| A mesh optimization method using machine learning technique and variational mesh adaptation |
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| 作者姓名： | Tingfan WU Xuejun LIU Wei AN Zenghui HUANG Hongqiang LYU |
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| 作者单位： | 1. MIITKey Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics;2. Collaborative Innovation Center of Novel Software Technology and Industrialization;3. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics |
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| 基金项目： | the Aeronautical Science Foundation of China (Nos. 2018ZA52002 and 2019ZA052011)； |
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| 摘 要： | Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical simulation. In light of the introduced large amount of computational costs for many adaptive mesh methods, moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational expense. As the state-of-the-art moving mesh method, the variational mesh adaptation approach has been introduced to CFD calculation. However, quickly estimating the flow fi...
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| 收稿时间： | 12 October 2020 |
A mesh optimization method using machine learning technique and variational mesh adaptation |
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| Tingfan WU,Xuejun LIU,Wei AN,Zenghui HUANG,Hongqiang LYU.A mesh optimization method using machine learning technique and variational mesh adaptation[J].Chinese Journal of Aeronautics,2022,35(3):27-41. |
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| Institution: | 1. MIITKey Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China;3. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China |
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| Abstract: | Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical simulation. In light of the introduced large amount of computational costs for many adaptive mesh methods, moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational expense. As the state-of-the-art moving mesh method, the variational mesh adaptation approach has been introduced to CFD calculation. However, quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging. A mesh optimization method, which embeds a machine learning regression model into the variational mesh adaptation, is proposed. The regression model captures the mapping between the initial mesh nodes and the flow field, so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression model. After the optimization, the density of the nodes in the high gradient area increases while the density in the low gradient area decreases. Benchmark examples are first used to verify the feasibility and effectiveness of the proposed method. And then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our method. Results show that the proposed method significantly improves the accuracy of the local flow features on the adaptive meshes. Our work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation. |
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| Keywords: | CFD Flow field Machine learning Moving mesh method Regression models Variational mesh adaptation |
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