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arXiv · 1703.02808

Identification of coronal heating events in 3D simulations

Abstract

The solar coronal heating problem is an open question since 1939. One proposed model for the transport and release of mechanical energy generated in the sub-phorospheric layers and photosphere is the nanoflare model that incorporates Ohmic heating which releases a part of the energy stored in the magnetic field via magnetic reconnection. The problem with the verification of this model is that we cannot resolve observationally small scale events. Histograms of observable characteristics of flares, show powerlaw behavior, for both energy release rate, size and total energy. Depending on the powerlaw index of the energy release, nanoflares might be an important candidate for coronal heating; we seek to find that index. In this paper, we employ a numerical 3D-MHD simulation produced by the numerical code Bifrost, and a new technique to identify the 3D heating events at a specific instant. The quantity we explore is the Joule heating, which is explicitly correlated with the magnetic reconnection because depends on the curl of the magnetic field. We are able to identify 4136 events in a volume $24 \times 24 \times 9.5 \ \textrm{Mm}^3$ (i.e. $768 \times 786 \times 331$ grid cells) of a specific snapshot. We find a powerlaw slope of the released energy per second, and two powerlaw slopes of the identified volume. The identified energy events do not represent all the released energy, but of the identified events, the total energy of the largest events dominate the energy release. Most of the energy release happens in the lower corona, while heating drops with height. We find that with a specific identification method that large events can be resolved into smaller ones, but at the expense of the total identified energy releases. The energy release which cannot be identified as an event favours a low energy release mechanism.

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BibTeXRIS

Charalambos Kanella, Boris V. Gudiksen. 2017-03-08. Identification of coronal heating events in 3D simulations. https://doi.org/10.1051/0004-6361/201730748

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