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lo studio di due dei sei piani (secondo e quinto) dei quali è composto l'edificio permette una valutazione ..... Designers' fee depends on investment cost of ventilation components ...... narrow o ce buildings, Building and Environment. 39 (2004) ...
POLITECNICO DI MILANO Facoltà di Ingegneria Industriale Corso di Laurea in Ingegneria Energetica

Energy Saving Potential of Night Natural Ventilation in the Urban Environment: the effect of wind shielding and solar shading

Relatore: Dott.sa Adriana ANGELOTTI Co relatore: Ing. Rubina RAMPONI

Tesi di laurea di: Isabella I. GAETANI DELL’AQUILA D’ARAGONA Matr. 752786

Anno Accademico 2012 - 2013



Contents Abstract Estratto in Lingua Italiana 1. Introduction 1.1. General Framework 1.2. Literature Survey on Natural Ventilation in Buildings 1.2.1. The cooling Potential of night Natural Ventilation 1.2.2. The Barriers to the Application Natural Ventilation 1.3. Literature Survey on Energy Evaluations at the Urban Scale 1.3.1. The Effect of External Shading on Cooling Requirement 1.3.2. The Effect of Urban Environment on Airflow Rate

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2. Natural Ventilation Fundamentals 2.1. Principles 2.1.1. Airflow through Cracks 2.1.2. Airflow through large Openings 2.1.2.1. The Discharge Coefficient 2.1.3. Airflow in and around Buildings 2.2. The driving Forces of Natural Ventilation 2.2.1. Wind driven Natural Ventilation 2.2.1.1. The Pressure Coefficient 2.2.1.2. Wind Profiles in the Urban Environment 2.2.2. Buoyancy driven Natural Ventilation 2.2.3. Combined Effect of Wind and Buoyancy 2.3. Modelling Natural Ventilation in Buildings 2.3.1. Multizone Airflow Network modelling 2.3.2. Cp Sources 2.3.2.1. TPU aerodynamic Database for non-isolated low-rise Buildings 2.3.2.2. TNO Cp Generator

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3. Building Energy Simulation Fundamentals 3.1. Building Energy Simulation Software 3.1.1. The indoor Air Heat Balance 3.1.2. The Surface Heat Balance 3.1.2.1. The Outside Surface 3.1.2.2. The Inside Surface

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3.1.3. Models of transient heat conduction 3.2. EnergyPlus 3.2.1. The thermal Model 3.2.2. The Airflow Model 3.2.3. Modelling the Urban Environment with EnergyPlus 3.2.3.1. Solar Radiation 3.2.3.2. Wind Profile

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 4. The Case Study 4.1. The Building 4.1.1. Thermal Characteristics 4.1.2. Occupancy and Systems 4.1.3. Ventilation Model 4.2. The Climatic Zones 4.3. The Urban Environment 4.3.1. Physical Description of surrounding Buildings in EnergyPlus 4.3.2. Impact of Urban Environment on Boundary Conditions 4.3.2.1. Simplification of the Urban Heat Island (UHI) effect 4.3.2.2. Calculation of Pressure Coefficients for non-isolated buildings using Cp Generator 4.4. List of Cases simulated in EnergyPlus 
 5. Results 5.1. Impact of Urban Environment on Heat Fluxes 5.2. The Effect of Solar Shading 5.3. The Effect of Wind Shielding 5.4. Energy Saving Potential of Night Natural Ventilation in the Urban Environment 5.5. Sensitivity to Albedo Values 5.6. Sensitivity to Increased Air Temperature 5.7. Sensitivity to Pressure Coefficients Source 5.8. Discussion of Results 
 6. Conclusions 6.1. Limits and Further Developments

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Appendix 1 Appendix 2 Appendix 3 References

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List of Figures Figure 1.1: The total cooled area in Europe from 1990 to 2020 Figure 2.1: Natural ventilation can take place in three different manners: a) single-sided ventilation, b) cross ventilation, c) stack ventilation Figure 2.2: The action of the wind on building plan and prospectus Figure 2.3: The main components of the urban atmosphere Figure 2.4: Wind profile development for different terrain roughness Figure 2.5: The Davenport diagram Figure 2.6: The influence of temperature difference between indoor and outdoor Figure 2.7: Urban configurations with different area densities tested in the wind tunnel Figure 2.8: Disposition of wind pressure measurement taps Figure 2.9: Graphic output of TNO Cp Generator for a given urban environment Figure 2.10: Comparison between calculated and measured pressure coefficients Figure 3.1: Energy flowpaths of a building Figure 3.2: Outside heat balance control volume diagram Figure 3.3: Inside heat balance control volume diagram Figure 3.4: The overall EnergyPlus structure is made of three components: simulation manager, heat and mass balance simulation, building systems simulation Figure 3.5: Simplified scheme of a nodal approach Figure 3.6: Schematic representation of rays going outward from a point on a receiving surface Figure 3.7: Scheme of the calculation of beam-to-beam reflection from an obstruction Figure 4.1: Building’s three-dimensional representation (a), floor plan (b) and floor section (c) with ventilation hypothesis Figure 4.2: Representation of the second floor and the fifth floor of the studied building Figure 4.3: Selected European locations: overview Figure 4.4: Physical modelling of the nearby urban environment in EnergyPlus Figure 4.5: Modified daily temperature profile in Rome due to the application of UHI effect Figure 4.6 Hypothesis of pressure coefficients distribution on North-South and East-West façade. (1) Mid points of windows, (2) horizontal refinement, (3) vertical refinement, (4) (2)∪(3) Figure 4.7: ΔC p between North and South façade calculated for the isolated

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building and for UD=0.3 75 Figure 4.8: Average North façade pressure coefficients calculated with Cp Generator using 76 € the urban configuration UD=0.3 Figure 4.9: Comparison between average North façade Cp calculated with Cp Generator and experimentally derived in a wind tunnel by TPU in the isolated case (a) and at UD 0.1 (b), UD 0.3 (c) and UD 0.6 (d) 77 Figure 4.10: Comparison between North-South facades ΔC p calculated with Cp Generator and experimentally derived in a wind tunnel by TPU in the isolated case (a) and at UD 0.1 (b), UD 0.3 (c) and UD 0.6 (d) Figure 4.11: Average percentage discrepancy € in North-South facades ΔC p

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between Cp Generator calculated values and TPU experimental values; the average difference increases at denser urban configurations Figure 4.12: Average floor pressure coefficients from TPU wind tunnel experiments at varying urban density Figure 4.13: Average floor pressure coefficients calculated with TNO Cp Generator at varying urban density Figure 5.1: Thermal behaviour of a South facing wall for a sunny summer day in Amsterdam at the 2nd floor and at the 5th floor Figure 5.2 Seasonal Window Heat Gains for a South facing window in Amsterdam for different Urban Densities, at the 2nd floor and at the 5th floor Figure 5.3: Cooling ED of the same building in different locations and within diversely dense urban environments Figure 5.4: Total floor seasonal Window Heat Gains of the same building in different locations and within diversely dense urban environments Figure 5.5: Cooling energy demand versus Urban Density according to location and floor Figure 5.6: Night average Air Changes per Hour (ACH) for different locations and floors at increasing Urban Density Figure 5.7: Energy demand and ACH at increasing Urban Density Figure 5.8: Energy saving due to natural ventilation for different locations and floors at increasing UD Figure 5.9: Energy demand and ACH at increasing Urban Density with albedo 0.3 and 0.7 Figure 5.10: Cooling energy demand versus UD according to location and floor with albedo=0.7 Figure 5.11: Energy saving due to natural ventilation for different locations and floors at increasing UD with albedo=0.3 and 0.7 Figure 5.12: Energy demand and ACH at increasing Urban Density without air temperature increase and with a maximum temperature increase of 3°C Figure 5.13: Energy saving due to natural ventilation for different locations at the 5th floor at increasing UD without outdoor air temperature increase and with a maximum temperature increase of 3°C Figure 5.14: Night average ACH obtained with pressure coefficients from TPU wind tunnel and TNO Cp Generator tool Figure 5.15: Energy demand and ACH at increasing Urban Density obtained with pressure coefficients from TPU wind tunnel Cp and TNO Cp Generator tool Figure 5.16: Energy saving due to natural ventilation for different locations and floors at increasing UD obtained with pressure coefficients from TPU wind tunnel Cp and TNO Cp Generator tool


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List of Tables Table 1.1 Overview of some main barriers in designing with natural ventilation Table 2.1: A number of parameters affects pressure coefficients Table 2.2: Typical values for terrain dependent parameters Table 2.3: Terrain categories proposed by Choi (2009) [42] Table 3.1: Reference values for the exponent of the power-law α and the thickness of the boundary layer δ for different terrain types Table 4.1: Composition of each construction typology Table 4.2: Thermal properties of construction materials used in the modelling of the building Table 4.3: Thermal properties of the building constructions Table 4.4: Selected European locations: average daily, night-time and daytime temperature and average daily, night-time and daytime wind speed for the simulated time span June -September Table 4.5: Modelling of the nearby urban environment at increasing Urban Density Table 4.6: Values of the aerodynamic roughness length z0 at increasing urban density used as input in the Cp Generator Table 4.7: Discrepancy in North-South façades ΔC p between Cp Generator

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calculated values and TPU experimental values Table 4.8: Combination of parameters investigated with EnergyPlus Table 5.1: Cooling energy need for the€non-ventilated building in different Urban Densities, for different locations and floors Table 5.2: Cooling Degree Hours calculated from June to September Table 5.3: The effect of parameters on energy demand and ventilation potential trend as functions of the Urban Density

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Abstract La ventilazione naturale è considerata oggi un’efficiente tecnica di raffrescamento passivo degli edifici. L’ambiente circostante tuttavia ha un’influenza determinante sull’efficacia del metodo, sebbene si tenda spesso a trascurare tale aspetto in fase di simulazione. Gli edifici infatti agiscono sia da schermatura per la radiazione solare, provocando una diminuzione del fabbisogno di raffrescamento, sia da ostacoli per il vento, riducendo così le velocità medie di questo. Per tenere conto di tali effetti nella simulazione energetica è necessario utilizzare modelli integrati di recente sviluppo. Ad oggi si ha una scarsa comprensione delle effettive prestazioni della ventilazione naturale in ambiente urbano. Questo studio si propone di effettuare con l’ausilio del software EnergyPlus un’analisi comparativa tra la domanda di raffrescamento di un edificio commerciale all’interno di un contesto isolato e il medesimo edificio posto in un ambiente urbano di crescente densità. Il fabbisogno è considerato sia per i casi non ventilati che per i casi ventilati, e in relazione a varie località climatiche e diverse altezze di piano. La ventilazione naturale è simulata utilizzando il modello Airflow Network contenuto in EnergyPlus. I risultati ottenuti evidenziano che l’efficacia della ventilazione naturale nel diminuire il fabbisogno di raffrescamento in contesto urbano può essere ridotta fino al 60% rispetto al contesto isolato. Generalmente, il rendimento in contesto urbano della ventilazione naturale notturna è fortemente influenzato dal potenziale iniziale: climi caratterizzati da bassi potenziali sono solitamente più soggetti all’effetto del contesto urbano. Questa tesi conferma l’importanza di valutare il reale posizionamento di un edificio all'interno del paesaggio architettonico per poter stimare in modo accurato il potenziale di raffrescamento notturno della ventilazione naturale. Inoltre, evidenzia la necessità di considerare contemporaneamente gli effetti termici e la riduzione delle portate d’aria e propone un esempio di applicazione dei software di simulazione energetica all’ambiente urbano. Questi risultati potranno essere di supporto ai progettisti nella definizione dell’appropriato potenziale di ventilazione naturale di un edificio all’interno contesto urbano.

Parole Chiave Potenziale di ventilazione naturale; ambiente urbano; vento; coefficienti di pressione; schermatura solare; simulazione energetica degli edifici


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Abstract Night natural ventilation is regarded today as an energy efficient cooling strategy. However, the specific setting where it takes place can undermine its effectiveness, and hence needs to be taken into account. On the contrary natural ventilation evaluations are often carried out considering the isolated building. The urban environment has two major outcomes on boundary conditions: neighbouring buildings serve on one side as obstacles to the wind, causing a reduction in wind speeds, and on the other side they serve as solar shading surfaces, reducing the cooling energy demand of buildings. Assessing the impact of the urban form on both thermal and airflow conditions would require airflow models to be coupled with thermal models. As a result, a lack of knowledge is found on the performance of night natural ventilation in a urban environment. In the present study, a comparison between energy demands of a low-rise isolated commercial building and of the same building within an increasingly dense urban environment is carried out using EnergyPlus. Energy demands are studied both for the unventilated and for the ventilated case, in various locations and at different floor heights. Wind-driven natural ventilation is simulated by means of Airflow Network modelling. Results show that the energy saving potential of night natural ventilation in a dense urban environment can be reduced by up to 60% if compared to an isolated case. The overall urban performance of night natural ventilation is strongly dependent on the initial energy saving capacity: climates with an original low cooling potential generally undergo a stronger impact of the urban environment. This thesis confirms the importance of taking into account the real setting of a building in order to properly estimate the potential of night cooling. Moreover, it highlights the need of a combined investigation of thermal and airflow behaviour and provides also an example of an application of building energy simulation tools at a urban scale. Such findings can be of use to support building designers in assessing the energy saving potential of natural ventilation for a building within the urban environment.

