基于Pareto聚类免疫进化算法的发动机悬置系统优化与稳健性分析

Optimization of engine mounting system based on Pareto clustering immune evolutionary algorithm and robustness analysis

文章在分析各种优化方法优缺点的基础上,建立发动机悬置系统6自由度动力模型。以6自由度方向的解耦率最大为优化目标,以各悬置点三向刚度为设计变量,选用免疫进化算法对发动机的悬置刚度参数进行优化,同时应用Pareto聚类算法从记忆种群中提取多个优化解,最后用Monte Carlo法对悬置系统进行稳健性分析。结果表明,优化解不仅能保证6自由度方向的高解耦率,还能保证悬置系统的稳健性,提高了产品的质量。

On the basis of the analysis of both the strong and weak points of different optimization de- sign techniques, a 6 DOFs dynamics model for engine mounting system is built. Optimization of the mounting stiffness parameters of the engine is conducted by using the immune evolutionary algorithm with the maximum decoupling rate of 6 DOFs mounting system as objective, and the stiffness in three directions of each mount as design variable. More than one optimal solutions are obtained from memo- ry set by using the Pareto clustering algorithm. Then, a robustness analysis of the mounting system is performed by Monte Carlo method. The results show that the optimal solutions can not only ensure high decoupling rate of 6 DOFs, but also ensure the robustness of the mounting system, and enhance the quality of products.