Boosting the convergence of low-variance DSMC by GSIS
Liyan Luo, Qi Li, Lei Wu

TL;DR
This paper introduces a coupling of low-variance DSMC with GSIS to significantly accelerate convergence to steady state in near-continuum rarefied gas flow simulations, reducing computational costs by two orders of magnitude.
Contribution
It develops a stochastic-deterministic coupling method based on the BGK model, integrating macroscopic synthetic equations with kinetic simulations to improve efficiency.
Findings
Convergence to steady state is greatly accelerated.
Computational cost is reduced by two orders of magnitude.
Method effectively handles near-continuum flow regimes.
Abstract
The low-variance direct simulation Monte Carlo (LVDSMC) is a powerful method to simulate low-speed rarefied gas flows. However, in the near-continuum flow regime, due to limitations on the time step and spatial cell size, it takes plenty of time to find the steady-state solution. Here we remove these deficiencies by coupling the LVDSMC with the general synthetic iterative scheme (GSIS) which permits the simulation at the hydrodynamic scale rather than the much smaller kinetic scale. As a proof of concept, we propose the stochastic-deterministic coupling method based on the Bhatnagar-Gross-Krook kinetic model. First, macroscopic synthetic equations are derived exactly from the kinetic equation, which not only contain the Navier-Stokes-Fourier constitutive relation, but also encompass the higher-order terms describing the rarefaction effects. Then, the high-order terms are extracted from…
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Taxonomy
TopicsGas Dynamics and Kinetic Theory · Fluid Dynamics and Turbulent Flows · Particle Dynamics in Fluid Flows