Key Words Natural ventilation potential; urban environment; wind; pressure coefficient; solar shading; Building Energy Simulation











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Estratto in Lingua Italiana Il settore edilizio rappresenta oggi una tra le voci più importanti dei consumi energetici mondiali. La domanda di energia è negli ultimi anni in aumento, anche a causa del crescente fabbisogno di raffrescamento, soprattutto nel settore terziario [1]. Allo stesso tempo, l’impatto ambientale è divenuto un argomento di dibattito internazionale provocando un rinnovato interesse nell’architettura sostenibile. Tra le pratiche di raffrescamento passivo è stata riscoperta la ventilazione naturale. La ventilazione naturale notturna è particolarmente indicata per gli edifici commerciali, in quanto avviene durante i periodi di non occupazione. D’altra parte, la grande maggioranza degli edifici commerciali o adibiti ad uffici è posizionata nel contesto urbano, che presenta una serie di condizioni sfavorevoli all’applicazione della ventilazione naturale. I principali svantaggi sono una ridotta velocità del vento e l’incremento di temperatura dell’aria. In presenza di occupanti a questi si aggiungerebbero rumore ed inquinamento. A causa del riconosciuto impatto dell’ambiente urbano sulle condizioni esterne, una serie di autori ha studiato gli effetti termici e la distribuzione di pressione causata dal vento all’interno di canyons e al crescere della densità urbana [2,3]. Solo pochi di questi studi, però, mettono in diretta relazione l’influenza dell’ambiente esterno e il potenziale di ventilazione naturale; un numero ancor minore considera il comportamento energetico complessivo dell’edificio e non solo le temperature interne o le variate masse d’aria passanti. Sebbene alcuni studi esistano, essi sono perlopiù di carattere sperimentale ed effettuati in scala reale [4–6]. Un approccio di campo è però sconsigliato nello studio delle prestazioni della ventilazione naturale, in quanto introduce necessariamente una serie di variabili quali la tipologia di occupazione, le caratteristiche termiche dell’edificio e l’ambiente circostante. Esso rende impossibile variare sistematicamente dei parametri, operazione necessaria ad evidenziare il peso delle diverse variabili e trarre delle linee guida applicabili ad altri contesti. È quindi raccomandabile trattare questo argomento mediante software di simulazione energetica. Inoltre, gli studi esplorano raramente l’intera stagione di raffrescamento o una varia gamma di climi [7]. Pertanto, nonostante il rinnovato interesse nei confronti della ventilazione naturale, le sue performance nel contesto cittadino sono ancora incerte. Questa tesi si propone di investigare in modo parametrico l’influenza del contesto urbano sul potenziale di ventilazione naturale notturna. Per raggiungere questo obiettivo, il software EnergyPlus viene utilizzato per simulare il comportamento energetico di un edificio in contesto isolato e del medesimo











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edificio all’interno di un ambiente urbano di crescente densità. Come prima cosa il fabbisogno di raffrescamento è analizzato in assenza di ventilazione per studiare gli effetti degli edifici circostanti sui flussi di scambio termico. In seguito, l’applicazione della ventilazione naturale permette di poterne provare l’efficienza e allo stesso tempo verificare l’impatto dell’ambiente urbano su quest’ultima. Le simulazioni sono state effettuate in cinque città caratterizzate da climi diversi tra loro: Amsterdam, Berlino, Milano, Palermo e Roma. Inoltre, lo studio di due dei sei piani (secondo e quinto) dei quali è composto l’edificio permette una valutazione dell’incidenza di diverse altezze. Ad ultimo, una serie di parametri che descrivono l’intorno urbano sono stati variati in modo parametrico per valutare il loro effetto sui risultati: l’albedo degli edifici, l’incremento di temperatura ad emulare l’effetto isola di calore, e la fonte dei coefficienti di pressione. Si ritiene che un incremento di conoscenza della ventilazione naturale possa essere utile a chi di competenza per stimare già in fase di progettazione il potenziale di raffrescamento dato da questa tecnica, evitando inutili investimenti e dando origine ad edifici migliori dal punto di vista energetico. Un secondo intento di questa tesi, soprattutto dal punto di vista metodologico, è la verifica della possibilità di modellare un ambiente urbano utilizzando i software di simulazione energetica. Potenziali e limiti di tali strumenti sono argomento di dibattito in questo studio. Il Capitolo 1 costituisce una panoramica del contesto generale e fornisce una rassegna della letteratura pertinente. Nel Capitolo 2 vengono illustrati i principi teorici della ventilazione naturale, mentre il funzionamento dei programmi di simulazione energetica, in particolare EnergyPlus, è presentato nel Capitolo 3. Il Capitolo 4 chiarisce il caso studio e le ipotesi fatte sulla modellazione dell’edificio e del suo intorno. I risultati sono argomento del Capitolo 5, mentre le conclusioni e possibili sbocchi futuri sono riportati nel Capitolo 6. Caso studio L’edificio analizzato nel corso di questa tesi è un edificio per uffici di dimensioni 16 x 24 x 18 m3 (Fig. 1a). Ognuno dei sei piani è composto da dodici moduli per uffici di 3.4 x 6.1 x 2.7 m3 allineati lungo le pareti Nord e Sud dell’edificio e separati da un corridoio centrale. I locali di servizio sono dislocati nell’area centrale. L’edificio è occupato per un totale di 11 h al giorno, dalle 7 alle 18, per cinque giorni alla settimana (festivi esclusi). Un sistema di raffrescamento ideale garantisce una temperatura interna di progetto di 26°C in presenza degli occupanti, mentre la ventilazione naturale mediante finestre vas-istas è attiva unicamente durante le ore notturne (dalle 20 alle 7).


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Figura 1: Rappresentazione tridimensionale dell’edificio (a), pianta (b) e sezione (c) con ipotesi di ventilazione

La pressione del vento sull’involucro è descritta attraverso i coefficienti di pressione ricavati da due fonti differenti: dati sperimentali per edificio non isolato dalla galleria del vento della Tokyo Polytechnic University e dati calcolati mediante lo strumento Cp Generator sviluppato dal TNO. Un’analisi comparativa dei coefficienti di pressione ottenuti mediante le due fonti è attuata al fine di valutare la capacità di approssimazione ai dati sperimentali da parte di uno strumento di calcolo. Gli edifici limitrofi sono modellati in EnergyPlus come superfici ombreggianti; l’edificio oggetto di studio è posto all’interno di un contesto urbano ideale di densità crescente e composto da edifici analoghi a quello studiato. Il parametro utilizzato per definire la densità urbana è la Urban Density, o il rapporto tra area dell’edificio in pianta e area del lotto sul quale l’edificio è costruito. Tabella 1: Modellazione dell’intorno vicino a crescente densità urbana (UD); le distanze tra gli edifici sono 51.89 e 34.60 m in direzione Ovest/Est e Nord/Sud rispettivamente a UD=0.1, 19.82 e 13.21 m a UD=0.3 e 6.98 e 4.66 m a UD=0.6. UD=0.1

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STREET SECTION

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STREET SECTION

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Risultati In un primo momento l’analisi dell’impatto dell’ambiente urbano sulla domanda di energia per il raffrescamento è effettuata in assenza di ventilazione naturale. Per quanto riguarda gli elementi opachi, si nota una diminuzione di guadagni solari causata dalla schermatura da parte degli edifici limitrofi, ma allo stesso tempo una riduzione delle perdite per convezione e radiazione termica. I risultanti scambi conduttivi sono generalmente poco influenzati.

Figura 2: Comportamento termico di una superficie opaca rivolta a Sud in un giorno soleggiato d’estate ad Amsterdam al 2nd piano (alto) e al 5th piano (in basso). Le linee continue si riferiscono al caso isolato, le linee tratteggiate si riferiscono a Urban Density = 0.6

Un impatto preponderante degli edifici limitrofi si nota invece sui guadagni solari attraverso i componenti trasparenti, che appaiono ridotti drasticamente soprattutto al secondo piano.

Figura 3: Guadagni solari stagionali attraverso le finestre per una superficie rivolta a Sud ad Amsterdam al variare della UD (secondo piano a sinistra, quinto piano a destra)


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Generalmente le località in climi più freddi, laddove i guadagni solari hanno relativamente un peso maggiore rispetto alla trasmissione termica dell’involucro, dimostrano una maggior sensitività della domanda di raffrescamento all’effetto del contesto urbano.

Figura 4: Domanda di energia per il raffrescamento dello stesso edificio in località differenti e all’interno di contesti urbani di densità crescente (secondo piano in alto, quinto piano in basso)

Le portate in ambiente urbano subiscono una forte diminuzione.

Figura 5: Ricambi d’aria notturni (ACH) per diverse località e piani al crescere della UD (secondo piano in alto, quinto piano in basso)










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In generale, il contesto urbano penalizza il potenziale di raffrescamento della ventilazione naturale notturna. È utile però indicare che la sua influenza varia molto a seconda della località e del piano; in particolare, la riduzione di potenziale dipende dal potenziale iniziale di una certa località. Laddove la ventilazione naturale in contesto isolato contribuiva in modo significativo alla riduzione della domanda di energia per raffrescamento, il contesto urbano ha un impatto significativo ma il potenziale rimane a livelli accettabili. Al contrario, in località dove il contributo iniziale della ventilazione era relativamente scadente, un contesto urbano denso può provocare una riduzione fino al 60% del potenziale di risparmio energetico. Il comportamento di Milano sta ad indicare una limitata sensitività al contesto urbano in presenza di valori di ricambi ora molto bassi. Per considerazioni più approfondite si veda il Capitolo 5, dove sono contenute anche le analisi di sensitività dei risultati all’albedo degli edifici, a un aumento di temperatura dell’aria e ad una diversa fonte di coefficienti di pressione.

Figura 6: Risparmio energetico dato dalla ventilazione naturale notturna per diverse località e piani all’aumentare della UD (secondo piano in alto, quinto piano in basso)


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Conclusioni La tesi verifica la possibilità di modellare in modo soddisfacente il contesto urbano utilizzando software di simulazione energetica originariamente destinati ad edifici in contesto isolato. L’utilizzo di input manuali per i coefficienti di pressione e i profili di vento è determinante alla riuscita di tale approssimazione. L’obiettivo principale di questa tesi è la quantificazione dell’impatto del contesto urbano sul potenziale di raffrescamento. I risultati mostrano come tale impatto sia fortemente dipendente dal potenziale iniziale e sia generalmente più visibile a densità urbana da 0.3 a 0.6. Due aspetti fondamentali concorrono a queste conclusioni: la variazione della domanda di raffrescamento e la diminuzione delle portate d’aria in contesto urbano. Il primo è fondamentalmente causato dall’ombreggiamento dagli edifici circostanti, mentre il secondo è determinato dall’elevata rugosità del terreno e dalla presenza di ostacoli al passaggio indisturbato del vento. L’analisi combinata del comportamento di domanda e potenziale è essenziale alla corretta interpretazione delle performance di ventilazione naturale in ambiente urbano. La possibilità di risparmio data dalla ventilazione in ambiente urbano è generalmente più compromessa al quinto piano rispetto al secondo. Questo avviene poiché gli edifici limitrofi causano una maggior riduzione della domanda al secondo piano, il quale risulta ombreggiato per più tempo. Un aumento dell’albedo e della temperatura dall’aria causano una minor sensitività della domanda al contesto urbano, e nel secondo caso anche una ridotta efficacia della ventilazione naturale causata dalla minor differenza di temperatura tra portate d’aria entrante e aria interna all’edificio. Conseguenza di entrambi questi parametri è dunque un maggior impatto del contesto urbano sul potenziale di raffrescamento della ventilazione naturale notturna. Dal punto di vista della modellazione, l’effetto dei coefficienti di pressione sui risultati è fondamentale. Tra le due fonti di Cp analizzate (dati sperimentali dalla galleria del vento del TPU e Cp ottenuti mediante Cp Generator) si nota una dispersione di valori per densità urbane elevate. In particolare, i coefficienti calcolati con Cp Generator provocano una globale sottostima degli effetti del contesto urbano sulla ventilazione naturale. In conclusione, la tesi conferma la necessità di considerare il reale posizionamento dell’edificio in fase di valutazione dell’efficacia della ventilazione naturale. Specialmente laddove il potenziale iniziale è scarso, un ambiente urbano ad alta densità può avere effetti determinanti sulla performance della ventilazione naturale. L’analisi di un edificio in caso isolato porterebbe ad una considerevole sovrastima del potenziale di risparmio energetico dato dalla ventilazione naturale notturna.










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1

Introduction

The building sector accounts for a great share of the total worldwide energy consumption. This share has been increasing during the last years and space cooling represents a rising item, especially due to the growing demand of air conditioning for the tertiary sector [1]. However, the environmental debate has lately gained much attention and regulations on emissions and energy consumption have enhanced the so called sustainable building practice. Sustainable building aims at buildings that consume as little superfluous energy as possible through an integrated approach that takes into account the outdoor environment. In this context, a general renewed interest emerged for a cooling technique used for centuries by both humans and animals: natural ventilation. Offices are particularly suitable for night natural ventilation, as it is active during no occupancy hours. On the other hand, the typical setting of offices is the urban environment, which presents several drawbacks to the application of passive cooling. Typical disadvantages are lower wind speeds, higher external air temperatures, noise and air pollution; the last two aspects are less or nonrelevant in absence of occupants. Due to the recognized impact of the urban environment on boundary conditions, a number of authors investigated thermal effects, wind flow and wind pressure distributions within street canyons, at varying urban density or with different neighbouring configurations [2,3]. Only a few of these studies related the effects of the urban environment to the potential of natural ventilation and even fewer made the step to consider the building’s energy requirements rather than indoor temperatures or airflow rates alone. Although some authors did investigate this topic, the majority of their work is based on experiments and empirical data on real scale buildings [4–6]. A field approach necessarily adds variables (such as building structure, environment and occupation typology) to the analysis of natural ventilation’s efficiency, which make it hard to obtain guidelines to use in different circumstances. Experimental studies are set aside when a systematic variation of parameters appears essential to achieve effective understanding of natural ventilation potential in the urban environment. Furthermore, the existing studies rarely concentrate on the whole cooling season and on different climates; generally only one location and a few representative days are under investigation [7]. Despite an increasing attention on natural ventilation as a passive cooling technique, the actual influence of the urban environment on its performance is still unclear. In fact, given the experimental nature of most of the studies in this field it seems very hard to discern the variables concurring to a building’s energy performance and to get practical guidelines. Among the main barriers to the practical diffusion of natural ventilation are: the few sources of urban case










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studies and guidelines, the lack of know-how and expertise and the uncertainty to get adequate airflow rates within a certain environment. Due to the great number of parameters involved, it is recommended to simplify real cases and to deal with this topic in a parametric way using a Building Energy Simulation (BES) software. The aim of the present study is to assess the influence of the urban environment on the cooling potential of night ventilation in a low-rise office building. A parametric analysis is carried out with the energy simulation software EnergyPlus. The present study performs a comparative analysis of the seasonal cooling energy requirement of an isolated building and of the same building within urban configurations of increased density. The parameter Urban Density (UD, defined as the ratio of the area occupied by a building on the lot area) is used to describe the urban environment. At first, the effect of the surrounding buildings on thermal exchanges is considered; then natural ventilation is applied during the night to investigate the influence of urban environment on airflow rates. Five different locations (Amsterdam, Berlin, Milan, Palermo and Rome) are considered to study the impact of outdoor conditions on both rural and urban night natural ventilation; the analysis of two different floors (2nd and 5th) allows to make important considerations on the effect of height. At last, the influence of relevant parameters on results is investigated. The analysed factors are: the source of pressure coefficients, the environmental albedo and the outdoor air temperature increase, used to simulate in a simplified way the possible effect of Urban Heat Island (UHI). A broader knowledge of the phenomenon will allow designers to assess the potential of natural ventilation for a specific building and location at the beginning phase of the design process. An increased understanding of natural ventilation will lead to energy-saving designs with lower capital and time investments. A second intent of this thesis, more on the methodological side, is to verify the possibility of modelling the urban environment using a dynamic building energy simulation software. Potential and limits of the available tools are inquired in this thesis. Following an introduction regarding the general context and relevant literature surveys (Chapter 1), the fundamental theoretical basis of natural ventilation and its modelling are outlined in Chapter 2. The energy simulation software is presented in Chapter 3, while Chapter 4 goes into detail on the case study (base building, urban environment and climatic assumptions) investigated throughout the research. Results are shown and analysed in Chapter 5, while conclusions and future prospects are the argument of Chapter 6.


