Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel

TL;DR

This paper introduces an advanced 3D-PTV method with real-time particle segmentation, enabling detailed Lagrangian turbulence measurements in canopy flows, overcoming previous data transfer bottlenecks and limitations of Eulerian methods.

Contribution

The authors developed a real-time particle segmentation extension to 3D-PTV, drastically reducing data transfer and allowing longer, more accurate Lagrangian turbulence experiments in complex flows.

Findings

Acceleration statistics influenced by mean shear at canopy top

Small-scale dispersion dominated by wake turbulence

Method overcomes Eulerian measurement limitations

Abstract

Direct estimation of Lagrangian turbulence statistics is essential for the proper modeling of dispersion and transport in highly obstructed canopy flows. However, Lagrangian flow measurements demand very high rates of data acquisition, resulting in bottlenecks that prevented the estimation of Lagrangian statistics in canopy flows hitherto. We report on a new extension to the 3D Particle Tracking Velocimetry (3D-PTV) method, featuring real-time particle segmentation that outputs centroids and sizes of tracer particles and performed on dedicated hardware during high-speed digital video acquisition from multiple cameras. The proposed extension results in four orders of magnitude reduction in data transfer rate that enables to perform substantially longer experimental runs, facilitating measurements of convergent statistics. The extended method is demonstrated through an experimental wind tunnel investigation of the Lagrangian statistics in a heterogeneous canopy flow. We observe that acceleration statistics are affected by the mean shear at the top of the canopy layer and that Lagrangian particle dispersion at small scales is dominated by turbulence in the wake of the roughness elements. This approach enables to overcome major shortcomings from Eulerian-based measurements which rely on assumptions such as the Taylor's frozen turbulence hypothesis, which is known to fail in highly turbulent flows.