Space-time statistics of a linear dynamical energy cascade model
Gabriel B. Apolin\'ario, Laurent Chevillard

TL;DR
This paper analyzes a linear dynamical model for turbulent energy cascades, deriving explicit temporal correlation predictions and exploring how forcing correlation affects spatial and temporal statistics, revealing limitations in modeling turbulence regularity.
Contribution
It provides explicit asymptotic predictions for the temporal correlation function and examines the effects of forcing correlation on spatial and temporal turbulence statistics.
Findings
Temporal correlation regularity depends on forcing time correlation.
Spatial homogeneity is maintained for small forcing correlation times.
Large forcing correlation causes spatial concentration and breaks homogeneity.
Abstract
A linear dynamical model for the development of the turbulent energy cascade was introduced in Apolin\'ario \emph{et al} (J. Stat. Phys. \textbf{186}, 15 (2022)). This partial differential equation, randomly stirred by a forcing term which is smooth in space and delta-correlated in time, was shown to converge at infinite time towards a state of finite variance, without the aid of viscosity. Furthermore, the spatial profile of its solution gets rough, with the same regularity as a fractional Gaussian field. We here focus on the temporal behavior and derive explicit asymptotic predictions for the correlation function in time of this solution and observe that their regularity is not influenced by the spatial regularity of the problem, only by the correlation in time of the stirring contribution. We also show that the correlation in time of the solution depends on the position, contrary to…
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Taxonomy
TopicsFractional Differential Equations Solutions · Statistical Mechanics and Entropy · Optical properties and cooling technologies in crystalline materials
