CFD-Based Multi-Objective Optimization Of SWATH Ship

Rough sea often cause problem for ship sailing for the sway and heave of ship will sharply increase, and certainly decrease the comfort. At this circumstance, SWATH ship which has good seakeeping performance appears. SWATH ship consists of submerged body, column and hull above water. Compared to mono hull ship, the wave resistance, seakeeping performance and area of deck of SWATH ship are better thus makes SWATH ship get more attention.The multi-objective optimization of ship is always a difficulty for ship engineering. For the traditional optimization method always fall into local optimum and it is difficult to balance the optimum solution of different performances of ship during the optimization process. These years some hydrodynamic software are very popular, but it is very time consuming which makes it impossible to do optimization work directly. Surrogate model technique like surface respond method can solve this problem, for it can build an approximate model which need little computing resource with a slightly lower accuracy. With this method, the computational efficiency can be sharply improved which means the computational expense can be lower down.The paper focus on the optimization of a SWATH ship, set four parameters (length of submerged body, half width of submerged body, half width of column and separation distance of two sheets) as design variables, with these four parameters a parametric ship hull can be obtained thus makes optimization possible. After verification of the computational method, a series of simulations are performed and surface respond analysis is carried out. With the results of surface respond analysis the relationship between ship performance and design variables are gained. Then using genetic algorithm to optimize the single performance of SWATH ship.With all the five results of surface respond analysis, combined genetic algorithm and method of index weight to optimize five performance together. Finally gain a optimum ship hull and fulfilled the multi-objective optimization.