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Design of Compressor Impeller Using Evolutionary Optimization Technique

机译:使用进化优化技术设计压缩机叶轮

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Configuration design on an impeller using to the centrifugal compressor of turbocharger was conducted to improve its performance.Impeller shape was adjusted by changing its meridional contours and blade profile.Total nine design variables were chosen with constraints.ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time.This ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment).This ANN was continuously improved its accuracy for each generation of which population was one hundred.New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result.After 7th generation,the difference of efficiency and pressure ratio predicted by ANN and CFD (Computational Fluid Dynamics) was less than 0.6%.From more than 1,200 design variable sets,a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function.On this optimized impeller,the efficiency and pressure ratio were improved by 1% and 9.3%,respectively.
机译:物进行使用于涡轮增压器的离心式压缩机的叶轮配置设计,以提高其performance.Impeller形状通过改变与constraints.ANN(人工神经网络)被选为9个设计变量获得通过作为其子午轮廓和叶片profile.Total调整为了减少优化time.This ANN悉并用其使用DOE(实验设计)中得到的设计变量集训练了与PSO(粒子群优化)一个主要的优化算法。这ANN连续提高其精度为每个代,其人口在每一代一个hundred.New设计变量集合使用PSO的非基于梯度的方法,以获得全局优化result.After第7代,效率和压力比的差由ANN和预测选择CFD(计算流体动力学)为小于0.6%。从超过1200个设计变量集,效率与帕累托得到的压力比和基于多目标function.On这种优化的叶轮,效率和压力比分别通过1%和9.3%,提高了被选择的优化结果。

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