Monte Carlo Aggregation Code (MCAC) Part 1: Fundamentals

[Display omitted] •Accurate MC simulations of agglomeration is achieved based on LD simulations.•A persistent distance and its time step are introduced for Monte Carlo simulations.•A new and unrestricted probability of particles displacement is introduced. The application of Monte Carlo methods to s...

Full description

Saved in:
Bibliographic Details
Published in:Journal of colloid and interface science 2020-06, Vol.569, p.184-194
Main Authors: Morán, J., Yon, J., Poux, A.
Format: Article
Language:English
Subjects:
Agglomeration
Coagulation
Engineering Sciences
Langevin dynamics
Monte Carlo
Reactive fluid environment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:[Display omitted] •Accurate MC simulations of agglomeration is achieved based on LD simulations.•A persistent distance and its time step are introduced for Monte Carlo simulations.•A new and unrestricted probability of particles displacement is introduced. The application of Monte Carlo methods to simulate the agglomeration of suspended nanoparticles is currently limited to specific agglomeration regimes with reduced accuracy in terms of the particle’s physical residence time. The definition of specific particles persistent distance, its corresponding time step and subsequent probabilities for particle displacements may improve the accuracy of this method. To solve these issues, a new persistent distance and its corresponding time step based on Langevin dynamics simulations are introduced. Additionally, a probability of particle displacements, not restricted to a specific agglomeration regime, is introduced. All the modifications are validated by comparison with Langevin dynamics simulations. Finally, the above mentioned modifications considerably improve the accuracy of Monte Carlo methods to predict the dynamics and agglomeration of suspended nanoparticles.
ISSN:0021-9797
1095-7103
DOI:10.1016/j.jcis.2020.02.039