Two sub-pixel processing algorithms for high accuracy particle centre estimation in low seeding density particle image velocimetry

This article presents two algorithms for spatial processing of low seeding density PIV (particle image velocimetry) images which lead to sub-pixel precision in particle positioning. The particle centres are estimated to accuracies of the order of 0.1 pixel, yielding 1% error in velocity calculation. The first algorithm discriminates valid particles from the rest of the image and determines their centres in Cartesian coordinates by using a two-dimensional Gaussian fit. The second algorithm performs local correlation between particle pairs and determines instantaneous two-dimensional velocities. The methods have been applied initially to simulated data. Gaussian noise and distortion has then been added to simulate experimental conditions. It is shown that, in comparison with conventional methods, the new algorithms offer up to an order of magnitude higher accuracy for particle centre estimation. Finally, the Gaussian fit approach has been used to map an experimental transonic flow field from the stator trailing edge wake region of a cascade with an estimated error of 1%. The experimental results are found to be in good agreement with previous theoretical steady-state viscous calculations.