Sum-of-squares bounds on correlation functions in a minimal model of turbulence

TL;DR

This paper introduces a computer-assisted method using sum-of-squares polynomials to bound correlation functions in a minimal turbulence model, revealing statistical properties and phase behaviors at high Reynolds numbers.

Contribution

It develops a novel approach combining analytical proofs and numerical algorithms to analyze turbulence models through sum-of-squares bounds on correlation functions.

Findings

Derived bounds on correlation functions and phase variance.

Identified phase tendencies towards ±π/2 at high Reynolds numbers.

Obtained probability densities for modes in inverse cascade.

Abstract

We suggest a new computer-assisted approach to the development of turbulence theory. It allows one to impose lower and upper bounds on correlation functions using sum-of-squares polynomials. We demonstrate it on the minimal cascade model of two resonantly interacting modes, when one is pumped and the other dissipates. We show how to present correlation functions of interest as part of a sum-of-squares polynomial using the stationarity of the statistics. That allows us to find how the moments of the mode amplitudes depend on the degree of non-equilibrium (analog of the Reynolds number), which reveals some properties of marginal statistical distributions. By combining scaling dependence with the results of direct numerical simulations, we obtain the probability densities of both modes in a highly intermittent inverse cascade. We also show that the relative phase between modes tends to $\pi/2$ and $-\pi/2$ in the direct and inverse cascades as the Reynolds number tends to infinity, and derive bounds on the phase variance. Our approach combines computer-aided analytical proofs with a numerical algorithm applied to high-degree polynomials.