pressure sensor, and a ducted shaft for multiple injection points. More than one fan can be used depending on the height of the staircase. According to Turkish fire code [2], fire stairs greater than 25 m shall be pressurized with multiple injection system and if the structural height of the building is greater than 51.5 m, there must be an injection point into the staircase at least every three floors. The design requirements for stair pressurizations systems included in the various codes and standards indicate a minimum and maximum pressure differential [2], [3]. The minimum pressure differential differs between 12.5–15 Pa, and is for counteracting the penetration of hot smoke resulting from a compartment fire adjacent to the stair. The maximum pressure differential specified ranges between 60–80 Pa and is derived from the maximum allowable

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door opening force allowed for doors entering the stairs, which is specified to be 110 N in Turkish Fire Code. The door opening force is defined for the building occupants to be capable of opening the door to the staircase in an emergency event. Excessive force against the door could prevent occupants from entering the staircase, which could be a dangerous condition even in the event of a small fire in a high-rise building. Stair pressurization systems are difficult to design; considering different cases, such as all doors of the staircase are closed and some of the doors are opened. The pressurization fan capacity will be much more than the required pressurization air demand in the case of “closed doors”. This can be achieved in combination of two methods, which have to be applied simultaneously; first reducing the rotation speed of the pressurization fan by frequency convertors and second implementing overpressure relief dampers to discharge the excessive amount of air. Air Movement in Staircase The driving forces of smoke movement include naturally occurring stack effect, the wind effect and fan-powered ventilation systems. Stack effect is induced by temperature differences between staircase and outside. When it is cold outside, air in the staircase has a buoyant force because it is warmer and therefore less dense than outside air. The buoyant force causes air to rise within the staircase. However, a downward flow of air can occur in air conditioned buildings when it is hot outside, which is known as reverse stack effect [4]. The impact of the stack effect on a pressurization system design is given by eq.1. Where ΔPSO represents the pressure differences between building stairwell and outside, TS is absolute stairwell temperature, TO is outside temperature, z is the height from neutral plane and Ks = 3460 kg K/(m2s2). ,

(1)

The pressure, PW is the wind exerts on a wall of a building can be expressed by eq.2. Cw is wind velocity at the upwind wall of height H, UH is the velocity at H and ρ0 is density of outside. ,

(2)

Upon detection of a fire, HVAC system should be designed such that either the fans are shut down or the system goes into a special smoke control mode operation, if normal HVAC operation continues, the HVAC system will transport smoke to every area the system serves [4]. METHODS oth numerical and experimental studies are executed to investigate the importance of the use of relief damper in pressurization system.

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Description of the Field Test Staircase The stairwell to be studied is located in a shopping center. The stairwell is 29.5 m in height and it serves to 7 stories, where 5 of these floors are below ground. Also staircase has a roof access door, which is normally kept locked and only used for maintenance purposes also it can be characterized as airtight therefore no leakage is specified for this door for calculations. The floor area of the staircase is 17.6 m2. Exit floor is on ground floor (0th floor). The schematic of staircase and pressurization system are shown in Figure 1.

Figure 1.Schematic description of the staircase and pressurization system. Pressurization system has a centrifugal supply fan with the capacity of 35,000 m3/h on the roof level. The fan supplies the outside air to the staircase through grilles in the vertical supply shaft on every floor except ground floor. Motorized relief damper of the pressurization system is also located on the roof level, which is directly connected to the discharge duct (Figure1). Such a design is mostly preferred when it is very difficult to find a place for a relief damper connecting the staircase directly to outside. However, placing the relief damper at the discharge duct causes excessive pressure differences across the stair doors and the location of the relief damper cannot serve to relief this excessive pressure, because the relief point is too far from the pressurized staircase. A single pressure transducer is used to monitor the pressure difference and serve as input to a linear control system that operates both frequency regulator for the fan and the motorized relief damper, which is located at first basement floor. The whole system is connected to the alarm system that enables both manual and automatic startup. Description of the Tests The measured parameters in the tests include supply air velocity through the grille, positive pressure differences in between stairwell and the building and temperatures of stairwell, inside and the outside of the building.

