熵判别粒子群优化算法在发动机模型修正中的应用

因生产、安装工艺差别导致单台发动机部件特性的差异,使得模型计算结果与单台发动机的性能差异较大,提出了一种基于熵判别粒子群优化算法.通过判别粒子群的熵值,调整种群的多样性,对适应度差的粒子进行迁移,克服了易陷入局部极小点的缺陷.从仿真结果可知:基于熵判别粒子群优化算法的修正效果显然优于影响系数矩阵的修正方法.经验证,模型修正后的低压涡轮出口温度等8个目标性能参数的误差在1%以内,达到较好的修正效果,使单台发动机模型能够与真实发动机进行匹配. 

Engine model correction based on entropy criterion PSO

The difference of single component characteristics which is caused by manufacture and installation can make the performance discrepant.A new entropy criterion particle swarm optimization (PSO) has been presented to revise the engine model based on the trial run data.The new algorithm adjusted the speed of inertia weight and migrated the particles of part poor fitness at the same time based on entropy discrimination.The presented algorithm overcame the defect of the original algorithm.The simulation results indicate that the single engine model correction based on entropy criterion PSO is better than the correction based on influence coefficient matrix (ICM).It is verified that the maximum error of the performance parameter is under 1.5%,which means the single engine model and real engine match better.