Data-driven compression of plunging airfoil wakes

Plunging airfoil generates diverse wake patterns depending on its plunging frequency and plunging amplitude. Developing a reduced-order model for such high-dimensional wakes is challenging due to their strong nonlinearity but crucial for understanding the complex airfoil wakes and enabling real-time flow analysis. In this study, we leverage an observable-augmented autoencoder to obtain a reduced-order model that captures the underlying physics of plunging airfoil wakes. Our autoencoder, which incorporates drag coefficients into latent manifold identification, compresses the plunging airfoil wake data into only three variables. While these three variables may appear minimal, they hold sufficient information to accurately reconstruct the full flow fields for a variety of plunging cases. The three-dimensional representation by the autoencoder provides physically-tractable coordinates that express characteristic modes of the wakes, distinguishing different wake types and angles of attack. These observations suggest the present approach can promote the understanding of the complex airfoil wakes in a simple and trackable manner. Furthermore, we will show in the talk that the wake data can be reconstructed from sparse sensors with the discovered latent coordinates.