Optimization design of forebody shape of an underwater axisymmetric body for transition delay using a genetic algorithm

Abstract

The forebody shape of an underwater vehicle significantly influences the transition position of the boundary layer, which, in turn, impacts the flow-induced noise that can interfere with the sonar in the head. Typically, underwater vehicle forebodies are axisymmetric. Optimizing the forebody shape of an axisymmetric body can effectively delay the boundary layer transition and reduce the interference caused by flow noise on the sonar. This study applies an artificial intelligence algorithm for the optimization of the forebody shape. Specifically, a genetic algorithm is adopted to establish the optimization design method. The non-uniform rational B-spline (NURBS) method is used to parameterize the forebody shape, and the eN method based on linear stability theory is employed to predict the transition position. The optimization is conducted at the scale of a real vehicle and at a fixed velocity. After 45 generations, the genetic algorithm converges to an optimized shape. The transition position of the optimized shape is significantly delayed in comparison to that of the optimal original shape, with the relative delay amounting to an impressive 20.8%. Furthermore, the optimized shape effectively suppresses wall pressure fluctuation (WPF) in the unstable laminar region, also contributing to a reduction in flow-induced noise.