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Pressure difference values are measured by a differential pressure transducer (Testo 512) with a measurement range of 0 to 2 hPa. Air velocity values are measured by a hot wire anemometer (Testo 425) with a measurement range of 0 to 20 m/s. Both of the measuring instruments are calibrated with an inclined manometer. Relative error is obtained as 2.1% for the pressure difference transducer and 5.2% for hot wire anemometer. A series of tests were conducted under non-fire conditions. During the tests, the temperature values are measured in the range of 15.3°C to 16.0°C for outside air, 21°C to 25°C for the staircase and 25°C for the building compartments. At the beginning of each test, motorized relief damper was manually adjusted and the fan started manually. The staircase pressurization systems were tested for the following cases: 1- Case 1: The fan adjusted to supply a flow rate of 3027 m3/h, relief damper was fully opened and all of the doors were closed. 2- Case 2: The fan adjusted to supply a flow rate of 1511 m3/h , relief damper was fully opened and all of the doors were closed. 3- Case 3: The stair door at second basement floor was half opened to see check the change in door opening forces at other floors. RESULTS Experiental Results In this section, various comparisons are made between different experimental cases. In Figure2, the supply air flow rate is altered while the other two parameters (the status of the relief damper and the stair doors). Reducing the supply air rate by half has caused a decrease in the pressure difference between the stairwell and the building approximately by 20% at each floor.

Figure 2.Supply air flow rate effect (Case 1: Q = 3027 m3/h; Case 2: 1511 m3/h).

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The most significant result obtained from Figure 2 is that, the pressure difference range in Case 2 fits very well to what is defined in the Fire Codes, except for the abrupt change at the third basement floor, which is thought to be caused by a possible leakage problem inside the pressurization shaft. The abrupt pressure reduction at the third basement could not be justified by theoretical explanations but it is thought that the pressurization shaft needs to be checked for leakage. In “Case 3”, one of the stair doors (at second basement floor) is opened while the pressurization fan supplied a capacity more than 20,000 m3/h and the relief damper is opened. In this case, it is observed that the pressure difference across all the closed doors of the staircase has remained in an acceptable range specified in the fire codes (Figure 3). It must be noticed that these desired conditions are obtained for a pressurization flow rate of approximately 7 times that for Case 1, where all the doors were closed. And yet, the pressure difference is much lower in Case 3, comparing to Case 1. This is a consequence of a wellknown fact that [7], if the stair door at the exit discharge level could be automatically opened in case of fire, then there would be no need for a relief damper in the design of the pressurization systems. As far as known by the author of this paper, there is no automatic door opening mechanism that is approved to be used in fire stairs. And it is out of scope of this study to check the applicability of such a mechanism

Figure 3. Effect of an open staircase. Numerical Results Numerical analyses of the experimental cases are studied and the results are shown in Figure 4. Numerical results are 28% and 32% different from the cases in which the supply air flow rate is 3137.04 m3/h and 1511.164 m3/h respectively with closed doors and opened damper. The reason for these differences could be the prospective leakage problem inside supply shaft at the third basement floor.The increasing trend towards upper floors of the pressure difference in all cases, both numerical and experimental, is thought to be due to the stack effect.

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Figure 4.Comparison of numerical results with experiments. DISCUSSION Fire staircases in shopping malls are only used in case of fire except in special cases. Doors of the fire staircases are closed with automatic door closers under normal operation. In the fire event or activation of the fire scenario, staircase pressurization system is activated without any delay. The results of the study show that, doors of the staircases cannot be opened without the relief damper and yet if the relief damper does not discharge the excessive pressure from staircase to outside, in other words which discharges from the supply duct, unacceptable pressure levels are encountered. Also, direct connection of the relief damper to the supply duct causes a delay in discharging excess air from the staircases. Relief damper is essential for the first moments of the system activation, in which all of the stair doors are closed. If the exit door is opened and remains open continuously during evacuation, the need for the relief damper will be eliminated. REFERENCES 1.

2. 3. 4. 5.

Ferreira, M J and Cutonilli, J. 2008. Protecting the stair enclosure in tall buildings impacted by stack effect, Proceedings of the 8th Council on Tall Buildings and Urban Habitat World Congress Dubai. Turkey’s Regulation on Fire Protection. 2009, Regulation Referring to the Fire Protection of Buildings. NFPA 92. 2012. Standard for Smoke Control Systems, Quincy Massachusetts: National Fire Protection Association. Klote, J H and Milke J A. 2002. Principles of Smoke Management. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. Beceren, K, Soyel, K and Kılıç A. 1998. Pressurization of the stairwell by using single injection systems. Proceedings of the 3rd International Mechanical Installation Science & Technology Symposium Istanbul.

