摘要
为进行辅助动力装置(Auxiliary Power Unit,APU)特性计算,以某型APU为研究对象,基于MATLAB/Simulink建立其部件级模型。鉴于N-R法的固有缺陷,引入PSO(Particle Swarm Optimization)算法,并提出叛逆粒子和自适应学习因子以改进算法全局收敛性和收敛速度,用于模型求解。对比基本PSO算法和改进算法,验证改进方法的有效性。同时将计算结果与实际试车数据比较,表明所采用的建模与求解方法满足工程应用。
Firstly,in order to support the characteristic calculation of Auxiliary Power Unit(APU),a component level model of APU was built with MATLAB/Simulink.Secondly,in order to calculate the model,Particle Swarm Optimization(PSO)was introduced to replace the N-R algorithm with inherent defections and optimized with new method.The validity of the improved PSO was proved after compared with the primary one.Finally,the comparison between the results from the calculation and the experimental shows that the APU model is suitable for engineering us-age.
作者
胡冬
桂先立
蒋海明
吴桦
HU Dong;GUI Xian-li;JIANG Hai-ming;WU Hua(Aviation Science and Technology Key Laboratory of Aviation Mechanical and Electrical System,China Aviation Industry Nanjing Electrical and Hydraulic Engineering Research Center,Nanjing Jiangsu 211106,China)
出处
《计算机仿真》
北大核心
2020年第1期35-39,共5页
Computer Simulation
关键词
辅助动力装置
部件级模型
粒子群算法
模型求解
Auxiliary power unit(APU)
Component level model
Particle swarm optimizer(PSO)
Model sol-ving
