Uncertainty quantification in particle image velocimetry and advances in time-resolved image and data analysis

Abstract

Particle Image Velocimetry (PIV) is a well-established technique for the measurement of the flow velocity in a two or three-dimensional domain. As in any other technique, PIV data is affected by measurement errors, defined as the difference between the measured velocity and its actual value, which is unknown. Aim of uncertainty quantification is estimating an interval that contains the (unknown) actual error magnitude with a certain probability. The present work introduces a novel methodology for the uncertainty quantification of PIV data. The method relies upon the concept of image matching: the PIV recordings are matched based on the measured velocity field. The positional disparity between paired particle images is then computed to retrieve the measurement uncertainty. Both the numerical assessment via Monte Carlo simulations and the experimental assessment show that the image matching approach allows estimating the measurement uncertainty in good agreement with the actual error value. Furthermore, advanced methodologies for time-resolved image and data analysis are investigated. Those methodologies include: the enhancement of the image quality via a temporal filter applied to the PIV images; a multi-frame processing algorithm (pyramid correlation) that improves precision and robustness of PIV measurements; a post processing approach based on the solution of the Navier-Stokes equations for estimating the velocity field in regions where no experimental data is available.