Multi-scale turbulence modeling and maximum information principle. Part 2

TL;DR

This paper develops a multi-scale turbulence model for 2D shear turbulence using optimal control, analyzing transient and asymptotic states, and demonstrating solutions via convex programming with relevance to flow stability.

Contribution

It introduces a novel optimal control framework for multi-scale turbulence modeling based on the Navier-Stokes equations and correlation constraints, with solutions obtained through convex programming.

Findings

Asymptotic solutions have a zero exponential growth rate.

Feasible solutions exist for the control variables in the reduced model.

The formulation is relevant to flow stability analysis.

Abstract

We consider two-dimensional homogeneous shear turbulence within the context of optimal control, a multi-scale turbulence model containing the fluctuation velocity and pressure correlations up to the fourth order; The model is formulated on the basis of the Navier-Stokes equations, Reynolds average, the constraints of inequality from both physical and mathematical considerations, the turbulent energy density as the objective to be maximized, and the fourth order correlations as the control variables. Without imposing the maximization and the constraints, the resultant equations of motion in the Fourier wave number space are formally solved to obtain the transient state solutions, the asymptotic state solutions and the evolution of a transient toward an asymptotic under certain conditions. The asymptotic state solutions are characterized by the dimensionless exponential time rate of growth $2\sigma$ which has an upper bound of $2\sigma_{\max}$ = 0; The asymptotic solutions can be obtained from a linear objective convex programming. For the asymptotic state solutions of the reduced model containing the correlations up to the third order, the optimal control problem reduces to linear programming with the primary component of the third order correlations or a related integral quantity as the control variable; the supports of the second and third order correlations are estimated for the sake of numerical simulation; the existence of feasible solutions is demonstrated when the related quantity is the control variable. The relevance of the formulation to flow stability analysis is suggested.