Two-point Turbulence Closures in Physical Space

TL;DR

This paper introduces a novel physical-space two-point turbulence closure framework that models ensemble-averaged turbulence without Fourier transforms, verified against simulations and classical models.

Contribution

It develops a physical-space closure model for homogeneous isotropic turbulence, incorporating non-local effects and enabling applications to complex flows.

Findings

Closure model accurately predicts turbulence statistics compared to DNS.

Physical-space formulation captures non-local effects and pressure contributions.

Framework extends to inhomogeneous and anisotropic turbulence.

Abstract

This work presents a predictive two-point statistical closure framework for turbulence formulated in physical space. A closure model for ensemble-averaged, incompressible homogeneous isotropic turbulence (HIT) is developed as a starting point to demonstrate the viability of the approach in more general flows. The evolution equation for the longitudinal correlation function is derived in a discrete form, circumventing the need for a Fourier transformation. The formulation preserves the near-exact representation of the linear terms, a defining feature of rapid distortion theory. The closure of the nonlinear higher-order moments follows the phenomenological principles of the Eddy-Damped Quasi-Normal Markovian (EDQNM) model of Orszag (1970). Several key differences emerge from the physical-space treatment, including the need to evaluate a matrix exponential in the evolution equation and the appearance of triple integrals arising from the non-local nature of the pressure-Poisson equation. This framework naturally incorporates non-local length-scale information into the evolution of turbulence statistics. Verification of the physical-space two-point closure is performed by comparison with direct numerical simulations of statistically stationary forced HIT and with classical EDQNM predictions for decaying HIT. Finally, extensions to inhomogeneous and anisotropic turbulence are discussed, emphasizing advantages in applications where spectral methods are ill-conditioned, such as compressible flows with discontinuities.