基于深度置信网络与多目标粒子群算法的通用飞机机翼优化设计

Optimization design on the wing of a general aviation airplane based on deep belief network and multi-objective particle swarm optimization algorithm

采用主成分分析技术对基于型函数/类函数变换(CST)参数化方法进行改进,以减少机翼的设计变量。建立了机翼的数据库,以数据库中机翼的几何参数和气动参数为训练样本,建立并训练了深度置信网络作为气动优化设计的代理模型。为了提高标准多目标粒子群算法的收敛速度和全局搜索能力,发展了基于α-stable分布函数的改进型多目标粒子群算法。将深度置信网络代理模型嵌入改进的多目标粒子群算法,开展了某通用飞机机翼的多目标气动优化设计。优化结果表明,采用主成分分析技术可大幅减少计算量。对优化所得机翼构型开展数值模拟验证,结果表明,在阻力系数不增加的条件下,优化机翼构型在多个工作状态下的升力系数显著提升。

For the purpose of reducing the number of the design variables of the wing,the PCA(principal component analysis)technique is employed to improve the CST parameterization method.A database of the wings is established,including geometrical parameters and aerodynamic parameters.Based on the database,the DBN(deep belief network)is established and trained as the surrogate model in the aerodynamic optimization.In order to improve the convergence rate and global searching ability of the standard MOPSO(multi-objective particle swarm optimization)algorithm,an improved MOPSO algorithm is developed based on theα-stable distribution functions.By embedding the DBN surrogate model into the improved MOPSO algorithm,the multi-objective aerodynamic optimizations are performed for the wing of a general aviation airplane.The optimization results indicate that,the computation amount is decreased dramatically by introducing the PCA technique.Numerical validation is conducted for the designed wing configuration,and the results indicate that,comparing with the baseline configuration,the lift coefficients of the designed configuration are increased obviously under multiple states with the drag coefficients not increased.