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In particle image velocimetry (PIV) the measurement signal is contained inthe recorded intensity of the particle image pattern superimposed on a varietyof noise sources. The signal-to-noise-ratio (SNR) strength governs theresulting PIV cross correlation and ultimately the accuracy and uncertainty ofthe resulting PIV measurement. Hence we posit that correlation SNR metricscalculated from the correlation plane can be used to quantify the quality ofthe correlation and the resulting uncertainty of an individual measurement. Inthis paper we present a framework for evaluating the correlation SNR using aset of different metrics, which in turn are used to develop models foruncertainty estimation. The SNR metrics and corresponding models presentedherein are expanded to be applicable to both standard and filteredcorrelations. In addition, the notion of a valid measurement is redefined withrespect to the correlation peak width in order to be consistent withuncertainty quantification principles and distinct from an outlier measurement.Finally the type and significance of the error distribution function isinvestigated. These advancements lead to robust uncertainty estimation models,which are tested against both synthetic benchmark data as well as actualexperimental measurements. For all cases considered here, standarduncertainties are estimated at the 68.5% confidence level while expandeduncertainties are estimated at 95% confidence level. For all cases theresulting calculated coverage factors approximate the expected theoreticalvalues thus demonstrating the applicability of these new models for estimationof uncertainty for individual PIV measurements.

Particle image velocimetry correlation signal-to-noise ratio metrics and measurement uncertainty quantification