• Bulletin of the Korean Chemical Society
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• v.31 no.8
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• pp.2402-2405
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• 2010

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1922-1927
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• 2008

In the present study, deposition of discrete and small particles, which diameter is less than $1{\mu}m$, on a filter element was simulated by stochastic method. Trajectory of each particle was numerically solved by Langevin equation and Brownian random motion was treated by Brownian dynamics. Lattice Boltzmann method (LBM) was used to solve flow field around the filter collector and deposit layer. Interaction between flow field and deposit layer was obtained from a converged solution from an inner-loop calculation. Simulation method is properly validated and collection efficiency due to different filtration parameters are examined and discussed. Morphology of deposit layer and its evolution was visualized in terms of the particle size. The particle loaded effect on collection efficiency was also discussed.

• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.169-182
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• 2014

이 연구에서는 Lennard-Jones (LJ) particle을 이용하여 상분리 현상을 이해하기 위한 컴퓨터 시뮬레이션 연구를 수행하였다. 초기에 균일하게 분포되어 있는 LJ 입자들을 시뮬레이션 하면 상대적으로 dense phase와 dilute phase로 상분리 현상이 일어나게 된다. 상분리 현상의 첫 번째 단계를 핵 생성 (nucleation) 이라고 한다. 본 연구에서는 Brownian Dynamics (BD) Simulation과 Molecular Dynamics (MD) Simulation을 이용하여 상평형 그림을 구하고 초기에 일어나는 LJ 입자들의 nucleation rates를 구하였다.

• Journal of the Korean Chemical Society
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• v.51 no.3
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• pp.227-231
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• 2007

We improve Wilehemski-Fixmann theory for intrachain reaction dynamics of a polymer chain by taking into account excluded volume effects between reactive groups in the polymerchain. An approximate analytic expression for the intra-chain reaction dynamics is obtained for Gaussian chain model and compared to Brownian dynamics simulation results. The results of the present theory are in a better agreement to Brownian dynamics simulation results than those calculated by previously reported theories.

• Korea-Australia Rheology Journal
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• v.14 no.4
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• pp.161-174
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• 2002

A new technique for numerical calculation of viscoelastic flow based on the combination of Neural Net-works (NN) and Brownian Dynamics simulation or Stochastic Simulation Technique (SST) is presented in this paper. This method uses a "universal approximator" based on neural network methodology in combination with the kinetic theory of polymeric liquid in which the stress is computed from the molecular configuration rather than from closed form constitutive equations. Thus the new method obviates not only the need for a rheological constitutive equation to describe the fluid (as in the original Calculation Of Non-Newtonian Flows: Finite Elements St Stochastic Simulation Techniques (CONNFFESSIT) idea) but also any kind of finite element-type discretisation of the domain and its boundary for numerical solution of the governing PDE's. As an illustration of the method, the time development of the planar Couette flow is studied for two molecular kinetic models with finite extensibility, namely the Finitely Extensible Nonlinear Elastic (FENE) and FENE-Peterlin (FENE-P) models.P) models.

• Korea-Australia Rheology Journal
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• v.21 no.1
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• pp.1-12
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• 2009

This paper reports the suitability of a domain decomposition technique for the hybrid simulation of dilute polymer solution flows using Eulerian Brownian dynamics and Radial Basis Function Networks (RBFN) based methods. The Brownian Configuration Fields (BCF) and RBFN method incorporates the features of the BCF scheme (which render both closed form constitutive equations and a particle tracking process unnecessary) and a mesh-less method (which eliminates element-based discretisation of domains). However, when dealing with large scale problems, there appear several difficulties: the high computational time associated with the Stochastic Simulation Technique (SST), and the ill-condition of the system matrix associated with the RBFN. One way to overcome these disadvantages is to use parallel domain decomposition (DD) techniques. This approach makes the BCF-RBFN method more suitable for large scale problems.

• Korea-Australia Rheology Journal
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• v.16 no.1
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• pp.1-15
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• 2004

The computation of viscoelastic flow using neural networks and stochastic simulation (CVFNNSS) is developed from the point of view of Eulerian CONNFFESSIT (calculation of non-Newtonian flows: finite elements and stochastic simulation techniques). The present method is based on the combination of radial basis function networks (RBFNs) and Brownian configuration fields (BCFs) where the stress is computed from an ensemble of continuous configuration fields instead of convecting discrete particles, and the velocity field is determined by solving the conservation equations for mass and momentum with a finite point method based on RBFNs. The method does not require any kind of element-type discretisation of the analysis domain. The method is verified and its capability is demonstrated with the start-up planar Couette flow, the Poiseuille flow and the lid driven cavity flow of Hookean and FENE model materials.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.959-964
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• 2010

In this work, equilibrium molecular dynamics (MD) simulations in a microcanonical ensemble are performed to evaluate the friction coefficient of a Brownian particle (BP) in a Lennard-Jones (LJ) solvent. The friction coefficients are determined from the time dependent friction coefficients and the momentum autocorrelation functions of the BP with its infinite mass at various ratios of LJ size parameters of the BP and solvent, ${\sigma}_B/{\sigma}_s$. The determination of the friction coefficients from the decay rates of the momentum autocorrelation functions and from the slopes of the time dependent friction coefficients is difficult due to the fast decay rates of the correlation functions in the momentum-conserved MD simulation and due to the scaling of the slope as 1/N (N: the number of the solvent particle), respectively. On the other hand, the friction coefficient can be determined correctly from the time dependent friction coefficient by measuring the extrapolation of its long time decay to t=0 and also from the decay rate of the momentum autocorrelation function, which is obtained by time integration of the time dependent friction coefficient. It is found that while the friction coefficient increases quadratically with the ratio of ${\sigma}_B/{\sigma}_s$ for all ${\sigma}_B$, for a given ${\sigma}_s$ the friction coefficient increases linearly with ${\sigma}_B$.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.10
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• pp.811-819
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• 2009

In the present study, deposition of discrete and small particles on a filter fiber was simulated by stochastic method. Trajectory of each particle was numerically solved by Langevin equation. And Lattice Boltzmann method (LBM) was used to solve flow field around the filter collector for considering complex shape of deposit layer. Interaction between the flow field and the deposit layer was obtained from a converged solution from an inner-loop calculation. Simulation method is properly validated with filtration theory and collection efficiency due to different filtration parameters are examined and discussed. Morphology of deposit layer and its evolution was visualized in terms of the particle size. The particle loaded effect on collection efficiency was also discussed.

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.21-22
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• 2003

We perform two kinds of computer simulations on polydisperse hard-sphere systems; a molecular-dynamics simulation on atomic systems and a Brownian-dynamics simulation on colloidal suspensions. Analyses of the mean square displacement, the radial distribution function, and the pressure suggest that there exist three phase regions, a liquid phase region, a metastable phase region, and a crystal phase region, where the freezing and melting points are shifted to the values higher than in monodisperse case. It is also shown that the long-time behavior of colloidal suspensions is exactly the same as that of atomic systems.