AFRAMAX Tanker Design

MARIC, 7 May 2018, Shanghai

HOLISTIC-MULTIOBJECTIVE SHIP DESIGN OPTIMISATION: Applications to the Design of Tankers, Containerships and Passenger Ships by Professor Apostolos Papanikolaou National Technical University of Athens - NTUA Hamburger Ship Model Basin - HSVA

List of contents 1. Introduction to Holistic Ship Design Optimisation Important Ship Design Optimization Notions ◼ Holistic Ship Design Optimisation ◼ Multi-objective Optimisation ◼ Risk based Ship Design ◼

2. Multi-objective Optimisation of Tanker Design – EU project SAFEDOR & GL-NTUA Project BEST

3. Multi-objective Optimisation of Containerships •

GL-NTUA project CONTIOPT

4. Multi-objective Optimisation of Passenger Ships – EU Project GOALDS – EU Project HOLISHIP

5. Conclusions- The Way Ahead

A. Papanikolaou

HOLISTIC SHIP DESIGN OPTIMISATION

2

Important Design Optimization Notions (1) ◼

Holism (philosophical notion, from Greek όλος, meaning entire, total)- adj. holistic The properties of a system cannot be determined or explained by looking at its component parts alone; instead of, the system as a whole determines the behaviour of the

part components, i.e. interactions between the components cannot be neglected.

◼

◼

“The whole is more than the sum of the parts” (Aristotle ‘’Metaphysics’’) Reductionism-reduction: is sometimes interpreted as the opposite of holism. ” A complex system can be approached by reduction to its fundamental parts” Holism and reductionism need, for proper account of complex systems, to be regarded as complementary approaches to system analysis. A. Papanikolaou


3

Important Design Optimization Notions (2) ◼

Risk (from Greek ρίζα , meaning root, later used in Latin for cliff) – was originally introduced in economic sciences – Definition of financial risk: “A quantifiable likelihood (probability) of loss or of less-than-expected returns” – Later introduction in the assessment of safety critical engineering systems (nuclear power plants) – Much later introduction to the assessment of safety of ships through the pioneering work of Prof. K. Wendel (Germany) in the probabilistic theory of ship’s damage stability (early 60ties)

◼

Risk (general): “A quantifiable likelihood (probability) for the upset of an acceptable state or of a worse-than-expected state condition”

◼

Risk = E (loss) = Σ Pi x Ci = Σ frN(i) x loss (i)

◼

Safety: may be defined as “A societally acceptable state of risk” A. Papanikolaou


4

Important Design Optimization Notions (3) Optimization: “The identification of the best out of a series of many feasible options” ◼ Holistic Ship Design Optimisation is… “The multi-objective optimisation of ship design considering simultaneously all (holistically) design aspects, like ◼

– – – – – –

Intact and damage stability, Resistance and propulsion, seakeeping, added resistance in waves, manoeuvrability, Structural design, Arrangements and outfitting, Shipbuilding and operational costs, in- and out cash flow, etc…

for the entire ship life cycle”.

A. Papanikolaou


5

Important Design Optimization Notions (4) – Typical assessment criteria-indices for

holistic ship design optimization

◼

◼

◼ ◼ ◼

◼

◼ ◼

◼

◼

Required Freight rate (RFR): The freight rate per ton of transported cargo which covers all expenses, with a remainder sufficient for a reasonable return on the shipbuilding investment. Actual freight rates are set by the market and fluctuate during ship’s life cycle. Net Present Value (NPV): is the sum of the present values of all cash flows (in and out) discounted at a rate consistent with the ship investment's risk. The RFR is the freight rate producing discounted cash flows with zero NPV, i.e. the break even rate. EEDI: Energy Efficiency Design Index Building Cost, Yearly Operational Cost. Life Cycle Cost Cost for Averting fatalities (CAF): originally estimated 3.0 Mio US$ (1996), updated by project GOALDS (2012) 7.45 Mio US$ Gross Cost for Averting Fatalities (GCAF) =ΔCost of applied Risk Control Options (RCOs)/ ΔRisk of fatalities of human lives Net Cost of Averting Fatalities (NCAF) = ΔCost-ΔBenefit/ΔRisk Oil Outflow Index (OOI) = the % of carried oil which is statistically expected to be released to the marine environment over ship’s life cycle due to collision and grounding damages Attained Subdivision Index (A): the probability of ship’s loss (capsize and/or sinking) due to side collision and flooding Required Subdivision Index (R): is set by regulation as a function of ship size and People OnBoard POB (effect of extent of live boats)…..A > R

A. Papanikolaou


6

Holistic Design Optimisation-Generic Problem VARIATION OF DESIGN PARAMETERS • Hull form • Arrangement of spaces • Arrangements of (main) outfitting • Structural arrangements • Network arrangements (piping, electrical, etc) • etc… Parametric Model of Ship Geometry and Outfitting INPUT DATA GIVEN BY OWNER REQUIREMENTS AND/OR PARENT HULL •Deadweight, payload •Speed •Maximum Draft •Initial Arrangement •etc..

