Tailoring Generative Adversarial Networks for Smooth Airfoil Design
This work addresses the need for smooth and diverse airfoil designs in aerospace engineering, representing an incremental improvement over existing GAN-based approaches.
The paper tackled the problem of generating smooth airfoil designs using GANs by introducing a customized loss function, resulting in seamless contours and a substantial increase in design diversity compared to conventional methods with post-processing filters.
In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed generative AI technique, has proven instrumental in synthesizing airfoil designs. However, a common limitation of GAN is the inherent lack of smoothness in the generated airfoil surfaces. To address this issue, we present a GAN model featuring a customized loss function built to produce seamlessly contoured airfoil designs. Additionally, our model demonstrates a substantial increase in design diversity compared to a conventional GAN augmented with a post-processing smoothing filter.