基于粒子群算法的涡扇发动机气路性能修正

针对由于建模过程中条件简化及发动机零部件的差异性导致的发动机数学模型计算结果与整机性能实测数据偏差较大的问题,提出基于粒子群算法(PSO)的发动机模型修正方法,运用修正因子提高模型计算精度。将修正后发动机模型的计算结果与实测数据对比,结果表明:运用PSO算法对模型进行的修正能够显著提高模型的精度,修正前模型计算值与实测值的最大误差达4.85%,修正后最大误差只有0.97%,修正效果良好,且涡轮等后端部件比压气机等前端部件精度提高更为明显。

Turbofan Engine Gas Performance Model Modification Based on Particle Swarm Optimization

In order to solve the problem of large deviation between the calculated results of engine mathematical model andthe measured data of engine performance due to the simplification of conditions and the difference of engine parts in theprocess of modeling, an engine model correction method based on particle swarm optimization (PSO) was proposed, and theaccuracy of model calculation was improved by using the correction factor. Comparing the calculated results of the modi?fied engine model with the measured data, the results show that the PSO algorithm can significantly improve the accuracyof the model. The maximum error between the calculated and measured values of the model is 4.85% before correction, butthe maximum error is only 0.97% after correction. The correction is effective, and the improvement of the rear-end compo?nents accuracy such as turbines is more obvious than that of the front-end components such as compressors.