等离子体电磁干扰下圆柱绕流壁面压力信号AI实时降噪

陈杰, 宗豪华, 宋慧敏, 梁华, 刘诗敏, 方子淇

陈杰, 宗豪华, 宋慧敏, 等. 等离子体电磁干扰下圆柱绕流壁面压力信号AI实时降噪[J]. 实验流体力学, 2023, 37(4): 59-65. DOI: 10.11729/syltlx20230030
引用本文: 陈杰, 宗豪华, 宋慧敏, 等. 等离子体电磁干扰下圆柱绕流壁面压力信号AI实时降噪[J]. 实验流体力学, 2023, 37(4): 59-65. DOI: 10.11729/syltlx20230030
CHEN J, ZONG H H, SONG H M, et al. AI-based real-time noise reduction of flow field pressure signals under plasma electromagnetic interference[J]. Journal of Experiments in Fluid Mechanics, 2023, 37(4): 59-65. DOI: 10.11729/syltlx20230030
Citation: CHEN J, ZONG H H, SONG H M, et al. AI-based real-time noise reduction of flow field pressure signals under plasma electromagnetic interference[J]. Journal of Experiments in Fluid Mechanics, 2023, 37(4): 59-65. DOI: 10.11729/syltlx20230030

等离子体电磁干扰下圆柱绕流壁面压力信号AI实时降噪

基金项目: 国家自然科学基金青年科学基金项目(12002384);基础加强计划技术领域基金项目(2021-xxxx-JJ-0786);“两机”基础科学中心项目(P2022-DC-I-003-001)
详细信息
    作者简介:

    陈杰: (1994—),男,陕西榆林人,博士研究生。研究方向:机器学习在流动控制领域的应用。通信地址:陕西省西安市灞桥区霸陵路1号空军工程大学航空动力系统与等离子体技术全国重点实验室(710038)。E-mail:chenjie9402@foxmail.com

    通讯作者:

    宗豪华: E-mail:haohua_zong@163.com

    宋慧敏: min_cargi@sina.com

  • 中图分类号: V211.71

AI-based real-time noise reduction of flow field pressure signals under plasma electromagnetic interference

  • 摘要: 面向闭环主动流动控制的可靠传感需求,提出采用人工神经网络模型对不同等离子体电磁干扰下受污染流场信号进行实时降噪处理。以安装在圆柱表面的动态压力传感器为研究对象,分别采集了正弦交流介质阻挡放电(AC–DBD)的“稠密峰”型干扰信号和纳秒脉冲介质阻挡放电(NS–DBD)的“稀疏突刺”型干扰信号,对人工合成含干扰的流场压力数据进行了监督学习训练,并对模型的泛化能力进行了测试验证。结果表明:基于人工神经网络模型的实时降噪方法可以有效抑制等离子体激励带来的电磁干扰影响,还原真实的压力信号,且对AC–DBD“稠密峰”型干扰信号的降噪效果更好,降噪后信号波形平滑,与真实信号拟合度高;将模型用于真实的流场压力测量中,通过对比降噪后信号与真实信号均值,验证了降噪神经网络的信号还原精度。
    Abstract: For the reliable sensing requirements of closed-loop active flow control, a real-time noise reduction method based on the artificial neural network was proposed for solving the plasma actuation electromagnetic interference on flow field signals. Taking the dynamic pressure sensor installed on the cylinder surface as the experimental subject, the “dense peak” type noise signals of alternating current dielectric barrier discharge (AC–DBD) and the “sparse spike” type noise signals of nanosecond pulsed dielectric barrier discharge (NS–DBD) were collected respectively. Artificial synthetic noise signals were used for supervised learning, and the generalization of the artificial neural network model was tested and verified. The results show that this method can effectively suppress the influence of electromagnetic interference caused by plasma actuation and restore the real pressure signal. It has better denoising performance on the AC–DBD “dense peak” type noise signal. The denoised signal is smoother and better fitted with the real one. This model is also applied to the real flow field pressure measurement, and the accuracy of the denoising network prediction is further verified by comparing the mean value of the denoised signal and the real signal.
  • 图  1   实验设置

    Fig.  1   Experimental setup

    图  2   介质阻挡放电激励器

    Fig.  2   Dielectric barrier discharge actuator

    图  3   介质阻挡放电的典型电压、电流波形

    Fig.  3   Typical voltage and current waveforms of dielectric barrier discharge

    图  4   激励电压波形

    Fig.  4   Excitation voltage waveform

    图  5   实验方法

    Fig.  5   Experimental method

    图  6   噪声信号

    Fig.  6   Noise signal

    图  7   人工神经网络训练损失曲线

    Fig.  7   Artificial neural network training loss curve

    图  8   AC–DBD模型验证

    Fig.  8   AC–DBD model validation

    图  9   NS–DBD模型验证

    Fig.  9   NS–DBD model validation

    表  1   人工神经网络模型参数

    Table  1   Parameters of artificial neural network model

    网络模型参数设置值
    输入层节点数402(NS–DBD),403(AC–DBD)
    隐含层数4
    隐含层节点数512,1024,2048,1024
    输出层节点数400
    激活函数Leaky ReLU
    优化器Adam
    损失函数均方根误差(RMSE)
    初始学习率0.001
    学习率衰减系数0.96
    下载: 导出CSV

