Details

Autor(en) / Beteiligte

• Jiang, Jundou
• Li, Guanxiong
• Jiang, Yi
• Zhang, Laiping
• Deng, Xiaogang

Titel

TransCFD: A transformer-based decoder for flow field prediction

Ist Teil von

• Engineering applications of artificial intelligence, 2023-08, Vol.123, p.106340, Article 106340

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The computational fluid dynamics (CFD) method is computationally intensive and costly, and evaluating aerodynamic performance through CFD is time-consuming and labor-intensive. For the design and optimization of aerodynamic shapes, it is essential to obtain aerodynamic performance efficiently and accurately. This paper proposed TransCFD, a Transformer-based decoding architecture for flow field prediction. The aerodynamic shape is parameterized and used as input to the decoder, which learns an end-to-end mapping between the shape and the flow fields. Compared with the CFD method, the TransCFD was evaluated to have a mean absolute error (MAE) of less than 1%, increase the speed by three orders of magnitude, and perform very well in generalization capability. The method simplifies the input requirements compared to most existing methods. Although the object of this work is a two-dimensional airfoil, the setup of this scheme is very general. TransCFD is promising for rapid aerodynamic performance evaluation, with potential applications in accelerating the aerodynamic design. •Fast prediction of pressure and velocity fields method with high accuracy.•A Transformer-based decoder to model the shape features of the flow field.•Achieved lower prediction errors on a low resolution.