强非均匀磁场中带电粒子动力学的半离散化和全离散化

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Esaim-Probability and Statistics Pub Date : 2023-07-01 DOI:10.1051/m2an/2023058

Yao-Lin Jiang

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引用次数: 0

摘要

本文的目的是建立和分析强非均匀磁场中带电粒子动力学(CPD)的数值离散化。首先提出了二维CPD的半离散化和全离散化策略，提高了精度。当磁场强度增强时，这种精度在位置和速度上得到提高。这比通常所谓的“均匀精确方法”有更好的特点。为了获得这种精确的精度，将问题的一些重新表述和双尺度指数积分结合起来，并从这种新方法中得到了精度的提高。然后，在二维情况下给出的策略的基础上，构造了一类具有简单格式的一致精度的三维CPD方法。通过数值试验对理论结果进行了数值验证。

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Semi-discretization and full-discretization with improved accuracy for charged-particle dynamics in a strong nonuniform magnetic field

The aim of this paper is to formulate and analyze numerical discretizations of charged-particle dynamics (CPD) in a strong nonuniform magnetic field. A strategy is firstly performed for the two dimensional CPD to construct the semi-discretization and full-discretization which have improved accuracy. This accuracy is improved in the position and in the velocity when the strength of the magnetic field becomes stronger. This is a better feature than the usual so called ``uniformly accurate methods”. To obtain this refined accuracy, some reformulations of the problem and two-scale exponential integrators are incorporated, and the improved accuracy is derived from this new procedure. Then based on the strategy given for the two dimensional case, a new class of uniformly accurate methods with simple scheme is formulated for the three dimensional CPD in maximal ordering case. All the theoretical results of the accuracy are numerically illustrated by some numerical tests.

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来源期刊

Esaim-Probability and Statistics STATISTICS & PROBABILITY-

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The journal publishes original research and survey papers in the area of Probability and Statistics. It covers theoretical and practical aspects, in any field of these domains. Of particular interest are methodological developments with application in other scientific areas, for example Biology and Genetics, Information Theory, Finance, Bioinformatics, Random structures and Random graphs, Econometrics, Physics. Long papers are very welcome. Indeed, we intend to develop the journal in the direction of applications and to open it to various fields where random mathematical modelling is important. In particular we will call (survey) papers in these areas, in order to make the random community aware of important problems of both theoretical and practical interest. We all know that many recent fascinating developments in Probability and Statistics are coming from "the outside" and we think that ESAIM: P&S should be a good entry point for such exchanges. Of course this does not mean that the journal will be only devoted to practical aspects.