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1.1 General Framework Urban population is rapidly growing worldwide, leading to important social, energy and environmental consequences. Over 75% of the inhabitants of wealthy countries live today in cities, and it is estimated that, by the end of the century, 50% of the total human population will be urban [8]. Cities in the developed world are affected by over-consumption and the ecological footprint per capita of wealthy countries is ten times larger than the one of the less developed world [9]. An increase of 1% in the urban population leads to a growth of energy consumption by 2.2% [10], which means that the urban environment affects energy consumption twice as much as the rate of urbanization. An exponential growth of worldwide energy consumption is hence the likely consequence. In the United States commercial buildings account for just under one-fifth of U.S. primary energy consumption. Heating, lighting and space cooling are the most energy-consuming end uses. Commercial buildings consumed in 2009 46.0% of building sector primary energy, which represented 18.9% of U.S. total energy consumption. The residential sector accounted for 22.3% [11]. Although air conditioning penetration in Europe is still smaller than in the U.S., the market is far from saturation and it is expected to expand steadily [12] as shown in Fig. 1.1.

Figure 1.1: The total cooled area in Europe from 1990 to 2020

The growth is not only related to climatic conditions (Italy and Spain accounted in 2003 for more than half of the entire EU market) but also to the development of the tertiary sector, especially offices [1]. Economic growth is resulting in an










 19


increase of Air Conditioning (AC) levels in regions where the tertiary sector is significant, independently on the climate (e.g. Belgium and Germany). The evolution of the demand of various economic sectors is very diverse; only trade and offices are expected to grow in relative terms and to account alone for 70% of total cooled floor area by 2020 [1]. Over the last twenty years European electricity consumption in non-residential buildings has increased by ca. 74% (Eurostat), mainly due to the diffusion of IT and air conditioning. The average specific final energy consumption in the nonresidential sector is estimated to be 280 kWh/m2 (all end-uses), at least 40% larger than the residential sector [13]. Italy, with its 10% of households with air conditioning, was in 1996 the European country with the highest penetration [9,14]. According to Odyssee [15], diffusion of air conditioning in the service sector is much more important than in the residential sector; estimates show that it increased from 30% to 70% between 2000 and 2008 (eERG [14] estimation from Odyssee and Energy Efficiency and Certification of Central Air Conditioners (EECCAC) [1] data). In Spain data illustrate that in the service sector air conditioning represents 31% of total energy consumption and it is much more significant than in the residential sector [16]. Also in colder climates such as Germany air conditioning is supposed to be progressively increasing and to be more significant in the nonresidential sector. The previous trends have been in the last years the basis of arguments of several directives on energy efficiency in buildings. The European Energy Efficiency Directive 2012 stresses the importance of taking into account climatic and local conditions to improve the energy performance of buildings. In Italy the DPR 59 (2009) [17] sets maximum values for summer cooling requirements according to the climatic zone of a given location. Furthermore, natural ventilation is suggested as an important strategy to limit the energy demand of buildings, as well as solar protection. UK building regulations [1] impose to avoid solar overheating by limiting glazing area, providing adequate shading or designing buildings to include night cooling techniques. The above mentioned statistics and trends through the U.S. and Europe show a strong necessity for energy-efficient cooling techniques for non-residential buildings in the urban environment. This need is as also mentioned, demanded and encouraged by current regulations. Natural ventilation can be an effective solution in improving Indoor Air Quality (IAQ), comfort and in reducing the energy consumption from mechanical air conditioning. As stated before, however, the urban context has some negative effects that can largely undermine its effectiveness. In order to design more efficient buildings,


20
 








it is essential to understand the role of built environment in natural ventilation potential for cooling.

1.2

Literature Survey on Natural Ventilation in Buildings

The benefits of natural ventilation have been experimentally demonstrated by a number of authors. Nevertheless, passive cooling design represents, in some cases, an additional difficulty for architects and designers. In the present section, a brief literature review on the potential of natural ventilation for cooling is presented, along with the main drawbacks and barriers to its implementation. 1.2.1 The cooling Potential of Night Natural Ventilation The performance of night ventilation depends on a series of factors related to the local climate, the building characteristics and the ventilation strategy. Among others, diurnal temperature swing, inside-outside air temperature difference, thermal inertia of the building and natural ventilation control strategy play a major role in the success of passive cooling [5,18]. The potential of natural ventilation (NVP) in reducing energy demand of buildings is widely recognized and has been investigated by several authors. Several experiments, theoretical studies and real applications have shown that night ventilation applied to air-conditioned buildings allows a saving potential of over 50% of the cooling load of massive buildings for a set point temperature of 26°C. The peak electricity demand can also be reduced by up to 40% [19]. Santamouris et al. [4] monitored a sample of 214 air-conditioned residential buildings using natural ventilation techniques in Greece. Geros et al. [5] carried out experimental measurements in three different buildings in Athens during the summers of 1995 and 1996. The buildings were then simulated using TRNSYS to evaluate the efficiency of night ventilation. The maximum reduction of cooling load due to ventilation in a heavy-weight structure building was 56% (set point temperature 25°C, 30 ACH). The impact of natural ventilation was very different for a building with light weight structure in the same conditions; the contribution in diminishing the cooling need was in this case 36%. Furthermore, the authors found empirical evidence that, when applied to massive buildings, night ventilation causes a time delay of the indoor peak temperature. The maximum temperature is also decreased by up to 3°C. On the other hand, Kolokotroni, Webb and Hayes [6] pursued research on cellular offices in London, Manchester and Plymouth (UK), and stated that, although night natural ventilation works better in heavy weight buildings, it is a worthwhile strategy to follow even in lightweight buildings. Gratia, Bruyère and De Herde [7] examined in a parametric way a middle-size office building in Uccle, Belgium using the energy simulator TAS. Results










 21


proved that night ventilation generally works better than daytime ventilation, causing a greater economy and less discomfort for occupants. When singlesided night ventilation was applied, cooling demand decreased of about 41%. The importance of size, shape and location of windows is also stressed in the paper. 1.2.2 The Barriers to the Application of Natural Ventilation A number of projects formerly investigated the barriers to natural ventilation; among them are the AIOLOS project [20], the NatVent project [21] and the HybVent project [22]. Tab. 1.1 shows the main barriers to design with natural ventilation identified by the three projects. Table 1.1 Overview of some main barriers in designing with natural ventilation (HybVent [22]) Type of barrier

Economy

Knowledge

Regulations, guidelines and tools

Architecture

Unwillingness

Barrier Designers’ fee depends on investment cost of ventilation components Fear of increased costs for designers Designers’ fear of not succeeding Fear of increased space demand Fear of increased overall investment costs Uncertainty due to lack of information, knowledge and experience about hybrid ventilation and lack of examples of documented and successful hybrid ventilated buildings Reduced number of design options for ventilation system and increased investment costs due to fire compartmentation and noise regulation Uncertainty among designers due to lack of suitable design tools Uncertainty due to lack of suitable standards and regulations Fear of the impact of chimneys, towers, building envelope, etc. on the architecture and overall design Smart control devices which may overcome other barriers are not being implemented due to unwillingness among building owners and promoters

Source AIOLOS HybVent HybVent HybVent HybVent NatVent/HybVent

AIOLOS/HybVent

AIOLOS/NatVent AIOLOS AIOLOS AIOLOS

The European project NatVent [21] is based on interviews to architects, consultant engineers, contractors, developers, owners and governmental decision makers from Belgium, Denmark, Great Britain, Norway, The Netherlands, Sweden and Switzerland. Results show that there is an overall lower knowledge and experience in special designed natural ventilation for office buildings compared to mechanical ventilation applications. Naturally ventilated offices are thought to be harder to design as they provide less design freedom, higher dependence on external factors and need of specialized professionals.


22
 








Interviewees generally expected a better cooling performance and a higher controllability of mechanical ventilation. Experts lamented a lack of source and information in standards, guidelines and case studies. Furthermore, there is a strong requirement for simple design tools, calculation rules and easy-to-use computer programs. The same deficiency was perceived by AIOLOS as one of the main barriers during building design. The HybVent project is a survey focused on hybrid ventilation and carried out on educational and office buildings in twelve different European countries. After an accurate analysis of several issues involved in hybrid ventilation design, the project confirms a lack of design tools and guidelines. The authors state that only few designers have sufficient competence to deal with this type of ventilation, leading to a risk of higher investment costs than in mechanical systems. Besides knowledge barriers, the complexity of the urban environment poses a great challenge itself to simulation. In fact, while dynamic simulation models or simplified calculations can accurately predict the thermal behaviour of individual buildings, tools are not optimized for urban areas. The magnitude of urban effects is generally underestimated and typical outcomes such as mutual shading of buildings are often neglected. The present study provides an answer to the growing need of parametric information and order-of-magnitude guidelines on naturally ventilated buildings, both stand-alone and within the urban environment. It contributes to overcome some of knowledge and tools barriers to the application of natural ventilation. Moreover, manual input options within EnergyPlus are used to model more accurately the multiple effects of the urban environment.

1.3

Literature Survey on Energy Evaluations at the Urban Scale

Surrounding buildings have two main opposite outcomes on a building’s energy need. On one hand, their shading limits solar gains and reduces the necessity of cooling; on the other hand, wind shielding weakens the benefit of natural ventilation and causes an increase of mechanical energy demand for cooling. The present section is a review of relevant literature on both these aspects. 1.3.1 The Effect of External Shading on Cooling Requirement Nikoofard et al. [23] investigated in a parametric way the effect of site shading on the energy requirement of a residential two-storey detached house in Canada. The impact of two different obstacles (house and tree) situated at a distance of 2.3, 4.7, 9.5, 14.2 m in four distinct climates (Halifax, Toronto, Calgary,










 23


Vancouver) is simulated using the energy simulator ESP-r. Results are compared to a base case house without shading surfaces. Neighbouring houses are simulated in different number (1, 2, 3) and orientations (S-N/E-W or S/N/E, S/N/W, S/E/W and N/E/W). Results show that shading from a single neighbouring house on the South side generates at most a modest increase in heating energy requirement (+2.7% in Calgary). On the other hand, shading by a house on the West side decreases cooling energy requirement the most (-25% in Vancouver). In fact, the solar azimuth arc is longer in the summer than in winter, therefore causing more shade from East and West exposure neighbouring structures. The shading caused by two obstructions to the East and West sides decreases the cooling energy requirement more than the shading caused by two obstructions on any other orientation. When three obstructions are simulated (S/E/W), the maximum reduction in cooling energy demand is 40% in Vancouver. The closer the obstruction, the greater the impact (differences up to 35% between UD 0.13 and UD 0.61). Finally, an extreme case of shading with UD 0.61 and obstacles (S/E/W) being twice the size of the house was simulated. In this situation, the cooling energy need was reduced by 90% for the house in Vancouver and heating energy need was increased by 10% for the house in Calgary. The authors P. Bhiwapurkar and D. Moschandreas [24] analyse the influence of street geometry on the annual heating and cooling energy needs of a mid-rise office building in Chicago, U.S.. Urban density is taken into account by changing the Street Aspect Ratio (SAR, calculated as H/W, where H and W are the height and the width of the canyon, respectively), which is set on 1:1, 1:2, 1:4 and 1:8. The calculated energy cooling savings are respectively 17%, 26%, 34% and 37%. One of the main limitations of the study is that both the increase of urban air temperature due to heat island effect and the reduced wind speed within the urban environment are not taken into account by the energy simulation software. Therefore, in order to consider urban microclimate in a parametric way, a peak cooling day and a peak heating day were simulated at Street Aspect Ratio 1:4 by applying five temperature incremental changes of 1,2,3,4 and 5°F, and a decrease in wind speed of 2, 4, 6, 8 and 10 m/s. When these effects are taken into account, an increase in cooling energy need is visible. However, it does not balance the shading effect of buildings, which appears in this study to be dominant. In order to make outcomes obtained by P. Bhiwapurkar and D. Moschandreas comparable to the results that are achieved in this thesis and reported in Chapter 5, it is essential to describe the urban environment consistently. In the present thesis, the urban environment is described by the parameter Urban Density (UD); three increasingly dense configurations are investigated: 0.1, 0.3 and 0.6.


24
 








SAR 1:1 for the building studied in [24] corresponds to UD 0.38 calculated on the same building, while SAR 1:2 is equivalent to UD 0.58. Tereci et al. [25] investigated the impact of the urban form on the energy demand of residential buildings using EnergyPlus. The Scharnhauser Park in Ostfildern, near Stuttgart (Germany) was the chosen setting for simulations. The varying parameters were: site density or Urban Density (varying between UD 0.3 and 0.6), settlement and building typology, building age and national thermal standard. Both heating and cooling energy requirements were considered. The cooling demand for the isolated reference case was found to be relatively low or 11.4 kWh/m2a for a multi-family house and it diminished with maximum increased site density to 6.4 kWh/m2a or 44%. The study showed that higher surface albedo might increase cooling energy demand by up to 10%. The articles by Nikoofard et al. (2011) [23], Bhiwapurkar and Moschandreas (2010) [24] and Tereci et al. (2013) [25] can be of use in comparing results obtained for the first part of the present research, where the shading effect of buildings on energy cooling requirement is assessed. They do not concern the second part. In fact, as the authors focus on the effect of urbanization on cooling and heating energy demand, an investigation on natural ventilation potential goes beyond their scope. 1.3.2 The Effect of Urban Environment on Airflow Rate Moeseke, Gratia, Reiter, & Herde (2005) [2] analyse airflow rate, and particularly pressure coefficients, for open, suburban and urban environment on a mid-rise office building in Uccle, Belgium. The effect of wind incidence is also investigated by setting it at 0°, 45° and 90° respectively. One representative day (July 24th) is examined and results are given in comparison to a reference simulation characterised by normal South wind of 4 m/s (monthly Belgian mean wind speed at 10 m). The authors conclude that wind incidence has a qualitative effect on air movements, while buildings density influences ACH levels, which appear reduced of about 70% in the urban environment with respect to the open environment. The following power-law equation is used to calculate wind profile in the built environment:

 z 1 α vz = vg   δ 

(1.1)

where vz is wind speed at height z [m s-1], vg wind speed outside the boundary layer [m s-1], δ the layer height [m] and α a coefficient describing terrain € roughness [dimensionless].










