An accuracy-enhanced transonic flow prediction method fusing deep learning and reduced-order model
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909209569787904 |
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| author | Jia, Xuyi Gong, Chunlin Ji, Wen Li, Chunna |
| author_facet | Jia, Xuyi Gong, Chunlin Ji, Wen Li, Chunna |
| contents | It's difficult to accurately predict the flow with shock waves over an aircraft due to the flow's strongly nonlinear characteristics. In this study, we propose an accuracy-enhanced flow prediction method that fuses deep learning and reduced-order model to achieve fast flow field prediction for various aerodynamic shapes. First, we establish the convolutional neural network-proper orthogonal decomposition (CNN-POD) model for mapping geometries to the entire flow field. Next, local flow regions containing nonlinear flow structures are identified through POD reconstruction for enhanced modeling. Then, a new CNN model is employed to map geometries to the local flow field. The proposed method is finally applied in predicting transonic flow over airfoils. The results indicate that the proposed enhanced DNN method can reduce the prediction error of flow properties, particularly in the regions with shock waves (up to 13%-46%). Additionally, the better efficiency and robustness of the proposed methods have been validated in comparison to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_07702 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An accuracy-enhanced transonic flow prediction method fusing deep learning and reduced-order model Jia, Xuyi Gong, Chunlin Ji, Wen Li, Chunna Fluid Dynamics It's difficult to accurately predict the flow with shock waves over an aircraft due to the flow's strongly nonlinear characteristics. In this study, we propose an accuracy-enhanced flow prediction method that fuses deep learning and reduced-order model to achieve fast flow field prediction for various aerodynamic shapes. First, we establish the convolutional neural network-proper orthogonal decomposition (CNN-POD) model for mapping geometries to the entire flow field. Next, local flow regions containing nonlinear flow structures are identified through POD reconstruction for enhanced modeling. Then, a new CNN model is employed to map geometries to the local flow field. The proposed method is finally applied in predicting transonic flow over airfoils. The results indicate that the proposed enhanced DNN method can reduce the prediction error of flow properties, particularly in the regions with shock waves (up to 13%-46%). Additionally, the better efficiency and robustness of the proposed methods have been validated in comparison to existing methods. |
| title | An accuracy-enhanced transonic flow prediction method fusing deep learning and reduced-order model |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2307.07702 |