The purpose of this study is to discuss how to improve the maneuverability of lifting and diving for underwater vehicle’s vertical motion. Therefore, to solve these problems, applied the 3-D numerical simulation, Taguchi’s Design of Experiment (DOE), and intelligent parameter design methods, etc. We planned four steps as follows: firstly, we applied the 2-D flow simulation with NACA series, and then through the Taguchi’s dynamic method to analyze the sensitivity (β). Secondly, take the data of pitching torque and total resistance from the Taguchi orthogonal array (L₉), the ignal-to-noise ratio (SNR), and analysis each factorial contribution by ANOVA. Thirdly, used Radial Basis Function Network (RBFN) method to train the non-linear meta-modeling and found out the best factorial combination by Particle Swarm Optimization (PSO) and Weighted Percentage Reduction of Quality Loss (WPRQL). Finally, the application of the above methods gives the global optimum for multi-quality characteristics and the robust design configuration, including L/D is 9.4:1, the foreplane on the hull (Bow-2), and position of the sail is 0.25 Ls from the bow. The result shows that the total quality is improved by 86.03% in comparison with the original design.

这项研究的目的是讨论如何提高水下航行器垂直运动的提升和潜水的可操纵性。因此，为解决这些问题，应用了3-D数值模拟，田口实验设计（DOE）和智能参数设计方法等。我们计划了以下四个步骤：首先，我们将二维流模拟与NACA结合使用。系列，然后通过田口的动力学方法来分析灵敏度（β）。其次，从田口正交阵列获取俯仰转矩和总阻力的数据（L ₉），点火噪声比（SNR），并通过ANOVA分析每个因子贡献。第三，采用径向基函数网络（RBFN）方法训练非线性元模型，并通过粒子群优化（PSO）和加权质量损失降低百分比（WPRQL）找出最佳因子分解组合。最后，上述方法的应用为多种质量特性和稳健的设计配置提供了全局最优，其中L / D为9.4：1，船体的前平面（Bow-2），帆的位置为0.25 Ls从弓。结果表明，与原始设计相比，总质量提高了86.03％。