Extreme Aerodynamic Manifold: Vortex-Airfoil Interactions

Small-scale air vehicles encounter severe flight conditions in urban areas and turbulent wakes behind large structures during adverse weather. While understanding interactions between a strong gust and a wing is important, sweeping over the huge parameter space of extreme aerodynamic flows with expensive simulations and experiments is impractical, calling for data-driven approaches. This talk discusses how such complex aerodynamics under the vortex gust-airfoil interaction can be expressed in a low-order manner by leveraging machine learning. We consider wakes over a NACA0012 airfoil at Re = 100 covering a range of angles of attack with a strong disturbance modeled by the Taylor vortex, producing a variety of complex wake patterns due to vortex-airfoil interaction. Such unsteady and violent vortical flows over the parameter space can be compressed into only three variables with a lift-augmented nonlinear autoencoder while capturing the fundamental physics of nonlinear interaction under extreme aerodynamics. We also show that the present approach can be used for real-time state estimation using sparse sensors.