Optimized design of the overall shapes of supercavitating vehicles based on a multi-objective adaptive genetic algorithm

The supercavitating vehicle is a new generation of high-speed underwater vehicles enveloped by a large bubble of gas or vapor (i.e., a supercavity) to reduce navigation resistance by an order of magnitude, except for the head and tail. However, due to the limited hydrodynamic forces, it is difficult to ensure stable motion. The overall shape of the vehicle is not only the critical factor to ensure a suitable relative position between the vehicle and its supercavity for stable motion, but also directly affects the ease of generation and development of the supercavity. Therefore, this study focuses on the characteristic parameters of the overall shape of the vehicle and first establishes a multi-objective optimization model based on the dynamic model of the supercavitating vehicle with the incipient supercavitation number and the vehicle volume as the optimization objectives. Then, given the relatively good global, robust and universal characteristics of the adaptive genetic algorithm compared to other algorithms, a multi-objective optimization algorithm is developed and improved using the subsection optimization method for the main optimization variable. The characteristic parameters of the overall shapes are optimized for natural and ventilated supercavitating vehicles using the algorithm based on the multi-objective optimization model so that it is easier to create a supercavity under the conditions of the same flow and force equilibrium and the vehicle has a larger volume. By optimizing the vehicles under different navigation depths and slenderness ratios, the design rules for the characteristic parameters of the overall shape and the control surface parameters are obtained based on the optimization objectives, and the motion state and hydrodynamic characteristics of the optimized vehicles are revealed.

Automated summary

The supercavitating vehicle is a new generation of high-speed underwater vehicles with reduced navigation resistance by utilizing a supercavity. However, stable motion is difficult to achieve due to limited hydrodynamic forces. This study focuses on optimizing the overall shape of the vehicle to ensure stable motion and easier formation of a supercavity, using a multi-objective optimization model and an adaptive genetic algorithm. By optimizing the vehicle's characteristics and control surface parameters, design rules are established, and the motion state and hydrodynamic characteristics of the optimized vehicles are revealed.