Diploma Thesis Lampros Nikolopoulos, MEng. Prof. Dr. Ing. Apostolos Papanikolaou

Transcription

1 A Holistic Methodology for the Optimization of Tanker Design and Operation and its Applications National Technical University of Athens-School of Naval Architecture and Marine Engineering Ship Design Laboratory Better Economics with a Safer Tanker (BEST++) Diploma Thesis Lampros Nikolopoulos, MEng Thenamaris Ships Management Inc. Prof. Dr. Ing. Apostolos Papanikolaou Director Ship Design Laboratory Professor Kostas Spyrou, Associate Professor George Zaraphonitis SNAME Greek Section Thesis Competition

2 Contents Introduction-Background to the Tanker Shipping Industry Developed Methodology and Sensitivity Analysis Case Studies on the Optimization of AFRAMAX Tankers Perspective: Case Study on the Optimization of a VLCC Conclusions, Discussion and Future Perspectives

3 Introduction: Background to the Tanker Shipping Industry

4 Background to the Tanker Shipping Industry The Evolution of a Giant: First crude oil carrier: Glucklauf (1910), 3000 tonnes of Kerozene in 16 tanks 1945: T-2 and T-3 Tankers, and DWT 1966: Idemitzu Maru, DWT Large magnitude of scale economies 1975: Batillus Class, DWT A re-active regulatory framework: 1967: Torrey Canyon (navigational error-grounding) 1978: Amoco Cadiz (loss of steering gear-grounding) MARPOL 73/ : Exxon Valdez OPA 90 and introduction of Double Hull 1998: Erika Phase out of Single Hull, SIRE and Vetting Inspections 2002: Prestige Evolution of Tank Arrangement:

5 Background to the Tanker Shipping Industry Modern Economic Challenges Modern Tanker categories: Charter Rates Development: Trade Routes: New Building Prices:

6 Background to the Tanker Shipping Industry Modern Environmental Challenges New Regulations for Ship Emissions Control: MARPOL Annex VI for Emission Control Areas (ECAs). SOx and Nox limits for new engines (Tiered reduction) Regulations for Efficiency (CO2 and fuel consumption performance): Technical Measures: EEDI, EEOI, SEEMP Market Based Measure: CO2 fund, Fuel Levy etc. International Convention for Ballast Water Management: Applicable from Need for use of treatment technologies.

7 Thesis Objectives Diploma Thesis of L. Nikolopoulos at NTUA: A Holistic Methodology for the Optimization in Tanker Design and Operation and its Applications, supervised by Prof. A. Papanikolaou Emphasis of method on the following design aspects: Safety, especially minimization of probabilistic oil outflow Efficiency, minimization of emissions, fuel costs Competitiveness, increased profitability with reduced OPEX Global and Risk-Based Approach, with the aim to generate a safer tanker which is also more competitive. Holistic: Aristotle definition of «όλον» being greater than the sum of all parts Employed software platform for this project is the Friendship Framework Two Case Studies: Innovative, shallow draft, twin skeg/screw AFRAMAX Tanker of 5X3 Tank Arrangement. Conventional, single screw VLCC with a 6X3 Tank Arrangement (less detailed application-proof of applicability)

8 Design Methodology and Theory The computations, assumptions and workflow of the analytical model

9 Calculations Workflow Geometric Model EEDI Calculation Initial Hydrostatic Calculation Required Freight Rate Calculation Lackenby Variation Oil Outflow Calculation Tank Arrangement Modeling Capacity Calculation Stability and Loadline Check Water Ballast Calculation Capacity and Cargo Special Gravity Check Resistance Prediction Machinery Calculations Deadweight Analysis Lightship Calculation

