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Optimal Design of a Centrifugal Compressor Impeller Using Evolutionary Algorithms

机译：离心压缩机叶轮的进化算法优化设计

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摘要

An optimization study was conducted on a centrifugal compressor. Eight design variables were chosen from the control points for the Bezier curves which widely influenced the geometric variation; four design variables were selected to optimize the flow passage between the hub and the shroud, and other four design variables were used to improve the performance of the impeller blade. As an optimization algorithm, an artificial neural network (ANN) was adopted. Initially, the design of experiments was applied to set up the initial data space of the ANN, which was improved during the optimization process using a genetic algorithm. If a result of the ANN reached a higher level, that result was re-calculated by computational fluid dynamics (CFD) and was applied to develop a new ANN. The prediction difference between the ANN and CFD was consequently less than 1% after the 6th generation. Using this optimization technique, the computational time for the optimization was greatly reduced and the accuracy of the optimization algorithm was increased. The efficiency was improved by 1.4% without losing the pressure ratio, and Pareto-optimal solutions of the efficiency versus the pressure ratio were obtained through the 21st generation.
机译：在离心压缩机上进行了优化研究。从贝塞尔曲线的控制点中选择了八个设计变量，这些变量对几何变化有很大的影响。选择了四个设计变量来优化轮毂和护罩之间的流动通道，并使用其他四个设计变量来改善叶轮叶片的性能。作为一种优化算法，采用了人工神经网络（ANN）。最初，通过实验设计来建立ANN的初始数据空间，并在优化过程中使用遗传算法对其进行了改进。如果人工神经网络的结果达到较高水平，则可以通过计算流体力学（CFD）重新计算该结果，并将其用于开发新的人工神经网络。因此，第6代后，ANN和CFD之间的预测差异小于1％。使用这种优化技术，极大地减少了优化的计算时间，并提高了优化算法的准确性。在不损失压力比的情况下，效率提高了1.4％，并且在第21代中获得了效率与压力比的帕累托最优解。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第10期|752931.1-752931.22|共22页
  • 作者

    Soo-Yong Cho; Kook-Young Ahn; Young-Duk Lee; Young-Cheol Kim;

  • 作者单位

    Department of Mechanical and Aerospace Engineering (RECAPT), Gyeongsang National University, 900 Gajwa-dong, Gyeongnam, Jinju 660-701, Republic of Korea;

    Department of Eco-Machinery, Korea Institute of Machinery and Materials, 171 Jang-dong, Daejeon 305-343, Republic of Korea;

    Department of Eco-Machinery, Korea Institute of Machinery and Materials, 171 Jang-dong, Daejeon 305-343, Republic of Korea;

    Department of Eco-Machinery, Korea Institute of Machinery and Materials, 171 Jang-dong, Daejeon 305-343, Republic of Korea;

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