14th International Symposium on Particle Image Velocimetry – ISPIV 2021 August 1–5, 2021 1 Physics-based universal outlier detector for flow statistics E. Saredi 1* , A. Sciacchitano 1 and F. Scarano 1 1 Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands * e.saredi@tudelft.nl Abstract Outlier detection for PIV velocity fields is still nowadays an active field of research. In the last decades, several image pre-processing and processing algorithms have been developed aiming at increasing the dynamic velocity range of PIV measurements and reducing the measurement uncertainty. Nevertheless, PIV velocity fields are still often characterised by the presence of outliers, which potentially hamper the correct interpretation of the flow physics and negatively affect the evaluation of the flow statistics. The outlier detection strategies presented in literature are mainly based on the statistical analysis of the velocity vector with its immediate neighbour. Most of these algorithms have been demonstrated to be effective for instantaneous flow fields, where the errors associated with the outliers are order of magnitude larger than the expected measurement uncertainty. However, these approaches are not as effective for the flow statistics, where the outliers yield errors of the same order of the measurement uncertainty. To overcome this limitation, this paper proposed an outlier detection approach based on the agreement of the flow statistics to the constitutive equations, more specifically to the turbulent kinetic energy (TKE) transport equation. The focus is posed on the ratio between the local advection terms of TKE and a robust estimation of the TKE production along the local streamline. It is demonstrated that, in presence of outliers, the proposed principle yields a clear separation between the correct and the erroneous vectors. In order to assess the performance of the proposed principle, three different test cases are considered. For all of them, the results are compared with a reference outlier detection methodology, namely the universal outlier detection method proposed by Westerweel and Scarano (2005). The proposed turbulence transport-based approach exhibits higher performance in terms of percentage of outliers correctly identified in the flow statistics. 1 Introduction In Particle Image Velocimetry (PIV), outliers are spurious vectors that exhibit large unphysical variations in magnitude and direction from neighbouring valid vectors (Westerweel, 1994). Several factors can lead to the appearance of outliers. Among them, unwanted light reflections, lack of seeding and inadequate processing methodology are the main ones (Hart, 2000; Lazar et al., 2004; Sciacchitano, 2019; among others). Several methods for outlier detection have been presented in the literature, with the focus on the analysis of instantaneous flow fields. The most common approach relies on the analysis of the difference between a vector and its neighbours, either in the temporal or in the spatial domain. In this category of outlier detection algorithms, the approach proposed by Westerweel and Scarano (2005) is nowadays widely applied. The latter approach builds upon the median filter presented by Westerweel (1994) and achieves a larger degree of