10 Sensitivity Analysis of the Developed Methodology RFR Sensitivity

11 Case Study on the Holistic Optimizaiton of AFRAMAX Tankers

12 CASE STUDIES ON AFRAMAX TANKERS The G5 Tanker: Tender Preliminary Concept Study (VISIONS 2011) Investigation of the Potential Use of Deep Well Pumps for AFRAMAX Tankers (BEST++ Project) Investigation of Structural Aspects of NX3 Tank Arrangements for AFRAMAX Tankers (BEST++ Project) PART ONE HOLISTIC OPTIMIZATION STUDIES OF TWIN SCREW AFRAMAX TANKER PART TWO MINIMIZATION OF: RFR OOI EEDI TOTAL NUMBER OF GENERATED DESIGNS:20000 MULTI VENTURE Enviromentally Friendly and Efficient Tankers: PART THREE LNG as a fuel Optimal Operating Speed Bulb Optimization New Propulsion Systems (VISIONS 2012)

13 Part One: Initial Design, Research and Analysis

14 Part One of Case Study The G 5 Tanker Original concept for shallow draft, twin skeg tanker with two longitudinal bulkheads was submitted as a proposal ( The G 5 Tanker ) for the VISIONS Olympics Competition in March Shortlisted idea, took the 4 th place in the competition. No optimization, just a concept illustrating the potential. Need for better and more efficient hullform. "A Holistic Method for the Optimization of Tanker Design and Operation and its Applications", SNAME Thesis Competition, Lampros Nikolopoulos, NTUA-SDL,

15 Part One of Case Study Potential Use of Deep Well Pumps Undertaken for the joint BEST++ project. Indicated a 2.5 mil $ additional costs for the installation of the pumps at a 6x2 AFRMAX tanker, without taking into account possible shipyard savings (cost estimates acc. to pump supplier). Superior efficiency (shorter time at port) and drastic reduction of fuel costs for unloading (from 80000$/discharge to $/discharge, when operating in an ECA area) Faster port operations means a better RFR performance (more annual trips, with the payback time of the equipment being approx. one year) Estimated (then) cargo capacity increase of 2-3% due to the elimination of the pump room.

16 Part One of Case Study Structural Weight Comparison for NX3 Designs Undertaken in in order to validate the assumptions taken in the previous related projects (BEST, BEST+). Used an available 6X3 Reference Design, M/T NAVION BRITANNIA. Modeled in POSEIDON as a reference design by use of the BEST+ structural design template. Design I.D Longitudinal Members (t/m) Longitudinal Members (tonnes) Trans. Members (tonnes) Trans. BHD (tonnes) Total (tonnes) Template_6X3 54,8 1932,92 383,7 230,1 2546,72 Template_5X ,2 252,4 233,5 2391,10 Template_6X2 53,1 1888,48 392,5 167,8 2448,78 BEST_Optimized , ,8 2179

17 Part Two: Global Optimization Studies

18 Design Concept Twin Skeg Arrangement

19 Design Concept Elliptic Bilge Geometrical Modeling Elliptic Bilge of the midship section:

20 Design Concept Tank Arrangement NX3 tank arrangement was herein chosen by default: due to the superior performance in terms of accidental oil outflow (as indicated by the TANKOPT results). Number of longitudinal tanks reduced to 5 (instead of 6): compensate the increase in structural weight due to the introduction of a second longitudinal bulkhead (approx. 200 tones weight). Other ways to compensate the increase of structural weight: increase the tank size and capacity together increase in displacement (due to a bigger Cb thanks to twin skeg). Use of deep well pumps (instead of conventional pump room): Engine room bulkhead moved towards the aft of the ship. Initial estimation of 2-3% increase Increase was up to 6%. Tank Variables: Double Bottom height Double Hull Width Mid Tank Width Hopper angle and length in accordance with BEST+ results

21 Design Concept Tank Arrangement-Modeling

22 Optimization Principles Design Variables Generation of Design Variants Design Engine: NSGA II Design Evaluation: -Oil Outflow Index -Required Freight Rate -EEDI Design Constraints Constraint Limit Upper Special Cargo Gravity <0.92 Lower Special Cargo Gravity >0.82 Deadweight < tonnes Double Bottom Height (MARPOL limit) >2.0m Double Hull Width (MARPOL limit >2.0m Accidental Oil Outflow Parameter (MARPOL limit) <0.015 Draft (Port Restrictions) <14.8m

