Probabilistic evaluation of streamline topologies for the detection of preferential flow configurations in PIV applications

While PIV allows the extraction of instantaneous streamlines, the availability of typically thousands of patterns hampers a statistical understanding of the underlying flow topology. A particular application is oscillating gas jets, encountered in scientific and industrial studies dealing with mass transfer and flow control. Multiple methods, such as statistical moments (including mean, variance, skewness, and kurtosis), proper orthogonal decomposition (POD), and Hartigan's dip test are common post-processing methodologies. However, as demonstrated in this paper, they do not provide sufficient in-depth information from a statistical perspective to attribute probabilities to potential flow topologies. In this work, a novel approach to describe probability distributions based on extracted streamline patterns is proposed. Based on the available streamline patterns, the convolution with adaptive Gaussian kernels reveals the probability density function of the flow topology. The proposed methodology, as well as more traditional post-processing approaches, are assessed on the basis of synthetic flow fields and experimental PIV data of oscillating impinging gas jets, demonstrating the added value of the streamline probability map in characterising the scrutinised flow.