Ensemble Kalman filter for vortex models of disturbed aerodynamic flows

We use an ensemble Kalman filter (EnKF) to sequentially estimate low Reynolds number aerodynamic flows using an inviscid vortex model and distributed surface pressure readings. We look at two scenarios: an impulsively translating plate subject to flow actuation near the leading edge or placed in a cylinder wake. In each case, the ensemble transform Kalman filter (ETKF) - a deterministic version of the EnKF - is consistently more robust than the stochastic EnKF and is qualitatively better at representing the coherent structures of the true flow. We analyze the mapping from pressure discrepancies to state update through a singular value decomposition of the Kalman gain.