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Application of the Monte Carlo Method in Modeling Dusty Gas, Dust in Plasma, and Energetic Ions in Planetary, Magnetospheric, and Heliospheric Environments
Author(s) -
Tenishev Valeriy,
Shou Yinsi,
Borovikov Dmitry,
Lee Yuni,
Fougere Nicolas,
Michael Adam,
Combi Michael R.
Publication year - 2021
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1029/2020ja028242
Subject(s) - physics , direct simulation monte carlo , monte carlo method , distribution function , boltzmann equation , computational physics , cosmic ray , statistical physics , planetary science , astrobiology , astronomy , dynamic monte carlo method , statistics , mathematics , quantum mechanics
Typical planetary and planetary satellite exospheres are in nonequilibrium conditions, which means that a distribution function that describes these environments is far from Maxwellian. It is even more true when considering transportation of energetic ions in planetary magnetospheres, making it necessary to solve the Boltzmann equation in order to capture kinetic effects when modeling evolution of the distribution function describing such environments. Among various numerical methods, the Monte Carlo approach is one of the most used one for solving kinetic equations. That is because of the relative simplicity of implementing and a high degree of flexibility in including new physical processes specific to a particular simulated environment. Adaptive Mesh Particle Simulator (AMPS) was developed as a general‐purpose code for solving the Boltzmann equation in conditions typical for planetary and planetary satellite exospheres. Later, the code was generalized for modeling dusty gas, dust, and plasma, and for simulating transportation of solar energetic particles and galactic cosmic rays in planetary magnetospheres. Here, we present a brief overview of the design, list the implemented physics models, and outline the modeling capabilities of AMPS. The latter is supported by several examples of prior applications of the code.