Large time convergence of the non-homogeneous Goldstein-Taylor Equation

TL;DR

This paper introduces a new method using a pseudodifferential Lyapunov functional to analyze the large time convergence of the non-homogeneous Goldstein-Taylor equation with variable relaxation functions, extending understanding beyond constant cases.

Contribution

It develops a general, robust approach with explicit convergence rates for the non-constant relaxation function case, applicable to multi-velocity models.

Findings

Established a new Lyapunov functional for variable relaxation functions.

Derived explicit convergence rates, though not optimal.

Extended analysis to multi-velocity Goldstein-Taylor models.

Abstract

The Goldstein-Taylor equations can be thought of as a simplified version of a BGK system, where the velocity variable is constricted to a discrete set of values. It is intimately related to turbulent fluid motion and the telegrapher's equation. A detailed understanding of the large time behaviour of the solutions to these equations has been mostly achieved in the case where the relaxation function, measuring the intensity of the relaxation towards equally distributed velocity densities, is constant. The goal of the presented work is to provide a general method to tackle the question of convergence to equilibrium when the relaxation function is not constant, and to do so as quantitatively as possible. In contrast to the usual modal decomposition of the equations, which is natural when the relaxation function is constant, we define a new Lyapunov functional of pseudodifferential nature, one that is motivated by the modal analysis in the constant case, that is able to deal with full spatial dependency of the relaxation function. The approach we develop is robust enough that one can apply it to multi-velocity Goldstein-Taylor models, and achieve explicit rates of convergence. The convergence rate we find, however, is not optimal, as we show by comparing our result to the that found in the work of Bernard and Salvarani from 2013.