Fast aerodynamics prediction of laminar airfoils based on deep attention network

The traditional method for obtaining aerodynamic parameters of airfoils by solving Navier-Stokes equations is a time-consuming computing task. In this article, a novel data-driven deep attention network (DAN) is proposed for reconstruction of incompressible steady flow fields around airfoils. To extract the geometric representation of the input airfoils, the grayscale image of the airfoil is divided into a set of patches, and these are input into the transformer encoder by embedding. The geometric parameters extracted from the transformer encoder, together with the Reynolds number, angle of attack, flow field coordinates, and distance field, are input into a multilayer perceptron to predict the flow field of the airfoil. Through analysis of a large number of qualitative and quantitative experimental results, it is concluded that the proposed DAN can improve the interpretability of the model while obtaining good prediction accuracy and generalization capability for different airfoils and flow-field states.

Automated summary

In this article, a novel data-driven deep attention network (DAN) is proposed for reconstructing incompressible steady flow fields around airfoils. The traditional method of obtaining aerodynamic parameters through solving Navier-Stokes equations is time-consuming. The DAN utilizes a transformer encoder to extract geometric representations of the airfoils and then predicts the flow fields using a multilayer perceptron. Experimental results demonstrate that the proposed DAN improves model interpretability and achieves good prediction accuracy and generalization capability for different airfoils and flow-field states.