23 FOS FOB B D T

24 Design Variables Design Variable Lower Bound Upper Bound Length Between Perpendiculars (m) Beam (m) Deck Height (m) Draft (m) Cb LCB (% Lbp) FOB (% B) FOS (%D) End of Parallel Midbody (% Lbp) Beggining of Parallel Midbody (% Lbp) Bulb Length (% Lbp) Double Bottom Height (Tanks 2-5, m) Double Hull Width (m) Mid Tank Width (% Bcargo) Design Speed (knots) "A Holistic Method for the Optimization of Tanker Design and Operation and its Applications", SNAME Thesis Competition, Lampros Nikolopoulos, NTUA-SDL,

25 Optimization Stages A multi-staged Approach 1st Stage: Design Space Exploration Design of Experiment (DoE) Sobol Algorithm producing 3000 variants 2nd Stage: Design Space Exploration DoE 2 and Design Speed Effect Investigation Sobol Algorithm producing 6000 variants I.D 314 3rd Stage: Formal Optimization with Genetic Algorithms NSGA II Design Engine producing 2500 variants I.D 2590, 1838,2515,2738 4th Stage: Formal Optimization with Genetic Algorithms ( I.D 2515) NSGA II Design Engine producing 3000 (150X20) and 4500 (150X30) variants Dominant Variants for 2500 variants: I.D 2896, 1943, 2294, 2210, 1686, 2954 Dominant Variants for 4500 variants: I.D 3210, 1431, 4567, 4416, 4247, 559, 2111

26 Optimization Stages A multi-staged Approach 1st Stage: Design Space Exploration Design of Experiment (DoE) Sobol Algorithm producing 3000 variants 2nd Stage: Design Space Exploration DoE 2 and Design Speed Effect Investigation Sobol Algorithm producing 6000 variants I.D 314 3rd Stage: Formal Optimization with Genetic Algorithms NSGA II Design Engine producing 2500 variants (100X25) I.D 2590, 1838,2515,2738 4th Stage: Formal Optimization with Genetic Algorithms ( I.D 2515) NSGA II Design Engine producing 3000 (150X20) and 4500 (150X30) variants Dominant Variants for 2500 variants: I.D 2896, 1943, 2294, 2210, 1686, 2954 Dominant Variants for 4500 variants: I.D 3210, 1431, 4567, 4416, 4247, 559, 2111

27 Optimization Stages A multi-staged Approach 1st Stage: Design Space Exploration Design of Experiment (DoE) Sobol Algorithm producing 3000 variants 2nd Stage: Design Space Exploration DoE 2 and Design Speed Effect Investigation Sobol Algorithm producing 6000 variants I.D 314 3rd Stage: Formal Optimization with Genetic Algorithms NSGA II Design Engine producing 2500 variants I.D 2590, 1838,2515,2738 4th Stage: Formal Optimization with Genetic Algorithms ( I.D 2515) NSGA II Design Engine producing 3000 (150X20) and 4500 (150X30) variants Dominant Variants for 2500 variants: I.D 2896, 1943, 2294, 2210, 1686, 2954 Dominant Variants for 4500 variants: I.D 3210, 1431, 4567, 4416, 4247, 559, 2111

28 4 th Stage: 2 nd Genetic Algorithm Run Background Two runs were made: First: 3000 variants generated by 150 Generations of 20 Population Second: 4500 variants generated by 150 Generations of 30 Population Reason for two runs: see the effect of population size on the solution and push the boundaries of optimization The variables, constraints and boundaries were the same for both runs. First Run (150X20): Gaps in certain areas indicate that the population size was not adequate Two peaks created: low OOI and low RFR values. Designs dominate the 6X2, both in terms of OOI and cargo capacity. Large cargo capacity for the majority EEDI-RFR pattern almost constant. RFR dominant variants appear to be EEDI dominant too Inferior RFR-OOI performance

