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Accelerating aerodynamic design optimization based on graph convolutional neural network.
- Source :
-
International Journal of Modern Physics C: Computational Physics & Physical Computation . Jan2024, Vol. 35 Issue 1, p1-14. 14p. - Publication Year :
- 2024
-
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 ×. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01291831
- Volume :
- 35
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- International Journal of Modern Physics C: Computational Physics & Physical Computation
- Publication Type :
- Academic Journal
- Accession number :
- 173743466
- Full Text :
- https://doi.org/10.1142/S0129183124500074