Dynamical and statistical properties of large scales in turbulence

We present a review of results concerning the properties of scales larger than the forcing scale in turbulent flows. In three-dimensional turbulence, when the power driving the flow is injected at a well-defined scale, the injection scale separates the small-scale range, where the Kolmogorov cascade takes place, from the large-scale range across which the mean energy flux is zero, suggesting that these modes are in equilibrium. We show that, in the case of spatially periodic forcing involving only a few modes, an equipartition energy spectrum is generated at large scales, and we discuss the deviations from equipartition observed with more complex forcings. In two-dimensional turbulence, the injection scale separates the direct enstrophy cascade towards small scales from the inverse energy cascade towards large scales. Because of the inverse cascade, one would not expect equilibrium statistical physics tools to be suitable for describing the large-scale structures. Yet this is precisely the situation in which such tools have been most widely used. We indeed show that the bifurcations observed between different large-scale regimes of two-dimensional turbulence, both in experiments and in direct numerical simulations (DNS), can be qualitatively described by the truncated Euler equations (TEE), whose solutions satisfy statistical equilibrium. This article is part of the theme issue 'Frontiers of turbulence and statistical physics'.

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Publication Details

Journal
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Published
2026-07-30
DOI
https://doi.org/10.1098/rsta.2025.0034
Primary Topic
Fluid Dynamics and Turbulent Flows
Type
article
Field-Weighted Citation Impact
0.00

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article

Dynamical and statistical properties of large scales in turbulence

Marc Brächet, S. Fauve, François Pétrelis, Alexandros Alexakis
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
Fluid Dynamics and Turbulent Flows
article

Dynamical and statistical properties of large scales in turbulence

Marc Brächet, S. Fauve, François Pétrelis, Alexandros Alexakis
article en

Abstract

We present a review of results concerning the properties of scales larger than the forcing scale in turbulent flows. In three-dimensional turbulence, when the power driving the flow is injected at a well-defined scale, the injection scale separates the small-scale range, where the Kolmogorov cascade takes place, from the large-scale range across which the mean energy flux is zero, suggesting that these modes are in equilibrium. We show that, in the case of spatially periodic forcing involving only a few modes, an equipartition energy spectrum is generated at large scales, and we discuss the deviations from equipartition observed with more complex forcings. In two-dimensional turbulence, the injection scale separates the direct enstrophy cascade towards small scales from the inverse energy cascade towards large scales. Because of the inverse cascade, one would not expect equilibrium statistical physics tools to be suitable for describing the large-scale structures. Yet this is precisely the situation in which such tools have been most widely used. We indeed show that the bifurcations observed between different large-scale regimes of two-dimensional turbulence, both in experiments and in direct numerical simulations (DNS), can be qualitatively described by the truncated Euler equations (TEE), whose solutions satisfy statistical equilibrium. This article is part of the theme issue 'Frontiers of turbulence and statistical physics'.

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering SciencesVol. 384(2325)
Centre National de la Recherche Scientifique (FR), Université Paris Cité (FR), Sorbonne Université (FR), Sorbonne Paris Cité (FR), École Normale Supérieure (BI)
Centre national d'études spatiales, Agence Nationale de la Recherche
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Openalex Percentile: Top 11%
Fluid Dynamics and Turbulent Flows
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