The optimization of high-pressure ratio impeller with splitter blades is difficult because of large-scale design parameters, high time cost, and complex flow field. So few relative works are published. In this paper, an engineering-applied centrifugal impeller with ultrahigh pressure ratio 9 was selected as datum geometry. One kind of advanced optimization strategy including the parameterization of impeller with 41 parameters, high-quality CFD simulation, deep machine learning model based on SVR (Support Vector Machine), random forest, and multipoint genetic algorithm (MPGA) were set up based on the combination of commercial software and in-house python code. The optimization objective is to maximize the peak efficiency with the constraints of pressure-ratio at near stall point and choked mass flow. Results show that the peak efficiency increases by 1.24% and the overall performance is improved simultaneously. By comparing the details of the flow field, it is found that the weakening of the strength of shock wave, reduction of tip leakage flow rate near the leading edge, separation region near the root of leading edge, and more homogenous outlet flow distributions are the main reasons for performance improvement. It verified the reliability of the SVR-MPGA model for multiparameter optimization of high aerodynamic loading impeller and revealed the probable performance improvement pattern.

中文翻译：

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机器学习方法研究高压比叶轮的优化设计及流动机理

由于具有大规模的设计参数，较高的时间成本和复杂的流场，因此难以优化带有分流叶片的高压比叶轮。因此相关的著作很少发表。本文选择了工程应用的超高压比9的离心叶轮作为基准几何形状。在此基础上，建立了41种叶轮参数化，高质量CFD仿真，基于SVR（支持向量机）的深度机器学习模型，随机森林和多点遗传算法（MPGA）的一种高级优化策略。商业软件和内部python代码的结合。优化目标是在接近失速点和阻塞质量流量的情况下，在压力比的约束下最大化峰值效率。结果表明，峰值效率提高了1.24％，整体性能同时得到提高。通过比较流场的细节，发现冲击波强度减弱，前缘附近的尖端泄漏流速降低，前缘根部附近的分离区域以及更均匀的出口流量分布是性能改善的主要原因。它验证了SVR-MPGA模型对高气动负载叶轮的多参数优化的可靠性，并揭示了可能的性能改进模式。靠近前缘根部的分离区域以及更均匀的出口流量分布是提高性能的主要原因。它验证了SVR-MPGA模型对高气动负载叶轮的多参数优化的可靠性，并揭示了可能的性能改进模式。靠近前缘根部的分离区域以及更均匀的出口流量分布是提高性能的主要原因。它验证了SVR-MPGA模型对高气动负载叶轮的多参数优化的可靠性，并揭示了可能的性能改进模式。