2015
DOI: 10.1098/rsta.2014.0260
|View full text |Cite
|
Sign up to set email alerts
|

Modelling nanoflares in active regions and implications for coronal heating mechanisms

Abstract: Recent observations from the Hinode and Solar Dynamics Observatory spacecraft have provided major advances in understanding the heating of solar active regions (ARs). For ARs comprising many magnetic strands or sub-loops heated by small, impulsive events (nanoflares), it is suggested that (i) the time between individual nanoflares in a magnetic strand is 500–2000 s, (ii) a weak ‘hot’ component (more than 10 6.6  K) is present, and (iii) nanoflare energies may be as low as a few 10 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

5
64
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
5
1
1

Relationship

1
6

Authors

Journals

citations
Cited by 64 publications
(69 citation statements)
references
References 65 publications
5
64
0
Order By: Relevance
“…Indeed wave heating is also naturally transient; for example, due to the nonlinear coupling between plasma heating and damping. In fact, all heating mechanisms so far proposed will give impulsive heating to some extent [7], but one of the fundamental questions to be resolved is the 'degree of unsteadiness' [18]. Comparison of predictions from modelling with recent observations from missions such as SDO is beginning to shed light on this [8,18].…”
Section: Modelling Coronal Heatingmentioning
confidence: 99%
See 1 more Smart Citation
“…Indeed wave heating is also naturally transient; for example, due to the nonlinear coupling between plasma heating and damping. In fact, all heating mechanisms so far proposed will give impulsive heating to some extent [7], but one of the fundamental questions to be resolved is the 'degree of unsteadiness' [18]. Comparison of predictions from modelling with recent observations from missions such as SDO is beginning to shed light on this [8,18].…”
Section: Modelling Coronal Heatingmentioning
confidence: 99%
“…One approach to observational tests of models is to use scaling laws, investigating how heating rates vary with field strength and loop length [17]. Analysis of differential emission measure distributions is proving a powerful tool for potentially distinguishing between heating mechanisms [8,18]. The presence of non-thermal particles could provide an important signature of energy dissipation mechanisms, often being associated with magnetic reconnection, and recent results from IRIS [19] present exciting evidence, albeit indirect, of their creation in small-scale heating events.…”
Section: (B) Observational Tests Of Coronal Heating Modelsmentioning
confidence: 99%
“…However, because of the small cell sizes needed to resolve the transition region and consequently small time steps demanded by thermal conduction, the use of such models in large parameter space explorations is made impractical by long computational runtimes (Bradshaw & Cargill 2013). We use the popular 0D enthalpy-based thermal evolution of loops (EBTEL) model (Klimchuk et al 2008;Cargill et al 2012aCargill et al , 2012bCargill et al , 2015 in order to efficiently simulate the evolution of a coronal loop over a large parameter space. This model, which has been successfully benchmarked against the 1D hydrodynamic HYDRAD code of Bradshaw & Cargill (2013), computes, with very low computational overhead, time-dependent, spatially averaged loop quantities.…”
Section: Numerical Modelmentioning
confidence: 99%
“…Nanoflare heating can be classified as being either high or low frequency (HF or LF, respectively). In the case of HF heating, t N , the time between successive events, is such that t N =τ cool , where τ cool is a characteristic loop cooling time, and in the case of LF heating t N ?τ cool (Mulu-Moore et al 2011;Warren et al 2011;Bradshaw et al 2012;Reep et al 2013;Cargill et al 2015). Steady heating is just HF heating in the limit  t 0…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation