Volumetric Particle Tracking Velocimetry (PTV) Uncertainty Quantification

TL;DR

This paper presents a comprehensive method to quantify uncertainty in volumetric Particle Tracking Velocimetry (PTV), accounting for errors from particle detection, calibration, and reconstruction, validated through synthetic and experimental flow data.

Contribution

It introduces a novel uncertainty quantification framework for volumetric PTV that integrates multiple error sources into a unified estimate, enhancing measurement reliability.

Findings

Predicted RMS uncertainty aligns well with synthetic data.

Uncertainty prediction remains accurate across different seeding densities.

Method validated with experimental laminar pipe flow measurements.

Abstract

We introduce the first comprehensive approach to determine the uncertainty in volumetric Particle Tracking Velocimetry (PTV) measurements. Volumetric PTV is a state-of-the-art non-invasive flow measurement technique, which measures the velocity field by recording successive snapshots of the tracer particle motion using a multi-camera set-up. The measurement chain involves reconstructing the three-dimensional particle positions by a triangulation process using the calibrated camera mapping functions. The non-linear combination of the elemental error sources during the iterative self-calibration correction and particle reconstruction steps increases the complexity of the task. Here, we first estimate the uncertainty in the particle image location, which we model as a combination of the particle position estimation uncertainty and the reprojection error uncertainty. The latter is obtained by a gaussian fit to the histogram of disparity estimates within a sub-volume. Next, we determine the uncertainty in the camera calibration coefficients. As a final step the previous two uncertainties are combined using an uncertainty propagation through the volumetric reconstruction process. The uncertainty in the velocity vector is directly obtained as a function of the reconstructed particle position uncertainty. The framework is tested with synthetic vortex ring images. The results show good agreement between the predicted and the expected RMS uncertainty values. The prediction is consistent for seeding densities tested in the range of 0.01 to 0.1 particles per pixel. Finally, the methodology is also successfully validated for an experimental test case of laminar pipe flow velocity profile measurement where the predicted uncertainty is within 17% of the RMS error value.