摘要
针对弹用涡喷发动机风车起动过程的特点,提出一种仿真求解机制:点火前的风车过程利用径向基函数神经网络(RBFN)方法建模仿真;点火后的加速过程则采用部件匹配法建立发动机动态模型仿真求解。动态模型的非线性方程组,利用粒子群优化算法(PSO)求解,解决了传统迭代解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,证明所建模型具有较高精度。利用模型进行仿真,得到点火时间对发动机加速性能的影响规律,为弹用涡喷发动机起动时间的优化提供了理论依据。
A new simulation strategy was proposed for the start process of missile turbojet engine windmill according to its characteristic.The simulation consists of two phases： the windmill process before ignition and the acceleration process after ignition.The former is simulated using radial basis function networks（RBFN）,and the latter which model is a set of nonlinear equations is solved using particle swarm optimization（PSO） algorithm.The introduction of PSO helps to solve the divergence problem of nonlinear equations caused by traditional iteration method when the initial condition is far from real solution.The results are in agreement with test data,which shows that the presented model has a higher accuracy.The start processes at the different ignition times were simulated,and the results were analyzed synthetically.The analysis shows how the ignition time affects the acceleration performance of the engine windmill start,which provides a theoretical foundation for igniting time optimization.
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2011年第1期37-44,共8页
Acta Armamentarii
基金
航空科学基金(20095584006)
关键词
航空、航天推进系统
导弹推进
涡轮喷气发动机
风车
滑动最小二乘
粒子群算法
加速性能
propulsion system of aviation ＆ aerospace
missile propulsion
turbojet engine
windmill
moving least square
particle swarm optimization
acceleration performance
