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

arXiv · 2404.11816

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

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Abstract

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.

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BibTeX RIS

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. 2024-04-18. Tailoring Generative Adversarial Networks for Smooth Airfoil Design. https://arxiv.org/abs/2404.11816

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