中文摘要：

涡扇发动机稳态数学模型为一高度非线性的复杂系统，用传统的迭代方法求解发动机非线性方程组存在受初值影响不易收敛的问题。为此利用粒子群算法求解，并针对算法的局部收敛现象对其进行改进：借鉴免疫算法思想，设计出基于Logstic混沌变异的粒子克隆选择机制，基于概率的粒子多样性保持机制。对测试非线性方程组求解的结果表明引入免疫机制的改进算法较被比较的典型算法在搜索性能和收敛速度方面有明显改善。将改进的粒子群算法应用到某型涡扇发动机稳态性能计算，从仿真结果可知，基于免疫粒子群算法计算过程收敛速度明显加快，计算误差显然小于其他改进算法。经验证，该算法在不同的工作状态下与试车试验值的误差在0．5％左右，达到较高的计算精度。

英文摘要：

Turbo fan engine mathematical model is a highly complex nonlinear system. Solving engine mathematical model with traditional iteration methods turns out to be difficult as these methods are very sensitive to initial values and inclined to divergence. Therefore particle swarm optimization is used to solve the model. To solve the local convergence problem of PSO, some mechanisms in immune algorithm are introduced. Clone selection mechanism based on Logistic chaotic mutation and diversity maintaining mechanism based on probability are designed. Results on test nonlinear equations show that the proposed algnrithm has better searching performance and convergence speed than other compared algorithms. And ideal results are obtained with new algorithm when modeling a mixed exhaust turbofan engine.