Research on Drag Reduction Optimization of Foldable Solar Wings for UUVs

To respond to the endurance bottleneck faced by unmanned undersea vehicles(UUVs) in missions such as ocean observation and resource exploration, this paper studied the hydrodynamic performance optimization of a novel foldable solar wing. To balance computational efficiency and optimization accuracy, a parametric model of the wing was established in CAESES software with variables including wing point coordinates, rounding factors of wing edges, wing gaps, and gaps between the wing and the hull. Innovatively, a hybrid optimization framework combining Sobol global sampling and the non-dominatedsorting genetic algorithm II(NSGA-II) optimization algorithm was constructed. Firstly, the Sobol algorithm was used to generate 80 sample points within the threshold space of each variable to fully explore the design space, followed by multi-generation optimization through NSGA-II. To avoid the accuracy degradation of traditional surrogate models, a coupled computational process integrating high-precision hydrodynamic solutions and optimization algorithms was established, enabling automatic co-simulation between CAESES and STAR-CCM + software. Hydrodynamic analyses were conducted on UUVs equipped with wings of different shapes to explore the impact of different parameter combinations on total drag. The optimization results indicate that a certain height difference between the two wing sections protruding from the hull is beneficial for reducing total drag. Flow field analysis shows that the optimized shape effectively suppresses energy dissipation caused by turbulence. The proposed technical route of parametric modeling, intelligent optimization, and high-precision verification not only reduces the straight-line drag of the UUV with a new configuration but also provides a methodological reference for the optimization of complex appendages, possessing significant engineering value for improving the energy utilization efficiency of underwater equipment.