    表  2   AC–DBD模型训练电参数

    Table  2   Electrical parameters of training data for AC–DBD model

    电压Up/kV载波频率fbase/kHz调制频率fm/Hz
    4,6,8,10,126,8,10,12100,300,500,700,900
    下载: 导出CSV

    表  3   NS–DBD模型训练电参数

    Table  3   Electrical parameters of training data for NS–DBD model

    电压Up/kV脉冲频率fp/Hz
    4,6,8,10,12200,400,600,800,1000,1200,1400
    下载: 导出CSV

    表  4   AC–DBD模型测试电参数

    Table  4   Electrical parameters of testing data for AC–DBD model

    电压Up/kV载波频率fbase/kHz调制频率fm/Hz
    5,7,9,115,7,9,11200,400,600,800,1000
    下载: 导出CSV

    表  5   不同电参数下AC–DBD模型降噪前后均方根误差

    Table  5   RMSE value of AC–DBD model under different testing electrical parameters

    激励电参数RMSE1RMSE2
    Up = 5 kV,fbase = 7 kHz,fm = 200 Hz32.863.73
    Up = 5 kV,fbase = 9 kHz,fm = 400 Hz39.563.27
    Up = 5 kV,fbase = 11 kHz,fm = 600 Hz42.223.66
    Up = 7 kV,fbase = 7 kHz,fm = 400 Hz40.232.89
    Up = 7 kV,fbase = 9 kHz,fm = 600 Hz44.103.12
    Up = 7 kV,fbase = 11 kHz,fm = 800 Hz48.363.24
    Up = 9 kV,fbase = 7 kHz,fm = 600 Hz48.303.36
    Up = 9 kV,fbase = 9 kHz,fm = 800 Hz56.233.78
    Up = 9 kV,fbase = 11 kHz,fm = 1000 Hz62.353.82
    Up = 11 kV,fbase = 7 kHz,fm = 800 Hz66.232.99
    Up = 11 kV,fbase = 9 kHz,fm = 1000 Hz70.513.64
    Up = 11 kV,fbase = 11 kHz,fm = 200 Hz66.162.81
    下载: 导出CSV

    表  6   NS–DBD模型测试电参数

    Table  6   Electrical parameters of testing data for NS–DBD model

    电压Up/kV脉冲频率fp/Hz
    5,7,9,11300,500,700,900,1100
    下载: 导出CSV

    表  7   不同电参数下NS–DBD模型降噪前后均方根误差

    Table  7   RMSE value of NS–DBD model under different testing electrical parameters

    激励电参数RMSE1RMSE2
    Up = 5 kV,fp = 300 Hz40.364.36
    Up = 5 kV,fp = 500 Hz51.634.89
    Up = 5 kV,fp = 700 Hz62.224.78
    Up = 7 kV,fp = 500 Hz50.244.86
    Up = 7 kV,fp = 700 Hz59.334.88
    Up = 7 kV,fp = 900 Hz68.364.98
    Up = 9 kV,fp = 700 Hz66.324.48
    Up = 9 kV,fp = 900 Hz72.564.36
    Up = 9 kV,fp = 1100 Hz79.854.90
    Up = 11 kV,fp = 300 Hz68.494.22
    Up = 11 kV,fp = 900 Hz90.224.23
    Up = 11 kV,fp = 1100 Hz105.624.58
    下载: 导出CSV

    表  8   降噪后的压力信号均值与干净压力信号均值对比

    Table  8   Mean value comparison between the denoised pressure signal and the clean pressure signal

    激励电参数降噪后信号与
    真实信号的
    均值差/Pa
    AC–DBD
    Up = 5 kV,fbase = 6 kHz,fm = 200 Hz 1.26
    Up = 5 kV,fbase = 8 kHz,fm = 400 Hz1.15
    Up = 7 kV,fbase = 6 kHz,fm = 400 Hz1.36
    Up = 7 kV,fbase = 8 kHz,fm = 600 Hz1.44
    Up = 9 kV,fbase = 6 kHz,fm = 600 Hz1.52
    Up = 9 kV,fbase = 8 kHz,fm = 800 Hz1.21
    Up = 11 kV,fbase = 6 kHz, fm = 800 Hz1.58
    Up = 11 kV,fbase = 8 kHz, fm = 1000 Hz1.61
    NS–DBD
    Up = 6 kV,fp = 200 Hz2.98
    Up = 7 kV,fp = 300 Hz2.32
    Up = 8 kV,fp = 400 Hz2.36
    Up = 9 kV,fp = 500 Hz2.22
    Up = 10 kV,fp = 600 Hz2.43
    Up = 11 kV,fp = 700 Hz2.58
    Up = 13 kV,fp = 800 Hz2.46
    Up = 14 kV,fp = 900 Hz2.77
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-03-12
  • 修回日期:  2023-04-24
  • 录用日期:  2023-04-26
  • 刊出日期:  2023-08-29

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