Print Email Facebook Twitter Towards Universal Parameterization: Using Variational Autoencoders to Parameterize Airfoils Title Towards Universal Parameterization: Using Variational Autoencoders to Parameterize Airfoils Author Swannet, K. (TU Delft Flight Performance and Propulsion) Varriale, Carmine (TU Delft Flight Performance and Propulsion) Doan, Nguyen Anh Khoa (TU Delft Aerodynamics) Date 2024 Abstract A design can only be as good as its mathematical representation. In engineering design optimization, the chosen method of parameterization can have significant impact on the outcomes. This paper introduces a novel methodology for airfoil design parameterization utilizing variational autoencoders (VAEs), a class of neural networks known for their proficiency in reducing dimensionality. However, a significant challenge with VAEs is the interpretability of the encoded latent space. This work aims to address this issue by creating a network with an interpretable latent space, yielding parameters that are understandable to humans. The effectiveness of this approach is evaluated using the comprehensive UIUC airfoil database, which offers a diverse range of airfoil shapes for analysis. We show that a VAE can successfully extract key features of airfoil geometries and parameterize them using six parameters, which show a clear correlation with airfoil properties in a way that remains understandable by the designer. Additionally, it smoothly interpolates the data points, allowing the generation of new airfoils and thus offering a practical and interpretable airfoil parameterization. To reference this document use: http://resolver.tudelft.nl/uuid:2ebb06e8-2c85-40e2-8630-1a3d2ce1750d DOI https://doi.org/10.2514/6.2024-0686 Publisher American Institute of Aeronautics and Astronautics Inc. (AIAA) ISBN 978-1-62410-711-5 Source Proceedings of the AIAA SCITECH 2024 Forum Event AIAA SCITECH 2024 Forum, 2024-01-08 → 2024-01-12, Orlando, United States Part of collection Institutional Repository Document type conference paper Rights © 2024 K. Swannet, Carmine Varriale, Nguyen Anh Khoa Doan Files PDF swannet_et_al_2024_toward ... rfoils.pdf 2.45 MB Close viewer /islandora/object/uuid:2ebb06e8-2c85-40e2-8630-1a3d2ce1750d/datastream/OBJ/view