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- Title
Accelerating aerodynamic design optimization based on graph convolutional neural network.
- Authors
Li, Tiejun; Yan, Junjun; Chen, Xinhai; Wang, Zhichao; Zhang, Qingyang; Zhou, Enqiang; Gong, Chunye; Liu, Jie
- Abstract
Computational fluid dynamics (CFD) plays a critical role in many scientific and engineering applications, with aerodynamic design optimization being a primary area of interest. Recently, there has been much interest in using artificial intelligence approaches to accelerate this process. One promising method is the graph convolutional neural network (GCN), a deep learning method based on artificial neural networks (ANNs). In this paper, we propose a novel GCN-based aerodynamic design optimization acceleration framework, GCN-based aerodynamic design optimization acceleration framework. The framework significantly improves processing efficiency by optimizing data flow and data representation. We also introduce a network model called GCN4CFD that uses the GCF framework to create a compact data representation of the flow field and an encoder–decoder structure to extract features. This approach enables the model to learn underlying physical laws in a space-time efficient manner. We then evaluate the proposed method on an airfoil aerodynamic design optimization task and show that GCN4CFD provides a significant speedup compared to traditional CFD solvers while maintaining accuracy. Our experimental results demonstrate the robustness of the proposed framework and network model, achieving a speedup average of 3. 0 ×.
- Subjects
CONVOLUTIONAL neural networks; GRAPH neural networks; DEEP learning; COMPUTATIONAL fluid dynamics; PHYSICAL laws; ARTIFICIAL intelligence; DECODING algorithms
- Publication
International Journal of Modern Physics C: Computational Physics & Physical Computation, 2024, Vol 35, Issue 1, p1
- ISSN
0129-1831
- Publication type
Article
- DOI
10.1142/S0129183124500074