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循环神经网络在智能天平研究中的应用

聂少军 王粤 汪运鹏 赵敏 隋婧

聂少军, 王粤, 汪运鹏, 赵敏, 隋婧. 循环神经网络在智能天平研究中的应用. 力学学报, 2021, 53(8): 2336-2344 doi: 10.6052/0459-1879-21-168
引用本文: 聂少军, 王粤, 汪运鹏, 赵敏, 隋婧. 循环神经网络在智能天平研究中的应用. 力学学报, 2021, 53(8): 2336-2344 doi: 10.6052/0459-1879-21-168
Nie Shaojun, Wang Yue, Wang Yunpeng, Zhao Min, Sui Jing. Application of recurrent neural network in research of intelligent wind tunnel balance. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8): 2336-2344 doi: 10.6052/0459-1879-21-168
Citation: Nie Shaojun, Wang Yue, Wang Yunpeng, Zhao Min, Sui Jing. Application of recurrent neural network in research of intelligent wind tunnel balance. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(8): 2336-2344 doi: 10.6052/0459-1879-21-168

循环神经网络在智能天平研究中的应用

doi: 10.6052/0459-1879-21-168
基金项目: 国家自然科学基金资助项目(11672357)
详细信息
    作者简介:

    汪运鹏, 副研究员, 主要研究方向: 激波风洞测力试验与风洞天平技术. E-mail: wangyunpeng@imech.ac.cn

  • 中图分类号: O354

APPLICATION OF RECURRENT NEURAL NETWORK IN RESEARCH OF INTELLIGENT WIND TUNNEL BALANCE

  • 摘要: 激波风洞地面试验对高超声速飞行器高焓气动特性研究至关重要, 而高精度气动力测量是其中的关键技术. 在脉冲型激波风洞中进行测力试验时, 风洞起动时流场瞬间建立, 对测力系统会产生较大的冲击. 测力系统在瞬时冲击作用下受到激励, 系统的惯性振动信号在短时间内无法快速衰减, 天平的输出信号中会包含惯性振动干扰量, 导致脉冲型风洞测力试验精准度的进一步提高遇到瓶颈. 为了解决短试验时间内激波风洞快速准确测力问题, 发展高精度的动态校准技术是提升受惯性干扰天平性能的关键方法. 因此, 本文采用循环神经网络对天平动态校准数据进行训练和智能处理, 旨在消除输出动态信号中的振动干扰信号. 本文对该方法进行了误差分析, 验证了该方法的可靠性, 并将该方法应用于激波风洞测力试验中, 切实有效降低了惯性振动对天平输出信号的干扰影响. 根据智能模型的样本验证分析, 各分量载荷相对误差比较小, 其中高频轴向力分量处理结果的相对误差约1%. 在风洞试验数据验证中, 也得到了比较理想的结果, 同时与卷积神经网络模型处理的结果进行了对比分析.

     

  • 图  1  循环神经网络单元结构示意图

    Figure  1.  Diagram of RNN unit

    图  2  阶跃载荷采集装置[21]

    Figure  2.  Step load acquisition device[21]

    图  3  阶跃载荷信号

    Figure  3.  Step load signal

    图  4  轴向力通道输入信号与经过网络模型处理后信号的对比

    Figure  4.  Comparison of the input signal and validation data by training model (axial force)

    图  5  法向力通道输入信号与经过网络模型处理后信号的对比

    Figure  5.  Comparison of the input signal and validation data by training model (normal force)

    图  6  俯仰力矩通道输入信号与经过网络模型处理后信号的对比

    Figure  6.  Comparison of the input signal and validation data by training model (pitching moment)

    图  7  LSTM模型loss值随训练轮数的变化

    Figure  7.  Loss changes with epochs in LSTM model

    图  8  Bi-LSTM模型loss值随训练轮数的变化

    Figure  8.  Loss changes with epochs in Bi-LSTM model

    图  9  轴向力通道风洞试验信号与模型处理的信号

    Figure  9.  Wind tunnel test signal and model processed signal (axial force)

    图  10  法向力通道风洞试验信号与模型处理的信号

    Figure  10.  Wind tunnel test signal and model processed signal (normal force)

    图  11  俯仰力矩通道风洞试验信号与模型处理的信号

    Figure  11.  Wind tunnel test signal and model processed signal (pitching moment)

    表  1  LSTM和Bi-LSTM模型的相对误差

    Table  1.   Relative error of LSTM and Bi-LSTM model

    Components$\bar F$$\bar F_{\rm{L}}^*$$\bar F_{\rm{B}}^*$${\delta _{\rm{L}}}$${\delta _{\rm{B}}}$
    normal force/N5.9436.0815.9472.32%0.07%
    pitching moment/(N·m)2.4092.4502.4031.70%−0.25%
    axial force/N4.6994.6834.6997−0.34%0.01%
    下载: 导出CSV

    表  2  RNN与CNN模型处理数据的相对误差

    Table  2.   The relative errors of RNN and CNN models in processing data

    CoefficientsCNNLSTMBi-LSTM${\delta _{\rm{L}}}$${\delta _{\rm{B}}}$
    normal force0.1550.1600.1573.23%1.27%
    pitching moment0.1070.1030.109−3.74%1.87%
    axial force0.1080.1110.1062.78%−1.85%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-22
  • 录用日期:  2021-06-27
  • 网络出版日期:  2021-06-27
  • 刊出日期:  2021-08-18

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