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6.

7.

Beceren, K and Balık, G. 2004. Merdivenyuvalarıbasınçlandırmatasarımesaslarıvebirhesaplamayöntemi. TesisatMühendisliğiDergisi, Vol.83, pp 33 – 51. Tamura, G T. 1990. Field tests of stair pressurization systems with overpressure relief. ASHRAE Transactions. Vol.96 (1), pp.951 – 958.

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THE EFFECTS OF SET-POINTS AND DEAD-BANDS OF THE HVAC SYSTEM ON THE ENERGY CONSUMPTION AND OCCUPANT THERMAL COMFORT Ongun Berk Kazanci, Bjarne W. Olesen1 1

International Center for Indoor Environment and Energy, Dept. of Civil Engineering, Technical University of Denmark, Nils Koppels Alle, Building 402, Kgs. Lyngby, Denmark Corresponding email: [email protected] SUMMARY A building is a complex system where many components interact with each other therefore the control system plays a key role regarding the energy consumption and the occupant thermal comfort. This study is concerned with a detached, one-storey, single family, energy-plus house. It is equipped with a ground heat exchanger, a ground coupled heat pump, embedded pipes in the floor and in the ceiling, a ventilation system (mechanical and natural), a domestic hot water tank and photovoltaic/thermal panels on the roof. Preliminary evaluations showed that for Madrid, change of indoor set-point in cooling season from 23°C to 25°C (±1 K) can decrease the cooling need by 23%. Hence, an interest arose in order to quantify the energy saving potential with respect to different set-points and deadbands. However occupant comfort should not be neglected for the sake of energy savings. This study focuses on the effects of the set-points and dead-bands of different components on the energy consumption together with the occupant thermal comfort. Evaluations are carried out with TRNSYS for Copenhagen and Madrid in order to compare climatic effects. INTRODUCTION As the fossil fuels are gradually depleting, focus on the renewable energy resources and their integration into various systems has been increasing. Even though replacing fossil fuels with renewable energy resources is an important step, energy efficiency should not be neglected. People spend most of their time indoors [1] therefore providing a comfortable and healthy indoor environment should be placed in the center of every HVAC system design. This goal should be achieved as efficiently and as effectively as possible. This study is concerned with the house, Fold, which Technical University of Denmark competed in the worldwide student competition Solar Decathlon Europe 2012 [2]. During the design of the HVAC system of the house, the above mentioned points were studied. The house was designed to be energetically self-sufficient and in fact it performs as an energy-plus house [3]. It is equipped with a ground heat exchanger (GHX), a ground coupled heat pump, embedded pipes in the floor and in the ceiling, a ventilation system (mechanical and natural), a domestic hot water (DHW) tank and photovoltaic/thermal (PV/T) panels. It was observed during the design and operation phases that in order to obtain optimal performance, it is not enough for one component to perform optimally but all of the components should perform optimally and interact with each other in the best possible way. Hence set-points and dead-bands of different components emerge as crucial parameters. This

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study is concerned with the indoor temperature set-point and dead-band, set-point of supply temperature to the embedded pipes and mass flow rate in the ground loop and their effects on energy demand, energy consumption and occupant thermal comfort. Evaluations are carried out for Copenhagen and Madrid, with commercially available simulation software, TRNSYS. CASE STUDY DESCRIPTION The house is a detached, one-storey, single family, energy-plus house with an interior area of 66,2 m2 and with a conditioned volume of 213 m3. The house’s largest glazing façade is oriented to the North, with a 19˚ turn towards West. The house can be seen in Figure 13:

Figure 13: Southwest and North sides of the house The glazing surfaces in North and South sides are covered by the overhangs which eliminate direct solar radiation to the house during summer. During winter direct solar radiation enters the house and creates a favorable effect. Only active shading system was for the skylight window. Inside the house, there is one space combining kitchen, living room and bedroom. The surface areas and the thermal transmittance values are presented in Table 2: Table 2: Construction details of the house [3] External walls South North East West Floor Roof 2 Area [m ] 19,3 37,2 66,2 53 0,09 0,09 0,09 0,09 U-value [W/m2K] Windows South North East West Floor Roof Area [m2] 21,8 36,7 0,74 U-value 1,04 1,04 1,04 [W/m2K] Solar 0,3 0,3 0,3 transmission The design conditions required for the house to be fully functioning in two different climates: Denmark (Copenhagen) and Spain (Madrid). Summer maximum, summer average and winter average temperatures are taken for Madrid while winter minimum temperature is taken for Copenhagen. Design temperatures and respective loads are as follows: –

