摘要
对弹用涡喷发动机风车起动过程进行仿真求解:点火前风车过程用径向基函数神经网络(RBFN)建模,引入相关先验知识对网络输入变量加以变换,以满足小样本情况下网络建模;依据试验数据运用滑动最小二乘法(MLS)获得压气机特性;对于点火加速过程的动态模型,采用改进的粒子群优化(PSO)算法求解.解决了N-R(Newton-Raphson)法受初值影响不易收敛的问题.计算结果与试验数据吻合较好,可作为发动机起动过程性能分析和优化的理论依据.
The windmill start process of a missile turbojet engine was simulated and also modeled before ignition using radial basis function networks（RBFN）.Under the condition of few sample data,some experiential relations were introduced to reduce the input variable numbers.Compressor characteristic maps were generated with moving least square based on experimental data.Start process after ignition was modeled and the model was solved by particle swarm optimization（PSO） algorithm.PSO can avoid the problem of divergence for nonlinear equations caused by Newton-Raphson（N-R） method when the initial condition is far from real solution.Calculated results show a great agreement with the test data.The presented method provides some reference for the performance analysis and optimization of turbo engine windmill start process.
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2010年第8期1776-1782,共7页
Journal of Aerospace Power
