Shape Optimization of the Cavitator for a Supercavitating Vehicle Based on Genetic Algorithm

The cavitator is one of the most crucial parts that influence the navigability performance of supercavitating vehicles. Because the analysis relates to a free boundary value problem(BVP), its shape optimum becomes much difficultly. In the paper, this free boundary value problem is transformed into an equivalent shape optimization problem by defining the objective function as a square error integral of pressure difference over the cavity. Accordingly, the shape optimum of cavitator and the calculation of the cavity boundary are merged into a multi-objective optimization problem. The optimum problem has been solved efficiently through combining the commercial soft ANSYS which computes the potential flow with GA which uses to optimize the design parameters. Comparisons show that genetic algorithm is feasible and effective used in this flow analysis, the method is good enough for the reduction of computational complexity and more digestible. Meanwhile, the framework can be expanded to study the overall geometrical dimension optimization in which the cavitator optimum can be as a sub-optimization.