Novel Hybrid Hard Sphere Model for Direct Simulation Monte Carlo Computations

2018 ◽  
Vol 32 (1) ◽  
pp. 156-160 ◽  
Author(s):  
Jin Li ◽  
Xiangren Geng ◽  
Jianqiang Chen ◽  
Dingwu Jiang
Keyword(s):  
Monte Carlo ◽  
Hard Sphere ◽  
Direct Simulation Monte Carlo ◽  
Hard Sphere Model ◽  
Direct Simulation ◽  
Sphere Model ◽  
Simulation Monte Carlo

DIRECT SIMULATION MONTE CARLO FOR DENSE HARD SPHERES

2013 ◽  
Vol 25 (01) ◽  
pp. 1340023 ◽  
Author(s):  
LIU CHAO ◽  
SANG KYU KWAK ◽  
SANTOSH ANSUMALI
Keyword(s):  
Monte Carlo ◽  
Hard Sphere ◽  
Hard Spheres ◽  
Direct Simulation Monte Carlo ◽  
Direct Simulation ◽  
Enskog Theory ◽  
Dsmc Method ◽  
Dense System ◽  
Simulation Monte Carlo ◽  
Excellent Accuracy

We propose a modified direct simulation Monte Carlo (DSMC) method, which extends the validity of DSMC from rarefied to dense system of hard spheres (HSs). To assess this adapted method, transport properties of hard-sphere (HS) systems have been predicted both at dense states as well as dilute, and we observed the excellent accuracy over existing DSMC-based algorithms including the Enskog theory. The present approach provides an intuitive and systematic way to accelerate molecular dynamics (MD) via mesoscale approach.

Numerical Methods for the Kinetic Theory of Fluids

2018 ◽  
Author(s):  
Sauro Succi
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Numerical Methods ◽  
Kinetic Theory ◽  
Computational Efficiency ◽  
Lattice Boltzmann ◽  
Direct Simulation Monte Carlo ◽  
Particle Methods ◽  
Direct Simulation ◽  
Simulation Monte Carlo

This chapter provides a bird’s eye view of the main numerical particle methods used in the kinetic theory of fluids, the main purpose being of locating Lattice Boltzmann in the broader context of computational kinetic theory. The leading numerical methods for dense and rarified fluids are Molecular Dynamics (MD) and Direct Simulation Monte Carlo (DSMC), respectively. These methods date of the mid 50s and 60s, respectively, and, ever since, they have undergone a series of impressive developments and refinements which have turned them in major tools of investigation, discovery and design. However, they are both very demanding on computational grounds, which motivates a ceaseless demand for new and improved variants aimed at enhancing their computational efficiency without losing physical fidelity and vice versa, enhance their physical fidelity without compromising computational viability.

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