Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning
The reduced-order model can accurately and efficiently predict unsteady problems in many aerospace engineering applications. The traditional reduced-order model based on proper orthogonal decomposition (POD) and Galerkin projection has poor robustness and large error in predicting complex problems....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/5575722 |
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| _version_ | 1832560662709010432 |
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| author | Zhe Liu Fangli Ning Hui Ding Qingbo Zhai Juan Wei |
| author_facet | Zhe Liu Fangli Ning Hui Ding Qingbo Zhai Juan Wei |
| author_sort | Zhe Liu |
| collection | DOAJ |
| description | The reduced-order model can accurately and efficiently predict unsteady problems in many aerospace engineering applications. The traditional reduced-order model based on proper orthogonal decomposition (POD) and Galerkin projection has poor robustness and large error in predicting complex problems. In this paper, a reduced-order model combining POD and deep learning is proposed to predict cavity flow oscillations under different flow conditions. Firstly, POD modes and corresponding coefficients are obtained by POD. Then, two deep learning frameworks, including multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks, are used to predict the future POD coefficients, respectively. Finally, the cavity flow oscillations across multi-Mach numbers are predicted by the POD modes and the future coefficients. The results show that both of these frameworks can accurately predict cavity flow oscillations when the flow conditions change, and the time cost is reduced by order of magnitude. In addition, due to the performance of LSTM is better than that of MLP, its calculation speed is faster. |
| format | Article |
| id | doaj-art-497a2bc854684f329e87adb422dcdb6f |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-497a2bc854684f329e87adb422dcdb6f2025-02-03T01:27:01ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/55757225575722Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep LearningZhe Liu0Fangli Ning1Hui Ding2Qingbo Zhai3Juan Wei4School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Communication Engineering, Xidian University, Xi’an 710071, ChinaThe reduced-order model can accurately and efficiently predict unsteady problems in many aerospace engineering applications. The traditional reduced-order model based on proper orthogonal decomposition (POD) and Galerkin projection has poor robustness and large error in predicting complex problems. In this paper, a reduced-order model combining POD and deep learning is proposed to predict cavity flow oscillations under different flow conditions. Firstly, POD modes and corresponding coefficients are obtained by POD. Then, two deep learning frameworks, including multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks, are used to predict the future POD coefficients, respectively. Finally, the cavity flow oscillations across multi-Mach numbers are predicted by the POD modes and the future coefficients. The results show that both of these frameworks can accurately predict cavity flow oscillations when the flow conditions change, and the time cost is reduced by order of magnitude. In addition, due to the performance of LSTM is better than that of MLP, its calculation speed is faster.http://dx.doi.org/10.1155/2021/5575722 |
| spellingShingle | Zhe Liu Fangli Ning Hui Ding Qingbo Zhai Juan Wei Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning Shock and Vibration |
| title | Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning |
| title_full | Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning |
| title_fullStr | Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning |
| title_full_unstemmed | Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning |
| title_short | Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning |
| title_sort | reduced order modeling of cavity flow oscillations across multi mach numbers using deep learning |
| url | http://dx.doi.org/10.1155/2021/5575722 |
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