In Autonomous Underwater Vehicles (AUVs) design, hull resistance is an important factor in determining the power requirements and range of vehicle and consequently affect battery size, weight, and volume requirement of the design. In this paper, we leverage on AI-based optimization algorithm along with Computational Fluid Dynamics (CFD) simulation to study the optimal hull design that minimizing the resistance. By running the CFD-based optimization at different operating velocities and turbulence intensity, we want to study/search the possibility of a universal design that will provide least resistance/near-optimal design across all operating conditions (operating velocity) and environmental conditions (turbulence intensity). Early result demonstrated that the optimal design found at low velocity and low turbulence condition performs very poor at high velocity and high turbulence conditions. However, a design that is optimal at high velocity and high turbulence conditions performs near-optimal across many considered velocity and turbulence conditions.

翻译：在自主式水下航行器（AUV）设计中，壳体阻力是决定航行器功率需求和航程的关键因素，进而影响设计中电池尺寸、重量和体积要求。本文利用基于人工智能的优化算法结合计算流体动力学（CFD）仿真，研究使阻力最小化的最优壳体设计。通过在不同运行速度和湍流强度下开展基于CFD的优化，我们旨在探索/寻找一种能够跨所有运行条件（运行速度）和环境条件（湍流强度）提供最小阻力/接近最优设计的通用方案的可能性。初步结果表明，在低速度和低湍流条件下获得的最优设计在高速度和高湍流条件下表现极差。然而，在高速度和高湍流条件下最优的设计，在所考虑的多项速度和湍流条件下均表现出接近最优的性能。