 25


The urban environment is modelled according to M. Melaragno [26], who proposes a boundary layer height of 275 m in open environment, 365 m in suburban environment and 460 m in urban environment, while values of 1/α
are respectively 0.14, 0.22 and 0.33. Pressure coefficients are calculated using the model by M. Grosso et al. [27]. Only one location and only one day a year is investigated, not allowing an accurate perception of the global season behaviour. Furthermore, the study stresses the fact that due to a lack of available description of urban flow, the procedure used to obtain ‘urban’ pressure coefficients is necessarily an abstraction. The research does not investigate the effects on natural ventilation effectiveness, which are nonetheless suggested in its conclusion as an important and concrete development to be undertaken. The parametric research carried out by Schulze and Eicker (2013) [3] aims to discuss the feasibility of natural ventilation and to determine the associated energy conservation for an office building in Istanbul, Turin and Stuttgart. In this study, although unclear how the urban environment has been modelled, air flow rates are calculated for three terrain typologies: country, urban and city. The values of ACH are given for two particular cases: wind driven only ventilation and mixed-driven ventilation, and they are equal to 11.7, 8.0 and 4.7 respectively for the first case, and 12.1, 8.7, 5.9 respectively for the second case. Although the fluid-dynamic study is directly linked to energy needs, the parameterization according to urban density is not deepened in the study. To the best knowledge of the author there is no accessible analysis which considers both solar shading and wind shielding, and relates the effects of such alterations to the cooling potential of night natural ventilation. This will be attempted in this parametric study which directly relates the effects of urban environment on the potential of natural ventilation for cooling. A variety of climates and configurations during the whole cooling season are investigated. A major asset of the present research is that the parameterization involves both the fluid-dynamical and thermal behaviour of the building. Urban density, in addition to solar shading and wind shielding, determines an alteration of the infrared heat transfer with sky and surrounding surfaces, it modifies the convective heat exchange between building envelope and outdoor and it influences air temperatures due to the Urban Heat Island effect. The present thesis considers these aspects as well, and it evaluates their weight relative to solar shading and wind shielding.


26
 








2

Natural ventilation fundamentals

2.1

Principles

The term natural ventilation refers to the supply and removal of indoor air without mechanical systems. The pressure differences are generated by two natural driving forces: the stack effect (or buoyancy) and the wind. These two effects can act separately or simultaneously, as it happens normally. Ventilation can be achieved intentionally, through large openings such as windows or doors, or unintentionally, through cracks or other leakages in the building’s envelope also called ‘infiltrations’. It can be distinguished according to its two main purposes: the improvement of indoor air quality (IAQ) and the removal of excessive heat. Natural ventilation for cooling has different outcomes depending on when it is used. During the daytime its aims are [20]: • • •

cooling of the indoor air when outdoor air temperatures are lower; cooling of the structure of the building; direct cooling effect over the human body through enhancement of convection and evaporation.

When natural ventilation takes place during the night, the building’s structure is cooled down and its thermal mass works as a storage medium; to a higher building mass corresponds a more effective passive cooling. Heat from the building materials is more rapidly dissipated because of air movement, and the warmer air is exhausted into the low temperature atmospheric heat sink. As a consequence, the following day occupants find a cooler environment. Strong diurnal temperature swings enhance the success of this technique, which is particularly indicated for countries where daytime temperatures are in any case above the comfort zone. Air movements play a major role and greater wind speeds are an advantage. Night cooling affects daytime internal conditions in the following ways [28]: • • • •





reducing peak air temperature; reducing air temperatures throughout the day, and in particular during morning hours; reducing slab temperatures and thus indoor mean radiant temperatures; creating a time delay between the occurrence of external and internal maximum temperatures.






 27


In the present research only night ventilation for cooling is addressed, and in particular when it is used in combination with daytime mechanical cooling. In this case the desired effect is to reduce the night indoor air temperature and consequentially the energy requirement of the mechanical cooling system on the following day. Typical limitations of daytime ventilation such as pollutants, noise and privacy are overcome when using night natural ventilation. 2.1.1 Airflow through Cracks The driving forces of natural ventilation result from pressure differences across all openings in the building envelope. Depending on the openings size the airflow rate can be expressed according to different formulas. For cracks, the air flow rate [m3 s-1] is calculated with the following expression:

Q = kL(ΔP) n

(2.1)

where k is the flow coefficient [m3s-1m-1Pa-n], L is a characteristic dimension of the crack such as length [m] and n is the flow exponent [dimensionless] and it indicates the degree of € turbulence. An n value of 0.5 represents fully turbulent flow and 1.0 represents fully laminar flow. The typical n value for whole buildings is 0.66. 2.1.2 Airflow through large Openings When large openings are considered, the air flow rate is calculated with the common orifice flow equation: (2.2)

Q = Cd A (2ΔP / ρ )

where Cd is the discharge coefficient of the opening [dimensionless], A is the area of the opening [m2], ΔP is the pressure difference across the opening [Pa] € density in the direction of airflow [kg m-3]. and ρ the average fluid



2.1.2.1 The Discharge Coefficient € The discharge coefficient is a dimensionless value that affects the flow rate between indoor and outdoor environment. It is a function of opening height, wind pressure and direction, temperature difference and of the flow Reynolds number. The definition of Cd is a controversial matter. Santamouris and Asimakopoulos [29] reported that, for small openings, a representative value of discharge coefficient is Cd=0.65, while for large openings it is close to unity. A reasonable value for a standard opening is Cd=0.78. On the other hand, Heiselberg and Sandberg (2006) [30] found Cd values ranging between 0.6 and 0.8 for an


28
 








opening area from 0.5 to 0.6 m2 and values between 0.8 and 1.0 for smaller openings. The building energy simulator EnergyPlus uses the following expression ([31], Eq. 39 in Chapter 16) to calculate Cd when the user does not manually set it: (2.3)

Cd = 0.40 + 0.0045 Tzone − Todb

where Tzone is the internal zone air dry-bulb temperature [K] and Todb is the local outdoor air dry-bulb temperature [K]. In this case, only ΔT is taken into account. € 2.1.3 Airflow in and around Buildings A good understanding of airflow is necessary to get most out of it; in other words, natural ventilation should not just ‘occur’, but it should be accurately planned according to needs and location’s characteristics. The designer has an active role in maximising the potential of passive cooling through a precise choice of windows’ size and disposition, interior design, thermal mass of the building and, where possible, building shape and plan. This aspect justifies the importance of taking passive cooling techniques into account at the beginning of a project. Intentional ventilation through large openings can happen in different ways depending on the wind and on the characteristics of a building. Three main operational manners can take place: single-sided ventilation, cross ventilation and stack ventilation. These three basic strategies can also happen simultaneously in a single building.

a) b) c) Figure 2.1: Natural ventilation can take place in three different manners: a) single-sided ventilation, b) cross ventilation, c) stack ventilation

Single-sided ventilation occurs when one or more openings are on the same side of a building and they connect internal and external environment. Single-sided single-opening ventilation is mainly driven by wind forces; the same opening works as air inlet and outlet. It is effective up to a room depth of two times the floor-ceiling height, typically 4-6 m. Despite being the most ordinary ventilation










 29


system, single-sided ventilation proves not to be effective for cooling if not properly planned; type, shape and size of the window(s) are of primary importance. If more openings are on different heights, the buoyancy effect concurs in increasing the ventilation rate. Architectonic elements such as wing walls can also be used to enhance airflow. Wind driven cross ventilation takes place when openings are on different sides of the building. Air flows from the windward side to the leeward side, and inlet and outlet are separated. Ventilation air can be directed through different spaces, bearing in mind that it will tend to warm during the passage, lowering its cooling effect. Cross ventilation is considered to be efficient up to a depth of five times the floor-ceiling height, typically 15 m [32]. As for single-sided ventilation, design plays a key role in the successful implementation of this technique. In particular, precise knowledge of the nearby wind profile is required in order not to overestimate airflow rates. Cross-ventilation is regarded as the most effective ventilation strategy as it normally involves higher Air Changes per Hour (ACH). Buoyancy-driven stack ventilation is based on the fact that warm air within a building tends to raise and flow out of upper-level exhausts, while cooler outdoor air flows in through lower inlets and replaces it. It is therefore dependent on the internal layout of a building; in fact, it can take place only if the building is designed with a sufficient height, as elevation is needed to promote this phenomenon. A desired height difference between inlet and outlet can be achieved in a number of ways, for example through a chimney or an atrium.

2.2 The driving Forces of Natural Ventilation 2.2.1 Wind driven Natural Ventilation Wind is the most significant agent of natural ventilation. Positive pressure is created on the windward side while negative pressure and suction regions take place on the leeward side. Due to this ΔP air flows within the building, namely from the windward side (inlet) to the leeward side (outlet).

Figure 2.2: The action of the wind on building plan and prospectus: wind creates positive pressure on the windward side and negative pressure on the leeward side


30
 








The pressure due to the wind flow on a certain surface is expressed as [33,34] C p ρv 2 Pw = 2

(2.4)

where Pw is the wind induced pressure [Pa], Cp is the pressure coefficient [dimensionless], ρ is the air density [kg m-3] and v is the wind speed at a reference height [m s-1€ ], usually taken as 10 m (the standard height of the weather station mast) or as the building height. 2.2.1.1 The Pressure Coefficient Eq. 2.4 can be seen also as a definition of the pressure Coefficient Cp. Cp depends on a number of variables that may be grouped into three categories, namely wind properties, environmental characteristics and building geometry. The main parameters affecting the distribution of Cp are shown in Tab. 2.2 [20]. Table 2.1: A number of parameters affects pressure coefficients Wind Wind velocity profile Wind incident angle

Environment Plan area density (PAD) Relative building height (RbH)

Building geometry Frontal aspect ratio (FAR) Side aspect ratio (SAR) Element positioning coordinates Roof slope tilt angle (N)

where [29]: • • • •

PAD or UD: ratio of built area to total lot area; RbH: ratio of the building height to the height of the neighbouring buildings; FAR: ratio of the length to the height of a considered building façade; SAR: ratio of the length to the height of the building façade adjacent to the considered façade.

The issue of pressure coefficients calculation will be discussed in detail in Section 2.3.2. 2.2.1.2 Wind Profiles in the Urban Environment In the urban environment boundary conditions are significantly changed and a new internal boundary layer develops downwind from the leading-edge of a town. This local phenomenon depends on the urban surface characteristic. The micro-climate which develops in the free areas between buildings (urban canyons) is primarily affected by topography, building geometry and dimension,










 31


traffic, streets width and other local features. The area affected by these changed conditions is the so called Urban Canopy Layer (UCL) [35,36], or the area up to the buildings’ height (see Fig. 2.3).

Figure 2.3: The main components of the urban atmosphere

In the Urban Canopy Layer the levels of temperature, noise and pollution are modified, and the wind flow is significantly different. The presence of buildings causes the terrain’s roughness to grow. Wind loses its momentum in order to overcome the frictional effect of the roughness and the loss of kinetic energy is converted into turbulent kinetic energy [37]; the Re number increases near the ground causing turbulence. Typically, wind flow in cities has lower speeds than the undisturbed one, its boundary layer develops at greater heights, it can have different prevalent direction and its turbulence is substantially modified (see Fig. 2.4).

Figure 2.4: Wind profile development for different terrain roughness

Meteorological wind speed data, normally measured at 10 m above ground in some peripheral weather station, need to be adjusted for a specific height and terrain roughness. There are three main methods to perform this calculation: •


32
 


Power-law type equations [26,34] such as:







α

v  z  =  v ref  zref 

(2.5)

where v is the mean wind speed [m s-1] at height z above ground, vref is the mean wind speed [m s-1] at some arbitrary reference height zref and α is the exponent of€ the power-law [dimensionless], based on the roughness of the considered terrain. Typical values for α are given in Tab. 2.2. •

Logarithmic type equations such as [38]:

 z−d  ln v v z0 = ∗  z − v ref v∗,ref  r dref  ln z  0,ref

     

(2.6)

where



v∗ v∗,ref

0.1

 z  = 0   z0,ref 

(2.7)

and vref is the mean wind speed at meteorological station [m s-1], v∗ represents the atmospheric friction speed [m s-1], z0 is the terrain € roughness [m] and d is the terrain displacement length [m]. Typical values for z0 and d are given in Tab. 2.2.



Deaves and Harris (D&H) model expressed by the equation [39]: v=

 z  z 2  z 3  z 4  v∗  z ln + 5.75  −1.88  −1.33  + 0.25    h h  h h  κ  z0

(2.8)

where v is the mean wind speed [m s-1] at height z above ground, κ= 0.40 is von Karman’s constant, v∗ is the atmospheric friction speed [m s-1], z0 € is the terrain roughness [m] and h is the equilibrium boundary layer height [m].










 33


Table 2.2: Typical values for terrain dependent parameters (h=building height) [40] Terrain Open flat country Country with scattered wind breaks Rural Urban City

α 0.17 0.20 0.25 0.33

z0 0.03

d 0.0

0.1

0.0

0.5 1.0 >2.0

0.7h 0.8h 0.8h

The power-law type equation is based on empirical assumptions and does not have any theoretical justification [33,34]; its profile does not meet the lower Atmospheric Boundary Layer (ABL) conditions and it has no upper boundary [39]. It remains a good model at moderately large heights (30 m < z < 300 m), but it is very poor when close to the ground. It is largely used due to its straightforward application. On the other hand, the log-law velocity profile well approximates the lower boundary condition but still has no upper boundary [39]. It is a poor model at large heights, typically z > 200 m, and it is therefore considered difficult to use when diverse terrain roughness are applied. The D&H model meets both the top and bottom boundary conditions of the ABL, and it is therefore the only model which ‘recognises’ the top of the ABL. This advantage allows precise translation between terrains of different roughness. The weakness of D&H model is to be defined by three parameters, v∗, z0 and h, which is a function of wind speed and latitude. The model does not provide a single curve, but a family of curves which depend on wind velocity. The Davenport diagram [41] is a useful tool (Fig. 2.5) to convert power-law terrain parameters (wind profile exponent α) to log-law terrain parameters (aerodynamic terrain roughness z0).