Summer maximum 40,0°C 52,0 W/m2 (cooling)

–

Summer average 26,0°C

35,2 W/m2 (cooling)

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–

Winter minimum -12,0°C

45,6 W/m2 (heating)

– Winter average 2,6°C 26,6 W/m2 (heating) Cooling and heating system of the house is water based with a low temperature heating and high temperature cooling concept, enabling the integration of renewable resources, ground in this case. It is a dry radiant system, piping grid is installed under the wooden layer. Space heating is obtained by the embedded pipes in the floor and space cooling is obtained by embedded pipes in the ceiling and, if necessary, in the floor. A mixing station is installed between ground and embedded pipes in order to control the water flow and temperature. In the ceiling, there is foam board system with aluminum heat conductive plates and PEX pipes (12x1,7 mm). There are 6 circuits, with maximum flow rate in one circuit of 0,07 m3/h. In the floor, there is chipboard system with aluminum heat conducting plates and PEX pipes (17x2,0 mm). There are 4 circuits, with maximum flow rate in one circuit of 0,07 m3/h for the cooling case and 0,15 m3/h for the heating case. In order to regulate the indoor air quality, mechanical and natural ventilation systems are installed. The distribution system consists of 2 supply diffusers and 4 exhausts (kitchen hood, bathroom, toilet and clothes dryer). Maximum flow rate that could be provided by the air handling unit, AHU, is 320 m3/h and this capacity fully covers the design value. AHU has two heat recovery systems; passive (cross flow heat exchanger) and active (reversible heat pump coupled with the DHW tank). Ventilation system is utilized to control humidity and indoor air quality expressed by CO2 levels. Mechanical ventilation is shut off when the outside air temperature is suitable for natural ventilation. Natural ventilation is possible via two windows in South and North façades and the operable skylight window. The only electrical energy source of the house is solar energy, utilized via PV/T panels placed on the entire roof area. The electrical system is designed to be grid-connected. The solar thermal system is coupled with the PV part of the PV/T panels. Thermal part absorbs the heat produced by PV panels and utilizes it in the DHW tank. Heat source/sink for space heating/cooling is the ground, utilized via a borehole heat exchanger. Free cooling is obtained during the cooling season and ground coupled heat pump is only used during the heating season. The ground heat exchanger is a borehole with a depth of 120 meters, single U-tube configuration and with a diameter of 0,12 m. METHODS and INVESTIGATIONS Presented results are from the commercially available dynamic building simulation software, TRNSYS [4]. Simulations were carried out for Copenhagen and Madrid, International Weather for Energy Calculations (IWEC) and Spanish Weather for Energy Calculations (SWEC) weather files were used, respectively. Same load profiles for occupants, lighting and equipment were implemented for Copenhagen and Madrid. There are 2 occupants in the house with 1,2 met. Occupants are assumed to be away from 8:00 to 16:00 during the weekdays and from 12:00 to 17:00 during the weekends. The lighting load is 222 W (3,4 W/m2). Lights are assumed to be ON from 05:00 to 08:00 and from 16:00 to 22:00 every day. Electrical power of the installed home appliances is 1,5 kW. Different equipment is ON and OFF during the day. The values are expressed with respect to the maximum value. For the weekdays, load is 5% all the time except from 02:00 to 03:00 where load is 20% and except from 19:00 to 20:00 where load is 62%. For Saturday, from

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7:00 to 8:00 the load is 15%, from 8:00 to 9:00 the load is 34% and for Sunday from 2:00 to 3:00 the load is 20%. Ventilation rate is 0,8 ach and infiltration is 0,1 ach. Natural ventilation is not taken into account in the simulations. G-value of the windows was taken as 0,28 (difference from the actual case is due to the available material library). May to September was the cooling season and the rest of the months were the heating season. In the reference case, set-points for the operative temperature has been defined as 21°C±1 K for heating and 25°C±1 K for cooling seasons, following category II of EN 15251:2007 [5]. The supply temperature set-points to the embedded pipes were 34°C and 16°C, for heating and cooling modes, respectively. The flow rates were determined according to EN 153772:2008 [6], design values were 619 kg/h for floor heating, 336 kg/h for floor cooling and 317 kg/h for ceiling cooling. The circulation pump in the ground loop has a design flow rate of 650 kg/h and a power of 68 W corresponding to this flow rate. The heat pump is water-to-water type. The performance data of the heat pump is presented in the following Figure 14 (heat pump is not used in the cooling season due to free cooling):

