Characterization of Sweeping Jet Actuator Flow Field using DMD analysis

The dynamic mode decomposition (DMD) algorithm captures the oscillating behavior of complex non-linear flow structures by identifying the low-rank coherent structures and superimposing the dominant modes to reconstruct the system. The basis vectors (modes) are determined using the Koopman theory. They are applied to develop an accurate shallow rank structure from a non-linear complex sweeping jet actuator flow field in the present study. With spectral decomposition, DMD can identify the dominant patterns in the oscillating flow structure - making it a valuable tool for spatio-temporal data extraction. Since the algorithm has no hyperparameter, it is flexible to implement in any periodic flow distribution to determine the temporal behavior. We have modified the classical DMD algorithm using amplitude-based data filtration for better accuracy and flexibility on computationally obtained raw data. For analysis, we considered velocity and pressure data series from two different sampling windows in a 2D sweeping jet actuator flow field for time step sensitivity and dominant mode requirement for reconstructing and predicting the coherent structure for a single mass flow rate. Finally, we demonstrated the model's effectiveness in data interpretation by combining two other sampling windows, which indicate its compatibility for working as a local tool to stitch the flow topology.