采用改进粒子群算法优化的涡轮增压器节能研究  

Study on energy saving of turbocharger optimized by improved particle swarm optimization

作　　者：王琦[1] 何仁[2] 翟辉辉[1] WANG qi;HE Ren;ZHAI Huihui(School of Transportation,Zhenjiang College,Zhenjiang 212018,Jiangsu,China;School of Automobile and Traffic Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China)

机构地区：[1]镇江高等专科学校交通学院,江苏镇江212018 [2]江苏大学汽车与交通工程学院,江苏镇江212013

出　　处：《中国工程机械学报》2020年第1期14-17,23,共5页Chinese Journal of Construction Machinery

基　　金：江苏省自然科学基金面上资助项目(BK20151225);镇江市工业控制网络重点实验室资助项目(GX2017005);镇江高专第2批科研团队建设资助项目。

摘　　要：涡轮增压器在工作过程时,由于参数设计的不合理,导致燃料能量损失较为严重。对此,创建了涡轮增压器平面简图模型,根据能量守恒定律建立燃料质量变化方程式。确定涡轮增压器设计参数变量,构造优化目标函数并且添加约束条件。对传统粒子群算法进行改进,将改进后的粒子群算法用于优化能量损失目标函数,给出优化过程的具体步骤。将优化后的参数输入Matlab软件中进行仿真,并与优化前能量损失进行比较和分析。仿真结果显示:优化前,涡轮增压器损失最大功率值为2.92×10^3W,功率曲线波动幅度较大;优化后,涡轮增压器功率损失最大值为1.98×10^3W,功率曲线波动幅度较小。采用改进粒子群算法优化涡轮增压器设计参数,能够提高其工作效率,从而节约能量。The unreasonable parameter design of turbocharger leads to a serious loss of fuel energy during its working process.In view of this,the plane sketch model of turbocharger is established,and the fuel mass change equation is established according to the law of energy conservation.The design parameters of turbocharger are determined,the optimization objective function is constructed and constraints are added.The traditional particle swarm optimization(PSO)is improved.The improved PSO is used to optimize the objective function of energy loss,and the specific steps of the optimization process are given. The optimized parameters are input into the Matlab software for simulation,and the energy loss before optimization is compared and analyzed.The simulation results show that before optimization,the maximum power loss of turbocharger is 2.92×103 W,and the fluctuation of power curve is large;after optimization,the maximum power loss of turbocharger is1.98×103 W,and the fluctuation of power curve is small.Using improved particle swarm optimization to optimize turbocharger design parameters can improve its working efficiency and consequently save energy.

关 键 词：改进粒子群算法 涡轮增压器 能量 优化 仿真 

分 类 号：TK424[动力工程及工程热物理—动力机械及工程]