Evolution, structure and topology of self-generated turbulent reconnection layers

Raheem Beg (Lead / Corresponding author), Alexander Russell, Gunnar Hornig

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Abstract

We present a 3D MHD simulation of two merging flux ropes exhibiting self-generated and self-sustaining turbulent reconnection (SGTR) that is fully 3D and fast. The exploration of SGTR is crucial for understanding the relationship between MHD turbulence and magnetic reconnection in astrophysical contexts including the solar corona. We investigate the pathway towards SGTR and apply novel tools to analyse the structure and topology of the reconnection layer. The simulation proceeds from 2.5D Sweet-Parker reconnection to 2.5D nonlinear tearing, followed by a dynamic transition to a final SGTR phase that is globally quasi-stationary. The transition phase is dominated by a kink instability of a large "cat-eye" flux rope and the proliferation of a broad stochastic layer. The reconnection layer has two general characteristic thickness scales which correlate with the reconnection rate and differ by a factor of approximately six: an inner scale corresponding with current and vorticity densities, turbulent fluctuations, and outflow jets, and an outer scale associated with field line stochasticity. The effective thickness of the reconnection layer is the inner scale of the effective reconnection electric field produced by turbulent fluctuations, not the stochastic thickness. The dynamics within the reconnection layer are closely linked with flux rope structures that are highly topologically complicated. Explorations of the flux rope structures and distinctive intermediate regions between the inner core and stochastic separatrices ("SGTR wings") are potentially key to understanding SGTR. The study concludes with a discussion on the apparent dualism between plasmoid-mediated and stochastic perspectives on SGTR.
Original languageEnglish
Article number94
Number of pages32
JournalAstrophysical Journal
Volume940
Issue number1
DOIs
Publication statusPublished - 23 Nov 2022

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