29 4 th Stage: 2 nd Genetic Algorithm Run 150 Generations with 30 Population: RFR vs. Oil Outflow 10 9,8 9,6 Second Optimization (150X30 Designs, Design Speed 15knots) RFR vs. Oil Outflow 5X3 Twin Skeg (4500 variants) BEST+ I.D 2590 (a) Required Freight Rate (USD/tonne, 1000$/t) BEST OOI 9,4 9,2 9 8,8 8,6 8,4 8,2 8 7,8 7,6 7,4 Improvement of 40% in OOI 7,2 BEST RFR 7 0,007 0,008 0,009 0,01 0,011 0,012 0,013 0,014 0,015 Accidental Oil Outflow (Acc. MARPOL Reg. 23) I.D 2515 (a) I.D 3210 (b) I.D 1431 (b) I.D 4567 (b) I.D 4416 (b) I.D 4247 (b) I.D 559 (b) I.D 2111 (b) 6X2 Reference Possible compromise

30 4 th Stage: 2 nd Genetic Algorithm Run 150 Generations with 30 Population: EEDI vs. RFR Second Optimization (150X30 Designs, Design Speed 15 knots) EEDI vs. RFR 4 3,9 5X3 Twin Skeg (4500 variants) BEST+ EEDI (Acc. IMO MEPC 62) 3,8 3,7 3,6 3,5 3,4 3,3 3,2 3,1 3 2,9 Frequent Dominant Variants indicated by utility functions 6 6,2 6,4 6,6 6,8 7 7,2 7,4 7,6 7,8 8 8,2 8,4 8,6 8,8 9 Required Freight Rate (USD/tonne, HFO 1000 $/t) ID 2590 (a) I.D 2515 (a) I.D 3210 (b) I.D 1431 (b) I.D 4567 (b) I.D 4416 (b) I.D 4247 (b) I.D 559 (b) I.D 2111 (b) BEST RFR BEST EEDI BEST OOI

31 4 th Stage: 2 nd Genetic Algorithm Run 150 Generations with 30 Population: Vcargo vs. Oil Outflow Accidental Oil Outflow (MARPOL Reg. 23) 0,015 0,014 0,013 0,012 0,011 0,01 0,009 Second Optimization (150X30 Designs, Design Speed 15 knots) Cargo Capacity vs. Oil Outflow 5X3 Twin Skeg (4500 variants) BEST+ I.D 2590 (a) I.D 2515 (a) I.D 3210 (b) I.D 1431 (b) I.D 4567 (b) I.D 4416 (b) I.D 4247 (b) I.D 559 (b) I.D 2111 (b) 0, Cargo Carrying Capacity (cubic meters) 6X2 Reference

32 4 th Stage: 2 nd Genetic Algorithm Run 150 Generations with 30 Population-Comments The scatter diagrams now are more dense. V-shaped pareto front instead of peaks The EEDI-RFR pattern remains the same. The Lowest OOI has a satisfactory RFR performance The Lowest RFR is at the upper boundaries of the variants size. BEST-Trade-off: Frequently appeared in the ranking of the utility functions Better RFR, OOI and EEDI performance than 6X2 AFRAMAX designs.

