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We introduce a novel approach to improving the resolution of Particle Image Velocimetry (PIV) measurements. The method merges information from different non-time resolved snapshots exploiting similarity of flow regions in different time instants. The main hypothesis is that the identification of similar flow structures at different time instants is feasible if a sufficiently large ensemble of statistically-independent snapshots is available. Merging individual vectors from different snapshots with similar flow organisation allows an artificial increase of the available information. This paves the way to refining of the interrogation region, i.e. increasing spatial resolution. The similarity can be enforced 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 implemented with an unsupervised K-nearest neighbours search in the space of significant flow features. Such features are identified with Proper Orthogonal Decomposition (POD) in subdomains of the original low resolution data. The refined bin size will depend on the number of “sufficiently close” snapshots: the more neighbours are identified, the higher is the “virtual” particle image density available, and consequently the smaller is the bin size. The statistical dispersion of the velocity vectors within the bin is then exploited in the estimation of the uncertainty. The optimal number of neighbours is the one corresponding to the minimum uncertainty. The method is tested and validated against datasets with a progressively increasing level of complexity: two virtual experiments based on direct simulations of the wake of a fluidic pinball and a turbulent channel flow; experimental data collected in a turbulent boundary layer.

We introduce a novel approach to improving the resolution of Particle Image Velocimetry (PIV) measurements. The method blends information from different snapshots without the need for timeresolved 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 to refine the interrogation region and, consequently, increase 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 unsupervised -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 lowresolution 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 close" snapshots identified. The more neighbours identified, the higher the "virtual" particle 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 which minimises it. The method is tested and validated against datasets with a progressively increasing level of complexity: two virtual experiments based on direct simulations of the wake of a fluidic pinball and a channel flow and the experimental data collected in a turbulent boundary layer.