Dynamical Fractional and Multifractal Fields

TL;DR

This paper introduces a class of stochastic PDEs inspired by turbulence modeling, incorporating fractional and multifractal features through linear and nonlinear interactions, with analytical predictions and numerical simulations revealing complex statistical behaviors.

Contribution

It proposes a novel stochastic PDE framework that captures fractional and multifractal turbulence features, combining analytical predictions with numerical validation.

Findings

Explicit statistical predictions for linear Gaussian cases.

Observation of non-Gaussian, skewed solution behaviors in simulations.

Introduction of quadratic interactions inspired by multifractal chaos.

Abstract

Motivated by the modeling of three-dimensional fluid turbulence, we define and study a class of stochastic partial differential equations (SPDEs) that are randomly stirred by a spatially smooth and uncorrelated in time forcing term. To reproduce the fractional, and more specifically multifractal, regularity nature of fully developed turbulence, these dynamical evolutions incorporate an homogenous pseudo-differential linear operator of degree 0 that takes care of transferring energy that is injected at large scales in the system, towards smaller scales according to a cascading mechanism. In the simplest situation which concerns the development of fractional regularity in a linear and Gaussian framework, we derive explicit predictions for the statistical behaviors of the solution at finite and infinite time. Doing so, we realize a cascading transfer of energy using linear, although non local, interactions. These evolutions can be seen as a stochastic version of recently proposed systems of forced waves intended to model the regime of weak wave turbulence in stratified and rotational flows. To include multifractal, i.e. intermittent, corrections to this picture, we get some inspiration from the Gaussian multiplicative chaos, which is known to be multifractal, to motivate the introduction of an additional quadratic interaction in these dynamical evolutions. Because the theoretical analysis of the obtained class of nonlinear SPDEs is much more demanding, we perform numerical simulations and observe the non-Gaussian and in particular skewed nature of their solution.