Accelerating aerodynamic design optimization based on graph convolutional neural network

Tiejun Li, Junjun Yan, Xinhai Chen, Zhichao Wang, Qingyang Zhang, Enqiang Zhou, Chunye Gong and Jie Liu
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Tiejun Li: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China
Junjun Yan: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China
Xinhai Chen: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China
Zhichao Wang: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China
Qingyang Zhang: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China
Enqiang Zhou: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China
Chunye Gong: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China
Jie Liu: Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology, Changsha 410073, Hunan, P. R. China2Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology, Changsha 410073, Hunan, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 01, 1-14

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×.

Keywords: Computational fluid dynamics; aerodynamic design optimization; deep learning; graph convolutional neural network (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124500074

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