33 4 th Stage: 2 nd Genetic Algorithm Run 150 Generations with 30 Population-Design Ranking 0,706 U1: 1/3 EEDI, 1/3RFR, 1/3 OOI 0,77 U2:0.1 EEDI, 0.8 RFR, 0.1 OOI 0,705 0,704 0,768 0,703 0,766 0,702 0,701 0,764 0,7 0,762 0,699 0,698 0,76 0,697 0, U3: 0.2 EEDI, 0.2 RFR, 0.6 OOI 0,756 0,695 0,709 0,708 0,707 0,706 0,705 0,704 0,703 0,702 0,701 0,7 U4: 0.4 EEDI, 0.3 RFR, 0.3 OOI 0,69 0,685 0,68 0,675 0,67 0,665 0,702 0,701 0,7 0,699 0,698 0,697 0,696 0,695 0,694 0,693 0,692 U5: 0.2 EEDI, 0.4 RFR, 0.4 OOI

34 Optimization Results Dominant Variants Comparison Results indicate an increase of the cargo structural weight which however is not influencing the RFR value which is decreased: 6X2 I.D 2515 I.D 3210 Reference OOI % % Wst cargo t % t % Cargo Capacity m m % m % RFR $/t $/t % $/t % Ballast Water m m 3-47% m % BEST+ I.D 2515 OOI % Wst cargo t % Cargo Capacity m m % RFR $/t $/t -0.1% Ballast Water m m 3-47% EEDI gco2/t gco2/t -2.95%

35 Comparison of OOI performance with previous concepts 0,015 0,014 6X2 Flat (TANKOPT) 0,013 6X2 Corrugated (TANKOPT) 0,012 0,011 0,01 0,009 6X3 Corrugated (TANKOPT) 6X3 Flat (TANKOPT) 7X2 Flat (TANKOPT) 0,008 5X3 Twin Skeg 0,007 BEST+ 0,

36 Optimization Results Main Particulars Principal Particular BEST+ I.D 2515 (a) I.D 4416 (b-2) L (m) B (m) D (m) T (m) Cb LCB (m) Not available FOB (%B) Not available FOS (%D) Not available Bulb Length (m) Not available Displacement (tonnes) Not available Height DB (m) Width DH (m) No. of Tanks 12 (6X2) 15 (5X3) 15 (5X3) Mid Tank Width (% B) NaN Cargo Capacity 98% Design Speed (knots) Installed Power (kw) Lightship Weight (tonnes) Deadweight (tonnes) Payload (tonnes) Not available EEDI (t CO2/tonne*mile) RFR (USD/tonne) Reg.23 Oil Outflow Index

37 Operational Analysis-Optimal Speed Investigation of the optimal ship speed: 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) 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, knots Speed-RFR Curves Operating Speed (knots) "A Holistic Method for the Optimization of Tanker Design and Operation and its Applications", SNAME Thesis Competition, Lampros Nikolopoulos, NTUA-SDL, HFO 1000 $/t HFO 750$/t HFO 500$/t 11.7 knots 13 knots

38 Part Three: Multi Venture All Electric, Dual Fuel Hybrid Propulsion System for a Tanker

39 Part Two: Multi Venture The All Electric Tanker Participation in VISIONS 2012 Ship Design Olympics Team: Lampros Nikolopoulos, Nikos Mantakos, Michalis Pytharoulis Objectives for the Competition: Energy Efficient Tanker Use of Alternative Fuels Multi Venture: Hull Efficiency: Results of Global Optimization Optimized Bow Propulsion Efficiency: Wake Adapted Propeller Use of LNG as a fuel Use of Hybrid Technologies (e.g Fuel Cells) Lifecycle Assessment of Environmental Performance

40 Bulb Optimization Objectives: Reduction of Wetted Surface, Reduction of Wave Making Resistance Computation: Geometry built in FFW, Minimization of Wetted Surface with NSGA II Assessed by CFD code SHIPFLOW, using Potential Flow Theory (XPAN code) Constraints: Displacement and Deadweight Constraint (up to 1% deviation) Tank Capacity Constraint (up to +1% deviation). Result: 42% reduction of the Wave Making Resistance (compared to original) 1.5% reductional of total resistance and installed power

