Multi-objective optimization of the shell in autonomous intelligent argo profiling float

To improve the performance of the first-generation profiling float and ensure that the float smoothly completes the profile motion, a shell optimization method based on response surface approximation model and multi-objective genetic algorithm is proposed. The second-order nonlinear response surface models of resistance, mass and envelope volume of the shell are established based on the data from Design of Experiments (DOE). Taking the minimum resistance, the minimum mass and the maximum envelope volume of the shell as optimization objectives, the Pareto Front is obtained based on the fast elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II). The influence rule and degree of each design variable on the optimization objective are gained through the main effect analysis. A set of solutions are selected as the design scheme of the shell for second-generation profiling float. The hydrodynamic performance, endurance and carrying capacity of the profiling float have been greatly improved compared with the first-generation. Finally, the feasibility and effectiveness of the optimization results are verified through simulation analysis and high pressure test. The proposed optimization process can greatly improve the performance of Argo profiling float, shorten the optimization period of shell design and improve the optimization efficiency. Therefore, it has a good reference value for the design optimization of other similar submarines.