CST-GANs: A Generative Adversarial Network Based on CST Parameterization for the Generation of Smooth Airfoils

Abstract

Generative adversarial networks (GANs) are well-known for their powerful generation ability. In recent years, GANs have been applied in the field of aerodynamic shape optimization (ASO). However, the existing airfoil generation methods based on GANs can only generate a discrete sequence of coordinates corresponding to fixed abscissas, and cannot be applied to the scenarios that generate airfoils directly. In this paper, class function / shape function transformation (CST), a parameterization method of the airfoil that forms a good representation of the geometric shape of the airfoil, is combined with GANs. Therefore, a CST-GANs method is proposed that can directly generate the CST parameterized variables of the airfoil instead of a sequence of airfoil points. Given the abscissa and parameterized variables, the corresponding coordinate can be calculated by CST expression. On the other hand, CST-GANs can generate airfoil geometry with smooth surface without intro-ducing the Bézier curve or the Savitzky-Golay filter. Experiments show that CST-GANs is a promising model, which can not only generate smoother airfoils with fewer neural network parameters but also generate more diverse airfoils.