Tailoring Generative Adversarial Networks for Smooth Airfoil Design
Joyjit Chattoraj Jian Cheng Wong Zhang Zexuan Manna Dai Xia Yingzhi Li Jichao Xu Xinxing Ooi Chin Chun Yang Feng Dao My Ha Liu Yong
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.