Particle image velocimetry (PIV) uncertainty quantification using moment of correlation (MC) plane

Bhattacharya, Sayantan; Charonko, John James; Vlachos, Pavlos P.

doi:10.1088/1361-6501/aadfb4

Particle image velocimetry (PIV) uncertainty quantification using moment of correlation (MC) plane

Journal Article · Thu Oct 11 00:00:00 EDT 2018 · Measurement Science and Technology

DOI:https://doi.org/10.1088/1361-6501/aadfb4· OSTI ID:1688748

Bhattacharya, Sayantan ^[1]; ^[2]; ^[1]

Purdue Univ., West Lafayette, IN (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Here, we present a new uncertainty estimation method for particle image velocimetry (PIV), that uses the correlation plane as a model for the probability density function (PDF) of displacements and calculates the second order moment of the correlation (MC). The cross-correlation between particle image patterns is the summation of all particle matches convolved with the apparent particle image diameter. MC uses this property to estimate the PIV uncertainty from the shape of the cross-correlation plane. In this new approach, the generalized cross-correlation (GCC) plane corresponding to a PIV measurement is obtained by removing the particle image diameter contribution. The GCC primary peak represents a discretization of the displacement PDF, from which the standard uncertainty is obtained by convolving the GCC plane with a Gaussian function. Then a Gaussian least-squares-fit is applied to the peak region, accounting for the stretching and rotation of the peak, due to the local velocity gradients and the effect of the convolved Gaussian. The MC method was tested with simulated image sets and the predicted uncertainties show good sensitivity to the error sources and agreement with the expected RMS error. Subsequently, the method was demonstrated in three PIV challenge cases and two experimental datasets and was compared with the published image matching (IM) and correlation statistics (CS) techniques. Results show that the MC method has a better response to spatial variation in RMS error and the predicted uncertainty is in good agreement with the expected standard uncertainty. The uncertainty prediction was also explored as a function of PIV interrogation window size. Overall, the MC method performance establishes itself as a valid uncertainty estimation tool for planar PIV.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA), Office of Defense Programs (DP)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1688748

Alternate ID(s):: OSTI ID: 22884623

Report Number(s):: LA-UR--19-29616

Journal Information:: Measurement Science and Technology, Journal Name: Measurement Science and Technology Journal Issue: 11 Vol. 29; ISSN 0957-0233

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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