基于多目标粒子群算法的配流副结构优化设计  

Optimization Design of Flow Distribution Pair Structure Based on Multi-Objective Particle Swarm Optimization Algorithm

作　　者：底本辉 赵书尚[1] 李阁强[1] 王俊杰 DI Benhui;ZHAO Shushang;LI Geqiang;WANG Junjie(College of Mechanical and Electrical Engineering,He’nan University of Science and Technology,He’nan Luoyang 471000,China)

机构地区：[1]河南科技大学机电工程学院,河南洛阳471000

出　　处：《机械设计与制造》2024年第12期243-249,共7页Machinery Design & Manufacture

基　　金：山东省重点研发计划(国际科技合作专项)资助项目(2019GHZ013)。

摘　　要：由于柱塞泵配流副的结构相对复杂,传统的试凑法不仅会加长设计周期,且难以获得最佳结构参数。为兼顾配流副多项性能指标,以某轴向柱塞泵端面配流副作为研究对象,建立端面配流副结构的数学模型,利用自适应网格与拥挤距离的多目标粒子群算法对模型求解。结果表明:算法的目标函数均保持良好的收敛性和全局适应性,定义的配流副参数变量能找到Pareto最优解,同时维持了解集良好的分布性,提高了结构优化设计的效率;优化后的配流副油膜刚度增大,且在不同油膜厚度与供油压力下,优化后的配流副泄漏流量比优化前降低8.6%左右,验证了模型和算法的适用性与优越性。Due to the relatively complex structure of piston pump valve pair,the traditional trial and error method will not only lengthen the design cycle,but also difficult to obtain the best structural parameters.In order to take account of multiple performance indexes of the end flow distribution pairs,a mathematical model of the end flow distribution pairs of an axial piston pump was established,and the model was solved by using the multi objective particle swarm optimization algorithm based on adaptive mesh and crowding distance.The results show that the objective functions of the algorithm keep good convergence and global adaptability,the parameters of the flow pair defined can find the Pareto optimal solution,and maintain good distribution of the obtained solution,and improve the efficiency of structural optimization design.The oil film stiffness of the optimized valve pair increases,and the leakage flow of the optimized valve pair decreases about 8.6%compared with that before optimization under different oil film thickness and oil supply pressure,which verifies the applicability and superiority of the model and algorithm.

关 键 词：配流副 多目标粒子群算法 优化设计 PARETO最优解 

分 类 号：TH16[机械工程—机械制造及自动化] TH322TH122