Application of the Monte Carlo method in modeling dusty gas, dust in plasma, and energetic ions in planetary, magnetospheric, and heliospheric environments.

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https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2020JA028242

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https://doi.org/10.1029/2020JA028242
 http://hdl.handle.net/11603/20973

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UMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II)
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2021-01-13

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journal articles postprints
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Valeriy Tenishev, Yinsi Shou, Dmitry Borovikov, Yuni Lee, Nicolas Fougere, Adam Michael and Michael R. Combi, Application of the Monte Carlo method in modeling dusty gas, dust in plasma, and energetic ions in planetary, magnetospheric, and heliospheric environments, JGR Space Physics, https://doi.org/10.1029/2020JA028242

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Abstract

Typical planetary and planetary satellite exospheres are in non‐equilibrium 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.