基于混沌粒子群优化算法的压气机盘低循环疲劳

压气机轮盘低循环疲劳寿命受很多随机参数的影响,具有很大的分散性,因此,对压气机盘低循环疲劳寿命进行稳健性设计具有重要的意义.在对疲劳寿命概率分析的基础上,结合RBF神经网络与混沌粒子群优化算法,利用混沌粒子群优化的动态收缩搜索区域的搜索特性,通过对随机参数进行优化进行压气机轮盘低循环疲劳寿命稳健性优化设计,使得疲劳寿命对参数的敏感度降低,概率区间减小,计算结果验证了该方法在工程应用中的可行性.

Robust Design of Compressor Disk Low Cycle Fatigue Life

The low cycle fatigue life of compressor disk is affected with many random parameters, which has a lot of dispersibilities. It is a vital significance of robustness design to the compressor disk low cycle fatigue life. By combining radial basis function(RBF) neural network with chaos particle swarm optimization Algorithm (CPSOA) and using CPSOA dynamically contracted search fields search ability to optimize the random variable which affects the fatigue life, a robust optimization design for low cycled fatigue life of compressor disk can be made based on probability analysis for compressor disk low cycled fatigue life. The robust optimization design of compressor disk low cycle fatigue life were preformed by the optimization of random parameters to reduced the sensitivity of the low cycled fatigue life on the random parameter and decrease the probability interval of fatigue life. The feasibility of the engineering application is verified by the calculation results