Figure 14: Heat pump efficiency curves for heating and cooling modes In the above figure, load side represents flow coming from the house and source side represents flow coming from the ground. The nominal thermal output of the heat pump is 3 kW with an electrical power of 600 W. The results from annual simulations are presented in Table 3: Table 3: Energy consumption by house needs Application [kWh/m2] / Location Heating Cooling Ventilation DHW Rest of the electricity consumption Total electricity consumption Total primary energy consumption Energy balance (electricity)

Copenhagen Need/consumption 100,7/30,6 23,4/0,6 1,5/0,7 32,2/7,1 5,4 44,4 111,0 67,9

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Madrid Need/consumption 54,2/17,3 28,2/1,0 5,3/5,2 32,2/3,7 4,0 31,3 93,8 141,0


In the above table, need indicates thermal need and consumption indicates electricity consumption. Rest of the electricity consists of consumptions of the pump of the embedded pipe loops and of the two pumps for the PV/T panels. Energy balance is consumption subtracted from production and it indicates that the house is an energy-plus house. The primary energy factor has been taken as 2,5 for Denmark [7] and as 3 for Spain [8]. RESULTS Results of the simulations In the following tables, values next to the temperatures indicate set-point temperatures. Values in the parentheses next to the flow rates indicate pump power. EN 15251:2007 has been used as the indicator of the occupant thermal comfort and results are shown in the percentage of time that the conditions fulfill respective comfort categories. During the simulations, indoor temperature set-points are adjusted according to the comfort categories. Dead-band analyses were not applied to all of the parameters; it was implemented on the indoor temperature set-point as a representative case of its effects. Coefficient of Performance (COP) values correspond to the COP of the heat pump (ratio of heat delivered to electricity consumed) and COPsys is the ratio of heat delivered to the sum of the electricity consumption of heat pump and the circulation pump. Free Cooling Coefficient (FCC) represents the ratio of cooling effect to circulation pump consumption. Presented results are only the embedded pipe system and do not consider ventilation however this is considered not to have a significant effect due to the design strategy (ventilation system is only intending to control humidity and CO2 levels). Results of the simulations are presented in the following tables, for each location and season: Table 4: Heating season, Copenhagen (CC is the comparison of consumption to the reference) Reference,21°C±1 K Tindoor, 19°C Tindoor, 20°C Tindoor, 21°C ±2K Tindoor, 22°C Tindoor, 23°C Tsupply, 30°C Tsupply, 31°C Tsupply, 32°C Tsupply, 33°C ṁ, 400 kg/h (63 W) ṁ, 900 kg/h (74 W) ṁ, 1150 kg/h (79 W) ṁ, 1400 kg/h (84 W) ṁ, 1650 kg/h (87 W)

Demand [kWh/m2] 100,7 79,9 90,6 97,7 109,7 118,3 90,6 95,2 98,4 100,2 100,9 100,5 100,5 100,5 100,5

Consumption [kWh/m2] 30,6 23,5 27,1 29,7 33,9 37,1 27,0 28,5 29,7 30,4 30,8 30,5 30,4 30,4 30,4

CC [-] -23,2% -11,5% -3,1% 10,7% 21,3% -11,9% -6,7% -2,9% -0,6% 0,8% -0,5% -0,6% -0,7% -0,8%

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COP [-] 3,29 3,4 3,35 3,29 3,24 3,19 3,36 3,34 3,31 3,3 3,27 3,3 3,31 3,31 3,31

COPsys [-] 3,05 3,15 3,1 3,05 3 2,96 2,99 3,01 3,02 3,04 3,05 3,04 3,02 3,01 3

Cat. I [-] 52% 16% 24% 48% 84% 96% 32% 38% 46% 52% 52% 52% 52% 52% 52%

Cat. II [-] 90% 26% 54% 69% 98% 99% 62% 78% 86% 90% 91% 89% 89% 89% 89%

Cat. III [-] 100% 95% 99% 100% 100% 100% 98% 99% 100% 100% 100% 100% 100% 100% 100%


