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Design of Compressor Impeller Using Evolutionary Optimization Technique

机译:基于进化优化技术的压气机叶轮设计

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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:为了提高涡轮增压器的离心压缩机的性能,对叶轮进行了结构设计,通过改变其子午线轮廓和叶片轮廓来调整叶轮的形状,总共选择了9个设计变量作为约束条件,并采用了人工神经网络(ANN)作为设计参数。为了减少优化时间,使用PSO(Particle Swarm Optimization)进行优化的主要优化算法.ANN是通过使用DOE(实验设计)获得的设计变量集进行学习和训练的.ANN不断提高了每种算法的准确性人口的一百个世代。使用基于非梯度的PSO方法选择每个世代中的新设计变量,以获得全局优化结果。在第7代之后,ANN预测的效率和压力比之差CFD(计算流体动力学)小于0.6%。从1200多个设计变量集中,效率与在多目标函数的基础上,获得了最佳的压力比,并选择了优化的结果。在这种优化的叶轮上,效率和压力比分别提高了1%和9.3%。

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