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首页> 外文期刊>International Journal of Heat and Mass Transfer >Aerodynamic robustness optimization and design exploration of centrifugal compressor impeller under uncertainties
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Aerodynamic robustness optimization and design exploration of centrifugal compressor impeller under uncertainties

机译：不确定性下离心压缩机叶轮的空气动力学鲁棒优化与设计探索

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摘要

Aerodynamic robustness optimization of centrifugal compressor impeller under multiple uncertainties is an arduous task, due to high dimension, meta modelling workload and trial and error optimization, and an automatic optimization process is often treated as a black box, the underlying mechanism are often not well understood and explored. This paper aims to provide an efficient robustness optimization procedure and explore the underlying physical mechanism for centrifugal compressor impeller under operational and geometric uncertainties. A novel approach is proposed for the multi-objective aerodynamic robustness optimization and design exploration of a centrifugal compressor impeller. A neural-network-based Kriging model is constructed and integrated into the aerodynamic robustness optimization The correlation between the design variables and performance parameters are explored from optimization and directly visualized by self-organizing mapping. Compared to the initial impeller, the average pressure ratio of the optimized impeller increases by 9.3%, the average isentropic efficiency increases by 6.7%, the standard deviations of pressure ratio and efficiency decrease by 7.5% and 15.4% respectively, and the acoustic power level decreases by 11dB. The neural-network-based Kriging model exhibits preferable accuracy for uncertain approximation modeling. The data visualization and interpretation facilitate designers to perform efficient design optimizations. The proposed approach supports design explorations for different applications of turbomachinery.
机译：离心式压缩机叶轮的空气动力学鲁棒性优化在多个不确定性下是一种艰苦的任务，由于高维，元建模工作量和试验和误差优化，而自动优化过程经常被视为黑匣子，潜在的机制往往不太了解并探索。本文旨在提供有效的稳健性优化程序，并探讨了在操作和几何不确定性下离心压缩机叶轮的潜在物理机制。提出了一种新的方法，用于离心式压缩机叶轮的多目标空气动力稳健性优化和设计探索。基于神经网络的Kriging模型构造并集成到空气动力学鲁棒性优化中，从优化和通过自组织映射直接可视化设计变量和性能参数之间的相关性。与初始叶轮相比，优化叶轮的平均压力比增加了9.3％，平均等熵效率增加了6.7％，压力比和效率的标准偏差分别降低了7.5％和15.4％，以及声学功率水平减少11dB。基于神经网络的Kriging模型对不确定近似建模表现出优选的精度。数据可视化和解释方便设计人员进行有效的设计优化。该拟议的方法支持涡轮机械的不同应用设计探索。

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  • 来源
    《International Journal of Heat and Mass Transfer》 |2021年第12期|121799.1-121799.13|共13页
  • 作者

    Xinzi Tang; Nengwei Gu; Wenbin Wang; Zhe Wang; Ruitao Peng;

  • 作者单位

    Engineering Research Center of Complex Track Processing Technology & Equipment Ministry of Education School of Mechanical Engineering Xiangtan University Xiangtan 411105 China;

    Engineering Research Center of Complex Track Processing Technology & Equipment Ministry of Education School of Mechanical Engineering Xiangtan University Xiangtan 411105 China;

    Engineering Research Center of Complex Track Processing Technology & Equipment Ministry of Education School of Mechanical Engineering Xiangtan University Xiangtan 411105 China;

    Institute of Aeronautical Power Machinery The Aviation Industry Corporation of China Ltd Zhuzhou 412002 China;

    Engineering Research Center of Complex Track Processing Technology & Equipment Ministry of Education School of Mechanical Engineering Xiangtan University Xiangtan 411105 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Centrifugal compressor; Surrogate based optimization; Aerodynamic stability; Uncertainty; Self-organizing map;

    机译：离心式压缩机;基于代理的优化;空气动力学稳定性;不确定;自组织地图;

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