Abstract
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.Graphical abstract
Highlights
In particle image velocimetry (PIV), outliers are spurious vectors that exhibit large unphysical variations in magnitude and direction from neighbouring valid vectors (Westerweel 1994)
Most proposed and used approaches for outliers detection are not based on flow physics, but rather on statistical data analysis
The latter stems from the open question as to what aspect of the flow physics should be considered to judge a velocity vector as unphysical
Summary
In particle image velocimetry (PIV), outliers are spurious vectors that exhibit large unphysical variations in magnitude and direction from neighbouring valid vectors (Westerweel 1994). Most proposed and used approaches for outliers detection are not based on flow physics, but rather on statistical data analysis The latter stems from the open question as to what aspect of the flow physics should be considered to judge a velocity vector as unphysical. The aforementioned outlier detection methods are typically effective for the instantaneous velocity fields where a single vector or a cluster thereof largely departs in magnitude and direction from the neighbouring points. Such outliers need to be detected and replaced, or omitted, when estimating statistical flow properties like the mean value and its fluctuations.
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