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Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels

Kay Gemba gemba@physics.csulb.edu. College of Engineering. Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels. Motivation & Introduction Concept and Methodology Models Catamaran Synthesis Design Model Cost Model Seakeeping Model Model Integration and Variables

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Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels

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  1. Kay Gemba gemba@physics.csulb.edu College of Engineering Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels

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

  3. 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 Motivation & Introduction

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

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

  6. 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 Cost Model (SPAR Software)

  7. 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 Seakeeping Model

  8. Variables

  9. Model Integration and Workflow iSight-FD

  10. 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 The Big Picture

  11. Results Preliminary results with Seakeeping Iterations: Iterations:

  12. 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 <http://www.engineous. com/iSIGHTFD.cfm> Hefazi H. and Henriksen., "Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels." CCDoTT Report, February 2008. Available on-line at www.ccdott.org. Resources

  13. Questions Paper and Presentation available at http://kai.gemba.org

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