Investigation of the Noise Floor of the Standard PIV Cross-Correlation Algorithm

Particle image velocimetry (PIV) is a powerful measurement technique used to acquire instantaneous measurements of entire flow fields at a given instant in time. Quantifying the uncertainty and error in PIV is a critical part of realizing the full potential of PIV as a flow measurement technique. The noise floor of PIV is the minimum amount of random error that can be achieved for a particular standard cross-correlation (SCC) algorithm. The noise floor of the SCC used by DaVis in correlating image pairs is explored. Two methods for creating image pairs for correlation are compared, namely pseudo image pairs and artificial image pairs. A common PIV experimental setup with seeded water in a glass tank was used to acquired images at dt approximately 0 seconds between images. The aperture or f# of the lens was varied in order to achieve a range of particle image diameters at two different magnifications. A Matlab code was written to upsample, shift and downsample the images by a prescribed, sub-pixel displacement. The shifted images were then imported into DaVis and correlated, resulting in displacement vector images. The random error of these images were calculated and each particle diameter is compared. The random and bias errors of the DaVis and PRANA SCC algorithms were also compared for a fixed, optimum particle image diameter and multiple sub-pixel displacements between 0 and 1 pixel