Plunging Airfoil Wakes in Low-Order Latent Space Coordinates

This study uncovers low-dimensional latent space representations of plunging airfoil wakes with nonlinear unsupervised machine learning. The dataset is prepared by direct numerical simulations of flow over a NACA0012 airfoil undergoing sinusoidal plunging oscillation at Re=100 across a range of plunging amplitude, reduced frequency, and angle of attack. In the present analysis, the flowfield data is first compressed using a drag-augmented autoencoder, which includes a drag-estimation subnetwork from the latent variables to capture the relationship between vortical structures and wake-related properties such as thrust generation. Using the current method, the diverse wake data can be represented only in three-dimensional coordinates. We find that for the wake dynamics, atomic orbit-like distributions for each wake type are extracted in the three-dimensional space. This enables the classification of different wake patterns in a data-driven manner with the characterization of physical parameters of the plunging airfoil geometrically in each orbit. Moreover, the low-order coordinates can be estimated only from sparse pressure sensors on the airfoil surface, allowing sensor-input-based unsupervised wake characterization. This low-dimensional representation achieved by nonlinear machine learning offers possibilities for low-dimensional modeling and control in feature space.