An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification

We introduce a novel end-to-end approach to improving the resolution of Particle Image Velocimetry (PIV) measurements. We conceptualised the algorithm as a tool that is able, starting from raw pictures, to obtain high-resolution flow fields and uncertainty estimations with minimal intervention from the user. The method blends information from different snapshots without the need for time-resolved measurements on grounds of similarity of flow regions in different snapshots. The main hypothesis is that, with a sufficiently large ensemble of statistically-independent snapshots, the identification of flow structures that are morphologically similar but occurring at different time instants is feasible. Since the particles randomly seed the flow, a randomised sampling of such structures is naturally achieved, providing different views of the same region. Measured individual vectors from different snapshots with similar flow organisation can thus be merged, resulting in an artificially increased particle concentration. This allows refining the interrogation region and, consequently, increasing the spatial resolution. The measurement domain is split in subdomains. The similarity is enforced only on a local scale, i.e. morphologically-similar regions are sought only among subdomains corresponding to the same flow region. The identification of locally-similar snapshots is based on K-nearest neighbours search in a space of significant flow features. Such features are defined in terms of a Proper Orthogonal Decomposition, performed in subdomains on the original low-resolution data, obtained either with standard cross-correlation or with binning of Particle Tracking Velocimetry data with a relatively large bin size. A refined bin size is then selected according to the number of sufficiently closesnapshots identified. The more neighbours identified, the higher the virtualparticle image density and the smaller is the bin size, provided that the number of particles to be contained in it is fixed. The statistical dispersion of the velocity vectors within the bin is then used to estimate the uncertainty and to select the optimal K which minimises it. The method is tested and validated against datasets with a progressively increasing level of complexity: two virtual experiments based on direct numerical simulations of the wake of a fluidic pinball and a channel flow and the experimental data collected in a turbulent boundary layer.

Automated summary

We present a novel end-to-end approach to enhance the resolution of Particle Image Velocimetry (PIV) measurements. Our method utilizes information from different snapshots to obtain high-resolution flow fields and uncertainty estimations with minimal user intervention.