Bluff body wakes in stratified fluids are known to exhibit a rich range of dynamic behavior that can be categorized into different regimes based on Reynolds number ($\text{Re}$) and Froude number ($\text{Fr}$). Topological differences in wake structure across these different regimes have been clarified recently through the use of dynamic mode decomposition (DMD) on direct numerical simulation (DNS) and laboratory data for a sphere in a stratified fluid for $\text{Re}\in [200,1000]$ and $\text{Fr}\in [0.5,16]$. In this work, we attempt to identify the dynamic regime from limited measurement data in a stratified wake with (nominally) unknown $\text{Re}$ and $\text{Fr}$. A large database of candidate basis functions is compiled by pooling the DMD modes obtained in prior DNS. A sparse model is built using the forward regression with orthogonal least squares (FROLS) algorithm, which sequentially identifies DMD modes that best represent the data and calibrates their amplitude and phase. After calibration, the velocity field can be reconstructed using a weighted combination of the dominant DMD modes. The dynamic regime for the measurements is estimated via a projection-weighted average of $\text{Re}$ and $\text{Fr}$ corresponding to the identified modes. Regime identification is carried out from a limited number of two-dimensional velocity snapshots from numerical and experimental data sets, as well as three point measurements in the wake of the body. A metric to assess confidence is introduced based on the observed predictive capability. This approach holds promise for the implementation of data-driven fluid pattern classifiers.

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• Stratified geophysical flows
• Machine learning