Hypersonic wind tunnel aerodynamic identification method considering noise suppression
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摘要:
对于风洞试验中全尺寸模型试验的非平稳信号进行载荷辨识仍存在诸多问题。针对全尺度模型试验的非平稳信号载荷辨识提出了一种基于深度残差收缩网络（DRSN）深度学习技术的智能载荷辨识方法，该方法通过深度学习提取测力系统输出数据中的气动力、惯性力和噪声等特征，通过注意力机制对每组数据进行获取阈值，再通过软阈值函数对特征进行滤波降噪，有效辨识出测力系统响应信号中的惯性力分量并进行剔除，实现气动力载荷辨识。在测试验证中，均值法的辨识精度为85%以上，DRSN模型的辨识精度为94%以上，证明DRSN模型能有效降低噪声和惯性力对于载荷辨识的干扰，用于非平稳信号的载荷辨识具有精度高、可靠性好等特点。
Abstract:There are still many problems in load identification of non-stationary signals of full-size model test in wind tunnel test. A full scale model test of non-stationary signal load identification was proposed based on a deep residual shrinkage network （DRSN） deep learning technology of intelligent load identification method. This method extracted load system output data of aerodynamic force and inertial force and noise characteristics by deep learning, through attention mechanism it obtained data access threshold for each group, then the soft threshold function was used to filter the characteristics and reduce the noise. The inertial force component in the response signal of the force measurement system was identified and eliminated effectively, so as to realize the identification of aerodynamic load. In the test and verification, the identification accuracy of the mean value method was above 85%, and that of the DRSN model was above 94%, proving that the DRSN model can effectively reduce the interference of noise and inertia force on the load identification. It presented the characteristics of high accuracy and good reliability for the load identification of non-stationary signals.
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图 12 悬挂测力系统结构及原理
Figure 12. Structure and principle of suspension force measuring system
表 1 DRSN参数
Table 1. DRSN parameters
参数 数值及说明 网络结构 DRSN 优化器 Adam 误差损失 MSE 初始学习率 0.05 最小批量 16 最大训练次数 500 
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表 2 试验结果对比
Table 2. Comparison of test results
组别序号 载荷识别精度/% DRSN法 均值法 1 97.486 85.170 2 95.563 87.029 3 98.922 85.105 4 99.813 84.127 5 99.746 82.226 6 96.910 85.263 7 98.897 87.492 8 98.214 85.600 9 94.715 83.827 10 96.413 83.690 11 98.673 86.260 12 99.512 85.830
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