论三维导电罗森斯韦格系统全局弱解的存在及其向准平衡的趋近

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Applied Mathematics and Optimization Pub Date : 2024-04-08 DOI:10.1007/s00245-024-10127-4
A. Ndongmo Ngana, P. A. Razafimandimby
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引用次数: 0

摘要

本文研究铁流体的导电罗森斯韦格模型,Hamdache 和 Hamroun(Appl Math Optim 81(2):479-509, 2020)研究了该模型的布洛赫-托雷正则化。我们主要证明了非正则化模型在一定的电导率小条件下弱解的全局存在性。因此,我们的结果不仅解决了哈姆达切和哈姆鲁恩提出的一个悬而未决的问题,而且还证实了铁流体是天然的不良电流导体。这个证明本身就很有趣,它依赖于对磁场的赫尔摩尔兹-勒雷分解和对磁化的重正化解,相当复杂。我们还对全局弱解在弛豫时间极限制度下向准平衡收敛的过程进行了严格而详细的描述。
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On the Existence of Global Weak Solutions to the 3D Electrically Conductive Rosensweig System and Their Convergence Towards Quasi-Equilibrium

In this article, we study an electrically conductive Rosensweig model for ferrofluids, whose Bloch–Torrey regularization was studied by Hamdache and Hamroun (Appl Math Optim 81(2):479–509, 2020). We mainly prove the global existence of weak solutions to the non-regularized model under a certain smallness condition on the electric conductivity. Hence, our result not only solves a problem that was left open by Hamdache and Hamroun, but it can also serve as a confirmation that ferrofluids are naturally poor conductors of electric current. The proof, which is interesting in itself, is quite involved and relies on the Helmohltz–Leray decomposition of the magnetic fields and the use of renormalized solutions for the magnetization. We also give a rigorous and detailed description of the convergence of the global weak solutions towards the quasi-equilibrium in the relaxation time limit regime \(\tau \rightarrow 0\).

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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