Adaptive vector validation in image velocimetry to minimise the influence of outlier clusters

The universal outlier detection scheme (Westerweel and Scarano, Exp. Fluids, 2005) and the distance-weighted universal outlier detection scheme for unstructured data (Duncan et al., Meas. Sci. Technol., 2010) are the most common PIV data validation routines. However, such techniques rely on a spatial comparison of each vector with those in a fixed-size neighbourhood and their performance subsequently suffers in the presence of clusters of outliers. This paper proposes an advancement to render outlier detection more robust while reducing the probability of mistakenly invalidating correct vectors. Velocity fields undergo a preliminary evaluation in terms of local coherency, which parametrizes the extent of the neighbourhood with which each vector will be compared subsequently. Such adaptivity is shown to reduce the number of undetected outliers, even when implemented in the afore validation schemes. In addition the authors present an alternative residual definition considering vector magnitude and angle adopting a modified Gaussian-weighted distance-based averaging median. This procedure is able to adapt the degree of acceptable background fluctuations in velocity to the local displacement magnitude. The traditional, extended and recommended validation methods are numerically assessed on the basis of flow fields from an isolated vortex, a turbulent channel flow and a DNS simulation of forced isotropic turbulence. The resulting validation method is adaptive, requires no user-defined parameters and is demonstrated to yield the best performances in terms of outlier under- and over-detection. Finally, the novel validation routine is applied to the PIV analysis of experimental studies focused on the near-wake behind a porous disc and on a supersonic jet, illustrating the potential gains in spatial resolution and accuracy.