Multi-objective shape optimization of submarine hull using genetic algorithm integrated with computational fluid dynamics

Abstract

A multi-objective optimization framework is developed for design of submarine hull shape. Internal volume of the vehicle and its hydrodynamic drag are optimized by seamlessly integrating non-dominated sorting genetic algorithm and Reynolds averaged Navier–Stokes solver in a single code. The methodology seeks a geometric shape with minimum drag and maximum volume satisfying the constraints on the geometric design parameters given by a 5-parameter formula that describes the submarine hull. The shape of the sail is not a part of the optimization process, and only its longitudinal location over the submarine hull is optimized. Two design optimization approaches are proposed, solved and compared. In the first approach, the combined hull–sail location is optimized, and in the second, the hull shape without sail is optimized first, and for this optimized hull shape, the sail location is optimized next. Our reported results show that the former approach yields significantly lower drag.