Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916213497528320 |
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| author | Li, Runze Zhang, Yufei Chen, Haixin |
| author_facet | Li, Runze Zhang, Yufei Chen, Haixin |
| contents | Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be trained on a case-by-case basis and require large training times. On the other hand, fast prediction and design tools are desired for aerodynamic shape designs, and data-driven mesh-based models have achieved great performance. Therefore, this paper proposes a data-driven mesh-agnostic decoder that combines the fast prediction ability of data-driven models and the flexibility of mesh-agnostic models. The model is denoted by an implicit decoder, which consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is based on implicit neural representation, and HyperNet is a simple neural network. The implicit decoder is trained for the fast prediction of supercritical airfoils. Different activation functions are compared, and a spatial constraint is proposed to improve the interpretability and generalization ability of the model. Then, the implicit decoder is used together with a mesh-based encoder to build a generative model, which is used for the inverse design of supercritical airfoils with specified physical features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design Li, Runze Zhang, Yufei Chen, Haixin Fluid Dynamics Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be trained on a case-by-case basis and require large training times. On the other hand, fast prediction and design tools are desired for aerodynamic shape designs, and data-driven mesh-based models have achieved great performance. Therefore, this paper proposes a data-driven mesh-agnostic decoder that combines the fast prediction ability of data-driven models and the flexibility of mesh-agnostic models. The model is denoted by an implicit decoder, which consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is based on implicit neural representation, and HyperNet is a simple neural network. The implicit decoder is trained for the fast prediction of supercritical airfoils. Different activation functions are compared, and a spatial constraint is proposed to improve the interpretability and generalization ability of the model. Then, the implicit decoder is used together with a mesh-based encoder to build a generative model, which is used for the inverse design of supercritical airfoils with specified physical features. |
| title | Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2402.17299 |