Wall model for surface pressure reconstruction from PIV data

We propose a wall model for estimating surface pressure from particle image velocimetry data. The model, which accepts pressure input from the Green’s function integral method, is derived from the thin-boundary layer equations and incorporates the effects of turbulence and wall curvature.



Most existing algorithms for extracting surface pressure from experimental measurements are hindered by unreliable data close to the wall, due to poor particle densities and surface reflections. Previous studies have addressed this issue by using polynomial extrapolation of off-wall pressure data, but accurate estimation of surface values remains challenging in regions with large pressure gradients, such as the leading edge of an airfoil, or in areas with massive separation. Additionally, when only surface pressure is of interest, retrieving the necessary off-wall pressure data entails significant additional computational effort. In this study, we benchmark our wall model using DNS flow data around a turbulent Gaussian bump, a challenging case due to the large flow separation and moderately high-pressure gradients near the suction peak. Our approach outperforms polynomial extrapolation while maintaining computational efficiency.