改进粒子群算法的蒸汽喷射器结构优化

Steam ejector structure optimized GA-PSO algorithm

蒸汽喷射器作为喷射式制冷系统的核心部件,传统单因素敏感度分析法对其进行结构优化设计时,忽略了多结构参数同时变化时的相互制约性,导致其优化效果并不理想.选取喷嘴喉部直径、等面积混合室直径、等面积混合室长度、扩压室长度及喷嘴出口位置5个关键结构参数,在单因素敏感度分析的基础上,利用粒子群算法对回归方程进行迭代寻优,并对相关优化结果进行分析.结果表明:标准粒子群算法(PSO)比单因素敏感度分析法得到的喷射系数提高了11.8%,改进粒子群算法(GA-PSO)比单因素敏感度分析法得到的喷射系数提高了14.7%;改进粒子群算法用于优化蒸汽喷射器的结构参数是可行的.

Steam ejector as a core component of jet cooling system,when the traditional single factor sensitivity analysis method is used to optimize the structural design,the mutual restriction of the simultaneous change of multi-structure parameters is ignored,which leads to the unsatisfactory result of optimization.In this paper,five key structural parameters of nozzle throat diameter,constant-area mixing chamber diameter,constant-area mixing chamber length,expansion chamber length and position of nozzle outlet are selected,based on the single factor sensitivity analysis,the particle swarm algorithm is used to optimize the regression equation.Finally.And the related optimization results are analyzed.The results show that,compared with the single factor sensitivity analysis,the standard particle swarm optimization(PSO)improves the ejection coefficient by 11.8%,meanwhile,the improved particle swarm optimization algorithm(GA-PSO)improves the ejection coefficient by 14.7%.It is feasible to use the improved particle swarm optimization algorithm to optimize the structural parameters of steam ejector.