On a skewed and multifractal uni-dimensional random field, as a probabilistic representation of Kolmogorov's views on turbulence

TL;DR

This paper constructs a one-dimensional stochastic field modeling turbulence that combines homogeneity, isotropy, negative skewness, and intermittency, using fractional Gaussian fields and Gaussian multiplicative chaos to replicate key turbulence features.

Contribution

It introduces a novel stochastic model that simultaneously captures turbulence axioms, including skewness and intermittency, by integrating fractional Gaussian fields with Gaussian multiplicative chaos.

Findings

Successfully models turbulence axioms in a 1D stochastic field

Replicates negative skewness and multifractal intermittency

Provides a new probabilistic framework for turbulence representation

Abstract

We construct, for the first time to our knowledge, a one-dimensional stochastic field $\{u(x)\}_{x\in \mathbb{R}}$ which satisfies the following axioms which are at the core of the phenomenology of turbulence mainly due to Kolmogorov: (i) Homogeneity and isotropy: $u(x) \overset{\mathrm{law}}= -u(x) \overset{\mathrm{law}}=u(0)$ (ii) Negative skewness (i.e. the $4/5^{\mbox{\tiny th}}$-law): \\ $\mathbb{E}{(u(x+\ell)-u(x))^3} \sim_{\ell \to 0+} - C \, \ell\,,$ \, for some constant $C>0$ (iii) Intermittency: $\mathbb{E}{|u(x+\ell)-u(x) |^q} \asymp_{\ell \to 0} |\ell|^{\xi_q}\,,$ for some non-linear spectrum $q\mapsto \xi_q$ Since then, it has been a challenging problem to combine axiom (ii) with axiom (iii) (especially for Hurst indexes of interest in turbulence, namely $H<1/2$). In order to achieve simultaneously both axioms, we disturb with two ingredients a underlying fractional Gaussian field of parameter $H\approx \frac 1 3 $. The first ingredient is an independent Gaussian multiplicative chaos (GMC) of parameter $\gamma$ that mimics the intermittent, i.e. multifractal, nature of the fluctuations. The second one is a field that correlates in an intricate way the fractional component and the GMC without additional parameters, a necessary inter-dependence in order to reproduce the asymmetrical, i.e. skewed, nature of the probability laws at small scales.