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基于机器学习的高速复杂流场流动控制效果预测分析

余柏杨 吕宏强 周岩 罗振兵 刘学军

余柏杨,吕宏强,周岩,等. 基于机器学习的高速复杂流场流动控制效果预测分析[J]. 实验流体力学,2022,36(3):44-54 doi: 10.11729/syltlx20210168
引用本文: 余柏杨,吕宏强,周岩,等. 基于机器学习的高速复杂流场流动控制效果预测分析[J]. 实验流体力学,2022,36(3):44-54 doi: 10.11729/syltlx20210168
YU B Y,LYU H Q,ZHOU Y,et al. Predictive analysis of flow control in high-speed complex flow field based on machine learning[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):44-54. doi: 10.11729/syltlx20210168
Citation: YU B Y,LYU H Q,ZHOU Y,et al. Predictive analysis of flow control in high-speed complex flow field based on machine learning[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):44-54. doi: 10.11729/syltlx20210168

基于机器学习的高速复杂流场流动控制效果预测分析

doi: 10.11729/syltlx20210168
基金项目: 航空科学基金(2018ZA52002,2019ZA052011);空气动力学国家重点实验室基金(SKLA20180102);气动噪声控制重点实验室基金(ANCL20190103)
详细信息
    作者简介:

    余柏杨:(1998—),男,江苏盐城人,硕士研究生。研究方向:机器学习在航空航天领域的应用。通信地址:南京市江宁区胜太路29号南京航空航天大学计算机科学与技术学院207实验室(211106)。E-mail:2425392690@qq.com

    通讯作者:

    E-mail:xuejun.liu@nuaa.edu.cn

  • 中图分类号: V211.3

Predictive analysis of flow control in high-speed complex flow field based on machine learning

  • 摘要: 流动控制激励器是主动流动控制技术的核心,其设计水平和工作性能直接决定了主动流动控制的应用效果和应用方向。为了获得流动控制激励器的作用规律,需要大量实验研究激励参数对控制效果参数的影响,实验代价较大。利用逆向等离子体合成射流激波控制实验数据,采用机器学习中的高斯过程回归模型,获得激励器参数(头锥直径、腔体体积、放电电容、出口直径)到控制效果参数(最大脱体距离)的映射规律,对比多种核函数下高斯过程回归的预测效果,采用特征重要性分析方法分析激励器参数对控制效果参数的影响程度。结果表明:对于小样本问题,采用2次多项式核函数Poly2的高斯过程回归预测精度最高。在特征重要性分析上,头锥直径对最大脱体距离的影响程度最大;其次是放电电容和腔体体积,2个参数的影响相近;出口直径影响最小。本文工作可为高速复杂流场流动控制实验中激励器各项参数的设置提供一定参考。
  • 图  1  研究方案

    Figure  1.  Research program

    图  2  最大脱体距离图像示例

    Figure  2.  An example image of maximum out of body distance

    图  3  基于GPR的控制效果参数预测模型框架

    Figure  3.  The framework of control effect parameter prediction model based on GPR

    图  4  训练数据集上GPR不同核函数对应的预测RMSE盒状图

    Figure  4.  Boxplot of RMSE for models with different kernel functions of GPR on training data set

    图  5  皮尔逊相关系数

    Figure  5.  Pearson correlation coefficients

    图  6  ARD特征重要性分析结果

    Figure  6.  Results of feature importance analysis from ARD

    图  7  LASSO特征重要性分析结果

    Figure  7.  Results of feature importance analysis from LASSO

    图  8  RF特征重要性分析结果

    Figure  8.  Results of feature importance analysis from RF

    图  9  最大脱体距离真实值和预测值

    Figure  9.  Real and predicted values of maximum out of body distance

    表  1  实验数据

    Table  1.   Experimental data

    序号头锥直径/mm腔体体积/mm3电极间距/mm放电电容/nF出口直径/mm击穿电压/kV最大脱体距离/mm
    1 50 1500 3.5 80 5.0 5.2 14.7
    2 50 1500 3.5 160 5.0 5.2 16.1
    3 50 1500 3.5 640 5.0 5.2 14.5
    4 50 1500 3.5 320 5.0 5.2 13.3
    5 50 1500 3.5 320 9.0 5.2 14.3
    6 50 1500 3.5 320 1.5 5.2 17.5
    7 50 1500 3.5 640 3.0 5.2 21.8
    8 50 1500 3.5 640 7.0 5.2 16.2
    9 50 1500 3.5 640 9.0 5.2 16.9
    10 30 1500 3.5 640 5.0 5.2 12.6
    11 70 1500 3.5 640 5.0 5.2 23.5
    12 50 3000 3.5 640 5.0 5.2 15.7
    13 50 500 3.5 640 5.0 5.2 18.7
    14 50 250 3.5 640 5.0 5.2 20.8
    15 50 1500 3.5 640 5.0 5.2 14.1
    下载: 导出CSV

    表  2  测试数据集上GPR不同核函数对应的预测RMSE均值

    Table  2.   Mean RMSE for models with different kernel functions of GPR on test data set

    核函数RMSE
    Poly22.4178$ \pm $2.5498
    Poly32.4198$ \pm $2.5531
    Poly42.4208$ \pm $2.5498
    SEiso2.4985$ \pm $2.5869
    SEard2.6906$ \pm $2.5919
    SM13.7693$ \pm $2.5546
    Add4.3876$ \pm $2.0921
    下载: 导出CSV

    表  3  实验数据及预测结果

    Table  3.   Experimental data and prediction results

    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-29
  • 修回日期:  2022-02-09
  • 录用日期:  2022-02-11
  • 网络出版日期:  2022-07-04
  • 刊出日期:  2022-07-04

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