Hull shape optimization for autonomous underwater vehicles using CFD

Drag estimation and shape optimization of autonomous underwater vehicle (AUV) hulls are critical to energy utilization and endurance improvement. In the present work, a shape optimization platform composed of several commercial software packages is presented. Computational accuracy, efficiency and robustness were carefully considered and balanced. Comparisons between experiments and computational fluid dynamics (CFD) were conducted to prove that a two-dimensional (2D) unstructured mesh, a standard wall function and adaptive mesh refinement could greatly improve efficiency as well as guarantee accuracy. Details of the optimization platform were then introduced. A comparison of optimizers indicates that the multi-island genetic algorithm (MIGA) obtains a better hull shape than particle swarm optimization (PSO), despite being a little more time consuming. The optimized hull shape under general volume requirement could provide reference for AUV hull design. Specific requirements based on optimization testify of the platform's robustness.