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

publication date

  • January 2023

start page

  • 1

end page

  • 12

issue

  • 110756

volume

  • 140

International Standard Serial Number (ISSN)

  • 0894-1777

Electronic International Standard Serial Number (EISSN)

  • 1879-2286

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.

subjects

  • Aeronautics
  • Biology and Biomedicine
  • Mathematics
  • Physics

keywords

  • piv; ptv; knn; data-driven measurement enhancement; uncertainty quantification