A data-driven modeling framework for predicting forces and pressures on a rapidly pitching airfoil

This work formulates a switched linear modeling procedure to understand and predict the unsteady aerodynamic forces arising from rapid pitching motion of a NACA 0012 airfoil at a Reynolds number of 50, 000. The system identification procedure applies a generalized dynamic mode decomposition algorithm to time-resolved wind tunnel measurements of the lift and drag forces, as well as the pressure at six locations on the suction surface of the airfoil. Linear state space models are identified for 5-degree pitch-up and pitch-down maneuvers within an overall angle of attack range of 0°— 20°. The identified models accurately capture the effects of flow separation and leading-edge vortex formation and convection. It is shown that switching between different linear models can give accurate prediction of the nonlinear behavior that is present in high-amplitude maneuvers. The models are accurate for a wide range of motions, including pitch-and-hold, sinusoidal, and pseudo-random pitching maneuvers. Providing the models access to a subset of the measured data channels can allow for improved estimates of the remaining states via the use of a Kalman filter, which could be of use for aerodynamic control applications.