An automated multidisciplinary design optimization method for multi-hull vessels

Currently, there are no practical, comprehensive computational tools available for the synthesis of multi-hull vessels. The thesis aims to apply a novel synthesis level multidisciplinary design and optimization method to multi hull vessels. The method combines Powering, Stability, Structural Weight, Payload capacity and Cost into a single design and optimization tool. The optimization is based on advanced multi-objective genetic algorithm, implemented in generic software called iSIGHT. The analysis of the results is done using the Pareto set of points generated by running the multi-objective genetic algorithm. Single objective and multi-objective cases are run for 3 types of multi-hull vessels. In order to reduce the computational cost, the application utilizes neural networks in the powering section to determine the coefficient of residual resistance. The design set up consists of various subsystems implemented into modules and integrated into iSIGHT. The points generated from the Pareto set are then used as design parameters for modeling the multi-hull vessel using I-DEAS software. The outcome is an optimum design that takes into account all major aspects affecting ship performance and cost and which can further be extended to subsystem design.