Evaluating Airfoil Mesh Quality with Transformer
Mesh quality is a major factor affecting the structure of computational fluid dynamics (CFD) calculations. Traditional mesh quality evaluation is based on the geometric factors of the mesh cells and does not effectively take into account the defects caused by the integrity of the mesh. Ensuring the...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/2/110 |
| _version_ | 1797623046225264640 |
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| author | Zhixiang Liu Huan Liu Yuanji Chen Wenbo Zhang Wei Song Liping Zhou Quanmiao Wei Jingxiang Xu |
| author_facet | Zhixiang Liu Huan Liu Yuanji Chen Wenbo Zhang Wei Song Liping Zhou Quanmiao Wei Jingxiang Xu |
| author_sort | Zhixiang Liu |
| collection | DOAJ |
| description | Mesh quality is a major factor affecting the structure of computational fluid dynamics (CFD) calculations. Traditional mesh quality evaluation is based on the geometric factors of the mesh cells and does not effectively take into account the defects caused by the integrity of the mesh. Ensuring the generated meshes are of sufficient quality for numerical simulation requires considerable intervention by CFD professionals. In this paper, a Transformer-based network for automatic mesh quality evaluation (Gridformer), which translates the mesh quality evaluation into an image classification problem, is proposed. By comparing different mesh features, we selected the three features that highly influence mesh quality, providing reliability and interpretability for feature extraction work. To validate the effectiveness of Gridformer, we conduct experiments on the NACA-Market dataset. The experimental results demonstrate that Gridformer can automatically identify mesh integrity quality defects and has advantages in computational efficiency and prediction accuracy compared to widely used neural networks. Furthermore, a complete workflow for automatic generation of high-quality meshes based on Gridformer was established to facilitate automated mesh generation. This workflow can produce a high-quality mesh with a low-quality mesh input through automatic evaluation and optimization cycles. The preliminary implementation of automated mesh generation proves the versatility of Gridformer. |
| first_indexed | 2024-03-11T09:18:53Z |
| format | Article |
| id | doaj.art-5781c2b528be42b0abac8f091018df4b |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T09:18:53Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-5781c2b528be42b0abac8f091018df4b2023-11-16T18:26:43ZengMDPI AGAerospace2226-43102023-01-0110211010.3390/aerospace10020110Evaluating Airfoil Mesh Quality with TransformerZhixiang Liu0Huan Liu1Yuanji Chen2Wenbo Zhang3Wei Song4Liping Zhou5Quanmiao Wei6Jingxiang Xu7College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaEast China Sea Bureau, Ministry of Natural Resources, Shanghai 200137, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaMesh quality is a major factor affecting the structure of computational fluid dynamics (CFD) calculations. Traditional mesh quality evaluation is based on the geometric factors of the mesh cells and does not effectively take into account the defects caused by the integrity of the mesh. Ensuring the generated meshes are of sufficient quality for numerical simulation requires considerable intervention by CFD professionals. In this paper, a Transformer-based network for automatic mesh quality evaluation (Gridformer), which translates the mesh quality evaluation into an image classification problem, is proposed. By comparing different mesh features, we selected the three features that highly influence mesh quality, providing reliability and interpretability for feature extraction work. To validate the effectiveness of Gridformer, we conduct experiments on the NACA-Market dataset. The experimental results demonstrate that Gridformer can automatically identify mesh integrity quality defects and has advantages in computational efficiency and prediction accuracy compared to widely used neural networks. Furthermore, a complete workflow for automatic generation of high-quality meshes based on Gridformer was established to facilitate automated mesh generation. This workflow can produce a high-quality mesh with a low-quality mesh input through automatic evaluation and optimization cycles. The preliminary implementation of automated mesh generation proves the versatility of Gridformer.https://www.mdpi.com/2226-4310/10/2/110mesh quality evaluationcomputational fluid dynamicstransformerdeep learningautomated mesh generation |
| spellingShingle | Zhixiang Liu Huan Liu Yuanji Chen Wenbo Zhang Wei Song Liping Zhou Quanmiao Wei Jingxiang Xu Evaluating Airfoil Mesh Quality with Transformer Aerospace mesh quality evaluation computational fluid dynamics transformer deep learning automated mesh generation |
| title | Evaluating Airfoil Mesh Quality with Transformer |
| title_full | Evaluating Airfoil Mesh Quality with Transformer |
| title_fullStr | Evaluating Airfoil Mesh Quality with Transformer |
| title_full_unstemmed | Evaluating Airfoil Mesh Quality with Transformer |
| title_short | Evaluating Airfoil Mesh Quality with Transformer |
| title_sort | evaluating airfoil mesh quality with transformer |
| topic | mesh quality evaluation computational fluid dynamics transformer deep learning automated mesh generation |
| url | https://www.mdpi.com/2226-4310/10/2/110 |
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