Figure 2.5: The Davenport diagram


34
 








It is widely recognized that wind profile varies with height according to the drag on the wind blowing over upstream areas. Among other factors, terrain roughness influences the drag coefficient. In order to assess different roughness effects for different terrain types, various terrain categories are specified in different wind load codes. Although Davenport [40] (Tab. 2.2) provides a reference to the correct identification of upstream wind profile category according to terrain description, there is a number of contradictory classifications related to this topic. Choi (2009) [42] attempts a comparison between Power Law and Log Law wind profiles given by various wind codes (i.e. AIJ, AS/NZ, BS6399, EN, ISO, ASCE, GB, NBCC) and proposes a set of unified terrain roughness categories (Tab. 2.3). Table 2.3: Terrain categories proposed by Choi (2009) [42] Category

Cat. I

Exposure (description) Open water (open sea or lake and coastal areas with few obstructions)

Roughness Length z 0 [m] 0.002



Power exponent

Current code specifications (z0 [m])

α 0.103



AIJ Cat I – open sea (0.0014) AS/NZ Cat 1 – open terrain (0.002) BS6399 – sea (0.003) EN Cat 0 – open sea (0.003) ISO Cat 1 – open sea (0.003) ASCE Exp D – flat area & water (0.0039) GB Cat A – sea, island, desert (0.0076) AIJ Cat II – open, few obst. (0.04) AS/NZ Cat 2 – open, few small obst. (0.02) BS6399 – country (0.03) EN Cat I – lake & area without obst. (0.01) EN Cat II – area with few obst. (0.05) ISO Cat 2 – open country (0.03) ASCE Exp C – open, few med. Obst. (0.048) GB Cat B – village, countryside (0.061) NBCC Exp A – open terrain (0.025) AIJ Cat III – suburban (0.21) AS/NZ Cat 3 – many medium obst. (0.2) BS6399 – town (0.3) EN Cat III – suburban, forest (0.3) ISO Cat 3 – suburban (0.3)

Cat. II

Open country (terrain with scattered obstructions up to 10 m high. Rural areas with a few low rise building)

0.04

0.15

Cat. III

Forest/Sub-urban scattered low (3-5m) buildings (Numerous closely space 3-5m obstructions) Urban, large town (many medium height (10-50m) buildings) City (medium height buildings mixed with tall (50m+) buildings) City centre (concentration of very tall buildings mixed with other buildings)

0.2

0.198

0.5

0.241

ASCE Exp B – urban (0.58) GB Cat C – city (0.34) NBCC Exp B – suburban & urban (0.58)

1.0

0.289

AIJ Cat IV – city medium height bldg. (0.78) EN Cat IV – are 15% bldg>15m (1.0) GB Cat D – city all bldg. (1.13)

>=2

0.362

AIJ Cat V – city tall bldg. (1.82) AS/NZ – city (2.0) ISO Cat 4 – urban (3.0) NBCC Exp C – city centre (1.97)

Cat. IV

Cat. V

Cat. VI

Tab. 2.3 implicitly illustrates the difficulty of identifying the correct terrain category and consequently the accurate values of α and z0. While in reality a










 35


general description of terrain type might be adequate to categorize a specific case roughness, in parametric simulations this aspect poses a challenge. Furthermore, some reference is only given for the upstream wind, whereas there is little indication on the values of such parameters in canyons. An other important aspect to take into account in Building Energy Simulation is the coherence between the simulation wind profile and the wind profile used for the pressure coefficients calculation of input Cps (Chapter 4). 2.2.2 Buoyancy driven natural ventilation Among the driving forces of natural ventilation are buoyancy forces. The difference in temperature, hence density, between two adjacent zones or between a zone and the outside environment provokes warm air to rise and cool air to flow in by infiltration from the bottom of the opening. Greater temperature differences cause greater airflow rates. Considering two openings at different height h, the height at which transition between inflow and outflow takes place is the neutral plane, where there is no difference between indoor and outdoor pressure (Fig. 2.6). The pressure induced by stack effect can be calculated in the following steps. The static pressure Ps at a reference height z is given by: (2.9)

Ps = P0 − ρgz

where P0 is the pressure at the bottom of the zone [Pa], g is the gravitational acceleration [m s-2] and ρ is the density of air [kg m-3] at the indoor temperature € T.

Figure 2.6: The influence of temperature difference between indoor and outdoor


36
 








Assuming as valid the approximation of air as an ideal gas, ρ is given by:

ρ=

ρ 0T0 T

(2.10)

where T is the absolute temperature, ρ0 [kg m-3] and T0 [K] are the reference values for air density and temperature (T0=273.15 K, ρ0 =1.29 kg m-3). € In general the stack or buoyancy pressure for all openings is calculated relatively to that of the lowest opening. The pressure difference between two external openings at h1 and h2 may be calculated by [43]: (2.11)

ΔP = − ρg[( z1 − h1 )(1− T0 /T1 ) + ( h2 − z1 )(1− T0 /T2 )]

where z1 is the height of the first floor [m], and T2 and T1 [K] are the mean air temperatures of zone 1 and zone 2. € 2.2.3 Combined effect of wind and buoyancy In order to obtain the total pressure difference across the opening, equation (2.4) and (2.9) are combined:

ρC p v12 ρC p v 22 ΔP = P1,0 − P2,0 + − + ( ρ1 − ρ 2 ) gz 2 2

(2.12)

where v1 and v2 are the wind speeds at the two different sides of the opening [m s-1] at a given height z. €

2.3

Modelling Natural Ventilation in Buildings

2.3.1 Multizone Air Flow Network Modelling Modelling of fluid flow is a complex task which is nevertheless necessary to simulate the behaviour of a building and to reasonably approximate its cooling/heating requirements. Three [44] increasingly complex methods can be used to solve the problem of airflow in buildings: 1 Simplified expressions. These models can give an approximate idea of airflow through cracks and openings in the building envelope. They typically offer correlations between airflow and wind speed and temperature difference, such as the following expression [45] for a construction of average permeability: (2.13)

Q = 0.15 + 0.0087ΔT + 0.02v

€ 









 37


where Q is the volume changes per hour, ΔT [°C] is the temperature difference between indoor and outdoor and v is the wind speed [m s-1] at the opening height. Such expressions are usually used at a very early stage of design and are not integrated in building simulation programs. 2 Zonal models. This method can predict both airflow between outdoor and indoor and within internal spaces. Every zone of a building and its surroundings is supposed as fully mixed and represented by a pressure node; nodes are then connected in series or in parallel. The mass transfer between zones is taken into account considering the inter-zone pressure difference across connectors such as ducts, doors, cracks, which work as resistances of an electrical grid. The advantage of these models (also called network or nodal models) is the ability to calculate airflow between two zones. There is a number of easy-to-use and fast computational tools based on the concept of the zonal method (e.g. COMIS, AIRNET, BREEZE, PASSPORT-AIR [46]) that can be easily integrated in an energy simulation program. 3 Computational fluid dynamic (CFD) modelling. Based on Navier-Stokes equations of energy, mass and momentum conservation, it provides detailed solutions for airflow, temperature distribution and contaminant concentrations. The interior space is divided in a number of small finite elements on a two or three-dimensional grid for which balance equations are discretised and solved. Although it provides a high degree of accuracy, CFD is a time consuming method that requires significant computational power and expertise. 2.3.2 Cp Sources As briefly stated in Section 2.2.1, pressure coefficients represent an argument of controversy in the deep knowledge of the natural ventilation phenomenon; their calculation is regarded as one of the main sources of uncertainties of Building Energy Simulation [47]. Cp values may be estimated in four different ways: •




38
 


Real scale measurements. They represent the most accurate values and give the exact notion of what is happening on a specific building in a specific environment. Of course they have very important time and cost limitations. Wind tunnel tests. Wind tunnels are physical chambers in scale dimension used to simulate wind flows. The first section of the tunnel







normally gives the required wind profile through obstacles and turbulence generators, while a scale model of the given building and its immediate surroundings is in the second section of the tunnel. Although there are not many tunnels and the process is time consuming, they have more flexibility than real scale measurements and provide today a good basis reference. •



Computational Fluid Dynamics. CFD offers all the benefits seen for wind tunnels but its accuracy still has some limits, especially when complex turbulent flows are simulated or in presence of complex building’s shape and surroundings. Furthermore, CFD implies a significant computational burden. Yet these models are constantly evolving and they represent today a good reference for simple building geometries. Parametrical models. These models are based on tunnel experiments and they provide the opportunity to model a building and its surroundings with great flexibility and a small time/cost effort. The degree of confidence is still lower than wind tunnels [47]. Nevertheless, parametric models and easy-to-use tools are of strategic importance for the future implementation of natural ventilation, as they offer an immediate, time and cost-efficient way to model complex airflow problems. Among the most renowned tools based on parametrical models are the TNO Cp Generator (Section 2.3.2.2) and Grosso model [27]. They typically allow the description of a real situation by taking into account climatic (wind profile and wind incidence), environmental (PAD and RbH) and geometrical parameters (frontal aspect ratio and side aspect ratio).

The building and the urban configurations analysed in this study (see Chapter 4) are analogous to those investigated with wind tunnel experiments at Tokyo Polytechnic University [48]. Therefore, accurate pressure coefficients for the present case study are available online1. However, in order to grant the flexibility typical of a parametric research, Cp values are also obtained through a web-based parametric tool: the TNO Cp Generator. As a preliminary step of this research, Cp values obtained by means of Cp Generator are compared with the experimental data (see Section 4.3.2.2).

























































 1

http://61.121.247.28/info_center/windpressure/grouplowrise/mainpage.html












 39


2.3.2.1 TPU aerodynamic Database for non-isolated low-rise Buildings The wind tunnel experiments were performed on reduced-scale urban configurations (scale 1:100) with different area densities (CA or Urban Density), as shown in Fig. 2.7. It was in our interest to compare results only for UD 0.1, 0.3 and 0.6. Building models are 0.24x0.16x0.18 m³ (24x16x18 m³ in fullscale). The roof is considered flat. The upstream profile used in the wind tunnel is described by a power-law with exponent 0.2 and gradient height of 450 m. This profile corresponds to a suburban terrain roughness of 0.2 in AIJ (2004) [49]. The given upstream wind profile is adopted for the whole range of urban densities. As represented in Fig. 2.7, only a limited number of surrounding buildings is physically modelled in the wind tunnel. Actually the test central building and the surrounding buildings are placed in the middle of the turn table with a 200 cm diameter (200 m in fullscale).

Figure 2.7: Urban configurations with different area densities tested in the wind tunnel

Wind pressure measurement taps were distributed homogeneously over the surface of the models. The distance between each tab was 20 mm corresponding to 2 m in full-scale. The dashed circles in Fig. 2.8 were not measured to lighten the calculation. Fluctuating wind pressures were measured nearly synchronously at the 384 points. The time series of wind pressure coefficients is calculated as: (2.14)

C p _ ori (i,t) = p(i,t) / p H

where Cp_ori(i,t) is the original wind pressure coefficient at measured tap i at time t; p(i,t) is the pressure measured wind pressure at tap i at time t; pH is the € of the approaching wind velocity at the reference height reference wind pressure 2 (10 m), 0.5ρvH , vH the mean longitudinal wind speed at reference height H; ρ


40
 








the air density. The time series of wind pressure coefficients were averaged every 0.0064s (or 0.2s in full scale) to make the Cps correspond to some duration as: (2.15)

C p (i,t) = C p _ ori (i,t − Δt /2 ≈ t + Δt /2)

where Δt is the duration of the wind pressure coefficients. €

Figure 2.8: Disposition of wind pressure measurement taps

In order to obtain the dynamic pressure correspondent to pressure coefficients calculated by TPU is necessary to refer to the measured upstream wind profile at a height of 10 m in full scale. 2.3.2.2 TNO Cp Generator TNO Cp Generator is a web-based tool which predicts dimensionless static wind pressure coefficients on the facades and roofs of block shaped buildings with or without pitched roofs. It is based on experimental data [50,51]. Nearby environment can be implemented in the tool by physically describing close obstacles. Obstacles further than 5 times their height are not considered to be relevant for the local shielding. Terrain roughness is taken into account by changing the parameter z0, which, according to TNO, ranges between 0.03 (plain pasture or prairie with sporadic obstacles at a distance > 50 times obstacle’s height) and 7 (large town with regular presence of high obstacles such as high storey buildings at a distance > 0.1 times obstacle’s height). The input file consists of a text file (see Appendix 2) containing building and obstacles coordinates and orientations, as well as coordinates of wind pressure measurement taps, which are defined by the user and can be a maximum of 40. The program output includes an array of pressure data, which can be linked to a ventilation simulation program such as COMIS, and a graphic output.










 41


In the graphic output the unshielded situation is shown, together with its correction due to the presence of obstacles and the final values given by the combination of the previous effects. This method allows the user to directly see the influence of neighbouring structures on Cp.

Figure 2.9: Graphic output of TNO Cp Generator for a given urban environment

TNO Cp Generator is undergoing constant improvements and is still at a development phase. However, there is a number of validations which show a good agreement with experimental data and support the use of the Generator even at this stage. In the example of Fig. 2.10 Cp Generator results have been evaluated against wind tunnel measured Cps [52]. Although the terrain roughness is not clearly reported, comparison shows a rather good agreement for facades and roof.

source: TNO Figure 2.10: Comparison between calculated and measured pressure coefficients


42
 








Moreover, TNO Cp Generator has been validated against Grosso [27] and Allen [53] models and was found to be coherent. Knoll et al. were among the experts selected by De Wit in occasion of his research on uncertainty in wind pressure coefficients for low-rise buildings [47].










 43



44
 








3 Building Energy Simulation Fundamentals The prediction of a natural ventilation strategy seasonal performance requires airflow models to be coupled with a thermal model. Chapter 3 introduces the building energy simulation software, presents its assumptions on thermal modelling and goes into detail on the thermal and airflow approach used by EnergyPlus. Moreover, the modelling of the urban environment within EnergyPlus is discussed.

3.1

Building Energy Simulation Software

Designing sustainable buildings which satisfy users requirements and regulations is a great challenge. The process involves a large number of variables, and the goal cannot be achieved without a holistic approach to design. Energy simulations help architects, engineers and energy consultants to take important design decisions at an early stage. Moreover, they allow comparison between different architectural solutions both for new buildings and in the refurbishing process. The performance of a building depends on its dynamic interactions with users, equipment, weather and HVAC systems. Contemporary simulations are based on mathematical models which attempt to emulate reality by taking into account every energy flowpath and their interrelations. Fig. 3.1 shows the flowpaths which dynamically interact to generate energy demand and achieve comfort.

