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

TL;DR

This paper presents an innovative end-to-end KNN-based PTV method that enhances high-resolution measurements and quantifies uncertainty by merging similar flow structures across multiple snapshots without requiring time-resolved data.

Contribution

The approach introduces a novel way to increase PIV resolution by leveraging morphological similarity in flow regions across independent snapshots using unsupervised KNN search and POD features.

Findings

Successfully validated on virtual and experimental datasets.

Achieved higher spatial resolution in PIV measurements.

Provided a method for uncertainty estimation in flow measurements.

Abstract

We introduce a novel end-to-end approach to improving the resolution of PIV measurements. 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. 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 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 close" snapshots identified. 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 simulations of the wake of a fluidic pinball and a channel flow and the experimental data collected in a turbulent boundary layer.