将CFD分析与优化构架相结合,建立了一个能够快速优化在不同工况下引射器构型设计的工具.首先对引射器构型进行参数化研究,确定需要优化的设计变量.然后用多目标排列遗传算法建立了包含建立和验证点的最优拉丁超立方体,其设计点的响应均由CFD计算分析得到.接着用移动最小二乘近似法建立工作流体和卷吸流体流率的描述模型.最后用遗传算法和顺序二次规划法对一系列的引射器设计进行优化,得到其全局最优值.通过使用这个优化方法,让不同适用范围的引射器设计得到了最优Pareto前沿,即其对应的最优化设计.%Aim. The introduction of the full paper reviews a number of papers in the open literature and then, in its fifth paragraph, outlines what we believe to be a new and better optimization, which is explained in sections 1, 2 and 3. The core of section 2 consists of: ( 1 ) we study the parameters of an ejector configuration and determine its design variables; (2) we build the surrogate model of an ejector to approximate the entrained flow rate and pressure lift; for the design of experiment, we build its optimal Latin hypercube (OLH) to ensure that every design point have its corresponding value and be evenly distributed to design object; (3) we use the multi-objective permutation genetic algorithm (GA) to optimize the fitness and uniformity of the building points and validation points; (4) we use the moving least squares (MLS) to establish the metamodel of ejector and carry out its global optimization with GA, whose response is calculated with the surrogate model; (5) we use the sequential quadratic programming to adjust the design variables optimized by GA and to ensure that they be optimal. Section 3 presents the optimization results, given in Figs. 6,9,10,11 and 12; the results and their analysis show preliminarily that our optimization method can obtain the Pareto optimal front and conduct rapid optimization of ejectors working in a wide range of entrained flow rate.
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