Efficient magnetohydrodynamic modelling of the time-evolving corona by COCONUT
Abstract
Compared to quasi-steady-state corona models that are constrained by a time-invariant magnetogram over a CR period, time-evolving corona models driven by time-varying photospheric magnetograms are more realistic and can maintain more useful information to accurately describe solar wind evolution and forecast CME propagation. This paper demonstrate that time-evolving corona simulations can be performed efficiently and accurately using an implicit method with relatively large time steps. We also evaluate differences between coronal structures captured by time-evolving and quasi-steady simulations over a CR period during solar minimum. We used a series of hourly updated photospheric magnetograms to drive the evolution of coronal structures from the solar surface to during two CRs around the 2019 eclipse in an inertial coordinate system. We compare the time-evolving and quasi-steady simulations to demonstrate that the differences in these two types of coronal modelling can be obvious even for a solar minimum. The relative differences in radial velocity and density can be over and at 20 during one CR period. We also evaluated the impact of time steps on the simulation results. Using a time step of approximately 10 minutes balances efficiency and necessary numerical stability and accuracy for time-evolving corona simulations around solar minima, with coronal evolution during a full CR simulated within only 9 hours (using 1080 CPU cores for 1.5M grid cells). The simulation results demonstrate that time-evolving MHD coronal simulations can be performed efficiently and accurately using an implicit method, offering a more realistic alternative to quasi-steady-state simulations. The fully implicit time-evolving corona model thus promises to simulate the time-evolving corona accurately in practical space weather forecasting.
Cite
@article{arxiv.2409.02043,
title = {Efficient magnetohydrodynamic modelling of the time-evolving corona by COCONUT},
author = {Haopeng Wang and Stefaan Poedts and Andrea Lani and Michaela Brchnelová and Tinatin Baratashvili and Luis Linan and Fan Zhang and Dawei Hou and Yuhao Zhou},
journal= {arXiv preprint arXiv:2409.02043},
year = {2025}
}
Comments
accept, 14 pages, 7 figures, 4 movies