基于改进粒子群算法的翼型稳健型气动优化设计

Robust Airfoil Optimization Based on Improved Particle Swarm Optimization Method

基于标准粒子群算法,将位移变化作为影响微粒速度的变量,使得粒子群算法关于粒子位置为二阶精度函数,加快了收敛速度;进一步地在粒子速度更新公式中引入振荡环节,提高了群体多样性,改善了算法的全局收敛性.以改进粒子群算法为基础,结合气动分析程序、代理模型以及翼型参数化方法,构建了翼型稳健型气动优化设计系统.针对某型客机的基本翼型以及翼梢小翼翼型气动优化设计结果表明,优化后的翼型气动特性相对于初始翼型在较宽的设计范围内都有了大幅度提高.

A robust airfoil optimization platform was constructed based on modified particle swarm optimization method（ i. e. second-order oscillating particle swarm method ） ,which con- sists of an efficient optimization algorithm, a precise aerodynamic analysis program, a highaecuracy surrogate model and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared to standard particle swarm method. Firstly, particle velocity was represented by the combination of particle position and variation of posi- tion, which makes the particle swarm algorithm become a second-order precision method with respect to particle position. Secondly, for the sake of adding diversity to the swarm and enlar- ging parameter searching domain to improve the global convergence performance of the algo- rithm, an oscillating term was introduced to the update formula of particle velocity. At last, taking two airfoils as examples, the aerodynamic shapes were optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils was greatly improved at a broad design range.