This paper describes the application of a principled estimation method that generates full flowfield estimates using data obtained from a limited number of pressure sensors on an actuated airfoil, based on Dynamic Mode Decomposition (DMD). DMD is a data-driven algorithm that approximates a time series of data as a sum of modes that evolve linearly. DMD is used here to create a linear system that approximates the flow dynamics and pressure sensor output from Particle Image Velocimetry (PIV) and pressure measurements of the flowfield around the airfoil. Sparsity Promoting DMD (SPDMD) selects a reduced number of modes in order to simplify the system while providing a sufficiently accurate approximation of the flowfield. A DMD Kalman Filter (DMD-KF) uses the pressure measurements to estimate the evolution of this linear system, and thus produce an approximation of the flowfield from the pressure data alone. The DMD-KF is implemented for experimental data from two different setups: a pitching cambered ellipse airfoil and a surging NACA 0012 airfoil. Filter performance is evaluated using the original flowfield PIV data, and compared with a DMD reconstruction.
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