两种涡扇发动机部件特性自适应模型对比

发展了航空发动机自适应模型．并对以两种优化算法为基础的自适应模型进行了对比分析。两种模型以通用特性为基础运用优化方法，以发动机主要性能参数和过程参数偏差函数最小为优化目标，以部件特性耦合因子为被优参数，可以预测出不同飞行条件下的发动机风扇、压气机、燃烧室、高压涡轮、低压涡轮等部件特性。运用单纯形和遗传算法为基础的自适应模型对某型涡扇发动机性能的计算结果表明：相对于单纯形算法模型．遗传算法模型对发动机主要性能参数和过程参数的计算偏差降低了20％～30％;对发动机各截面总温、总压计算偏差降低了15％～20％；遗传算法模型相对于单纯形模型具有更为宽广的自适应模拟范围。对某型已知部件特性的涡扇发动机模拟结果显示．遗传算法模型部件特性模拟结果与已知部件特性差别甚微。

Comparison of Two Simulation Models of Turbofan Component Performance

The comparison and analysis of two simulation models were present. Two models are based on nonlinear simplex and genetic algorithms allowing the simulation of turbofan component performance adapting to engine particularities. Two models can provide simulation for component maps using optimization with primary performance particularities＇ function and as objective function, The effectiveness of the proposed methods was demonstrated by application to a certain turbofan engine. Investigation shows that the calculation errors of primary performance particular and each engine section＇s total pressure and temperature based on genetic algorithms mode 1 decrease 20%-30%and 15%-20% respectively compared with simplex model. The genetic algorithms model has spacious adaptive simulation rang.