41 Multi Venture I.D 2515

42 All Electric Tanker Load Analysis Propulsion Loads: as before (2stroke) Use of redued resistance from bulb optimization Auxilliary Loads: calculated based on equivalent size Bulk carrier data Normal Sea going, Maneuvering, Cargo Unloading and Harbour Conditions FRAMO power pack for cargo pumps Propulsion Motors: Chosen from ABB according to Azipod range (same internal motor) Type 25 : ~12 MW at 100 RPM Generators: Medium Voltage (6600 V, 60 Hz)

43 All Electric Tanker Hybrid Dual Fuel Electric Propulsion FUEL CELLS STEAM TURBINE GENERATOR STEAM TURBINE GENERATOR FUEL CELLS 7330 kw 7330 kw GENSET 1 8L50 DF 2610 kw 2610 kw GENSET 4 8L50 DF GENSET 2 6L34 DF GENSET 3 6L34 DF G G G G 6600 V, 60 Hz 6600 V, 60 Hz LIGHTING LIGHTING FRAMO PUMPS POWER PACK FRAMO PUMPS POWER PACK M 2048 kw 2048 kw M PROPULSION LOAD PROPULSION LOAD 6646 kw SHORE CONNECTION SHORE CONNECTION 6646 kw

44 All Electric Tanker Engine Room and LNG tank Arrangement Engine Room Arrangement: Upper Deck Fire proof Type A60 longitudinal bulkhead Design for Safety Daily and Settling tanks modeled Control Room aft of the Generator Room Design for Security Steering Gear in the same position LNG Tank Arrangement: IMO C-Type tanks 4X400 m 3 Tanks on deck 3X200 m 3 Vertical tanks in E.R LNG Range: 4000nm Combined Range: 15000nm Modularized Engine Room Concept: Retractable Roof on main deck and hatches, Easily accessible engine room Engine Leasing Program

45 All Electric Tanker Engine Room and LNG tank Arrangement S.G/Control Room Generator Room Gas Preparation Room Vertical LNG tanks

46 All Electric Tanker Steam Turbine

47 All Electric Tanker Steam Turbine Waste Heat from Generators Exhaust Gas Boiler, used in LNG and Diesel modes, LNG mode can have better heat recovery (bigger LHV) Approx. 6.5 MW of exhaust gas energy Preheater, Evaporator and Superheater Single, High Pressure System Steam Turbine: High pressure turbine Steam pressure (inlet): 15bar Outlet pressure:0.05 bar Electrical Output of approx. 1.7 MW Combined with fuel cells 2MW of hybrid propulsion (15% of installed power)

48 All Electric Tanker Economic Asessment Increased CAPEX: 6.6 mil cost for LNG bunker installation Initial 15% twin screw extra taken to 20% for Diesel Electric Drive Reduced Lightship Reduced OPEX, VOYEX: Cheaper Fuel (LNG) Better engine loading in laden and ballast legs RFR difference from I.D 2515: HFO as fuel (750$/t): +1.41% LNG as fuel (500$/t): -11.7% HFO and LNG (eq. 650$/t): -4.16%

49 All Electric Tanker Environmental Assessment Environmental Assessment: EEDI is not applicable for DE applications! Need for Lifecycle Assesment Tool of Machinery Emissions Thesis of Mr. Nikos Mantakos (super. By Prof. Ventikos) Estimated Ship Life 25 years Emissions Breakdown: CO2, SOx, NOx, PM Increase of CH4 by 39% Major improvement in comparison with existing AFRAMAX ships Methane Slip can be tackled by: Improvement in combustion technology Use of afterburner

50 Life Cycle CO2 Emissions (Operation) Life Cycle NOX Emissions (Operation) 1,200E+06 1,000E+06 8,000E+05 6,000E+05 1,05E % % 4,000E+04 3,000E+04 2,000E+04 3,160E % 4,000E+05 1,000E % 2,000E+05 0,000E+00 Single Skeg HFO TwinSkeg HFO (I.D 2515) Multi Venture 0,000E+00 Single Skeg HFO TwinSkeg HFO (I.D 2515) Multi Venture Life Cycle PM Emissions (Operation) Life Cycle S02 Emissions (Operation) 3,000E+03 1,886E % 2,000E+04 2,840E % 2,000E+03 1,500E+04 1,000E+03 0,000E+00 Single Skeg HFO TwinSkeg HFO (I.D 2515) -95% Multi Venture 1,000E+04 5,000E+03 0,000E % Single Skeg HFO TwinSkeg HFO (I.D 2515) Multi Venture

