激波风洞测力信号的频域数据深度学习建模分析方法

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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.

Key words： shock tunnel aerodynamic force measurement inertial vibration deep learning frequency-domain analysis

Received: 27 April 2022 Published: 15 July 2023

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	Articles by authors
	NIE Shaojun1
	2
	WANG Yunpeng1
	2
	WANG Chun1
	2
	JIANG Zonglin1
	2

Cite this article:

NIE Shaojun1,2,WANG Yunpeng1, et al. 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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I13/296

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