Application of the Monte Carlo Method in Modeling Dusty Gas, Dust in Plasma, and Energetic Ions in Planetary, Magnetospheric, and Heliospheric Environments

• Published in: Journal of geophysical research. Space physics 2021-02, Vol.126 (2), p.n/a
• Main Authors: Tenishev, Valeriy, Shou, Yinsi, Borovikov, Dmitry, Lee, Yuni, Fougere, Nicolas, Michael, Adam, Combi, Michael R.
• Format: Article
• Language: English
• ISSN: 2169-9380; 2169-9402
• DOI: 10.1029/2020JA028242

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. Key Points Kinetic modeling is necessary for understanding various phenomena of planetary and space physics AMPS is a versatile and well‐tested code with a long track record of application to simulate various planetary and heliophysics phenomena The paper demonstrates the modeling capabilities of AMPS by presenting several examples of the code's prior application