Figure 3.1: Energy flowpaths of a building










 45


There are still four main barriers to the effective application of Building Energy Simulation or BES [44]: inadequacy of the user interface; absence of performance standards; little agreement on the data model used to define the building and its energy system; technical complexities in satisfying the growing expectations of users, especially with concerns to integrated modelling. Many of the previous barriers are caused by the fact that most BES tools are developed by technicians, building scientists and engineers and they are not fitting architects’ needs and methods. The first simplified models worked under the hypothesis of ‘steady-state’ or ‘quasi-steady-state’. Data were typically processed monthly or seasonally, and a building was considered as a collection of ‘nodes’ between which energy flows. The electrical analogy can be helpful to understand the process: each node has a different ‘variable of state’ such as temperature and pressure (analogous to voltage), heat flows between nodes (current), and the rate of transfer depends on the thermal resistance offered by the nodes (electrical resistance). Although they can be very handy when a low precision level is required, these models seemed inappropriate to process constantly changing variables such as weather, internal casual gains and energy storage/release by the thermal mass of the building. Nowadays the need of more accurate results led to the development of so-called dynamic simulation software. Time steps became hourly and energy-flows timedependent. However, dynamic models are also based on assumptions and they offer a simplified description of real behaviour. An initial simplification of the real behaviour of a building is the definition of thermal zones: portions of the space where air temperature can be approximated as constant (well-stirred hypothesis). Zones do not necessarily correspond to rooms or geometrical spaces, and they work as a control volume for heat balance. In order to study what happens in a zone, all heat transfer modes must be taken into account simultaneously. Heat balance models characterize the thermal response of the room air by solving energy balance equations for the zone air and for the interior and exterior surfaces. These equations are combined with those for transient conduction heat transfer and weather conditions’ data. The main principle is energy conservation, or ensuring that all energy flows are balanced in each zone. The present Chapter presents the indoor air heat balance, the heat balance on interior and exterior surfaces and the basic methods to solve transient conduction heat transfer, both in a general way and specifically for EnergyPlus. The basic assumptions made by EnergyPlus in urban modelling are the argument of Section 3.2.3.


46
 








3.1.1 The indoor Air Heat Balance The heat balance on the zone air is given by the following equation: N

sl dT Cz z = ∑ Q˙ i + dt i=1

N surfaces

N zones

∑ hi Ai (Tsi − Tz ) +

∑ m˙ C

i=1

i=1

i

p

˙ inf C p (T∞ − Tz ) + Q˙ sys (3.1) (Tzi − Tz ) + m

where: €

N sl

∑ Q˙ = sum of the convective internal loads [W] i i=1 N surfaces

∑ h A (T i

i

si

− Tz ) = convective heat transfer from the zone surfaces [W]

i=1 N zones



∑ m˙ C i

p

(Tzi − Tz ) = heat transfer due to inter-zone air mixing [W]

i=1



m˙ inf C p (T∞ − Tz ) = heat transfer due to infiltration of outside air [W] Q˙ = heat flow due to the heating/cooling system [W] sys

dTz = energy stored in zone air per unit time [W] dt

€ €

Cz



A finite difference approximation might be used to calculate the derivative term with respect to time, such as:



dT Ti (t + Δt) − Ti (t) ≅ dt Δt

(3.2)

where Ti is the air temperature [K] at a given time step and Δt is the temporal discretization. € If the air capacitance is neglected, Eq. 3.1 may be written as: N surfaces

N sl

−Q˙ sys = ∑ Q˙ i + i=1

N zones

∑ h A (T i

i=1

i

si

− Tz ) +

∑ m˙ C i

p

˙ inf C p (T∞ − Tz ) (3.3) (Tzi − Tz ) + m

i=1

to evaluate the necessary heating/cooling system loads for a fixed temperature. In case of an air heating/cooling system, the system energy provided to the zone, € ˙ Qsys , can be defined as the difference between the supply air enthalpy and the enthalpy of the air leaving the zone as in Eq. (3.4): ˙ sys = m ˙ sysC p (Tsup − Tz ) Q



(3.4)

€ 









 47


3.1.2 The Surface Heat Balance 3.1.2.1 The Outside Surface A heat balance must exist at the outside surface-air interface. The incoming direct and incident solar radiation absorbed by the surface, the radiation exchange between the surface, the sky and the ground, convective and conductive transfer must be in equilibrium. The heat balance at the outside surface can be expressed with Eq. (3.5) [54]: (3.5)

q''αsol +q''LWR +q''conv −q''ko = 0

where:

€ € € €

€ diffuse solar (short wavelength) radiation heat flux absorbed q''αsol = direct and at the surface [W m-2] q''LWR = net long wavelength (thermal) radiation flux exchange from the surroundings and the air [W m-2] q''conv = convective heat flux from outside air [W m-2] q''ko = conductive heat flux from outside surface to inside surface of the wall [W m-2]. All terms are considered positive for net flux entering the outside surface except the conduction term, which is conventionally considered positive in the direction from the outside surface into the wall, as explained in Fig. 3.2.

Figure 3.2: Outside heat balance control volume diagram

3.1.2.2 The Inside Surface The inside surface heat balance, just like the outside surface heat balance, ensures that contributions from convection to the air, absorbed and reflected short wavelength radiation and long wavelength radiant interchange balance the conduction through the building element. The longwave radiation exchange


48
 








includes absorption and emittance of low temperature sources such as other zone surfaces, equipment, and people. Solar radiation entering the zone through windows and high temperature indoor sources such as lights account for short wave radiation. The interior surface heat balance can be written as follows [54]: (3.6)

q''LWX +q''SW +q''LWS +q''sol +q''conv +q''ki = 0

where

€ € € € € €

q''LWX = net€longwave radiant flux from zone to the surface [W m-2] q''SW = net short wave radiation flux to surface from lights [W m-2] q''LWS = longwave radiation flux from equipment in zone [W m-2] q''sol = transmitted solar radiation flux absorbed at surface [W m-2] q''conv = convective heat flux from zone air to the surface [W m-2] q''ki = conductive heat flux from outside face to inside face of the wall [W m-2]

Figure 3.3: Inside heat balance control volume diagram

3.1.3 Models of transient heat conduction Conduction is the process through which a certain heat flux at one surface of a solid medium of thickness x finds its way to an other surface, being diminished in module and shifted in time due to the thermal inertia of the material [44]. Because of the strong time dependency of temperature, analysis of transient conduction is very complex and cannot be simplified as steady-state. Hence, its calculation poses a great challenge for Building Energy Simulation software.










 49


Three methods can be performed to solve transient heat conduction problems [55]: •





3.2

Z-transform methods: development of the so called room-air weighting factors, Z-transfer functions or room response factors are used to determine cooling load from heat gains. These methods include ‘response factors’ and ‘conduction transfer functions’. Conduction transfer functions calculate the heat flux on the inside or outside surface based on the temperature and the flux history. Among all the methods, Z-transform methods are most extensively used in simulation programs due to their computational efficiency and accuracy. Within this category are DOE-2 program, EnergyPlus and the ASHRAE Transfer Function Method (TFM). Numerical methods: models characterized by the numerical discretization of the building into a network of nodes with interconnecting energy flowpaths. Numerical methods essentially consist of three steps [44]: system discretization, definition of nodal equationset, simultaneous solution to obtain the distribution of state variables. They are based on an approximation of some governing partial differential equations, normally achieved through the truncated Taylor series expansion (finite differences) or by application of conservations principles to small control volumes (finite elements). Multiple zone air temperatures are provided. This benefit has the drawback of requiring more user effort and computational time. ESP-r is one of the few widely used software which operates this approach. Lumped parameter methods: normally used together with lumped capacitance zone models, they consider walls and roofs as discrete resistances and lumped capacitances.

EnergyPlus

EnergyPlus (EP) is used throughout the present research to carry out building energy simulations. It is a complete free constantly improving tool that is widely used and represents today a BES reference. EP results have been tested with various test suites including: analytical tests, ANSI/ASHRAE Standard 1402004, International Energy Agency Solar Heating and Cooling Program BESTest methods [56]. EnergyPlus is an energy simulation software developed in 1996 by the U.S. Department of Energy (DOE). The first working version for internal testing was completed in 1998 and released in April 2001 [57]. It is written in all-new


50
 








Fortran 90 code and it combines the best features and capabilities of the previous tools BLAST and DOE-2 along with new abilities [57]. In comparison to BLAST and DOE-2 the main innovation of EnergyPlus is to use an integrated (i.e. simultaneous) solution technique, which allows a more accurate prediction of indoor air temperatures and comfort levels. Furthermore, it simulates interzone airflow and it operates under realistic system controls. Other innovative features include sub-hourly, variable time steps and external modular HVAC systems integrated with a heat balance-based zone simulation. EnergyPlus simulator is based on text inputs, which increase the user effort to define all the necessary variables compared to engines with graphical user interfaces. Nevertheless, some interfaces for EnergyPlus have been developed by external companies and can be coupled with the software; the present study utilizes the free Open Studio plug-in for Google SketchUp, created by the National Renewable Energy Laboratory for the U.S. Department of Energy. Open Studio, however, is not developed to be fully-fledged front end for EnergyPlus, and much of the input has to be added via an EnergyPlus format Editor (.IDF editor). EP is made of three main components [57]: a simulation manager, a heat and mass balance simulation module and a building systems simulation module. The simulation manager’s purpose is to control the whole simulation process, especially concerning interactions between the heat balance engine and HVAC modules and between EnergyPlus and SPARK [58] and TRNSYS [59] simulations.

Figure 3.4: The overall EnergyPlus structure is made of three components: simulation manager, heat and mass balance simulation, building systems simulation










 51


3.2.1 The thermal Model Building thermal loads are calculated in EnergyPlus with a heat balance based technique that allows simultaneous calculation of radiant and convective effects at both the interior and exterior surface during each time step. Conduction Transfer Functions (CTF) are generally used to solve transient heat conduction through building elements, but the software includes an additional conduction finite differences method for particular situations. The indoor air balance and surface indoor and outdoor balances are treated in EnergyPlus as in Sections 3.1.1 and 3.1.2, while the transient conduction calculation is here presented in detail. EnergyPlus [54] carried over from BLAST the following response factor equation, which relates the present flux at one surface to an infinite series of temperature histories at both sides: ∞



(3.7)

q''ko (t) = ∑ X j To,t− jδ −∑Y j Ti,t− jδ j= 0

j= 0

where q'' is the conductive heat flux, X and Y are the response factors, T is the temperature, o and i represent the outside and the inside of the building element, € respectively, and t is the current time step. However, the infinite number of needed terms makes it necessary to replace them with flux history terms. The € new solution is called Conduction Transfer Function (CTF) solution and it is expressed by the equation: nz

nq

nz

(3.8)

q''ki (t) = −Z oTi,t − ∑ Z j Ti,t− jδ + YoTo,t + ∑Y j To,t− jδ + ∑ Φ j q''ki,t− jδ j=1

j=1

j=1

nz

nq

for the inside heat flux, and €

nz

(3.9)

q''ko (t) = −YoTi,t − ∑Y j Ti,t− jδ + X oTo,t + ∑ X j To,t− jδ + ∑ Φ j q''ko,t− jδ j=1

j=1

j=1

for the outside heat flux, € where:

X j =Outside CTF coefficient, j=0,1,...nz Y j =Cross CTF coefficient, j=0,1,...nz Z j =Inside CTF coefficient, j=0,1,...nz € € € 
52
 








€ € € € €

Φ j =Flux CTF coefficient, j=1,2,...nq Ti =Inside face temperature To =Outside face temperature q''ki =Conduction heat flux on outside face q''ko =Conduction heat flux on inside face The advantage of formulation (3.9) lays in the fact that it is a simple, linear equation with constant coefficients. The coefficients (CFTs) need to be determined only once for each construction type. Nevertheless, CTF series have some typical constraints of a transformationbased solution: because of constant coefficients, temperature dependent thermal properties cannot be included; CTF solutions span from the outside face to the inside face and do not produce results for the interior of the wall; they become progressively more unstable as the time step decreases, especially in thermally massive constructions. Furthermore, they do not accurately deal with more advanced constructions, such as Phase Change Materials (PCM). For these reasons, a Conduction Finite Difference (CondFD) solution algorithm has been integrated in EnergyPlus and it can be used to simulate PCM, variable thermal conductivity or particularly short zone time steps. For versions later than 7 (version 7.2 is used in the present study) the finite difference model is described by a fully implicit scheme, based on an Adams-Moulton solution approach. The model equation is: C p ρΔx

j +1 j +1 (Ti−1j +1 − Ti j +1)  Ti j +1 − Ti j  (Ti+1 − Ti ) =  kW + kE  Δt Δx Δx  

(3.10)

where Cp is the specific heat of the material [J kg-1 K-1], ρ is the density of the material [kg m-3], Δx is the finite difference layer thickness [m], T is the node € temperature [K], i is the node being modelled, (i+1) is the adjacent node to interior of construction, (i-1) is the adjacent node to exterior of construction, j is the previous time step, (j+1) is the new time step, Δt is the calculation time step, kW is the thermal conductivity for the interface between i node and (i+1) node [W m-1 K-1] and kE is the thermal conductivity for the interface between i node and (i-1) node [W m-1 K-1]. The surface discretization depends on the thermal diffusivity of the material and on the selected time step.










 53


3.2.2 The Airflow Model EnergyPlus uses a multizone nodal model to resolve the airflow within a building. The model is called Airflow Network (AFN) and it simplifies the building as a set of discrete airflow components such as doors, windows or cracks. Airflow components connect nodes through linkages. Fig. 3.5 is a schematic representation of the nodal model principle. The following steps are generally taken to set a nodal model: • • •

• •

Consideration of each separate zone and attribution of a specific node to each zone; Association of an unknown pressure variable Pi and a known (set point) temperature Ti to each zone; Identification of each flow component and association with an airflow path describing the pressure drop across the component and the consequent airflow rate; Establishment of the outdoor conditions and definition of reference pressure P0, air temperature T0, wind speed vref and direction; Association of a surface-averaged wind pressure coefficient Cp to each envelope flow component.

Figure 3.5: Simplified scheme of a nodal approach: zones and the environment are attributed a specific node; a possible airflow path when all doors and windows are open is shown

The governing equations are: pressure-flow relations in the flow elements, mass conservation at the nodes and hydrostatic pressure variations in the zones. The program solves the airflow network by adjusting the reference pressures to achieve the conservation of air mass in each node. The wind pressure is determined by Bernoulli’s equation (2.4). A linkage in the AFN model has two nodes, namely inlet and outlet, and is linked by a component which determines the correlation between airflow and pressure.


