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针对经典的航空发动机部件特性修正因子法的不足,提出一种于多状态试车数据的修正方法.利用发动机部件匹配模型方程以及试车参数计算值和实测值的差值组成目标方程组,采用改进的免疫粒子群算法优化求解获得部件特性修正系数.以多个不同状态下的修正系数为基础,利用滑动最小二乘方法拟合修正系数曲面,进而得到修正的部件特性图.数值试验表明:综合利用不同状态下发动机试车数据进行部件特性修正,克服了传统方法的不足.提高了修正部件特性在整个工作范围的精度.
Aiming at the deficiencies of the classic aeroengine component correction factor method, a method for correcting multi-state test data is proposed.According to the matching equations of engine components and the differences between the calculated and measured values of test parameters, Immune Particle Swarm Optimization (PSO) algorithm was used to obtain the correction coefficients of component properties. Based on the correction coefficients in different states, the least square method was used to fit the correction coefficient surfaces, and then the corrected component characteristics were obtained. Numerical experiments show that: Under different conditions, the test data of the engine is used to correct the component characteristics, which overcomes the shortcomings of the traditional methods and improves the accuracy of the corrected component characteristics over the entire working range.