Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: A computation and data-driven approach

A physics-based data-driven computational framework for the quantitative analysis of vortex kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows are characterized by dominant influence a small number structures, but complexity these makes it difficult to conduct this at level individual vortices. The method presented here combines machine learning-inspired clustering methods with rigorous mathematical partitioning aerodynamic enable detailed loads. We demonstrate utility approach applying an ensemble 165 distinct Navier-Stokes simulations flow past sinusoidally pitching airfoil. Insights enabled current methodology include identification period-doubling route chaos flow, precise quantification role that leading-edge vortices play driving aeroelastic pitch oscillations.