54
 








The pressure difference across each component in a linkage between node n at height zn and node m at height zm is assumed to be governed by Bernoulli’s Eq., as:

 ρv n2   ρv m2  ΔP =  Pn +  −  Pm +  + ρg( zn − zm ) 2   2  

(3.11)

Such equation might be rewritten in the format used by the airflow network model such as: € ΔP=Pn-Pm+PS+PW (3.12) where: Pn, Pm= total pressures at nodes n and m [Pa] PS= pressure difference due to density and height differences [Pa] PW= pressure difference due to wind [Pa]. EnergyPlus allows control of natural ventilation at three different levels, namely at zone level, surface level or component level: • the zone object specifies the ventilation control that concerns all openable exterior and interior windows and doors of the corresponding zone; • the surface object indicates weather a heat transfer surface (wall, window, etc.) has a crack or opening through which ventilation occurs; • the component object of a given element summarises the airflow characteristics of such element. The description of the relation between airflow and pressure for cracks and for a large horizontal opening is given hereunder, while an extensive documentation for each component can be found in [60]. The program uses the iterative Newton-Raphson method [61] based on a truncated Taylor-series approximation to define air pressure at each node. In order to initialize the method, first values can be estimated as equal to zero or they can be calculated relating airflow to pressure drop for each airflow component by means of a linear approximation:

 ΔP  m˙ i = Ci ρ i  ,  µ 

(3.13)












 55


where m˙ i is the air mass flow rate at i-th linkage [kg s-1], Ci is the air mass flow coefficient [m3], ρ is the air density [kg m-3], ΔPi is the pressure difference across the i-th linkage [Pa] and µ is the air viscosity [Pa s-1]. As for heat balance, the conservation of air mass flow rate at each linkage € € provides the convergence criterion. The air mass flow rate is determined by the € pressure difference (ΔP) across the opening. € EnergyPlus uses the following power law equation to calculate airflow rates through cracks as a function of the pressure difference across the crack:

Q = (CrackFactor)CT CQ (ΔP )

n

(3.14)

where Q= air mass flow [kg/s] CQ= air mass€ flow coefficient [kg/s at P=1 Pa] ΔP= pressure difference across crack [Pa] n= airflow exponent [dimensionless] CT= reference condition temperature correction factor defined as: n−1

 ρ o  ν o 2n−1 CT =      ρ  ν 

(3.15)

where ρ is the air density at specific air temperature and humidity ratio conditions [kg m-3], ν is the air kinetic viscosity at the specific air temperature € conditions [m2 s-1], ρo is the air density at reference air temperature [kg m-3] and νo is the air kinetic viscosity at the reference air temperature [m2 s-1]. A two zone building connected by a large opening is used to simplify the model for three different scenarios of ΔP between a lower (L) zone and an upper (U) zone: •

PL=PU (3.16)

m˙ U = m˙ L = 0

where PL is the air pressure in the lower zone [Pa], PU is the air pressure in the upper zone [Pa], m˙ U is the air mass flow rate from the lower zone € ˙ L is the air mass flow rate from the upper to the upper zone [kg s-1] and m -1 zone to the lower zone [kg s ]. € €


56
 










PL>PU (3.17)

m˙ U = 0  2ΔP  0.5 ˙ L = ρ L ACd  m   ρ ave  €

(3.18)

where ρL is the air density in the lower zone [kg m-3], A is the opening area [m2], Cd is the discharge coefficient [dimensionless], ρave is the € average air density between lower and upper zone [kg m-3] and ΔP is the pressure difference PL - PU [Pa]. •

PL0.1. Thus, using Cp Generator pressure coefficients rather than TPU wind tunnel values produces an overall increase of ventilation flow rates and a modest change in energy demand, due to modified convection coefficient values. Higher hc enhance convective heat transfer with the surrounding air and allow the building to release heat more efficiently, resulting in a slightly lower energy demand. However, the impact of urban environment on a non ventilated building energy demand is unvaried; for this reason, the energy demand variation at increasing Urban Density has not been updated in Fig. 5.15. The percentage reduction of airflow rates at UD = 0.1 given with TPU wind tunnel pressure coefficients is well approximated by TNO Cp Generator tool. On the 5th floor Cp Generator values well estimate TPU trends also for UD = 0.3. Major discrepancies between the two sources of pressure coefficients emerge for the 2nd floor at UD = 0.3 and for both floors at maximum buildings proximity (Fig. 5.15). Another interesting aspect visible in Fig. 5.15 is that ACH percentage differences at different floors are almost negligible when TNO Cp Generator values are used.










 97


Figure 5.15: Energy demand and ACH at increasing Urban Density obtained with pressure coefficients from TPU wind tunnel Cp and TNO Cp Generator tool

Fig. 5.16 finally illustrates the energy saving due to night natural ventilation in the urban environment calculated using TPU Cp values and TNO Cp Generator Cp values.

Figure 5.16: Energy saving due to natural ventilation for different locations and floors at increasing UD obtained with pressure coefficients from TPU wind tunnel Cp and TNO Cp Generator tool (top 2nd floor, bottom 5th floor)


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Results show that simulating night natural ventilation at the urban scale with inaccurate pressure coefficient values can determine misleading conclusions on the energy saving potential of this cooling technique. Discrepancies increase for denser environments. In particular, using pressure coefficients from TNO Cp Generator tool provokes a strong underestimation of the effects of a dense urban environment on natural ventilation. The only location which does not show particular sensitivity to the pressure coefficient source is Milan. Such result confirms the hypothesis according to which the urban environment has a modest influence on locations with very low initial ACH. In fact, although ACH in this case are reduced of about 60% rather than 85% using TPU wind tunnel Cps, the impact on final performance is insignificant.

5.8

Discussion of Results

Chapter 5 gives an overview of the cooling performance of natural ventilation at varying urban density in different locations. Various aspects which alter the levels of energy demand and airflow rates are investigated. The general conclusion is that a correct assessment of the night natural ventilation potential is inseparable from both the shading effect of neighbouring buildings and the impact of urban environment on airflow rates. In fact, only the investigation of the combined behaviour of such aspects allows an accurate prediction of the final energy saving potential of night natural ventilation for a given location. All changing parameters and simulation inputs investigated during the simulations can be divided according to their effect on the energy demand or on the airflow rates (Tab. 5.3). Energy savings in the urban environment are particularly penalized when the cooling energy demand is ‘less reduced’ and the ventilation potential is ‘more reduced’ with increasing Urban Density. Table 5.3: The effect of parameters on energy demand and ventilation potential trend as functions of the Urban Density

COOLING ENERGY DEMAND VENTILATION POTENTIAL (ACH)





‘LESS’ REDUCED

‘MORE’ REDUCED

warm climates

cold climates

V floor

II floor

albedo=0.7

albedo=0.3

simplified UHI

no UHI

TNO Cp Generator

TPU wind tunnel






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6

Conclusion

The present thesis investigates the cooling potential of natural ventilation for commercial buildings within the urban environment. A low-rise office building is simulated in EnergyPlus in the isolated context and as part of an urban configuration of increasing density. Thermal properties, ventilation strategy and heat loads of the building are maintained constant throughout the research. The cooling energy demand of the unventilated stand alone building works as a reference to explore the energy saving potential of natural ventilation and the impact of the urban environment on such potential. Simulations are carried out at two different floors to investigate the effect of altitude and on five diverse locations to study the impact of climatic boundary conditions on results. The possibility of modelling the impact of urban environment on the energy performance of a building using Building Energy Simulation software is verified to a satisfactory extent. Although such software are not optimized for nonisolated buildings, the opportunity to manually input relevant parameters, such as shading surfaces and urban wind flow pressure coefficients, provides an acceptable approximation of nearby environment and further upstream wind conditions. This study confirms the general effectiveness of night natural ventilation as a cooling strategy. However the main objective of the present work is to assess the impact of urban environment on cooling potential of natural ventilation. Results show that such impact is strongly dependent on initial potential conditions and is generally more visible for a range of Urban Density from 0.3 to 0.6. In general, locations with an initial high potential are considerably influenced by urban environment, but their energy saving potential remains significant even at high site densities. On the other hand, initially lower saving potentials typical of warmer climates can be halved within the urban environment. Two main aspects concur to such findings: the variation of cooling energy demand and the reduction of airflow rates at increasing urban densities. The first is mainly due to the shading effect of neighbouring buildings, while the latter is caused by the high terrain roughness and by the presence of obstacles, which are taken into account through modified values of pressure coefficients at increasing UD. An investigation of the combined behaviour of demand and potential is essential to explain how ventilation performance is modified within the urban environment. Energy savings on the 5th floor are generally more influenced by urban environment than those on the 2nd floor. This outcome can be explained as the shading effect of neighbouring buildings affects the cooling demand at lower










 101


altitudes more than at higher altitudes, due to the same geometrical description of surrounding obstacles. Some parameters have been systematically changed in order to assess their effect on results and explore a larger variety of simplified cases. An increased albedo value of surrounding buildings causes cooling demand to be less influenced by urban solar shading as a more relevant share of solar radiation is reflected. Airflow rates are modestly affected by such modification. Therefore, as the demand increases and the potential remains rather constant, the impact of urban environment on ventilation energy saving potential is enhanced. This effect is especially visible in cold climates, where solar gains play a relatively higher role in the cooling energy demand of a building. The possible effect of Urban Heat Island is taken into account increasing the weather file dry bulb temperature with a sinusoidal distribution throughout the day of a maximum of 1°C and 3°C. Such air temperature increases produce important effects in all locations and drastically reduce the saving potential within cities from max. +3°C. From a modelling point of view, the effect of input pressure coefficients was investigated processing simulations with Cp values from two different sources: TPU wind tunnel measurements and TNO Cp Generator tool. This aspect proves to be noteworthy as a dispersion of values between the two occurs at increasing urban density. The use of inaccurate pressure coefficient values can lead to significant underestimation of the impact of urban environment on ventilation energy saving potential. Also in this case, discrepancies depend on initial potential. As a conclusion, this thesis confirms the necessity of taking into account the real setting of a building when evaluating the potential of night natural ventilation. Especially in locations where the initial cooling capability is rather low, a dense urban environment can have determining effect on its final performance. The analysis of a building in the isolated case would lead to great overestimation of the energy saving potential of night natural ventilation.

6.1

Limits and further developments

The main limitation of the present study is that only a selected range of urban models is investigated. Aligned building arrays do not represent a realistic case study and they correspond to an ideal parameterisation of the urban environment. Diverse building typologies and urban forms should be investigated. Given the major dissimilarity of results obtained for different floor altitudes, an interesting development would be to consider uniform urban patterns of high rise buildings and then non uniform patterns with surrounding


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buildings of different heights. Furthermore, only the behaviour of the central building is studied and edge effects are neglected. The present research considers solar shading only in respect to cooling energy demand: some future study could investigate at the same time the impact of shading on energy demand for lighting, which could also affect the cooling energy demand. An other strong simplification concerns the way Urban Heat Island is treated: due to the complexity of the topic a realistic representation of UHI was not possible in such a study. At the same time, due to the relevant influence of air temperature increase, the need of taking UHI into account when exploring cooling energy demands within cities is confirmed.










 103



104
 








Appendix 1 Table A1.1: Selected European locations: average daily, night-time and daytime temperature for the simulated time span June - September with a maximum air temperature increase of 1°C and 3°C Location Amsterdam Berlin Milan Palermo Rome





Average T+1 [°C]

Average T+3 [°C]

16.3 17.9 21.1 25.3 23.3

17.1 18.7 21.9 26.2 24.1

Average Night T+1 [°C] 14.7 16.2 18.4 24.7 21.1 22.9

Average Night T+3 [°C] 16.4 17.8 20.1 26.4 22.9



Average Day T+1 [°C] 17.7 19.4 23.4 25.8 25.1

Average Day T+3 [°C] 17.7 19.5 23.5 25.9 25.2




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106
 








Appendix 2 Figure A2.1: Example of TNO Cp Generator input text file for UD = 0.6. Only a limited number of pressure coefficients can be required at each calculation (in this case on the South façade) {file=input} +----------------------------------+ | title | +----------------------------------+ title: building X version: 1.0 made by: X. Person comment: none +---------------------------------+ | wind.Zo | +---------------------------------+ Direction: 0 135 Zo: 5.06 5.06 +----------------------------------------+ | north arrow compass direction in plan | +----------------------------------------+ Direction: 0 +----------------------------------+ | obstacles (position in m(=meter))| +----------------------------------+ Ground level: 0. Roof height : 18.0 Name : building x,y : 0.0000 0.0000 Azimut : 180. L,W,H,#,‡,w: 24.0000 16.0000 18.0000 Name: meteo x,y: 1.0E6 -5.0 Azimut: 90. L,W,H: 0.1 0.1 10.0 Name: obstacle NORD x,y: 0.0000 20.66 Azimut: 180.0000 L,W,H: 24.0000 16.0000 18.0000 Name: obstacle EST x,y: 30.98 0.0 Azimut: 180.0000 L,W,H: 24.0000 16.0000 18.0000 Name: obstacle SUD EST x,y: 30.98 -20.66 Azimut: 180.0000 L,W,H: 24.0000 16.0000 18.0000 Name: obstacle NORD EST x,y: 30.98 20.66 Azimut: 180.0000 L,W,H: 24.0000 16.0000 18.0000 Name: obstacle SUD x,y: 0.00000 -20.66 Azimut: 180.0000 L,W,H: 24.0000 16.0000 18.0000 Name: obstacle OVEST x,y: -30.98 0.0000 Azimut: 180.0000 L,W,H: 24.0000 16.0000 18.0000 Name: obstacle SUD OVEST x,y: -30.98 -20.66 Azimut: 180.0000





0.

0.



0.




