Outlier detection for PIV statistics based on turbulence transport

The occurrence of data outliers in PIV measurements remains nowadays a problematic issue; their effective detection is relevant to the reliability of PIV experiments. This study proposes a novel approach to outliers detection from time-averaged three-dimensional PIV data. The principle is based on the agreement of the measured data to the turbulent kinetic energy (TKE) transport equation. The ratio between the local advection and production terms of the TKE along the streamline determines the admissibility of the inquired datapoint. Planar and 3D PIV experimental datasets are used to demonstrate that in the presence of outliers, the turbulent transport (TT) criterion yields a large separation between correct and erroneous vectors. The comparison between the TT criterion and the state-of-the-art universal outlier detection from Westerweel and Scarano (Exp Fluids 39:1096-1100, 2005) shows that the proposed criterion yields a larger percentage of detected outliers along with a lower fraction of false positives for a wider range of possible values chosen for the threshold. [GRAPHICS] .

Automated summary

This study proposes a novel approach for detecting outliers in PIV measurements by comparing the ratio between local advection and turbulent kinetic energy (TKE) production, and shows that it outperforms the state-of-the-art method in terms of accuracy.