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

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

余柏杨^{1, 2, 3, 4,},
吕宏强⁵,
周岩⁶,
罗振兵⁶,
刘学军^{1, 4, ,}

1.
南京航空航天大学计算机科学与技术学院/人工智能学院模式分析与机器智能工业和信息化部重点实验室，南京　211106
2.
空气动力学国家重点实验室，绵阳　621000
3.
气动噪声控制重点实验室，绵阳　621000
4.
软件新技术与产业化协同创新中心，南京　210023
5.
南京航空航天大学航空学院，南京　210016
6.
国防科技大学空天科学学院，长沙　410073

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:
刘学军: E-mail：xuejun.liu@nuaa.edu.cn

中图分类号: V211.3
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出版历程
- 收稿日期: 2021-10-28
- 修回日期: 2022-02-08
- 录用日期: 2022-02-10
- 网络出版日期: 2022-07-03
- 刊出日期: 2022-07-03

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

YU Baiyang^{1, 2, 3, 4,},
LYU Hongqiang⁵,
ZHOU Yan⁶,
LUO Zhenbing⁶,
LIU Xuejun^{1, 4, ,}

1.
Key Laboratory of Pattern Analysis and Machine Intelligence, Ministry of Industry and Information Technology, College of Computer Science and Technology / College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing　211106, China
2.
State Key Laboratory of Aerodynamics, Mianyang　621000 , China
3.
Laboratory of Aerodynamic Noise Control, Mianyang　621000, China
4.
Collaborative Innovation Centre of Novel Software Technology and Industrialization, Nanjing　210023, China
5.
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing　210016, China
6.
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha　410073, China

摘要

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。
- 主动流动控制 /
- 激励器 /
- 机器学习 /
- 高斯过程 /
- 特征重要性分析
Abstract: The flow control actuator is the core of the active flow control technology. The design level and performance of actuator directly determine the application direction and effect of active flow control. In order to obtain the action law of the flow control actuator, a large number of experiments are needed to study the influence of excitation parameters on control effect parameters, and the experimental cost is large. In this paper, the experimental data of jet shock control in reverse plasma synthesis are used, and the Gaussian process regression model in machine learning is used to obtain the mapping law from the actuator parameters （head cone diameter, cavity volume, discharge capacitance and outlet diameter） to the control effect parameters （maximum out of body distance）. We compare the prediction effects of Gaussian process regression under various kernel functions, and analyze the influence of actuator parameters on control effect parameters by using the characteristic importance analysis method. The results show that for this small sample problem, Gaussian process regression with the quadratic polynomial kernel function Poly2 obtains the highest accuracy; in characteristic importance analysis, the head cone diameter has the greatest influence on the maximum separation distance, followed by discharge capacitance and cavity volume. The influence of these two parameters is similar, and the influence of the outlet diameter is the least. The work of this paper can provide some guidance for the setting of various parameters of the actuator in the flow control experiment of the high-speed complex flow field.
- active flow control /
- exciter /
- machine learning /
- Gaussian process /
- feature importance analysis

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图 1 研究方案

Fig. 1 Research program

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图 2 最大脱体距离图像示例

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

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图 3 基于GPR的控制效果参数预测模型框架

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

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图 4 训练数据集上GPR不同核函数对应的预测RMSE盒状图

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

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图 5 皮尔逊相关系数

Fig. 5 Pearson correlation coefficients

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图 6 ARD特征重要性分析结果

Fig. 6 Results of feature importance analysis from ARD

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图 7 LASSO特征重要性分析结果

Fig. 7 Results of feature importance analysis from LASSO

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图 8 RF特征重要性分析结果

Fig. 8 Results of feature importance analysis from RF

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图 9 最大脱体距离真实值和预测值

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

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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

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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
Poly2	2.4178$ \pm $2.5498
Poly3	2.4198$ \pm $2.5531
Poly4	2.4208$ \pm $2.5498
SEiso	2.4985$ \pm $2.5869
SEard	2.6906$ \pm $2.5919
SM	13.7693$ \pm $2.5546
Add	4.3876$ \pm $2.0921

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表 3 实验数据及预测结果

Table 3 Experimental data and prediction results

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参考文献(32)

