Collision rates estimated from exact N-body simulations of a one-dimensional plasma

Author:

Gravier Etienne1ORCID,Drouot Thomas1ORCID,Lesur Maxime1ORCID,Guillevic Alejandro1ORCID,Lo-Cascio Guillaume1ORCID,Moritz Jérôme1ORCID,Escande Dominique2ORCID,Manfredi Giovanni3ORCID

Affiliation:

1. Université de Lorraine, CNRS, Institut Jean Lamour 1 , UMR 7198, F-54000 Nancy, France

2. Aix-Marseille Université, CNRS, PIIM 2 , UMR 7345, F-13000 Marseille, France

3. Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg 3 , UMR 7504, F-67000 Strasbourg, France

Abstract

In a plasma, the charged particles interact via long-range forces, and this interaction causes the plasma to exhibit collective effects. If the graininess or coupling parameter g goes to zero (ideal collisionless plasma), two-body collisions are negligible, while collective effects dominate the dynamics. In contrast, when g≈1 collisions play a significant role. To study the transition between a collisionless and a collisional regime, a N-body code was developed and used in this work. The code solves exactly, in one spatial dimension, the dynamics of N infinite parallel plane sheets for both ion and electron populations. We illustrate the transition between individual and collective effects by studying two basic plasma phenomena, the two-stream instability and Langmuir waves, for different values of g. The numerical collision rates given by the N-body code increase linearly with g for both phenomena, although with proportionality factors that differ by roughly a factor of two, a discrepancy that may be accounted for by the different initial conditions. All in all, the usual collision rates published in the literature (Spitzer collisionality) appear to compare rather well with the rates observed in our simulations.

Publisher

AIP Publishing

Subject

Condensed Matter Physics

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Learning the dynamics of a one-dimensional plasma model with graph neural networks;Machine Learning: Science and Technology;2024-05-28

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3