Detection of Flow-Regime Transitions Using Dynamic Mode Decomposition and Moving Horizon Estimation

The spatial and time behaviors of fluid flows at different Reynolds numbers and free-stream turbulence intensity levels are studied by combining dynamic mode decomposition (DMD) and moving horizon estimation to detect flow-regime transitions. In more detail, the norm of residuals provided by DMD when processing successive snapshots of the flow velocity field shows a trend that is identified by means of a moving horizon estimator based on a switching model. This allows detecting the change from stable to unstable flow regimes, which in turn enables to extract modes, frequencies, and growth rates of complex structures such as vortices, characterizing the fluid flow in the spatial and temporal domains. Different cases of experimental measurements given by a particle image velocimetry are analyzed to recognize the complexity of the underlying flow physics, while showing the effectiveness of the proposed approach.