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：在自主水下航行器设计中，艇体阻力是决定航行器功率需求与航程的关键因素，进而影响设计中的电池尺寸、重量及体积要求。本文利用基于人工智能的优化算法结合计算流体动力学仿真，研究实现阻力最小化的最优艇体设计。通过在不同运行速度与湍流强度下开展CFD优化计算，我们旨在探究一种能够在所有运行工况（运行速度）与环境条件（湍流强度）下提供最小阻力或近最优设计的通用艇体方案。初步结果表明，在低流速低湍流工况下获得的最优设计在高流速高湍流环境中表现极差，而在高流速高湍流条件下确定的最优设计则能在多种速度与湍流工况下保持近最优性能。