SIP-IFVM: A Time-evolving Coronal Model with an Extended Magnetic Field Decomposition Strategy - Wang, Haopeng; Yang, Liping; Poedts, Stefaan; Lani, Andrea; Zhou, Yuhao; Gao, Yuhang; Linan, Luis; Lv, Jiakun; Baratashvili, Tinatin; Guo, Jinhan; Lin, Rong; Su, Zhan; et al. (Institute of Physics Publishing, Inc., 2025)
Time-evolving magnetohydrodynamic (MHD) coronal modeling, driven by a series of time-dependent photospheric magnetograms, represents a new generation of coronal simulations. This approach offers more realistic results than traditional steady coronal models constrained by a static magnetogram. However, its practical application is significantly limited by the low computational efficiency and poor numerical stability in solving low-beta issues common in coronal simulations. To address this, we propose an extended magnetic field decomposition strategy and successfully implement it in an implicit MHD coronal model. The traditional decomposition strategies split the magnetic field into a time-invariant potential field and a time-dependent component B1. This works well for quasi-steady-state coronal simulations where divided by B1 divided by is typically small. However, when the inner-boundary magnetic field evolves, divided by B1 divided by can grow significantly, and its discretization errors often lead to nonphysical negative thermal pressure, ultimately causing the simulation to crash. In the extended magnetic field decomposition strategy, we split the magnetic field into a temporally piecewise-constant field and a time-varying component, B1. This effectively keeps divided by B1 divided by consistently small throughout the simulations and performs well in solving time-evolving low-beta issues, thereby outperforming traditional methods. We incorporate this improved strategy into our implicit MHD coronal model and apply it to simulate the evolution of coronal structures within 0.1 au over two solar-maximum Carrington rotations. The results show that this coronal model effectively captures observational features and performs more than 80 times faster than real-time evolutions using only 192 CPU cores, making it well suited for practical applications in simulating the time-evolving corona.