Persistent accelerations disentangle Lagrangian turbulence
| Autor: | Michael Wilczek, Lukas Bentkamp, Cristian Lalescu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Particle statistics Science FOS: Physical sciences General Physics and Astronomy 02 engineering and technology Article General Biochemistry Genetics and Molecular Biology Physics::Fluid Dynamics 03 medical and health sciences symbols.namesake Acceleration Fluid dynamics lcsh:Science Dispersion (water waves) Physics::Atmospheric and Oceanic Physics Computer Science::Databases Physics Multidisciplinary Turbulence Fluid Dynamics (physics.flu-dyn) Reynolds number Physics - Fluid Dynamics General Chemistry Mechanics Vorticity 021001 nanoscience & nanotechnology Lagrangian turbulence Particle acceleration 030104 developmental biology Flow (mathematics) symbols lcsh:Q Statistical physics 0210 nano-technology |
| Zdroj: | Nature Communications Nature Communications, Vol 10, Iss 1, Pp 1-8 (2019) |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-019-11060-9 |
| Popis: | Particles in turbulence frequently encounter extreme accelerations between extended periods of quiescence. The occurrence of extreme events is closely related to the intermittent spatial distribution of intense flow structures such as vorticity filaments. This mixed history of flow conditions leads to very complex particle statistics with a pronounced scale dependence, which presents one of the major challenges on the way to a non-equilibrium statistical mechanics of turbulence. Here, we introduce the notion of persistent Lagrangian acceleration, quantified by the squared particle acceleration coarse-grained over a viscous time scale. Conditioning Lagrangian particle data from simulations on this coarse-grained acceleration, we find remarkably simple, close-to-Gaussian statistics for a range of Reynolds numbers. This opens the possibility to decompose the complex particle statistics into much simpler sub-ensembles. Based on this observation, we develop a comprehensive theoretical framework for Lagrangian single-particle statistics that captures the acceleration, velocity increments as well as single-particle dispersion. Particles in turbulence, as encountered in the atmosphere or the oceans, experience strongly varying local flow conditions over time. Bentkamp et al. show that this statistical complexity can be broken down into simpler parts, allowing for insights into the space-time structure of turbulent flows. |
| Databáze: | OpenAIRE |
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