基于SVR近似模型的潜水器外形优化

流体仿真软件在船舶与海洋结构物概念设计阶段应用广泛。针对潜器外形设计过程中，仿真分析往往需要耗费大量的时间成本，无法直接与优化器结合的问题，本文研究基于支持向量回归机（Support Vector Regres-sion, SVR）的潜器外形优化方法，包括拉丁超立方试验设计选取样本点、基于 ICEM的潜器参数化建模和网格自动划分、基于 Fluent的阻力计算及 SVR模型的构造。采用改进的粒子群算法求解潜器外形优化设计问题，得到了阻力性能优良的潜器外形。

Optimization of submersible shape based on SVR surrogate

CFD simulation software greatly improves the efficiency and accuracy of optimization design of submers-ible shape. However, during the optimization, optimization results usually requires a lot of iterations to be achieved. Fluid simulation software, such as fluent will take enormous time and cost during the optimization process. Therefore, this paper proposes a Support Vector Regression (SVR) to study the optimization of submersible shape. The process of building matamodel include: Latin Hypercube experimental design selected sample points, automatic division based ICEM submers-ible parametric modeling and grid-based computing. Particle Swarm Optimization (PSO) algorithm is adopted to obtain the optimum with a minimum resistance.