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Simulations of Stellar Magnetoconvection using the Radiative MHD Code `StellarBox'

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arxiv 1507.07999 v1 pith:HGPQANVK submitted 2015-07-29 astro-ph.SR

Simulations of Stellar Magnetoconvection using the Radiative MHD Code `StellarBox'

classification astro-ph.SR
keywords codemagneticstarsconvectionradiativeresultsstructuresatmospheres
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Realistic numerical simulations, i.e., those that make minimal use of ad hoc modeling, are essential for understanding the complex turbulent dynamics of the interiors and atmospheres of the Sun and other stars and the basic mechanisms of their magnetic activity and variability. The goal of this paper is to present a detailed description and test results of a compressible radiative MHD code, `StellarBox', specifically developed for simulating the convection zones, surface, and atmospheres of the Sun and moderate-mass stars. The code solves the three-dimensional, fully coupled compressible MHD equations using a fourth-order Pad\'e spatial differentiation scheme and a fourth-order Runge-Kutta scheme for time integration. The radiative transfer equation is solved using the Feautrier method for bi-directional ray tracing and an opacity-binning technique. A specific feature of the code is the implementation of subgrid-scale MHD turbulence models. The data structures are automatically configured, depending on the computational grid and the number of available processors, to achieve good load balancing. We present test results and illustrate the code's capabilities for simulating the granular convection on the Sun and a set of main-sequence stars. The results reveal substantial changes in the near-surface turbulent convection in these stars, which in turn affect properties of the surface magnetic fields. For example, in the solar case initially uniform vertical magnetic fields tend to self-organize into compact (pore-like) magnetic structures, while in more massive stars such structures are not formed and the magnetic field is distributed more-or-less uniformly in the intergranular lanes.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Developing Machine Learning Models of Subgrid Turbulent Transport for Quiet Sun 3D Radiative Hydrodynamic Simulations

    astro-ph.SR 2026-07 conditional novelty 5.5

    A tuned 3DCNN recovers Reynolds stress tensor components from filtered quiet-Sun velocity and density fields with ~31% lower RMSE on diagonals and ~8% on off-diagonals versus physics baselines.

  2. Cluster-Weighted Training of Deep Surrogate Models for Subgrid Turbulent Transport

    astro-ph.SR 2026-07 conditional novelty 5.0

    K-Means cluster-weighted training cuts average MSE 34% and lifts R² from 0.54 to 0.80 for a 3D CNN predicting Reynolds stresses on StellarBox quiet-Sun data.