Exoplanets orbiting M-dwarfs within habitable zones are exposed to stellar environments more extreme than that terrestrial planets experience in our Solar System, which can significantly impact the atmospheres of the exoplanets and affect their habit
ability and sustainability. This study provides the first prediction of hot oxygen corona structure and the associated photochemical loss from a 1 bar CO2-dominated atmosphere of a Venus-like rocky exoplanet, where dissociative recombination of O2+ ions is assumed to be the major source reaction for the escape of neutral O atoms and formation of the hot O corona (or exospheres) as on Mars and Venus. We employ a 3D Monte Carlo code to simulate the exosphere of Proxima Centauri b (PCb) based on the ionosphere simulated by a 3D magnetohydrodynamic model. Our simulation results show that variability of the stellar wind dynamic pressure over one orbital period of PCb does not affect the overall spatial structure of the hot O corona but contributes to the change in the global hot O escape rate that varies by an order of magnitude. The escape increases dramatically when the planet possesses its intrinsic magnetic fields as the ionosphere becomes more extended with the presence of a global magnetic field. The extended hot O corona may lead to a more extended H exosphere through collisions between thermal H and hot O, which exemplifies the importance of considering nonthermal populations in exospheres to interpret future observations.
Space plasma simulations have seen an increase in the use of magnetohydrodynamic (MHD) with embedded Particle-in-Cell (PIC) models. This combined MHD-EPIC algorithm simulates some regions of interest using the kinetic PIC method while employing the M
HD description in the rest of the domain. The MHD models are highly efficient and their fluid descriptions are valid for most part of the computational domain, thus making large-scale global simulations feasible. However, in practical applications, the regions where the kinetic effects are critical can be changing, appearing, disappearing and moving in the computational domain. If a static PIC region is used, this requires a much larger PIC domain than actually needed, which can increase the computational cost dramatically. To address the problem, we have developed a new method that is able to dynamically change the region of the computational domain where a PIC model is applied. We have implemented this new MHD with Adaptively Embedded PIC (MHD-AEPIC) algorithm using the BATS-R-US Hall MHD and the Adaptive Mesh Particle Simulator (AMPS) as the semi-implicit PIC models. We describe the algorithm and present a test case of two merging flux ropes to demonstrate its accuracy. The implementation uses dynamic allocation/deallocation of memory and load balancing for efficient parallel execution. We evaluate the performance of MHD-AEPIC compared to MHD-EPIC and the scaling properties of the model to large number of computational cores.