[1]	BEN-GIDA H,STEFANINI J,STALNOV O,et al. Application of passive flow control techniques to attenuate the unsteady near wake of airborne turrets in subsonic flow[J]. Aerospace Science and Technology,2021,119:107129. doi: 10.1016/J.AST.2021.107129
[2]	战培国,程娅红,赵昕. 主动流动控制技术研究[J]. 航空科学技术,2010,21(5):2-6. DOI: 10.3969/j.issn.1007-5453.2010.05.001 ZHAN P G,CHENG Y H,ZHAO X. A review of active flow control technology[J]. Aeronautical Science and Technology,2010,21(5):2-6. doi: 10.3969/j.issn.1007-5453.2010.05.001
[3]	王林,罗振兵,夏智勋,等. 高速流场主动流动控制激励器研究进展[J]. 中国科学(技术科学),2012,42(10):1103-1119. DOI: 10.1360/ze2012-42-10-1103 WANG L,LUO Z B,XIA Z X,et al. Review of actuators for high speed active flow control[J]. SCIENTIA SINICA Technologica,2012,42(10):1103-1119. doi: 10.1360/ze2012-42-10-1103
[4]	BISHOP C . Pattern recognition and machine learning[M]. Germany: Springer, 2006.
[5]	MINELLI G,DONG T,NOACK B R,et al. Upstream actuation for bluff-body wake control driven by a genetically inspired optimization[J]. Journal of Fluid Mechanics,2020,893:A1. doi: 10.1017/JFM.2020.220
[6]	REN F,RABAULT J,TANG H. Applying deep reinforce-ment learning to active flow control in weakly turbulent conditions[J]. Physics of Fluids,2021,33(3):037121. doi: 10.1063/5.0037371
[7]	RABAULT J,KUCHTA M,JENSEN A,et al. Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control[J]. Journal of Fluid Mechanics,2019,865:281-302. doi: 10.1017/JFM.2019.62
[8]	侯宏,杨建华. RBF网络用于边界层转捩中抽吸流优化控制[J]. 航空学报,2002,23(6):556-559. DOI: 10.3321/j.issn:1000-6893.2002.06.003 HOU H,YANG J H. Plant identification in active control of laminar boundary-layer transition by suction using RBF neural network[J]. Acta Aeronautica ET Astronautica Sinica,2002,23(6):556-559. doi: 10.3321/j.issn:1000-6893.2002.06.003
[9]	卢俊峰, 龚小庆. 统计学[M]. 杭州: 浙江工商大学出版社, 2020.
[10]	罗亦泳,姚宜斌,张立亭,等. 基于高斯过程的GPS高程转换模型[J]. 测绘通报,2015(11):11-14,59. LUO Y Y,YAO Y B,ZHANG L T,ET AL. GPS height transformation model based on Gaussian process[J]. Bulletin of Surveying and Mapping,2015(11):11-14,59.
[11]	罗亦泳. 基于高斯过程的大坝变形预测模型[J]. 浙江工业大学学报,2016,44(5):543-546. DOI: 10.3969/j.issn.1006-4303.2016.05.015 LUO Y Y. A Gaussian process based model for predicting deformations of dams[J]. Journal of Zhejiang University of Technology,2016,44(5):543-546. doi: 10.3969/j.issn.1006-4303.2016.05.015
[12]	孙斌,姚海涛,刘婷. 基于高斯过程回归的短期风速预测[J]. 中国电机工程学报,2012,32(29):104-109,5. SUN B,YAO H T,LIU T. Short-term wind speed forecasting based on gaussian process regression model[J]. Proceedings of the CSEE,2012,32(29):104-109,5.
[13]	张韶辉,苏强,赵永亮,等. 基于LASSO回归对冠心病相关血脂指标的筛选[J]. 中国综合临床,2021,37(2):148-153. DOI: 10.3760/cma.j.cn101721-20200807-00060 ZHANG S H,SU Q,ZHAO Y L,et al. Screening of lipid parameters in coronary artery disease based on Lasso regression[J]. Clinical Medicine of China,2021,37(2):148-153. doi: 10.3760/cma.j.cn101721-20200807-00060
[14]	黄梅,朱焱. 基于随机森林特征重要性的K-匿名特征优选[J]. 计算机应用与软件,2020,37(3):266-270. DOI: 10.3969/j.issn.1000-386x.2020.03.045 HUANG M,ZHU Y. K-anonymity feature optimization based on the importance of random forest features[J]. Computer Applications and Software,2020,37(3):266-270. doi: 10.3969/j.issn.1000-386x.2020.03.045
[15]	刘鑫童. 深度卷积神经网络在甲状腺超声图像中的分类研究[D]. 银川: 宁夏大学, 2018. LIU X T. Classification of deep convolution neural networks in thyroid ultrasound images[D]. Yinchuan: Ningxia Univer- sity, 2018.
[16]	SUN L,AN W,LIU X J,et al. On developing data-driven turbulence model for DG solution of RANS[J]. Chinese Journal of Aeronautics,2019,32(8):1869-1884. doi: 10.1016/J.CJA.2019.04.004
[17]	胡伟杰,黄增辉,刘学军,等. 基于自动核构造高斯过程的导弹气动性能预测[J]. 航空学报,2021,42(4):289-302. HU W J,HUANG Z H,LIU X J,et al. Missile aerodynamic performance prediction of Gaussian process through auto-matic kernel construction[J]. Acta Aeronautica ET Astro-nautica Sinica,2021,42(4):289-302.
