激波风洞测力信号的频域数据深度学习建模分析方法

聂少军1,2,汪运鹏1,2,王春1,2,姜宗林1,2

振动与冲击 ›› 2023, Vol. 42 ›› Issue (13) : 296-302.

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (13) : 296-302.
论文

激波风洞测力信号的频域数据深度学习建模分析方法

  • 聂少军1,2,汪运鹏1,2,王春1,2,姜宗林1,2
作者信息 +

Deep learning modeling analysis method of frequency-domain data of shock wind tunnel force measurement signals

  • NIE Shaojun1,2, WANG Yunpeng1,2, WANG Chun1,2, JIANG Zonglin1,2
Author information +
文章历史 +

摘要

高精准度气动力测量是激波风洞试验中的关键技术。在开展测力试验时,测力系统在风洞流场起动瞬间的冲击激励下产生振动,振动信号无法在较短的有效试验时间内快速衰减,导致天平输出信号中耦合了惯性干扰。基于深度学习技术,对激波风洞天平信号在频域内开展数据处理,并针对动态信号的频域特征进行卷积神经网络建模分析,旨在消除测力信号中的惯性干扰。在频域模型训练样本和验证样本的结果分析中,天平信号的大幅惯性振动干扰被消除,达到了预期的结果,验证了频域建模分析方法的有效性和可靠性。此外,对处理结果进行了误差分析,进一步验证了该方法在激波风洞天平数据处理中具有较大的工程应用价值。

Abstract

High-accuracy force measurement is the key technology in shock tunnel tests. When a force test is conducted, the vibration of force measurement system is excited under the impact flow during the starting process of shock tunnel, and it cannot be attenuated rapidly during extremely short-duration (millisecond level). The balance output signal is coupled with aerodynamic force and inertial vibration. To eliminate inertial vibration, the balance signal was processed and the characteristics of dynamic samples were analyzed in frequency-domain based on deep learning. The results show that the most inertial vibration in output signal is removed and the expected results are obtained, verifying the validity and reliability of the modelling method in frequency-domain. In addition, error of processed results was analyzed, which further verifies that the modelling method in frequency-domain has great engineering application value in data processing of shock tunnel balance.

关键词

激波风洞 / 气动力测量 / 惯性振动 / 深度学习 / 频域分析

Key words

shock tunnel / aerodynamic force measurement / inertial vibration / deep learning / frequency-domain analysis

引用本文

导出引用
聂少军1,2,汪运鹏1,2,王春1,2,姜宗林1,2. 激波风洞测力信号的频域数据深度学习建模分析方法[J]. 振动与冲击, 2023, 42(13): 296-302
NIE Shaojun1,2, WANG Yunpeng1,2, WANG Chun1,2, JIANG Zonglin1,2. Deep learning modeling analysis method of frequency-domain data of shock wind tunnel force measurement signals[J]. Journal of Vibration and Shock, 2023, 42(13): 296-302

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