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.
Original language | English |
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Title of host publication | Proceedings of the AIAA SCITECH 2024 Forum |
Publisher | American Institute of Aeronautics and Astronautics Inc. (AIAA) |
Number of pages | 16 |
ISBN (Electronic) | 978-1-62410-711-5 |
DOIs | |
Publication status | Published - 2024 |
Event | AIAA SCITECH 2024 Forum - Orlando, United States Duration: 8 Jan 2024 → 12 Jan 2024 |
Conference
Conference | AIAA SCITECH 2024 Forum |
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Country/Territory | United States |
City | Orlando |
Period | 8/01/24 → 12/01/24 |