【摘要】 针对自主水下航行器（autonomous underwater vehicle, AUV）外形设计中低阻力与大容积之间的矛盾，探索以海豚为仿生对象的优化设计方法，以提升AUV的水动力性能与任务载荷能力。首先，采用9段Myring型曲线对海豚轮廓进行参数化拟合，建立AUV三维几何模型，并基于雷诺平均纳维-斯托克斯（Reynolds-averaged Navier-Stokes, RANS）方程和标准k-ε模型，通过CFD(computational fluid dynamics，计算流体力学)仿真获取初始AUV的总阻力与包络体积。随后，利用最优拉丁超立方抽样法生成样本点，构建描述AUV总阻力、包络体积与设计变量映射关系的Kriging代理模型。最后，以最小化总阻力和最大化包络体积为目标，采用NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ，二代非支配排序遗传算法)求解Pareto最优解集。优化后的AUV在2 m/s航速下的总阻力降低了5.74%，包络体积增大了5.87%。流场仿真分析表明：优化外形使AUV尾部的压力梯度趋于平缓，压差阻力降低了12.56%；同时，AUV尾部的速度梯度减小，有效抑制了边界层分离。阻力构成显示压差阻力降低是AUV总阻力下降的主要原因。水平面稳定性指数G_H>0，表明优化后的AUV具有动稳定性。融合参数化建模、CFD仿真、Kriging代理模型与NSGA-Ⅱ的多目标优化方法，为水下航行器的外形优化提供了参考。更多还原

【Abstract】 Aiming at the contradiction between low resistance and large volume in the shape design of autonomous underwater vehicles(AUVs), an optimization design method taking dolphins as bionic objects is explored to enhance the hydrodynamic performance and mission payload capacity of AUVs. Firstly, nine segments of Myring-type curves were used to parameterize and fit the dolphin contour, thereby establishing a three-dimensional AUV geometric model. Based on the Reynolds-averaged Navier-Stokes(RANS) equation and the standard k-ε model, the total resistance and envelope volume of the initial AUV were obtained through computational fluid dynamics(CFD) simulation. Subsequently, the optimal Latin hypercube sampling method was utilized to generate sample points, and Kriging surrogate models describing the mapping relationship between the total resistance and envelope volume of the AUV and design variables were constructed. Finally, with the objectives of minimizing total resistance and maximizing envelope volume, the Pareto optimal solution set was solved using NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ). After optimization, the total resistance of the AUV decreased by 5.74% and the envelope volume increased by 5.87% at a navigation speed of 2 m/s. Flow field simulation analysis indicated that the optimized shape flattened the pressure gradient at the AUV tail, reducing the pressure difference resistance by 12.56%. At the same time, the velocity gradient at the AUV tail decreased, effectively inhibiting boundary layer separation. The resistance composition showed that the reduction in pressure difference resistance was the main reason for the decrease in total resistance. The horizontal stability index G_H>0 indicated that the optimized AUV had dynamic stability. The multi-objective optimization method that integrates parametric modeling, CFD simulation, Kriging surrogate model and NSGA-Ⅱ provides a reference for the shape optimization of underwater vehicles.更多还原