Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network
An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enha...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2440075 |
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| author | Baigang Mi Wenqi Cheng |
| author_facet | Baigang Mi Wenqi Cheng |
| author_sort | Baigang Mi |
| collection | DOAJ |
| description | An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient.Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils.2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’. |
| format | Article |
| id | doaj-art-6ac00f5ab83e4488b8d75c1c8bd76980 |
| institution | DOAJ |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-6ac00f5ab83e4488b8d75c1c8bd769802025-08-20T02:49:14ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2025-12-0119110.1080/19942060.2024.2440075Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural networkBaigang Mi0Wenqi Cheng1School of Aeronautics, Northwestern Polytechnical University, Xi’an, People’s Republic of ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an, People’s Republic of ChinaAn intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient.Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils.2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’.https://www.tandfonline.com/doi/10.1080/19942060.2024.2440075Aerodynamic modeling methodsteady/unsteady flow fieldsairfoilsflow field imagesU-Net neural network |
| spellingShingle | Baigang Mi Wenqi Cheng Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network Engineering Applications of Computational Fluid Mechanics Aerodynamic modeling method steady/unsteady flow fields airfoils flow field images U-Net neural network |
| title | Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network |
| title_full | Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network |
| title_fullStr | Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network |
| title_full_unstemmed | Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network |
| title_short | Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network |
| title_sort | intelligent aerodynamic modelling method for steady unsteady flow fields of airfoils driven by flow field images based on modified u net neural network |
| topic | Aerodynamic modeling method steady/unsteady flow fields airfoils flow field images U-Net neural network |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2440075 |
| work_keys_str_mv | AT baigangmi intelligentaerodynamicmodellingmethodforsteadyunsteadyflowfieldsofairfoilsdrivenbyflowfieldimagesbasedonmodifiedunetneuralnetwork AT wenqicheng intelligentaerodynamicmodellingmethodforsteadyunsteadyflowfieldsofairfoilsdrivenbyflowfieldimagesbasedonmodifiedunetneuralnetwork |