Meta-Uncertainty for Particle Image Velocimetry

TL;DR

This paper introduces Meta-Uncertainty, a novel automated method to evaluate and improve the reliability of uncertainty estimates in Particle Image Velocimetry by perturbing particle images and combining multiple schemes.

Contribution

The paper presents a new meta-uncertainty approach that assesses the sensitivity of PIV uncertainty methods and combines them for improved reliability and accuracy.

Findings

Combined uncertainty method outperforms individual schemes

Meta-uncertainty effectively assesses reliability of PIV uncertainty estimates

Method applicable across various canonical flows

Abstract

Uncertainty quantification for Particle Image Velocimetry (PIV) is critical for comparing flow fields with Computational Fluid Dynamics (CFD) results, and model design and validation. However, PIV features a complex measurement chain with coupled, non-linear error sources, and quantifying the uncertainty is challenging. Multiple assessments show that none of the current methods can reliably measure the actual uncertainty across a wide range of experiments. Because the current methods differ in assumptions regarding the measurement process and calculation procedures, it is not clear which method is best to use for an experiment. To address this issue, we propose a method to estimate an uncertainty method's sensitivity and reliability, termed the Meta-Uncertainty. The novel approach is automated, local, and instantaneous, and based on perturbation of the recorded particle images. We developed an image perturbation scheme based on adding random unmatched particles to the interrogation window pair considering the signal-to-noise (SNR) of the correlation plane. Each uncertainty scheme's response to several trials of random particle addition is used to estimate a reliability metric, defined as the rate of change of the inter-quartile range (IQR) of the uncertainties with increasing levels of particle addition. We also propose applying the meta-uncertainty as a weighting metric to combine uncertainty estimates from individual schemes, based on ideas from the consensus forecasting literature. We use PIV measurements across a range of canonical flows to assess the performance of the uncertainty schemes.The results show that the combined uncertainty method outperforms the individual methods, and establish the meta-uncertainty as a useful reliability assessment tool for PIV uncertainty quantification.