Advection-based multiframe iterative correction on PIV fields for improved pressure estimation

A novel method to improve the accuracy of pressure field estimation for advection-dominated flows from time-resolved Particle Image Velocimetry (PIV) data is proposed. Leveraging an advection model we can propagate in time velocity fields from each snapshot. The multiple time-series obtained from time propagation are used to smooth the nonphysical features in the original time-series. This correction is done iteratively. The technique reduces spatial noise by leveraging temporal information while simultaneously correcting temporal jitters using spatial data. Two validation methods are proposed to evaluate the method without knowing the true value of the pressure field. In this paper we provide a proof of the concept using a 3D synthetic dataset based on channel flow and a 2D PIV data set on the wake of a wing foil. Various noise models are tested by superimposing them onto the synthetic dataset, including Gaussian white noise and errors with spatial coherence. The results demonstrate that the proposed method outperforms conventional filters in improving the velocity and pressure fields from noisy velocity data, and in suppressing time jittering of the estimated pressure fields.