Design Optimization OPTIMISATION CRITERIA •Maximization of Performance/Efficiency Indicators •Minimization of Environmental Impact Indicators •Minimization of Building and Operational Costs •Maximization of investment profit •Minimization of investment risk •etc…

A. Papanikolaou

Output

CONSTRAINTS • • • •

Regulations set by society Market demand/supply Cost for major materials, fuel and workmanship Other, case dependent constraints


7

Important Design Optimization Notions (5) – Major design optimization objectives (…merit functions, criteria, indices…) ◼ Performance/Efficiency ◼ Safety ◼ Cost – Major design optimization constraints ◼ Safety regulations ◼ State of market (demand, supply, cost of steel, fuel, etc) ◼ Other, more case specific ◼

Considering the risk of an investment in a new shipbuilding, the design of

which should be holistically optimized, we might interpret the Holistic Ship Design Optimisation also as a generic Risk-based Ship Design Optimisation, in which the risk of an investment with specific profit

expectation is minimised , or the profit maximised for an acceptable risk. A. Papanikolaou


8

Fundamentals of Mathematical MULTI-OBJECTIVE OPTIMIZATION ◼

Ship design is a typical mathematical optimization problem of multiple (in many cases contradicting) objectives and constraints. Typical objectives in ship design (to be minimized within a multi-objective optimization procedure), are: – – – –

◼

◼

◼

Steel weight, Powering and other hydrodynamic criteria (added resistance in waves, seakeeping, maneuvering, etc..) Economic criteria: Shipbuilding and Operational cost, Required Freight Rate, Net Present Value Environmental criteria: accidental oil outflow, wave wash-HSC, EEDI

The result of a multi-objective optimization is a set of “best designs”, i.e.

designs which in order to further improve one design attribute (objective) the Decision Maker (DM =designer) has to sacrifice the performance of another. This set of “best designs” is known as the Pareto Set and its graphical depiction is the Pareto Frontier. One of the most efficient methods for finding the Pareto Frontier is the Multi-objective Genetic Algorithms (MOGA) method. A. Papanikolaou


9

MULTI-CRITERIA DECISION MAKING (MCDM) ◼

◼

With the Pareto set of non-dominated designs in hand, the Decision Maker has to select the optimal solution according to his preferences. This can be done in a number of ways: – Use of the Utility Function technique for ranking the different designs – Use of Scatter 2D & 3D diagrams for visually identifying the more attractive designs, compare them on the basis of his criteriapreferences and intuitively (experience) select the optimum or setup relevant utility function (see above) – Use other visual tools (parallel plots, histograms, frequency plots, student plots etc.) and decide again according to his experience For the finally selected set of designs, a 2nd stage, local hull form hydrodynamic optimization may be performed leading to the final design A. Papanikolaou


10

Generic Ship Design Optimization Software Platform of NTUA-SDL

A. Papanikolaou


11

Basic Optimization Flowchart, V.1

Integrated NAPA®-FRONTIER®-POSEIDON® Platform

GL-NTUA Project BEST

Create Design Variable Vector NAPA Read Design Variables Vector Read Parameter Values Create Geometric Model Calculate Capacity Calculate Oil Outflow Calculate Intact & Damage Stability

Frontier

POSEIDON Create Structural model Check Required Scantlings Calculate Steel weight in cargo space area

A. Papanikolaou


12

Basic Optimization Flowchart, V.2

Integrated NAPA-FRONTIER-POSEIDON-SHIPFLOW Platform

GL-NTUA Project BEST Create Design Variable Vector

NAPA-1 Read Design Variables Vector Read Parameter Values Create Hullform Create Capacity Plan POSEIDON

Resistance Code (SHIPFLOW, CFD etc.)