Table 5: Heating season, Madrid (CC is the comparison of consumption to the reference) Reference,21°C±1 K Tindoor, 19°C Tindoor, 20°C Tindoor, 21°C ±2K Tindoor, 22°C Tindoor, 23°C Tsupply, 30°C Tsupply, 31°C Tsupply, 32°C Tsupply, 33°C ṁ, 400 kg/h (63 W) ṁ, 900 kg/h (74 W) ṁ, 1150 kg/h (79 W) ṁ, 1400 kg/h (84 W) ṁ, 1650 kg/h (87 W)

Demand [kWh/m2] 54,2 36,1 45,5 48,7 63,2 71,6 50,7 52,1 53,1 53,8 54,4 54,4 54,3 54,4 54,4


CC [-] -33,7% -16,2% -10,2% 16,9% 32,9% -6,6% -3,9% -2,1% -0,8% 0,9% 0,0% -0,2% -0,2% -0,3%

COP [-] 3,13 3,14 3,14 3,13 3,12 3,11 3,13 3,13 3,13 3,13 3,11 3,14 3,14 3,15 3,15

COPsys Cat. [-] I [-] 2,94 71% 2,95 37% 2,95 50% 2,94 57% 2,93 98% 2,92 100% 2,85 62% 2,88 65% 2,91 68% 2,93 70% 2,94 71% 2,93 71% 2,92 71% 2,91 71% 2,9 71%

Cat. II [-] 99% 51% 72% 76% 100% 100% 94% 97% 99% 99% 99% 99% 99% 99% 99%

Cat. III [-] 100% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Table 6: Cooling season, Copenhagen (CC is the comparison of consumption to the reference) Reference,25°C±1 K Tindoor, 22°C Tindoor, 23°C Tindoor, 24°C Tindoor, 25°C ±2K Tindoor, 26°C Tsupply, 17°C Tsupply, 18°C Tsupply, 19°C Tsupply, 20°C ṁ, 400 kg/h (63 W) ṁ, 900 kg/h (74 W) ṁ, 1150 kg/h (79 W) ṁ, 1400 kg/h (84 W) ṁ, 1650 kg/h (87 W)

Demand [kWh/m2] 23,4 35 30,2 27,2 21,2 19,8 22,6 21,6 21 19,4 23,1 23,4 23,4 23,2 23,1


CC [-] 101,6% 70,5% 27,9% -6,6% -21,3% 9,8% 18,0% 23,0% 36,1% -3,3% 9,8% 16,4% 27,9% 31,1%

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FCC [-] 38,22 28,43 29,17 34,72 37,13 41,17 33,45 29,89 27,89 23,42 39,24 35,15 32,92 29,89 28,75

Cat. I [-] 93% 96% 95% 95% 89% 89% 92% 91% 90% 88% 92% 93% 93% 93% 93%

Cat. II [-] 96% 98% 96% 97% 93% 92% 95% 94% 93% 91% 95% 96% 96% 95% 95%

Cat. III [-] 99% 99% 98% 99% 97% 97% 98% 97% 97% 96% 98% 99% 99% 99% 99%


Table 7: Cooling season, Madrid (CC is the comparison of consumption to the reference) Reference,25°C±1 K Tindoor, 22°C Tindoor, 23°C Tindoor, 24°C Tindoor, 25°C ±2K Tindoor, 26°C Tsupply, 17°C Tsupply, 18°C Tsupply, 19°C Tsupply, 20°C ṁ, 400 kg/h (63 W) ṁ, 900 kg/h (74 W) ṁ, 1150 kg/h (79 W) ṁ, 1400 kg/h (84 W) ṁ, 1650 kg/h (87 W)

Demand [kWh/m2] 28,2 41,9 36,7 31,1 26,9 26,3 28,3 27,9 27 25,5 28,2 28 27,7 27,7 27,5

Consumption [kWh/m2] 1,0 1,9 1,5 1,2 1,0 0,9 1,0 1,1 1,1 1,2 1 1,2 1,2 1,3 1,4

CC [-] 83,7% 45,2% 16,3% -1,9% -16,3% 0,0% 1,9% 3,8% 12,5% -3,8% 10,6% 18,3% 26,9% 32,7%

FCC [-] 27,19 21,89 24,34 25,56 26,45 30,18 27,25 26,37 24,98 21,84 28,27 24,39 22,56 21,04 19,95

Cat. I [-] 90% 100% 99% 98% 80% 79% 90% 89% 88% 85% 89% 89% 89% 89% 89%

Cat. II [-] 97% 100% 100% 100% 87% 87% 97% 97% 96% 93% 96% 97% 96% 96% 96%

Cat. III [-] 100% 100% 100% 100% 99% 99% 100% 100% 100% 99% 100% 100% 100% 100% 100%

