Latent Space Correlation for Interpretable Airfoil Parameterization Using Variational Autoencoders

This paper introduces an approach for parameterizing airfoil geometries using a Variational Autoencoder (VAE) with a focus on achieving a low-dimensional and interpretable model. The primary focus is to facilitate efficient use in design optimization environments by capturing essential airfoil features in a minimal number of latent dimensions. To address the black-box nature of VAEs and enhance interpretability, a correlation analysis is performed to uncover the relationships between the airfoil properties and these inferred latent dimensions. Key to this research is the incorporation of both geometric and aerodynamic properties in this analysis, enabling the generation of airfoils with desired aerodynamic characteristics through manual tuning of the latent vector by a designer. The method is evaluated using the extensive UIUC airfoil database, which includes a diverse range of airfoil categories. The VAE is trained on airfoil surface coordinate points, and the generated output geometries are refined using a composite Bezier curve to smooth out local imperfections. Results demonstrate that the VAE can successfully extract and parameterize key airfoil features using a limited number of interpretable latent parameters. These parameters show clear correlations with geometric and aerodynamic airfoil properties, providing a practical and understandable parameterization model that facilitates the intuitive generation of new airfoil designs through smooth interpolation of the training data.