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Visibility network analysis of large-scale intermittency in convective surface layer turbulence

Published online by Cambridge University Press:  31 August 2021

Subharthi Chowdhuri*
Affiliation:
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Dr Homi Bhaba Road, Pashan, Pune411008, India
Giovanni Iacobello
Affiliation:
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129Torino, Italy Department of Mechanical and Materials Engineering, Queen's University, Kingston, ONK7L 3N6, Canada
Tirtha Banerjee
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, CA92697, USA
*
Email address for correspondence: subharthi.cat@tropmet.res.in

Abstract

Large-scale intermittency is a widely observed phenomenon in convective surface layer turbulence that induces non-Gaussian temperature statistics, while such a signature is not observed for velocity signals. Although approaches based on probability density functions have been used so far, those are not able to explain to what extent the signals’ temporal structure impacts the statistical characteristics of the velocity and temperature fluctuations. To tackle this issue, a visibility network analysis is carried out on a field-experimental dataset from a convective atmospheric surface layer flow. Through surrogate data and network-based measures, we demonstrate that the temperature intermittency is related to strong nonlinear dependencies in the temperature signals. Conversely, a competition between linear and nonlinear effects tends to inhibit the temperature-like intermittency behaviour in streamwise and vertical velocities. Based on present findings, new research avenues are likely to be opened up in studying large-scale intermittency in convective turbulence.

Type
JFM Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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