51 Overview of AFRAMAX Case Study Final Product Safer Crude Oil Transport Increased Profitability 0,015 0,01 0,005 0 Accidental Oil Outflow Index (MARPOL Reg. 23) Convention al Multi Venture Cargo Capacity (m3) Conventional Multi Venture Competitiveness "Semi-Ballast Free" Tanker Required Freight Rate ($/t) Conventional Multi Venture Ballast Water Amount Required (m3) Conventional Multi Venture

52 Case Study of a VLCC Optimization A case study to illustrate the applicability of the method and provide future research potential

53 Scope of Work Need to demonstrate the applicability and robustness of the method. Try different size and more conventional geometry. Less detailed application: Only a few runs in DoE (1500 variants) Optimization using MOSA (Multi Objective Simulation Annealing) algorithm (1500 variants) Bigger Room for improvement: No applicable navigational restrictions for VLCCs Need for smaller tanks: 6 Transverse Bulkheads used (instead of 5) More impressive results are expected for smaller tank sizes (7X3)

54 VLCC Optimization Initial Results Accidental Oil Outflow Index (Acc. to MARPOL Reg. 23) 0,0149 0,0144 0,0139 0,0134 0,0129 0,0124 0,0119 0,0114 Optimization Run with MOSA (1500 variants) RFR vs. OOI 4 4,2 4,4 4,6 4,8 5 5,2 5,4 5,6 5,8 6 Required Freight Rate (USD/t, HFO price at 1000 $/t) 6X3 VLCC Aiolos Hellas (baseline) 3,2 Optimization Runs with MOSA (1500 variants) EEDI vs. RFR EEDI (Acc. to IMO MEPC 62) 3 2,8 2,6 2,4 2,2 2 4,5 4,6 4,7 4,8 4,9 5 5,1 5,2 5,3 Required Freight Rate (USD/t, HFO price 1000 $/t) 6X3 VLCC Aiolos Hellas (Baseline)

55 Conclusions, Discussion and Perspectives

56 Conclusion: Conclusion, Perpsectives A novel, holistic methodology was developed, using a Risk Based Approach and holistic ship theory in order to systematically assess and optimize Tanker Design. The application resulted in improved and innovative designs that illustrate the potential and applicability of the method. The method was entirely programmed in the Friendship Framework, with a fully parametric model using principles of Simulation Driven Design. The sensitivities of the model can be provided as design directives for the preliminary choice of the main dimensions. Awards/Perspectives: Pending Publication for methodlogy in peer reviewed journal ( submission) Further VLCC Optimization (spring 2013 publication) Ongoing adaptation of methodology for the case of containerships (OptiCON research project together with GL): Refinement of Lightship Calculation and Resistance Prediction Tools ABS Award for 2012/Collaboration with GL (BEST+, BEST++)

57 Acknowledgements The author needs to acknowledge the help and support of the following people, whose contributions have been critical for the completion of this work at various stages: Professor Dr.Ing. Habil. Apostolos Papanikolaou, Dr. Evangelos Bouloungouris (NTUA-SDL), Associate Professor Dr. George Zaraphonitis (NTUA-SDL), Professor Kostas J. Spyrou (NTUA) Assistant Professor Nikolaos Ventikos (NTUA), Professor Christos Frangopoulos (NTUA), Associate Professor John Prousalidis (NTUA) and Mr. Elias Sofras, Mr. Dimitrios Heliotis (Target Marine) Dr. Pierre C. Sames (GL) and Germanischer Lloyd SE, Dr. Harries, Mr. Park and Mr. Brenner from Friendship Systems Mr. Utvaer Alf-Morten (FRAMO) Mr. Kostas Anastasopoulos (NTUA-SDL) Ship Design Laboratory: Dr. Eliopoulou, Dr. Liu, Mr. Papatzanakis, Ms. Alisafaki My family and friends for their support and patience.

