3D Particle Image Velocimetry Uncertainty Quantification

3D Particle Image Velocimetry (PIV) is a non-invasive flow measurement technique, which has been widely used to study complex three-dimensional flow structures and resolve the three-component velocity fields. Accordingly, it can be used as a reliable validation data set for numerical studies as well as for design purposes in the industry, such as for Turbines, Jets, airplanes, etc. These applications intensify the importance of having an accurate measurement for 3D PIV and make uncertainty quantification a crucial factor in assessing the statistical significance of measurements in the experiment. However, this problem is non-trivial due to the complexity of possible uncertainty sources, their combination, and propagation through the measurement chain. For example, in Tomographic PIV, although there have been notable improvements in calibration error correction and object reconstruction, predicting the uncertainty bounds for each velocity vector measurement is still an open problem. Since both 3D PIV and conventional planar PIV are correlation-based methods, the existing 2D PIV uncertainty methods can be potentially extended to quantify the 3D PIV measurement uncertainty. However, the applicability and performance of such approaches have not been tested in 3D PIV measurements.  In the current work, we build upon two existing direct 2D uncertainty estimation methods, namely Image Matching (IM) and Moment of Correlation (MC), and make necessary implementation and algorithmic modifications to quantify the uncertainty in 3D velocity fields. The resulted uncertainty measurements are tested with artificial Poiseuille data, and it has been validated by experimental laminar Poiseuille data.