This study explores airfoil design optimization in propeller slipstreams, leveraging the capabilities of Generative Adversarial Networks (GANs). With advancements in AI, the research integrates a GAN-based airfoil generation algorithm, emphasizing its benefits in input dimension reduction and curve quality. Using Information Maximizing Generative Adversarial Networks (infoGAN), essential airfoil features are extracted, showcasing the network’s inferential prowess. The focus then shifts to propeller-wing coupled optimization, where a 6% drag reduction was achieved using rigorous validation techniques. The paper introduces a novel method, substituting expert feedback with GAN’s discriminator in airfoil optimization. This approach not only meets design point criteria but also enhances robustness and applicability, aligning with real-world engineering scenarios. In summary, this work presents a novel approach to airfoil design in propeller slipstreams through GANs.
Airfoil Optimization in Propeller Slipstreams Using Generative Adversarial Networks
Lect. Notes Electrical Eng.
Asia-Pacific International Symposium on Aerospace Technology ; 2023 ; Lingshui, China October 16, 2023 - October 18, 2023
2023 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2023) Proceedings ; Chapter : 110 ; 1412-1424
2024-07-05
13 pages
Article/Chapter (Book)
Electronic Resource
English
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