Create Structural model

Frontier

Check Required Scantlings

Calculate Steel weight Distribution

Create calculation grid

NAPA-2 Calculate Lightship & DWT

Calculate Total Resistance

Check MARPOL Requirements

Create Objectives A. Papanikolaou


13

Final Optimization Flowchart BEST+ Integrated FS/CAESES-NAPA-POSEIDON Platform

Optimization Flowchart Optimization Control (FFW) hydrodynamic response surface file

Hull Form Generation

Max. Speed Computation COT compartmentation file

Tank Computation

(Economic) Target Evaluation

hullform IGES file

Stability, Trim, Draft Computation poseidon template file

structural configuration file

Cargo Hold Mass Computation

Total Mass Computation

Oil Outflow Index Computation FFW

EEDI Computation

NAPA POSEIDON generated file (fix) configuration file

BEST plus – a novel AFRAMAX tanker design concept | last modified: 2011-01-20, PCS | No. 1

A. Papanikolaou


14

Implementation 1: Parametric Model in NAPA® – GA of Reference Ship

A. Papanikolaou


15

Implementation 2: Friendship Framework System® AFRAMAX Tanker GUI

A. Papanikolaou


16

Details of parametric models and design parameters: the structural design model General Arrangement created from FFW ....

... and structural template model ...

... translates into structural CSR model in Poseidon*

including arrangement of •girders •stiffeners •cutouts •plates •compartments

* slop tanks not modelled

HOLISTIC A. Papanikolaou

SHIP DESIGN OPTIMISATION

17

Structural detail for raised double bottom at COT no 1

A continuous ramp is used to link the inner bottom with varying heights near COT no 1.

A. Papanikolaou


18

Initial Studies of AFRAMAX Tanker Pareto Frontier Designs

A. Papanikolaou


1919

Multi-criteria Decision Making by Utility Functions Technique

Option 1: Equal Weights of Criteria Case Design ID Cargo.Vol

6x3 Flat 1710 (#1) 129804 (+2%)

Oil.Outflow

0.00777 (-23%)

Wst.cargo.area 10908 (-2%) Case Design ID Cargo.Vol

6x3 Flat 2122 (#2) 135950 (+7%)

Oil.Outflow

0.00942 (-6%)

Wst.cargo.area 11013 (-1%)

Reference Design Cargo.Vol 126765 Oil.Outflow 0.01006 Wst.cargo.area 11077

A. Papanikolaou


RB Optimization of Tanker Design - Apr 09

2020

Multi-criteria Decision Making by Utility Functions Technique –

Option 2: Unequal Weights

Case Design ID Cargo.Vol

6x3 Flat 2069 137494 (+8%)

Oil.Outflow

0.0111 (+10%)

Wst.cargo.area 10894 (-2%) Case Design ID Cargo.Vol

6x3 Flat 2122 (#2) 135950 (+7%)

Oil.Outflow

0.00942 (-6%)

Wst.cargo.area 11013 (-1%)

Reference Design Cargo.Vol 126765 Oil.Outflow 0.01006 Wst.cargo.area 11077

A. Papanikolaou


RB Optimization of Tanker Design - Apr 09

2121

BEST (Better Economics with a Safer Tanker)® A novel AFRAMAX tanker design by Germanischer Lloyd and NTUA-SDL

Background ◼Oil

Potential loss of cargo from oil tankers 24 20 16 12 8


ex

NA SF

pl o

si on

fir e

gr ou nd in g

ac t co nt

io n

4 0

co lli s

per ship year

tanker design was lately driven mainly by production aspects. The product has changed little. ◼A recent analysis(1) for large oil tankers documented that the risk to environment is dominated by collision, grounding and fire. ◼In response, GL and the National Technical University of Athens (NTUA) teamed up in 2008 and developed a novel AFRAMAX tanker design concept. ◼The design was called BEST – Better Economics with a Safer Tanker – and it won the Greek Lloyd’s List Shipping Award for technical innovation in 2009. ◼GL also received feedback from shipyards and operators regarding the desired features of the new design and now focuses on, compared to traditional designs, less extreme layout variations. ◼The novel BEST+ design enhances the attractiveness of the initial concept by also integrating hydrodynamic optimisation of the hull form.

