基于粒子群算法的转子支承参数优化设计

高速旋转飞轮转子的支承参数决定转子在过系统临界转速时的振幅大小和工作转速下的运行稳定性。选用具备自适应学习能力的粒子群算法(PSO),设计出能够表征系统过临界转速时的振幅和工作转速下衡量系统稳定性的目标函数式,利用PSO和该目标函数式,对系统进行参数优化。实验结果表明,利用PSO算法对该目标函数进行优化,最终得到的高速旋转飞轮转子系统参数,能有效地改善系统性能,提高系统运行稳定性。

Rotors Supporting Parameters Optimization Design Based on the Particle Swarm Algorithm

The high-speed rotate flywheel rotor＇s bearing parameters decide the amplitude of the rotor when crossing the system critical speed and the rotor＇s operational stability under the rated speed.This paper chooses the evolving learning algorithm equipped with self-adaptation learning ability-particle swarm optimization（PSO）.It designs a new objective function which can surface the amplitude when the system crossing the critical rotation speed and the stability of the system under working speed.After that,it uses the PSO and the objective function to optimize the whole system parameters.The relevant experiments indicate that,the objective function optimized by the PSO algorithm can obtain the parameters that promote the system performance and the stability of the operation effectively.