| Force measurement using strain-gauge balance in shock tunnel based on deep learning | |
Nie SJ(聂少军) ; Wang YP(汪运鹏); Jiang ZL(姜宗林)
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| Corresponding Author | Wang, Yunpeng([email protected]) |
| Source Publication | CHINESE JOURNAL OF AERONAUTICS
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| 2023-08-01 | |
| Volume | 36Issue:8Pages:43-53 |
| ISSN | 1000-9361 |
| Abstract | When a force test is conducted in a shock tunnel, vibration of the Force Measurement System (FMS) is excited under the strong flow impact, and it cannot be attenuated rapidly within the extremely short test duration of milliseconds order. The output signal of the force balance is coupled with the aerodynamic force and the inertial vibration. This interference can result in inaccurate force measurements, which can negatively impact the accuracy of the test results. To eliminate inertial vibration interference from the output signal, proposed here is a dynamic calibration modeling method for an FMS based on deep learning. The signal is processed using an intelligent Recurrent Neural Network (RNN) model in the time domain and an intelligent Convolutional Neural Network (CNN) model in the frequency domain. Results processed with the intelligent models show that the inertial vibration characteristics of the FMS can be identified efficiently and its main frequency is about 380 Hz. After processed by the intelligent models, the inertial vibration is mostly eliminated from the output signal. Also, the data processing results are subjected to error analysis. The relative error of each component is about 1%, which verifies that the modeling method based on deep learning has considerable engineering application value in data processing for pulse-type strain-gauge balances. Overall, the proposed dynamic calibration modeling method has the potential to improve the accuracy and reliability of force measurements in shock tunnel tests, which could have significant implications for the field of aerospace engineering. (c) 2023 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). |
| Keyword | Convolutional neural net-works Deep learning Frequency domain analysis Force measurement Time domain analysis Recurrent neural networks |
| DOI | 10.1016/j.cja.2023.05.009 |
| Indexed By | SCI ; EI ; CSCD |
| Language | 英语 |
| WOS ID | WOS:001147606200001 |
| WOS Keyword | SYSTEM ; DESIGN ; FLOWS |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Aerospace |
| Funding Project | National Natural Science Foundation of China[11672357] ; National Natural Science Foundation of China[11727901] |
| Funding Organization | National Natural Science Foundation of China |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Wang, Yunpeng |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/94232 |
| Collection | 高温气体动力学国家重点实验室 |
| Recommended Citation GB/T 7714 | Nie SJ,Wang YP,Jiang ZL. Force measurement using strain-gauge balance in shock tunnel based on deep learning[J]. CHINESE JOURNAL OF AERONAUTICS,2023,36,8,:43-53.Rp_Au:Wang, Yunpeng |
| APA | 聂少军,汪运鹏,&姜宗林.(2023).Force measurement using strain-gauge balance in shock tunnel based on deep learning.CHINESE JOURNAL OF AERONAUTICS,36(8),43-53. |
| MLA | 聂少军,et al."Force measurement using strain-gauge balance in shock tunnel based on deep learning".CHINESE JOURNAL OF AERONAUTICS 36.8(2023):43-53. |
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| Jp2023Fa055.pdf（2346KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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