Local hull form optimisation: CFD simulation within BEST+ ◼SHIPFLOW

by FLOWTECH – Well established and validated – Robust and relatively fast

◼Zonal

approach

– Potential flow analysis ◼ Free trim and sinkage ◼ Non-linear free surface boundary conditions – Boundary layer computation – RANSE simulation ◼ Overlapping grid technology

A. Papanikolaou


24

2nd stage hydrodynamic optimisation procedure ◼Pre-determine

set of variants

for a large

– Wave resistance from non-linear potential flow theory – Viscous resistance from RANSE ◼ Taking into account the propeller via an actuator disc ◼ Double-model assumption (waves ignored) – Wake quality from RANSE ◼ Correlate propulsive efficiency from a criterion that takes into account loading and wake homogeneity

A. Papanikolaou

◼Use

pre-determined hydrodynamics – Build response surface models (RSM) via ‘’kriging’’ interpolation approach ◼ Total resistance ◼ Propulsion characteristics, assuming standard propeller – Get fast feedback for each variant from RSM in holistic optimization


25

Panels and grids by CAESES®/FSS

A. Papanikolaou


26

Hydrodynamics of final hull at 13, 14, 15 and 16kn by SHIPFLOW

V = 13kn V = 14kn

Design draft = 13.7m

V = 15kn V = 16kn

Scantling draft = 14.8m

A. Papanikolaou


27

Final hull form visualisation by CAESES

A. Papanikolaou


28

Final hull form visualisation

A. Papanikolaou


29

Speed-power curves

(incl. variability for loading/ballast and trim conditions)

Scantling draft = 14.8m

Design draft = 13.7m

A. Papanikolaou


30

Scatter Diagrams-150 Generations each with 30 Population of 5x3 COT AFRAMAX tanker designs vs. 6x2 COT traditional (ref. ship) and improved designs (BEST+, 6x2) Second Optimization (150X30 Designs, Design Speed 15knots) RFR vs. Oil Outflow 5X3 Twin Skeg (4500 variants)

10 9.8

BEST+

Required Freight Rate (USD/tonne, 1000$/t)

9.6

I.D 2590 (a)

9.4 9.2

I.D 2515 (a)

9

I.D 3210 (b)

8.8 8.6

I.D 1431 (b)

Improvement of 34.7% in OOI

8.4

I.D 4567 (b)

8.2 I.D 4416 (b)

8 7.8

I.D 4247 (b)

7.6 I.D 559 (b)

7.4

BEST OOI

7.2 7 0.007

Possible compromise A. Papanikolaou

I.D 2111 (b) 0.008

0.009

0.01

0.011

0.012

0.013

0.014

0.015

6X2 Reference

BEST RFR HOLISTIC SHIP DESIGN OPTIMISATION

31

Operational Analysis-Optimal Speed ➢

Investigation of the optimal ship speed (slow steaming of 5x3 design): ➢ The ship were the RFR is minimum!

➢

Three scenarios for fuel cost: ➢ 500, 750 (current price) and 1000 USD/tonne. ➢ The 1000 $/t is very likely to come in effect with Ultra Low Sulphur Fuels

Speed Curves:

Required Freight Rate (USD/tonne)

➢

Speed-RFR Curves

9 8.8 8.6 8.4 8.2 8 7.8 7.6 7.4 7.2 7 6.8 6.6 6.4 6.2 6 5.8 5.6 5.4 5.2 5

10.75 knots

HFO 1000 $/t

HFO 750$/t

11.7 knots HFO 500$/t

6

7

8

9

10

11

12

13

14

15

16

17

13 knots

Operating Speed (knots)

A. Papanikolaou


32

Sample results: Oil outflow index vs. cost of transport • • •

Variation of angle of hopper plate [30°...60°] width of hopper plate [4m...6m] distance from inner hull to outer hull [2.1m...3m*]

0.967

MARPOL limit

0.966

Normalized Cost of Transport

◼

0.965

0.964

0.963

0.962

0.961

selected design

0.96

0.959 0.0115

0.0125

0.0135

0.0145

0.0155

0.0165

Oil Outflow Index

➔ An oil outflow index 20% less than the MARPOL limit can be reached for the considered 6x2 layout ➔ A minimum oil outflow index design has an increased cost of transport of 0.5%

*5m height of innerbottom in foremost tank

A. Papanikolaou HOLISTIC SHIP DESIGN OPTIMISATION

33

Feasibility Prospects ◼

◼

The proposed designs can be readily built at slightly higher cost (initial investment) and are economically very attractive to operate (significantly reduced freight rates). The introduction of raised double-bottom height in the forward part of the ship (and possibly raised deck by a linear sheer) is very simple and yet has a great potential as RCO (Risk Control Option); considering the frequency of grounding incidents in the bow region it appears to be an attractive safety measure to be quickly adopted.

