Discrete particle simulation

To perform simulations of relatively large systems for relatively long times, it is essential to optimize the computational strategy of discrete particle simulations. Obviously, the larger the time step 5t, the more efficient the simulation method. For the soft-sphere model, the maximum value for 5t is dictated by the duration of a contact. Since there are two different spring-dashpot systems in our current model, it is essential to assume that tcontact>n — tcontacUU so that... 

To relate the discrete particle simulations to the KTGFs, it is very useful to analyze the detailed information of the energy evolution in the system. The total energy balance of the system is obtained by calculating all relevant forms of energy as well as the work performed due to the action of external forces. 

Moysey, P.A. and Thompson, M.R., Investigation of Solids Transport in a Single-Screw Extruder Using a 3-D Discrete Particle Simulation, Polym. Eng. ScL, 44, 2203 (2004)... 

Moysey, P. A. and Thompson, M. R., Discrete Particle Simulation of Solids Compaction and Conveying in a Single-Screw Extruder, Polym. Eng. Set, 48, 62 (2008)... 

Y. Tsuji, T. Kawaguchi, T. Tanaka, Discrete particle simulation of two-dimensional fluidized bed, Powder Technol. 77 (1993) 79-87. 

In microporous materials where Knudsen diffusion prevails, De cannot be calculated by solving Fick s law. The use of a discrete particle simulation method such as dynamic MC is appropriate in such cases (Coppens and Malek, 2003 Zalc et al., 2003, 2004). In the Knudsen regime, relatively few gas molecules collide with each other compared with the number of collisions between molecules and pore walls. One of the fundamental assumptions of the Knudsen diffusion is that the direction in which a molecule rebounds from a pore wall is independent of the direction in which it approaches the wall, and is governed by the cosine law the probability d.v that a molecule leaves the surface in the solid angle dm forming an angle 0 with the normal to the surface is... 

Hoomans, B. P. B., Kuipers, J. A. M., and van Swaaij, W. P. M., Discrete particle simulation of a two-dimensional gas-fluidised bed Comparison between a soft sphere and a hard sphere approach. Submitted for publication (1998). 

Hoomans B.P.B et al (1996) Discrete particle simulation of bubble and slug formation in a two-dimensional gas-fluidised bed A hard-sphere approach. Chemical Engineering Science, 51, pp. 99-118... 

These principles ensure correct hydrodynamic behavior of DPD fluid. The advantage of DPD over other methods lies in the possibility of matching the scale of discrete-particle simulation to the dominant spatio-temporal scales of the entire system. For example, in MD simulation the timescales associated with evolution of heavy colloidal particles are many orders of magnitude larger than the temporal evolution of solvent particles. If the solvent molecules are coarse-grained into DPD droplets, they evolve much more slowly and are able to match the time scales close to those associated with the colloidal particles. 

V. A Concept of Problem-Solving Environment for Discrete-Particle Simulations. 769... 

V. A CONCEPT OF PROBLEM-SOLVING ENVIRONMENT FOR DISCRETE-PARTICLE SIMULATIONS... 

On the other hand, the LBG method can capture both mesoscopic and macroscopic scales even larger than those that can be modeled by discrete-particle methods. This advantage is due to computational simplicity of the method, which comes from coarse-grained discretization of both the space and time and drastic simplification of collision rules between particles. We can look at the validity of these simplifications by comparing them with more realistic discrete-particles simulation. We regard both DPD and LBG as being complementary computational tools for modeling the slow dynamics in porous media over wide spatio-temporal scales. 

Zhu, H.P. Zhou, Z.Y. Yang, R.Y. Yu, A.B. (2007) Discrete particle simulation of particulate systems Theoretical developments. Chemical Engineering Science 62, 3378-3396. 

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