Global convergence rates in the relaxation limits for the compressible Euler and Euler-Maxwell systems in Sobolev spaces
Timothée Crin-Barat, Yue-Jun Peng, Ling-Yun Shou
TL;DR
The paper analyzes global-in-time relaxation limits for two partially dissipative systems: the damped compressible Euler equations and the damped compressible Euler–Maxwell system. By developing a multi-dimensional stream-function framework and a systematic asymptotic expansion, the authors prove explicit convergence rates in Sobolev spaces for ill-prepared data and enhanced $O(^2)$ rates to first-order approximations under well-prepared data, with the Euler–Maxwell system converging to a drift-diffusion model. The work introduces initial-layer corrections and carefully manages nonlinear residuals to obtain uniform-in-time error bounds, avoiding frequency-localization techniques. These results deepen understanding of diffusion limits in high dimensions and establish quantitative rates relevant to plasma and fluid-EM coupling models, offering robust tools for further extensions to bounded domains or discrete settings.
Abstract
We study two relaxation problems in the class of partially dissipative hyperbolic systems: the compressible Euler system and the compressible Euler-Maxwell system. In classical Sobolev spaces, we derive a global convergence rate of $\mathcal{O}(\varepsilon)$ between strong solutions of the relaxed Euler system and the porous medium equation in $\mathbb{R}^d$ ($d\geq1$) for \emph{ill-prepared} initial data. In a well-prepared setting, we derive an enhanced convergence rate of order $\mathcal{O}(\varepsilon^2)$ between the solutions of the relaxed compressible Euler system and their first-order asymptotic approximation. Regarding the relaxed Euler-Maxwell system, we prove the global strong convergence of its solutions to the drift-diffusion model in $\mathbb{R}^3$ in an \emph{ill-prepared} setting. These results are achieved by developing a new asymptotic expansion approach that, combined with stream function techniques, ensures uniform-in-time error estimates.