58 Thank you for your kind attention! Questions?

59 Sensitivity Analysis of the Developed Methodology Annex I Accidental Oil Outflow: Tank variables: Oil Outflow Index entirely dependent on the tank size, position and geometry. Double bottom height is much less influencing the OOI than the side tank width Collision accidents more frequent and have bigger consequences than grounding accidents Main dimensions: influence on tank size and displacement Local hullform parameters: no influence on the Index (negligible changes of displacement only). Required Freight Rate: General impression: larger vessel sizes have a positive influence to the RFR thanks to the strong correlation to the tank capacity. Tank Variables: Larger Tank Sizes correspond to smaller RFR Local Hullform Parameters: Decrease of wetted surface leads to smaller RFR Correlation with EEDI sensitivities IMO Energy Efficiency Design Index (EEDI): General impression: the larger vessel sizes have a positive influence to the EEDI thanks to the strong correlation to the deadweight and the smaller increase of the installed power. Local hullform parameters via the wetted surface and thus the installed power

60 Sensitivity Analysis of the Developed Methodology EEDI Sensitivity

61 Sensitivity Analysis of the Developed Methodology OOI Sensitivity

62 1 st Stage: Design Space Exploration Design of Experiment-Sensitivities

63 2 nd Stage: Design Space Exploration Investigating the Effects of Design Speed EEDI performance: Promising results due to the strong correlation to the design speed. Robustness: The favored designs remained to be favored but, by using the lowest design speed. Final Decision: Not to include the speed in the calculations, Separate study of the optimal operating speed for a range of scenarios (depending on the fuel price). Some of the constraints were made more tight in order to make sure that the feasible designs are in fact feasible. The lower boundary for the double bottom was slightly increased. Selected Variants (by means of an objective function): Design I.D 314 and exported to be the baseline for the genetic algorithm runs (Stages 3 and 4).

64 Structural Analysis The effects of new dimensions, of the elliptic bilge and the new position of the longitudinal bulkheads needed to be examined. One of the dominant variants modeled in POSEIDON The functional elements and bulkheads were taken the same as in the structural weight investigation done. Results: Item Longitudinal Members Weight Transverse Members Weight Under-estimation of the structural weight by 4.5% It is lost within the correction factors which are up to 15%. Transverse weight is lower than expected. I.D 2515 Calculated I.D 2515 Calculated BEST+ NX3 Model in FFW in POSEIDON in POSEIDON t/m t/m 54 t/m 8.19 t/m 5.8 t/m 8.19 t/m

65 Hydrodynamic Analysis Due to uncertainties in the powering estimations and the design ranking, a CFD calculation had to take place. The focus was to see any irregular wave patterns with extreme bow and stern waves (due to bulky bow form and twin skeg stern). The SHIPFLOW package was used, and the XPAN code. The evaluation of the wave making resistance is done using wave cuts. The wave patterns of each subject is also examined in order to see any strange effects. The convergence was fast and the results were accurate. The effect of panel density and transom modeling were also taking into account. The trends are verified and the preliminary method can be considered accurate. The absolute values of Holtrop seem to be more conservative than the SHIPFLOW results. Current work: benchmark several CFD codes using I.D 2515 as a reference design.

66 Hydrodynamic Analysis Results Table Design I.D L bp B T F n Wetted Surface Cw wavecut Cw Holtrop * E * * * a E * a * a E * a * a * a *10-5 "A Holistic Method for the Optimization of Tanker Design and Operation and its Applications", SNAME Thesis Competition, Lampros Nikolopoulos, NTUA-SDL,