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We present a sparse sensing framework based on Dynamic Mode Decomposition(DMD) to identify flow regimes and bifurcations in large-scale thermo-fluidsystems. Motivated by real-time sensing and control of thermal-fluid flows inbuildings and equipment, we apply this method to a Direct Numerical Simulation(DNS) data set of a 2D laterally heated cavity. The resulting flow solutionscan be divided into several regimes, ranging from steady to chaotic flow. TheDMD modes and eigenvalues capture the main temporal and spatial scales in thedynamics belonging to different regimes. Our proposed classification method isdata-driven, robust w.r.t measurement noise, and exploits the dynamicsextracted from the DMD method. Namely, we construct an augmented DMD basis,with "built-in" dynamics, given by the DMD eigenvalues. This allows us toemploy a short time-series of data from sensors, to more robustly classify flowregimes, particularly in the presence of measurement noise. We also exploit theincoherence exhibited among the data generated by different regimes, whichpersists even if the number of measurements is small compared to the dimensionof the DNS data. The data-driven regime identification algorithm can enablerobust low-order modeling of flows for state estimation and control.

Sparse sensing and DMD based identification of flow regimes and bifurcations in complex flows