scholarly journals Electrophoretic properties of highly charged colloids: A hybrid molecular dynamics∕lattice Boltzmann simulation study

2007 ◽  
Vol 126 (6) ◽  
pp. 064907 ◽  
Author(s):  
Apratim Chatterji ◽  
Jürgen Horbach
Soft Matter ◽  
2020 ◽  
Vol 16 (2) ◽  
pp. 523-533 ◽  
Author(s):  
Byoungjin Chun ◽  
Taehyung Yoo ◽  
Hyun Wook Jung

Computer simulations of colloidal film drying including hydrodynamic interactions between the particles.


2020 ◽  
Vol 227 ◽  
pp. 115925 ◽  
Author(s):  
Bei Wei ◽  
Jian Hou ◽  
Michael C. Sukop ◽  
Qingjun Du ◽  
Huiyu Wang

2007 ◽  
Vol 18 (04) ◽  
pp. 667-675 ◽  
Author(s):  
S. SUCCI ◽  
A. A. MOHAMMAD ◽  
J. HORBACH

In a recent work, a dense fluid flow across a nanoscopic thin plate was simulated by means of Molecular Dynamics (MD) and Lattice Boltzmann (LB) methods. It was found that in order to recover quantitative agreement with MD results, the LB simulation must be pushed down to sub–nanoscopic scales, i.e. fractions of the range of molecular interactions. In this work, we point out that in this sub–nanoscopic regime, the LB method works outside the hydrodynamic limit at the level of a single cell spacing. A quantitative comparison with the Navier–Stokes (NS) solution shows however that LB and NS results are quite similar, thereby indicating that, apart for a small region past the plate, this nanoflow is still well described by hydrodynamic equations.


Author(s):  
Sauro Succi

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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