[18]	高赫,刘学军,郭晋,等. 基于高斯过程回归的连续式风洞马赫数控制[J]. 空气动力学学报,2019,37(3):480-487. DOI: 10.7638/kqdlxxb-2019.0018F GAO H,LIU X J,GUO J,et al. Mach number control of continuous wind tunnel based on Gaussian process regre-ssion[J]. Acta Aerodynamica Sinica,2019,37(3):480-487. doi: 10.7638/kqdlxxb-2019.0018F
[19]	NEMATZADEH Z, IBRAHIM R, SELAMAT A. Compa-rative studies on breast cancer classifications with k-fold cross validations using machine learning techniques[C]//Proc of the 2015 10th Asian control conference （ASCC）. IEEE, 2015: 1-6. doi: 10.1109/ASCC.2015.7244654
[20]	宗豪华,吴云,宋慧敏,等. 等离子体合成射流的理论模型与重频激励特性[J]. 航空学报,2015,36(6):1762-1774. ZONG H H,WU Y,SONG H M,et al. Analytical model and repetitive working characteristics of plasma synthetic jet[J]. Acta Aeronautica ET Astronautica Sinica,2015,36(6):1762-1774.
[21]	李铮, 史志伟, 魏晨瑶, 等. 高超声速流场中等离子体合成射流激励器逆向喷流激波控制研究[C]//第十届全国流体力学学术会议论文摘要集. 2018.
[22]	PRESS M. Approximation methods for Gaussian process regression[J]. MIT Press,2007,14(2):333-350.
[23]	王林,夏智勋,罗振兵,等. 两电极等离子体合成射流激励器工作特性研究[J]. 物理学报,2014,63(19):194702. DOI: 10.7498/aps.63.194702 WANG L,XIA Z X,LUO Z B,et al. Experimental study on the characteristics of a two-electrode plasma synthetic jet actuator[J]. Acta Physica Sinica,2014,63(19):194702. doi: 10.7498/aps.63.194702
[24]	姜慧,邵涛,章程,等. 不同电极间距下纳秒脉冲表面介质阻挡放电分布特性[J]. 电工技术学报,2017,32(2):33-42. JIANG H,SHAO T,ZHANG C,et al. Distribution cha-racteristics of nanosecond-pulsed surface dielectric barrier discharge at different electrode gaps[J]. Transactions of China Electrotechnical Society,2017,32(2):33-42.
[25]	王林,罗振兵,夏智勋,等. 三电极等离子体合成射流激励器工作特性参数影响实验[J]. 气体物理,2017,2(6):1-8. WANG L,LUO Z B,XIA Z X,et al. Experimental study of the parameters influence on flow characteristic of the three-electrode plasma synthetic jet actuator[J]. Physics of Gases,2017,2(6):1-8.
[26]	RASMUSSEN C E, WILLIAMS C K I. Gaussian processes for machine learning[M]. America: the MIT press, 2005. doi: 10.7551/MITPRESS/3206.001.0001
[27]	徐冲,刘保国,刘开云,等. 基于组合核函数的高斯过程边坡角智能设计[J]. 岩土力学,2010,31(3):821-826. DOI: 10.3969/j.issn.1000-7598.2010.03.028 XU C,LIU B G,LIU K Y,et al. Slope angle intelligent design based on gaussian process with combinatorial kernel function[J]. Rock and Soil Mechanics,2010,31(3):821-826. doi: 10.3969/j.issn.1000-7598.2010.03.028
[28]	王娟,慈林林,姚康泽. 特征选择方法综述[J]. 计算机工程与科学,2005,27(12):68-71. DOI: 10.3969/j.issn.1007-130X.2005.12.024 WANG J,CI L L,YAO K Z. A survey of feature selection[J]. Computer Engineering and Science,2005,27(12):68-71. doi: 10.3969/j.issn.1007-130X.2005.12.024
[29]	刘晓宁. 基于LASSO特征选择的方法比较[J]. 安徽电子信息职业技术学院学报,2014,13(1):26-30. DOI: 10.3969/j.issn.1671-802X.2014.01.009 LIU X N. Comparison of feature selection methods based on lasso[J]. Journal of Anhui Vocational College of Electronics & Information Technology,2014,13(1):26-30. doi: 10.3969/j.issn.1671-802X.2014.01.009
[30]	李欣海. 随机森林模型在分类与回归分析中的应用[J]. 应用昆虫学报,2013,50(4):1190-1197. DOI: 10.7679/j.issn.2095-1353.2013.163 LI X H. Using “random forest” for classification and regression[J]. Chinese Journal of Applied Entomology,2013,50(4):1190-1197. doi: 10.7679/j.issn.2095-1353.2013.163
[31]	徐鹏,林森. 基于C4.5决策树的流量分类方法[J]. 软件学报,2009,20(10):2692-2704. DOI: 10.3724/SP.J.1001.2009.03444 XU P,LIN S. Internet traffic classification using C4.5 decision tree[J]. Journal of Software,2009,20(10):2692-2704. doi: 10.3724/SP.J.1001.2009.03444
[32]	BENESTY J, CHEN J D, HUANG Y T, et al. Pearson correlation coefficient[M]//Noise reduction in speech pro-cessing. Berlin Heidelberg: Springer Berlin Heidelberg, 2009: 1-4. doi: 10.1007/978-3-642-00296-0_5