A. Papanikolaou


34

The BEST plus team

from left to right: Prof. Apostolos Papanikolaou, NTUA; Dr. Stefan Harries, Friendship Systems, Dr. Pierre. C. Sames, GL, Mattia Brenner, Friendship Systems, Prof. George Zaraphonitis, NTUA, Marc Wilken, GL

A. Papanikolaou


35

Holistic Optimization of High Efficiency & Low Emission Containership CONTiOPT : Bilateral NTUA-GL Research Project on Containership Optimization

A. Papanikolaou, L. Nikolopoulos National Tech. Univ. of Athens, Ship Design Laboratory, Athens/Greece

P. Sames, M Köpke Det Norske Veritas-Germanischer Lloyd , Hamburg/Germany

S. Harries FRIENDSHIP SYSTEMS GmbH, Potsdam/Germany 5th Transport Research Arena Conference – TRA2014 14-17 April 2014, Paris

Risk-based RoPax Design Optimisation EU project GOALDS

The case study focuses on the conceptual/preliminary stage by optimising ship’s main dimensions, hull form and internal compartmentation. Some details of the present study were presented at STAB2012 by a joint paper of NTUA and FINCANTIERI Zaraphonitis, G., Skoupas, S., Papanikolaou, A., Cardinale, M., "Multi-objective Optimization of Watertight Subdivision of ROPAX ships Considering the SOLAS 2009 and GOALDS s Factor Formulations", Proc. 11th International Conference on the Stability of Ships and Ocean Vehicles, 23-28 Sep. 2012, Athens)

A. Papanikolaou


37

Vienna Model Basin Ltd.

NTUA-SDL Coordinator A. Papanikolaou


38

State of the market: The problem and the challenges

Comparable max. data Titanic capacity (max): pass 2435, crew 892 total 3,327 Allure of the Seas capacity (max): Pass 6,296, crew 2,394 total 8,690

39

A. Papanikolaou


Conducted Ship Design Optimization Studies Investigation of the impact of the GOALDS probability of survival formulation on the design and operational characteristics of ROPAX and Cruise ships on the basis of a series of sample ships that were selected to be re-designed/optimised for: • enhanced survivability, namely minimum risk for Potential Loss of Lives (PLL), considering also • building cost • efficiency in operation

• and lifecycle cost

A. Papanikolaou


40

Original Design: Medium Size RoPax designed by Fincantieri/Italy

A. Papanikolaou


41

Main dimensions, Medium Size RoPax Length between perpendiculars Subdivision length Breadth Subdivision draught Height of bulkhead deck Number of passengers Number of crew Gross tonnage Deadweight Lane meters R index (SOLAS2009) A index (SOLAS2009) A index (GOALDS)

A. Papanikolaou

162.85 m 182 m 27.6 m 7.10 m 9.80 m 2080 120 abt. 36000 5000 t 1950m 0.79804 0.80305 0.82666


42

Medium size ROPAX: Developed customized full parametric model in NAPA® for change of main dimensions, hull form and internal compartmentation: here, change of beam

A. Papanikolaou


43

Basic Features of developed Parametric Model in NAPA® • •

•

•

Initialization phase (read input variables) Definition phase 1. Create the hullform and internal arrangement 2. Define loading conditions 3. Define openings, cross-connections, escape routes Evaluation phase 1. Evaluate geometric constraints 2. Calculate lanes length and transport capacity 3. Estimate resistance and propulsion power 4. Calculate intact stability 5. Calculate A-SOLAS 2009 and A-GOALDS 6. Calculate economic indicators 7. Calculate Potential Loss of Life (PLL) Print output files

A. Papanikolaou


44

Medium size RoPax: formulation of the optimization problem Optimization Variables (range) LREF 157m - 167m BREF 27.5m - 28.2m TREF 6.8m - 7.2m DBTHT-AFT 2.3m - 2.8m DBTHT-MID 1.5m - 2.0m DBTHT-FWD 1.4m - 1.9m DK1HT 2.3m - 2.6m DK2HT 2.1m - 2.3m DK3HT 3.0m - 4.2m DK4HT 5.4m - 5.8m Number of Feasible Designs: Number of Unfeasible Designs:

A. Papanikolaou

Objective Functions min(PLLGOALDS) min(Econ. Impact)

Constraints • ASOLAS-R>0 • PLLGOALDS