A New Global Nonlinear Force-Free Coronal Magnetic-Field Extrapolation Code Implemented on a Yin–Yang Grid

IF 2.7 3区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS Solar Physics Pub Date : 2023-02-07 DOI:10.1007/s11207-023-02109-6
Argyrios Koumtzis, Thomas Wiegelmann
{"title":"A New Global Nonlinear Force-Free Coronal Magnetic-Field Extrapolation Code Implemented on a Yin–Yang Grid","authors":"Argyrios Koumtzis,&nbsp;Thomas Wiegelmann","doi":"10.1007/s11207-023-02109-6","DOIUrl":null,"url":null,"abstract":"<div><p>The solar magnetic field dominates and structures the solar coronal plasma. Detailed insights into the coronal magnetic field are important to understand most physical phenomena there. While direct, routine measurements of the coronal magnetic field are not available, field extrapolation of the photospheric vector-field measurements into the corona is the only way to study the structure and dynamics of the coronal field. Here we focus on global coronal structures traditionally modeled using spherical grids and synoptic vector magnetograms as boundary conditions. We developed a new code that performs nonlinear force-free magnetic-field extrapolations in spherical geometry. Our new implementation is based on a well-established optimization principle on a Cartesian grid and a single spherical finite-difference grid. In the present work, for the first time, the algorithm is able to reconstruct the magnetic field in the entire corona, including the polar regions. The finite-difference numerical scheme that was employed in previous spherical-code versions suffered from numerical inefficiencies because of the convergence of those grids on the poles. In our new code, we implement the so-called Yin–Yang overhead grid, the structure of which addresses this difficulty. Consequently, both the speed and accuracy of the optimization algorithm are improved compared to the previous implementations. We tested our new code using the well known semi-analytical model (Low and Lou solution). This is a commonly used benchmark for nonlinear force-free extrapolation codes.</p></div>","PeriodicalId":777,"journal":{"name":"Solar Physics","volume":"298 2","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11207-023-02109-6.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solar Physics","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s11207-023-02109-6","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0

Abstract

The solar magnetic field dominates and structures the solar coronal plasma. Detailed insights into the coronal magnetic field are important to understand most physical phenomena there. While direct, routine measurements of the coronal magnetic field are not available, field extrapolation of the photospheric vector-field measurements into the corona is the only way to study the structure and dynamics of the coronal field. Here we focus on global coronal structures traditionally modeled using spherical grids and synoptic vector magnetograms as boundary conditions. We developed a new code that performs nonlinear force-free magnetic-field extrapolations in spherical geometry. Our new implementation is based on a well-established optimization principle on a Cartesian grid and a single spherical finite-difference grid. In the present work, for the first time, the algorithm is able to reconstruct the magnetic field in the entire corona, including the polar regions. The finite-difference numerical scheme that was employed in previous spherical-code versions suffered from numerical inefficiencies because of the convergence of those grids on the poles. In our new code, we implement the so-called Yin–Yang overhead grid, the structure of which addresses this difficulty. Consequently, both the speed and accuracy of the optimization algorithm are improved compared to the previous implementations. We tested our new code using the well known semi-analytical model (Low and Lou solution). This is a commonly used benchmark for nonlinear force-free extrapolation codes.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种新的全局非线性无力日冕磁场外推代码在阴阳网格上实现
太阳磁场主导并构造太阳日冕等离子体。详细了解日冕磁场对于理解那里的大多数物理现象非常重要。虽然日冕磁场的直接、常规测量是不可用的,但光球矢量场测量到日冕的场外推是研究日冕场结构和动力学的唯一方法。在这里,我们关注的是全球日冕结构,传统上是用球面网格和天气矢量磁图作为边界条件建模的。我们开发了一种新的代码,可以在球面几何中执行非线性无力磁场外推。我们的新实现基于笛卡尔网格和单球面有限差分网格的完善优化原理。在本工作中,该算法首次能够重建整个日冕的磁场,包括极地区域。由于这些网格在极点上的收敛,在以前的球码版本中采用的有限差分数值格式存在数值效率低下的问题。在我们的新代码中,我们实现了所谓的阴阳架空网格,其结构解决了这个困难。因此,与以前的实现相比,优化算法的速度和精度都得到了提高。我们使用众所周知的半解析模型(Low和Lou解决方案)测试我们的新代码。这是非线性无力外推代码的常用基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Solar Physics
Solar Physics 地学天文-天文与天体物理
CiteScore
5.10
自引率
17.90%
发文量
146
审稿时长
1 months
期刊介绍: Solar Physics was founded in 1967 and is the principal journal for the publication of the results of fundamental research on the Sun. The journal treats all aspects of solar physics, ranging from the internal structure of the Sun and its evolution to the outer corona and solar wind in interplanetary space. Papers on solar-terrestrial physics and on stellar research are also published when their results have a direct bearing on our understanding of the Sun.
期刊最新文献
Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning A New Solar Hard X-ray Image Reconstruction Algorithm for ASO-S/HXI Based on Deep Learning Calibration and Performance of the Full-Disk Vector MagnetoGraph (FMG) on Board the Advanced Space-Based Solar Observatory (ASO-S) Evaluation of Sunspot Areas Derived by Automated Sunspot-Detection Methods Helioseismic Constraints: Past, Current, and Future Observations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1