Particle Image Velocimetry (PIV) Uncertainty Quantification Using Moment of Correlation (MC) Plane

TL;DR

This paper introduces a novel uncertainty estimation method for Particle Image Velocimetry (PIV) using the moment of correlation plane, which improves accuracy and sensitivity over existing techniques by analyzing the shape of the correlation plane.

Contribution

The paper presents a new MC-based approach for PIV uncertainty quantification that accounts for local velocity gradients and outperforms existing methods in various test cases.

Findings

MC method accurately predicts PIV uncertainty in simulated and experimental data.

The approach shows better spatial variation response than existing techniques.

MC uncertainty estimates align well with expected RMS errors.

Abstract

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 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 PIV interrogation window size, and the MC method outperforms the other uncertainty methods.