 107


L,W,H: 24.0000 16.0000 18.0000 Name: obstacle NORD OVEST x,y: -30.98 20.66 Azimut: 180.0000 L,W,H: 24.0000 16.0000 18.0000 +---------------------------------+ | cp-positions | +---------------------------------+ unit: m Building side: facade 1 Pos.x,y: 1 17 Building side: facade 1 Pos.x,y: 3 17 Building side: facade 1 Pos.x,y: 5 17 Building side: facade 1 Pos.x,y: 7 17 Building side: facade 1 Pos.x,y: 9 17 Building side: facade 1 Pos.x,y: 11 17 Building side: facade 1 Pos.x,y: 13 17 Building side: facade 1 Pos.x,y: 15 17 Building side: facade 1 Pos.x,y: 17 17 Building side: facade 1 Pos.x,y: 19 17 Building side: facade 1 Pos.x,y: 21 17 Building side: facade 1 Pos.x,y: 23 17 Building side: facade 1 Pos.x,y: 1 15 Building side: facade 1 Pos.x,y: 3 15 Building side: facade 1 Pos.x,y: 7 15 Building side: facade 1 Pos.x,y: 11 15 Building side: facade 1 Pos.x,y: 15 15 Building side: facade 1 Pos.x,y: 19 15 Building side: facade 1 Pos.x,y: 23 15


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Table A2.1: Pressure coefficients obtained with Cp Generator and used in the comparison with TPU wind tunnel source (Isolated case) Isolated TNO-N 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360

TNO-E 0.81 0.79 0.70 0.45 0.03 -0.42 -0.80 -0.74 -0.61 -0.47 -0.41 -0.42 -0.43 -0.42 -0.41 -0.48 -0.62 -0.75 -0.81 -0.42 0.04 0.46 0.70 0.79 0.81

TNO-S

TNO-W

-1.03 -0.61 -0.08 0.40 0.70 0.77 0.75 0.76 0.69 0.40 -0.08 -0.60 -1.02 -0.94 -0.74 -0.53 -0.42 -0.41 -0.42 -0.41 -0.42 -0.53 -0.74 -0.94 -1.03

-0.43 -0.42 -0.41 -0.48 -0.62 -0.75 -0.81 -0.42 0.04 0.46 0.70 0.79 0.81 0.79 0.70 0.45 0.03 -0.42 -0.80 -0.74 -0.61 -0.47 -0.41 -0.42 -0.43

-1.02 -0.94 -0.74 -0.53 -0.42 -0.41 -0.42 -0.41 -0.42 -0.53 -0.74 -0.94 -1.03 -0.61 -0.08 0.40 0.70 0.77 0.75 0.76 0.69 0.40 -0.08 -0.60 -1.02

Table A2.2: Pressure coefficients obtained with Cp Generator and used in the comparison with TPU wind tunnel source (UD = 0.1) UD01 TNO-N 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360





TNO-E 0.49 0.51 0.54 0.33 0.03 -0.30 -0.59 -0.55 -0.45 -0.34 -0.30 -0.29 -0.30 -0.30 -0.30 -0.34 -0.45 -0.55 -0.59 -0.31 0.03 0.34 0.54 0.50 0.49

TNO-S

TNO-W

-0.85 -0.44 -0.06 0.30 0.51 0.55 0.54 0.55 0.50 0.29 -0.06 -0.44 -0.83 -0.50 -0.57 -0.40 -0.30 -0.30 -0.31 -0.30 -0.30 -0.40 -0.56 -0.51 -0.85

-0.30 -0.30 -0.30 -0.34 -0.45 -0.55 -0.59 -0.31 0.03 0.34 0.54 0.50 0.49 0.51 0.54 0.33 0.03 -0.30 -0.59 -0.55 -0.45 -0.34 -0.30 -0.29 -0.30



-0.83 -0.50 -0.57 -0.40 -0.30 -0.30 -0.31 -0.30 -0.30 -0.40 -0.56 -0.51 -0.85 -0.44 -0.06 0.30 0.51 0.55 0.54 0.55 0.50 0.29 -0.06 -0.44 -0.83




 109


Table A2.3: Pressure coefficients obtained with Cp Generator and used in the comparison with TPU wind tunnel source (UD = 0.3) UD03 TNO-N 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360

TNO-E 0.18 0.21 0.23 0.15 -0.02 -0.24 -0.45 -0.26 -0.29 -0.21 -0.20 -0.18 -0.19 -0.19 -0.20 -0.21 -0.28 -0.26 -0.46 -0.24 -0.02 0.15 0.24 0.21 0.18

TNO-S

TNO-W

-0.54 -0.36 -0.07 0.06 0.27 0.29 0.31 0.29 0.26 0.06 -0.06 -0.36 -0.52 -0.31 -0.19 -0.21 -0.20 -0.21 -0.20 -0.21 -0.20 -0.21 -0.18 -0.32 -0.54

-0.19 -0.19 -0.20 -0.21 -0.28 -0.27 -0.46 -0.25 -0.02 0.14 0.22 0.20 0.17 0.21 0.22 0.14 -0.02 -0.25 -0.45 -0.26 -0.28 -0.21 -0.20 -0.18 -0.19

-0.53 -0.32 -0.19 -0.22 -0.20 -0.21 -0.20 -0.21 -0.19 -0.20 -0.18 -0.33 -0.55 -0.36 -0.07 0.06 0.27 0.29 0.31 0.29 0.26 0.06 -0.06 -0.36 -0.53

Table A2.4: Pressure coefficients obtained with Cp Generator and used in the comparison with TPU wind tunnel source (UD = 0.6) UD06 TNO-N 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360


110
 


TNO-E -0.06 -0.03 0.01 -0.03 -0.14 -0.33 -0.64 -0.41 -0.20 -0.16 -0.18 -0.21 -0.22 -0.21 -0.18 -0.15 -0.19 -0.42 -0.65 -0.33 -0.13 -0.03 0.02 -0.02 -0.06

TNO-S

TNO-W

-0.77 -0.45 -0.31 -0.15 0.04 0.20 0.27 0.19 0.03 -0.15 -0.29 -0.45 -0.76 -0.41 -0.22 -0.15 -0.16 -0.22 -0.24 -0.21 -0.15 -0.15 -0.22 -0.44 -0.77

-0.21 -0.21 -0.18 -0.15 -0.21 -0.45 -0.65 -0.33 -0.14 -0.04 0.00 -0.04 -0.08 -0.04 0.01 -0.04 -0.13 -0.33 -0.65 -0.43 -0.20 -0.15 -0.18 -0.21 -0.21



-0.75 -0.43 -0.22 -0.16 -0.16 -0.21 -0.25 -0.21 -0.15 -0.15 -0.23 -0.46 -0.77 -0.44 -0.31 -0.15 0.05 0.20 0.27 0.20 0.05 -0.14 -0.29 -0.43 -0.75





Table A2.5: Pressure coefficients obtained by TPU wind tunnel and used in the comparison with Cp Generator tool (Isolated Case) Isolated TPU-N 0.0 22.5 45.0 67.5 90.0 112.5 135.0 157.5 180.0 202.5 225.0 247.5 270.0 292.5 315.0 337.5 360.0

TPU-E 0.515 0.556 0.343 -0.015 -0.439 -0.598 -0.505 -0.334 -0.237 -0.334 -0.505 -0.598 -0.439 -0.015 0.343 0.556 0.515

TPU-S -0.593 -0.157 0.297 0.555 0.557 0.555 0.297 -0.157 -0.580 -0.593 -0.392 -0.309 -0.198 -0.309 -0.392 -0.593 -0.580

TPU-W -0.237 -0.334 -0.505 -0.598 -0.469 -0.015 0.343 0.556 0.515 0.556 0.343 -0.015 -0.469 -0.598 -0.505 -0.334 -0.237

-0.580 -0.593 -0.392 -0.309 -0.198 -0.309 -0.392 -0.593 -0.593 -0.157 0.297 0.555 0.557 0.555 0.297 -0.157 -0.593

Table A2.6: Pressure coefficients obtained by TPU wind tunnel and used in the comparison with Cp Generator tool (UD = 0.1) UD01 TPU-N 0.0 22.5 45.0 67.5 90.0 112.5 135.0 157.5 180.0 202.5 225.0 247.5 270.0 292.5 315.0 337.5 360.0





TPU-E 0.307 0.292 -0.048 -0.208 -0.424 -0.651 -0.649 -0.408 -0.200 -0.408 -0.649 -0.651 -0.424 -0.208 -0.048 0.292 0.307

TPU-S -0.330 -0.048 0.316 0.407 0.311 0.407 0.316 -0.048 -0.358 -0.485 -0.424 -0.359 -0.285 -0.359 -0.424 -0.485 -0.358

TPU-W -0.200 -0.408 -0.649 -0.651 -0.421 -0.208 -0.048 0.292 0.307 0.292 -0.048 -0.208 -0.421 -0.651 -0.649 -0.408 -0.200



-0.358 -0.485 -0.424 -0.359 -0.285 -0.359 -0.424 -0.485 -0.330 -0.048 0.316 0.407 0.311 0.407 0.316 -0.048 -0.330




 111


Table A2.7: Pressure coefficients obtained by TPU wind tunnel and used in the comparison with Cp Generator tool (UD = 0.3) UD03 TPU-N 0.0 22.5 45.0 67.5 90.0 112.5 135.0 157.5 180.0 202.5 225.0 247.5 270.0 292.5 315.0 337.5 360.0

TPU-E -0.116 -0.107 -0.142 -0.215 -0.248 -0.299 -0.381 -0.393 -0.272 -0.393 -0.381 -0.299 -0.248 -0.215 -0.142 -0.107 -0.116

TPU-S -0.278 -0.123 -0.034 0.055 0.013 0.055 -0.034 -0.123 -0.270 -0.336 -0.319 -0.248 -0.222 -0.248 -0.319 -0.336 -0.270

TPU-W -0.272 -0.393 -0.381 -0.299 -0.246 -0.215 -0.142 -0.107 -0.116 -0.107 -0.142 -0.215 -0.246 -0.299 -0.381 -0.393 -0.272

-0.270 -0.336 -0.319 -0.248 -0.222 -0.248 -0.319 -0.336 -0.278 -0.123 -0.034 0.055 0.013 0.055 -0.034 -0.123 -0.278

Table A2.8: Pressure coefficients obtained by TPU wind tunnel and used in the comparison with Cp Generator tool (UD = 0.6) UD06 TPU-N 0.0 22.5 45.0 67.5 90.0 112.5 135.0 157.5 180.0 202.5 225.0 247.5 270.0 292.5 315.0 337.5 360.0


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TPU-E -0.199 -0.208 -0.206 -0.213 -0.243 -0.240 -0.256 -0.259 -0.215 -0.259 -0.256 -0.240 -0.243 -0.213 -0.206 -0.208 -0.199

TPU-S -0.230 -0.255 -0.182 -0.149 -0.189 -0.149 -0.182 -0.255 -0.234 -0.280 -0.257 -0.251 -0.242 -0.251 -0.257 -0.280 -0.234

TPU-W -0.215 -0.259 -0.256 -0.240 -0.237 -0.213 -0.206 -0.208 -0.199 -0.208 -0.206 -0.213 -0.237 -0.240 -0.256 -0.259 -0.215



-0.234 -0.280 -0.257 -0.251 -0.242 -0.251 -0.257 -0.280 -0.230 -0.255 -0.182 -0.149 -0.189 -0.149 -0.182 -0.255 -0.230





Appendix 3 Table A3.1: Energy Demand resulting from a maximum increase of air temperature of 1°C

UV

NV

Urban Density 0 0.3 0.6 0 0.3 0.6

Amsterdam 21.64 19.78 15.21 4.58 6.26 6.89

Energy Demand [kWh/m2] Berlin Milan Palermo 26.17 37.69 42.69 23.93 34.00 38.44 18.82 28.39 33.04 9.97 29.89 35.51 12.57 28.45 34.67 12.15 23.88 30.97

Rome 38.39 34.72 29.52 27.41 28.12 25.56

Table A3.2: Energy Saving resulting from a maximum increase of air temperature of 1°C Urban Density 0 0.3 0.6





Amsterdam 79% 68% 55%

Energy Saving [%] Berlin Milan Palermo 62% 21% 17% 47% 16% 10% 35% 16% 6%



Rome 29% 19% 13%




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List of Symbols





A α α Cd Ci Cp Cp CQ CT Cz d δ DH ε θ F fC,glazed g h hc hr k k κ L m˙ 
 n P P0 Ps Pw Q Q˙ q’’ ρ ρ σ sg SL





Area [m2] Exponent of the wind speed power-law [dimensionless] Solar incidence angle [°] Discharge coefficient of the opening [dimensionless] Air mass flow coefficient [m3] Pressure coefficient [dimensionless] Specific heat of the material [J kg-1 K-1] Air mass flow coefficient [kg/s at P=1 Pa] Reference condition temperature correction factor [dimensionless] Air capacity [J K-1] Terrain displacement length [m] Height of the boundary layer [m] Hydraulic diameter [m] Longwave emissivity of the surface Wind incidence angle [°] View factor Fraction of the window that is glazed Gravitational acceleration [m s-2] Average buildings height [m] Convection coefficient [W m-2K-1] Radiative coefficient [W m-2K-1] Flow coefficient [m3s-1m-1Pa-n] Thermal conductivity [W m-1 K-1] von Karman’s constant Characteristic dimension of the crack [m] Air
mass
[kg
s‐1] Flow exponent [dimensionless] Pressure [Pa] Pressure at the bottom of the zone [Pa] Static pressure [Pa] Wind-induced pressure [Pa] Air flow rate [m3 s-1] Heat flux [W] Heat flux per surface unit [W m-2] Air density [kg m-3] Reflectance Stefan-Boltzmann constant Average cross-section area presented to the wind by one element [m2] Lot size per element [m2]






 115


T

Temperature [K]

v v∗ Φ X Y Z z z0

Wind speed [m s-1] Atmospheric friction speed [m s-1] Flux CTF coefficient Outside CTF coefficient Cross CTF coefficient Inside CTF coefficient Height above ground [m] Terrain roughness [m]

List of Acronyms ABL AC ACH AFN AIJ ANSI ASHRAE ATC BES C CDH CFD CON CondFD CTF D&H DB DOE ED EECCAC EP FAR HVAC IAQ


116
 


Atmospheric Boundary Layer Air Conditioning Air Changes per Hour Airflow Network Architectural Institute of Japan American National Standards Institute American Society of Heating, Refrigeration, and AirConditioning Engineers Atlantic Central Building Energy Simulation Convection heat transfer Cooling Degree Hours Computational Fluid Dynamic Continental Conduction Finite Difference Conduction Transfer Functions Deaves and Harris model Dry Bulb temperature Department of Energy Energy Demand Efficiency and Certification of Central Air Conditioners EnergyPlus Frontal Aspect Ratio Heating, Ventilation, and Air Conditioning Indoor Air Quality







IDGD IWEC K MDM MDN MDS NTR NV NVP PAD PCM RbH SAR SR TNO TPU UCL UD UHI UV





Italian Climatic data collection Gianni De Giorgio International Weather for Energy Calculations Conduction heat transfer rate Mediterranean Mountains Mediterranean North Mediterranean South Net Thermal Radiation Naturally Ventilated Natural Ventilation Potential Plan Area Density Phase Change Materials Relative building Height Street Aspect Ratio Solar Radiation Dutch Organization for Applied Scientific Research Tokyo Polytechnic University Urban Canopy Layer Urban Density Urban Heat Island Unventilated






 117



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