Potential of ensemble-correlation techniques for adaptive PIV processing

Abstract

The presentation explores the possibilities of utilizing the statistical information provided through the analysis of PIV image series (not necessarily time-resolved) on the basis of the so-called ensemble-correlation. Originally introduced for the evaluation of micro-PIV images to overcome effects due to Brownian motion and to increase the spatial resolution in spite of sparse seeding, it has received little attention for its possible use in classical macroscopic PIV applications. The main concept behind ensemble correlation is to average cross-correlation planes over many images rather than averaging the extracted displacement vectors. The mean displacement is then obtained from the average correlation planes whose correlation peaks typically have a considerably larger signal-to-noise ratio than the correlation peaks in the correlation data obtained from the individual image pairs. The class of proposed algorithms suggested here uses ensemble correlation on a given PIV image series to recover the mean displacement and in addition evaluates the averaged correlation peak shape to extract detection bounds for processing the individual PIV image pairs. The algorithms inherently offer the capability of spatially varying detection criteria. Further, each individual search mask may be combined with its own weighting function based on the averaged correlation peak shape which provides an additional validation criterion.