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

Automated Multidisciplinary Design Optimization Method for Multi-hull Vessels

Kay [email protected]

College of Engineering

Agenda

•Motivation & Introduction•Concept and Methodology•Models

▫Catamaran Synthesis Design Model▫Cost Model▫Seakeeping Model▫Model Integration and Variables

•Results•Acknowledgements

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

Concept and Methodology

Schematic description of the synthesis level MDO process

CAT-SDM

Cat. Synthesis Design Model

Synthesis Design Model

Model developedby CSC AdvancedMarine

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

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

Variables

Design Variables Design Constraints Design Objectives

Length on Waterline

Power (Boost Speed) Deadweight to Displacement Ratio

Beam Overall Beam Displacement to Resistance Boost

Draft Difference in Balance Cost

Depth Feasibility Checks

Block Coefficient Seakeeping

Model Integration and Workflow

iSight-FD

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

ResultsPreliminary results with Seakeeping

Iterations: Iterations:

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 <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.

Questions

Paper and Presentation availableat http://kai.gemba.org