Energy performance The indoor temperature set-points have the greatest influence on the energy demand and consumption. In the heating season, higher indoor temperature set-points result in higher demand and consumption (21% and 33% higher for 2C increase for Copenhagen and Madrid, respectively) followed by a decrease in COP and COPsys. Changes in COP and COPsys are more pronounced in Copenhagen. In the cooling season, higher indoor temperature set-points result in lower demand (15% and 7% lower for 1C increase for Copenhagen and Madrid, respectively) and consumption. Free Cooling Coefficient increases with higher indoor setpoints. Dead-band increase (from ±1 K to ±2 K) results in a more flexible, less precise control, and its effects are visible in the decreased demand and consumption values for all cases. COP and COPsys are almost not affected while Free Cooling Coefficient is affected slightly. Due to the low temperature heating and high temperature cooling concept, when investigating the effects of different supply temperatures, lower temperatures than the design temperatures were investigated in the heating season. A similar approach was utilized in the cooling case in order to investigate the possibility of higher supply temperatures (also due to dew-point). In the heating season, energy demand and consumption (12% and 7% lower for 4C lower supply temperature for Copenhagen and Madrid, respectively) tend to decrease with the decrease of the supply temperature set-point. This results in higher COP but lower COPsys. COP is not affected in Madrid. In the cooling season, cooling demand decreases with increased supply temperature set-point (17% and 10% lower for 4C higher supply temperature for Copenhagen and Madrid, respectively) but this is not reflected to the consumption values due to the longer operation of the circulation pump. This trend is reflected to the Free Cooling Coefficient values. The results show that flow rate in the ground loop doesn’t have a significant effect on the demand and the consumption for the heating case. For the cooling case, effects are more

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pronounced on the consumption due to the free cooling concept (direct interaction of the house with the GHX). COPsys and Free Cooling Coefficient values tend to decrease with increasing flow rate mainly due to the higher consumption of the circulation pump. Comfort conditions The thermal comfort is most sensitive to the indoor temperature set-points. The effect of increased dead-band is more visible in the heating season. Increase of the dead-band results in a less strict control over the respective parameter therefore the comfort conditions tend to get worse (due to less operating hours of the heat pump and/or the circulation pump). In the cooling season, increased dead-band has a similar effect on the comfort conditions to an increased indoor temperature set-point. Comfort categories are less sensitive to the indoor temperature set-point changes during cooling season than in heating season. This could be explained with the higher heating demand than cooling demand. The same behavior is also observable for supply temperatures. The supply temperatures to the embedded pipes have more effect on the comfort conditions than the flow rate in the ground loop but less effect than the indoor temperature set-point. The results show that the flow rate in the ground loop doesn’t have a significant effect on the occupant thermal comfort neither in the heating season nor in the cooling season. Climatic effects can be observed in the case that Copenhagen is more sensitive to the supply temperatures in the heating case (different heating needs) however in the cooling case this effect is not possible to observe directly. Indoor operative temperatures are presented in the following figure for both of the locations during a representative week in July for Copenhagen and Madrid respectively (outdoor dry bulb temperature is shown on the right axis):

Figure 15: Operative temperatures during a week in July for both of the locations It is important to bear in mind that comfort categories only consider operative temperature but thermal comfort is a function of other parameters such as humidity. Therefore Predicted Mean Vote (PMV) could have been used in order to evaluate the occupant thermal comfort. Local thermal discomfort issues should also be considered. Every simulation software has its own advantages and limitations therefore the results presented in this paper will be validated with the full scale experiments in the near future.

