Visibility network analysis of large-scale intermittency in convective surface layer turbulence
| Autor: | Subharthi Chowdhuri, Tirtha Banerjee, Giovanni Iacobello |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Convection
atmospheric flows Fluids & Plasmas Flow (psychology) FOS: Physical sciences Probability density function Mathematical Sciences Surrogate data law.invention Physics - Geophysics Physics::Fluid Dynamics Engineering law Intermittency intermittency Surface layer Physics Turbulence Mechanical Engineering Applied Mathematics Fluid Dynamics (physics.flu-dyn) Physics - Fluid Dynamics Mechanics Condensed Matter Physics Geophysics (physics.geo-ph) Nonlinear system Mechanics of Materials |
| Zdroj: | Journal of Fluid Mechanics. 925 |
| ISSN: | 1469-7645 0022-1120 |
| DOI: | 10.1017/jfm.2021.720 |
| Popis: | Large-scale intermittency is a widely observed phenomenon in convective surface layer turbulence that induces non-Gaussian temperature statistics, while such 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 non-linear dependencies in the temperature signals. Conversely, a competition between linear and non-linear 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. 4 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|