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首页> 外文期刊>Journal of Mechanical Science and Technology >An integrated blade optimization approach based on parallel ANN and GA with hierarchical fair competition dynamic-niche
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An integrated blade optimization approach based on parallel ANN and GA with hierarchical fair competition dynamic-niche

机译:基于并行人工神经网络和遗传算法的公平竞争动态细分的集成叶片优化方法。

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摘要

This work presented an available multi-point blade optimization procedure for better aerodynamic performances. Based on the proposed Parallel ANN and GA with hierarchical fair competition dynamic-niche (GA-HFCDN), an integrated approach for the blade optimization design was put forward by combining Bezier parameterization with FINE/TURBO solver. In the optimization design, parallel ANN was employed to build a more proper approximate model. GA-HFCDN was proposed to obtain the global optimization solution more efficiently for the blade design. Two research cases, including plane cascades blade optimization and the optimization and experimental study of a low specific speed centrifugal blower blade, were performed by using the above approach. The conclusions showed the rationality and validity of the optimization approach.
机译:这项工作提出了一种可用的多点叶片优化程序,以获得更好的空气动力学性能。基于提出的具有分层公平竞争动态位的并行神经网络和遗传算法(GA-HFCDN),提出了一种将贝塞尔参数化与FINE / TURBO求解器相结合的叶片优化设计的集成方法。在优化设计中,并行神经网络被用来建立一个更合适的近似模型。提出GA-HFCDN是为了更有效地获得叶片设计的全局优化解决方案。利用以上方法进行了平面叶栅叶片优化和低比转速离心鼓风机叶片的优化与实验研究两个研究案例。结论表明了该优化方法的合理性和有效性。

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