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DISCUSSION and CONCLUSION Among the investigated parameters, the indoor temperature set-point is the most dominant parameter with respect to energy demand, consumption and occupant thermal comfort. Energy consumption and thermal comfort are also sensitive to the supply temperature to the embedded pipe loops but not as much as indoor temperature set-points. Flow rate in the ground has very low influence on energy consumption and occupant thermal comfort. This effect could be explained as closer the component/parameter to the indoors, higher the sensitivity (more components in between less the effect). When the indoor temperature setpoint is changed, every component in the system has to be adjusted accordingly but when the set-point of a component is changed, this change does not affect the system as much because the effects get dampened and not all of the other components need to be adjusted accordingly. It is possible to save 23% and 34% of energy consumption during heating season in Copenhagen and Madrid, respectively. In the cooling season, it is possible to reduce cooling demand by 17% and 10% in Copenhagen and Madrid, respectively. While these reductions result in fewer hours within Category I and II, Category III is satisfied for all of the cases for more than 95% of the time. Climatic differences are observable via different effects of modifications on the results. Increased dead-band results in lower energy consumption and demand but it also results in decreased occupant thermal comfort. It is possible to achieve 16C reduction in supply temperature in the heating mode and 16C increase in supply temperature in the cooling season with almost no change in the comfort conditions. However all of the other energy saving measures other than supply temperature modifications are accompanied with lower comfort for the occupants. Due to this trade-off between energy consumption and occupant thermal comfort, an optimum system operation point should be chosen based on the priority. REFERENCES 1. Olesen, B. W., & Seelen, J. (1993). Criteria for a comfortable indoor environment in buildings. Journal of Thermal Biology, 545-549 2. Solar Decathlon. (2012). Solar Decathlon Europe 2012, Rules,V.4.0. 3. Skrupskelis, M., & Kazanci, O. B. (2012). Solar sustainable heating, cooling and ventilation of a net zero energy house. Kgs. Lyngby: Technical University of Denmark. =2 22 " $ ) "2 4;99B52 :@0 %"*# :0 )) $ )')2 %"' $'- %')%'-0 $ +'( )-% (%$( $3 (%$2 5. EN 15251. (2007). Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. Brussels: European Committee for Standardization. ?2 :>?; ' - /*ECF 500 m² (buildings where the Administration is the owner) o Up to July 2015, surface > 250m² (buildings where the Administration is the owner)

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Up to December 2015, surface > 250 m² (rented buildings)

Procedure for obtaining the Energy Performance Certificate Outline steps

What CO2 emissions depend?

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Consumption: Influence of the Weather

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Consumption: Influence of the building

Consumption: Influence of the use Consumption: Influence of the environment

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Certification. Options

Energy Indicator

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ENERGY EFFICIENCY TAG

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RESEARCH ON THE CONDENSATE CARRYOVER PHENOMENA IN FINNED-TUBE EVAPORATOR OF AN AUTONOMOUS UNIT Miguel Zamora1, Natividad Molero1, José Miguel Corberán2, Emilio Navarro2 1. CIAT. R&D Department. Montilla, Córdoba, Spain. 2. Institute of Energy Engineering, University Polytechnic of Valencia, Spain. Corresponding email: [email protected]

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SUMMARY In the present work, an experimental evaluation and comparison of a dedicated coating for improving the hydrophilic behavior of evaporator fins is conducted in an autonomous air conditioning unit. Different fin materials and coatings are analyzed by measuring the surface tension angles. Hydrophilic coating presents contact angles lower than 10º. INTRODUCTION Current trends in the design of autonomous, roof-top and compact commercial air conditioning units tend to reduce the size of the indoor heat exchangers by reducing evaporator frontal area. Reasons of that are higher compactness requirements, and raw material savings. However high efficiency is nowadays a priority, thus air velocity must be increased in order to keep the heat transfer coefficient while the corresponding pressure drop raise must be limited in order to save fan electrical consumption. The effect of the condensate water layer in the heat transfer coefficient and in the pressure drop over wet finned-tube evaporators is a research topic well covered in the scientific literature. However a second harmful effect of air velocity increase is the condensate carryover phenomena. While there are many researches that have evaluated the effect of the hydrophilic and hydrophobic behavior of the fin coating treatment in the latent capacity, the frost formation, and the pressure drop, there is scarce literature that focuses in the condensate carryover. [1] and [2] have studied the condensate generation mechanism and the carryover phenomenon of different types of fin material with different surface treatments and coatings. According to these authors, the release mechanism of the drops in the air stream depends on the receding contact angle have (θR). Aluminum fins with traces of manufacturing oil have θR= 84º while fins cleaned with acetone have θR= 39º. These authors also measured the water retained on the fin surface. For lower values of θR (θR