On the optimal window size in optical flow and cross-correlation in particle image velocimetry: application to turbulent flows

Abstract

Two Particle Image Velocimetry (PIV) softwares applied to turbulent flows are compared. One is based on a standard Cross-Correlation (CC) algorithm and the other is based on an iterative multi-pyramid Optical Flow (OF) algorithm. First, still particle images are used to evaluate the cut-off frequency induced by each method. Then a step response analysis highlights the capabilities of each method to minimise the effect of unresolved velocity gradients. Two different benchmarks with various turbulent length-scales, down to the Taylor micro-scale, are then used to analyse the velocity spectra and the turbulent kinetic energy dissipation estimation. First, a synthetic PIV dataset of homogeneous isotropic turbulence is processed and compared with Direct Numerical Simulation (DNS) results. Then a grid turbulence wind tunnel experimental dataset is used to calculate velocity spectra and second-order structure functions, which are compared to Laser Doppler Velocimetry spectra.All these results point to the fact that, although OF is more diffusive and up to 5% less accurate than cross correlation, the numerical diffusion improves the calculation of sub-window unresolved gradients and allows for direct and more robust measurement of the onset of the viscous subrange in experimental turbulent flows.

Quantitative Compression Elastography With an Uncalibrated Stress Sensor

“…The algorithm with which speed is estimated also determines accuracy to some degree. If using windowed cross-correlation, for example, the choice of window size will influence the accuracy [39]. The second key step is the estimation of strain within both the sensor and the sample.…”

DPIVSoft-OpenCL: A multicore CPU–GPU accelerated open-source code for 2D Particle Image Velocimetry

“…Recently, other massively parallel optical flow methods have been developed that allow PIV processing which produce errors comparable to the aforementioned programs [14]. As an example, the FOLKI-PIV developed on GPUs allows for a speedup of 50 times with respect to the CPU version, and it provides very similar results to DPIVSoft [15,16].…”