Time-dependent boundary conditions for data-driven coronal global and spherical wedge-shaped models

Abstract

The development of an efficient and accurate method for boundary condition treatments is of fundamental importance to data-driven MHD modeling of the global solar corona and solar active region. Particularly, in a 3D spherical wedge-shaped volume, suitable to the numerical study of solar active region, the transverse terms calls for a delicate treatment at the computational domain’s edges and corners, and properly prescribed conditions for boundaries joining regions of different flow properties, so as to take account of the joint effect of incoming and outgoing waves. To provide a solution to the determination of boundary conditions, in this paper a systematic tactics is formulated for handling edges and corners and the prescribed conditions for inner/outer/edge/corner boundaries are proposed through the combination (CBC-ILW) of the time dependent characteristic boundary conditions (CBCs) and the inverse Lax-Wendroff (ILW) procedure. First, a data-driven 3D MHD simulation has been carried out to study the dynamic evolution of the solar corona from 1Rs to 6.7Rs during the period between 16 May and 6 August 2018. The simulated results of the global coronal evolution provide a good comparison with observed coronal images during the period investigated. Then, the validity of 3D MHD-CBC-ILW is verified for a 3D spherical wedge model, by producing almost the same results as those taken out of the global model on a 3D spherical wedge-shaped volume.

Data-driven Radiative Magnetohydrodynamics Simulations with the MURaM Code

“…The so-called "data-driven" model solves the continuous temporal evolution of the magnetic field in the domain in response to the change in the magnetic field at the boundary and can be done with MF (e.g., Cheung & DeRosa 2012;Pomoell et al 2019) or MHD (e.g., Wu et al 2006;Inoue et al 2018). The data-driven method has been used to investigate (for example) successive small eruptions (Kaneko et al 2021) and jets (Cheung et al 2015); the emergence (Cheung & DeRosa 2012), evolution (Hayashi et al 2018(Hayashi et al , 2019, and disposal (Mackay et al 2011) of active regions; the global coronal magnetic field (Fisher et al 2015;Weinzierl et al 2016;Hoeksema et al 2020;Hayashi et al 2022;Feng et al 2023); and the heliospheric impact of CMEs (Jin et al 2018). In recent years, data-driven simulations have become a powerful tool to model the evolution of flareproductive active regions (Toriumi & Wang 2019;Toriumi 2022), which has been done by Gibb et al (2014), Jiang et al (2016aJiang et al ( , 2016b, Price et al (2019Price et al ( , 2020, He et al (2020), Kilpua et al (2021), Yardley et al (2021), and Lumme et al (2022).…”