WeChat Mini Program
Old Version Features

Effect of Ionization and Recombination on the Evolution of the Harris-type Current Sheet in Partially Ionized Plasmas

The Astrophysical Journal(2019)

Kyoto Univ

Cited 7|Views10
Abstract
Two-dimensional magnetohydrodynamics (MHD) simulations, treating plasma and neutral populations (hereafter, neutrals) as two separate components of the magneto-fluid, are performed in order to investigate the effect of ionization and recombination (or I/R) on the time evolution of the Harris-type current sheet in partially ionized plasmas. Our MHD simulations, including the effect of ambipolar diffusion (arising due to ion-neutral interactions) along with the I/R, show that the current sheet thinning occurs due to the diffusion of neutral particles from the current sheet. In addition to ambipolar diffusion, frictional heating also appears and affects the evolution of the current sheet. In a current sheet that is formed in a partially ionized plasma, the neutral population tries to spread outward and the plasma population tries to converge toward the center of the current sheet, and the overall process is influenced by the I/R. One of the important feature that is captured in our 2D simulations is that the escape of neutrals from the current sheet is sometimes suppressed due to the increase in ionization rate at the center of the current sheet, for the case of collisional I/R. As long as the ionization degree is kept low inside the current sheet, the current sheet thinning and elongation takes place and the current sheet becomes unstable due to the tearing-mode and plasmoid formation. The ion-neutral interactions coupled with I/R and the dynamics of the magnetic reconnection play an important role in plasmoid-mediated reconnection, therefore, the present study on the current sheet thinning and plasmoid formation could serve as a key for understanding bursty and intermittent plasma ejections observed in the solar chromosphere.
More
Translated text
Key words
magnetic reconnection,magnetohydrodynamics (MHD),methods: numerical
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined