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A variable time step self-consistent mean field DSMC model for three-dimensional environments

MPG-Autoren
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Schullian,  Otto
Markus Miettinen, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Antila,  Hanne
Markus Miettinen, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Zitation

Schullian, O., Antila, H., & Heazlewood, B. R. (2022). A variable time step self-consistent mean field DSMC model for three-dimensional environments. The Journal of Chemical Physics, 156(12): 124309. doi:10.1063/5.0083033.


Zitierlink: https://hdl.handle.net/21.11116/0000-000A-53B8-9
Zusammenfassung
A self-consistent mean field direct simulation Monte Carlo (SCMFD) algorithm was recently proposed for simulating collision environments for a range of one-dimensional model systems. This work extends the one-dimensional SCMFD approach to three dimensions and introduces a variable time step (3D-vt-SCMFD), enabling the modeling of a considerably wider range of different collision environments. We demonstrate the performance of the augmented method by modeling a varied set of test systems: ideal gas mixtures, Poiseuille flow of argon, and expansion of gas into high vacuum. For the gas mixtures, the 3D-vt-SCMFD method reproduces the properties (mean free path, mean free time, collision frequency, and temperature) in excellent agreement with theoretical predictions. From the Poiseuille flow simulations, we extract flow profiles that agree with the solution to the Navier–Stokes equations in the high-density limit and resemble free molecular flow at low densities, as expected. The measured viscosity from 3D-vt-SCMF is ∼15% lower than the theoretical prediction from Chapman–Enskog theory. The expansion of gas into vacuum is examined in the effusive regime and at the hydrodynamic limit. In both cases, 3D-vt-SCMDF simulations produce gas beam density, velocity, and temperature profiles in excellent agreement with analytical models. In summary, our tests show that 3D-vt-SCMFD is robust and computationally efficient, while also illustrating the diversity of systems the SCMFD model can be successfully applied to.