基于全局信息的粒子群算法翼型综合优化设计

 翼型优化往往需要考虑众多的设计目标和约束条件,对此发展了稳健高效的翼型综合优化方法。在粒子群优化算法中用繁殖策略深度挖掘由Kriging代理模型所获取的全局信息,对基准函数优化、翼型几何外形重构与层流翼型优化问题进行了测试,结果表明该算法可大幅度提高优化速度。将改进的Hicks-Henne翼型参数化方法和雷诺平均Navier-Stokes(N-S)方程流场求解器与优化算法相结合,采用可方便确定权重系数的多目标非线性适应值加权方法,分别对多点、多目标和多约束的超临界翼型与低速翼型进行综合优化,计算结果表明该方法可大大提高气动外形优化的工程实用性。

Synthesis Airfoil Optimization by Particle Swarm Optimization Based on Global Information

An efficient synthesis optimization method is developed to obtain optimal airfoil designs that satisfy all design objectives and constraints. In the particle swarm optimization, reproduction of the particle strategy is used to dig the global information based on Kriging surrogate model. Efficiency is enhanced greatly by mapping the solution space for some benchmark function tests and airfoil geometry reshape and laminar flow airfoil design problems. A modified Hicks-Henne function is used to parameterize airfoils, Reynolds-averaged Navier-Stokes (N-S) flow solver is combined with the optimization arithmetic, and a new nonlinear weighted sum of multi-objective method which can determine the weight of different objects easily is developed. Supercritical airfoil and low speed airfoil design problems with multi-point, multi-objective and multi-restriction are tested. Results show that the optimization method can greatly improve the practicability of aerodynamic shape optimization.