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You are here: Home / Seminars / Experimental physics and modelling / Lagrangian Intermittency in Realistic Turbulent Canopy Flows

Lagrangian Intermittency in Realistic Turbulent Canopy Flows

Ron Shnapp (Weizmann Institute, Israel)
When Jun 15, 2021
from 11:00 to 12:00
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Attendees Ron Shnapp
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Air flows around vegetation or buildings in the lower atmosphere are called canopy flows. These flows are highly turbulent, making chief contributions to the transport and dispersion of matter in our environment, such as the spread of air pollution or viral particles in urban areas, or seeds and fungal spores in fields. Therefore, accurate dispersion modeling near Earth's surface requires a deep understanding of these complex flows. Nevertheless, turbulence in canopy flows has certain features that make solving such problems difficult. In particular, the variations of turbulence statistics in space (inhomogeneity) and temporal variations in turbulence energy production leave one in doubt regarding the validity of fundamental turbulence theories for such flows. 
In our study, we have conducted a novel 3D-particle tracking experiment in a wind-tunnel modeled canopy flow, which allowed us to extract crucial Lagrangian statistics for the first time. In this seminar, we will capitalize on this experiment, comparing the experimentally observed statistics with the information we already have about Lagrangian turbulence from previous homogeneous flows theory, experiments, and simulations. We will also put a particular emphasis on the so-called intermittency observed in Lagrangian statistics. Surprisingly, the analysis reveals that the statistical laws that characterize homogeneous turbulence are recovered at sufficiently small scales, aka local isotropy. Thus, it appears that for certain highly turbulent flows, the kinetic energy flux through the turbulent cascade outweighs the effect of inhomogeneity in maintaining the small-scale structure of the flow. This observation can provide a physical constraint for small-scale atmospheric dispersion models, and thus can help improve their accuracy.
[1] R. Shnapp, et al. (2019). Scientific Reports, 9.1. 
[2] R. Shnapp, Y. Bohbot-Raviv, A. Liberzon, and E. Fattal (2020). Physical Review Fluids, 5(9), 094601.
[3] R. Shnapp (2021). Journal of Fluid Mechanics, 913:R2.

[4] L. Chevillard (2021). Journal of Fluid Mechanics, 916:F1.