  Machine learning models are recently utilized for airfoil shape generation
methods. It is desired to obtain airfoil shapes that satisfies required lift
coefficient. Generative adversarial networks (GAN) output reasonable airfoil
shapes. However, shapes obtained from ordinal GAN models are not smooth, and
they need smoothing before flow analysis. Therefore, the models need to be
coupled with Bezier curves or other smoothing methods to obtain smooth shapes.
Generating shapes without any smoothing methods is challenging. In this study,
we employed conditional Wasserstein GAN with gradient penalty (CWGAN-GP) to
generate airfoil shapes, and the obtained shapes are as smooth as those
obtained using smoothing methods. With the proposed method, no additional
smoothing method is needed to generate airfoils. Moreover, the proposed model
outputs shapes that satisfy the lift coefficient requirements.
