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Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels Kay Gemba College of Engineering.

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Presentation on theme: "Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels Kay Gemba College of Engineering."— Presentation transcript:

1 Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels Kay Gemba College of Engineering

2 Agenda Motivation & Introduction Concept and Methodology Models ▫Catamaran Synthesis Design Model ▫Cost Model ▫Seakeeping Model ▫Model Integration and Variables Results Acknowledgements

3 Motivation & Introduction Mono hulls can not archive high speeds needed for commercial and military application Multi-hull form vessels offer favorable characteristics ▫Superior motion ▫Improved seakeeping in rough weather Apply MDO method to preliminary design stage of a catamaran vessel concept

4 Concept and Methodology Schematic description of the synthesis level MDO process CAT-SDM

5 Cat. Synthesis Design Model Synthesis Design Model Model developed by CSC Advanced Marine

6 Cost Model (SPAR Software) Estimate is based upon the hull’s structural components and the ship systems (piping, electrical, etc) Insurance Risk analysis for Construction (cost risk for labor and material), Re-work and Shipyard experience

7 Seakeeping Model 40 Neural Networks trained for a specific heading angle and for a specific output: ▫pitch, roll, bending moment, shear force…. Inputs are length, spacing, sea state and Froude number Result: Seakeeping Composite Index

8 Variables Design VariablesDesign ConstraintsDesign Objectives Length on WaterlinePower (Boost Speed)Deadweight to Displacement Ratio BeamOverall BeamDisplacement to Resistance Boost DraftDifference in BalanceCost DepthFeasibility Checks Block CoefficientSeakeeping

9 Model Integration and Workflow iSight-FD

10 The Big Picture Integrate modules into a workflow Define objective functions, constrains and inputs For each objective function run single objective MIGA to obtain feasible points spanning entire design space Utilize results from single optimization as initial population and optimize with multi-objective NCGA Result: pareto optimal solution

11 Results Preliminary results with Seakeeping Iterations:

12 Resources Schmitz A. "Constructive Neural Networks for Function Approximation and their Application to CFD Shape Optimization". Diss. Claremont Graduate University and California State University, Long Beach, 2007 SIMULIA Engineous Software, iSIGHT-FD. 05 February 2009 Hefazi H. and Henriksen., "Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels." CCDoTT Report, February Available on-line at

13 Questions Paper and Presentation available at


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