Insights from Time-Resolved Pressure Sensitive Paint Data using Momentum Potential Theory

Momentum potential theory (MPT) has yielded invaluable insights from scale-resolved simulations because of its ability to exactly separate acoustic, hydrodynamic and thermal content of arbitrarily large turbulent fluctuations without the need for linearization. Recently, sub-elements of MPT have been employed to analyze schlieren data by using the rate of change of directional density gradients as source terms to the Poisson equation that filters the irrotational (acoustic plus thermal) component. When combined with data-driven methods, the results have aided in assessing the dynamics of jet noise and hypersonic transition from schlieren data alone. In this work, we process pressure fluctuations, as obtained from pressure-sensitive paints (PSP) in an analogous fashion. Focusing on 2D, simple-swept and compound shock/boundary layer interactions, we show that MPT extracts acoustic components of pressure fluctuations near the surface. These are then interpreted either in terms of local information propagation pathways (feedback) or as imprints of structures from the outer parts of the separated flows that are not directly accessible to time-resolved diagnostics. A parallel assessment of simulated pressure fluctuation data from large-eddy simulations provides further insights into the interpretation of MPT-filtered PSP data.