Abstract

This study focuses on utilizing image techniques for river velocity measurement, with a specific emphasis on natural surface floating patterns. Employing a multi-camera system, we conducted 3D measurements on river surfaces, including surface velocity and water surface reconstruction. A pattern-based tracking approach has been adopted to improve the performance of image measurements on different types of natural floating tracers. The study employs the following approaches: 3D Lagrangian Pattern Tracking Velocimetry (3D-LPTV), 2D Lagrangian Pattern Velocimetry (2D- LPTV), and Large-scale Particle Image Velocimetry (LSPIV), for surface velocity estimation. The outcomes revealed that all three approaches yielded consistent results in terms of averaged velocity. However, the LSPIV method produced about two times higher uncertainty in measured velocities compared to the other methods. A strategy to assess the quality of river surface patterns in velocity estimation is presented. Specifically, the sum of squared interrogation area intensity gradient (SSIAIG) was found to be strongly correlated with measurement uncertainty. Additionally, a term related to the peak sidelobe ratio (PSR) of the cross-correlation map was found as an effective constraint, ensuring the image-tracking process achieves high reliability. The precision of measurements increases corresponding to the increase of image intensity gradient and PSR.

Keywords

Image velocimetry; Photogrammetry; 3D-LPTV; Surface flow velocity; Image tracking

Published in

Flow Measurement and Instrumentation
2024, Volume: 96, article number: 102557
Publisher: ELSEVIER SCI LTD

SLU Authors

• Andreasson, Patrik

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    
  • Vattenfall Vattenkraft AB

UKÄ Subject classification

Oceanography, Hydrology, Water Resources


Publication identifier

DOI: https://doi.org/10.1016/j.flowmeasinst.2024.102557

Permanent link to this page (URI)

https://res.slu.se/id/publ/129379