Correlated Scattering and Clustering of Very Dense Random Spherical Particles Using Monte-Carlo Numerical Realization

2006 ◽  
Author(s):  
Ya-Qiu Jin ◽  
Qing Yang
Keyword(s):  
Monte Carlo ◽  
Numerical Realization ◽  
Spherical Particles

scholarly journals Clusters Formed by Dumbbell-like One-Patch Particles Confined in Thin Systems

2021 ◽  
Author(s):  
Masahide Sato
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Particle Interaction ◽  
Three Dimensional ◽  
The Other ◽  
Spherical Particles ◽  
Dimensionless Distance ◽  
Large Island ◽  
Cluster Shape

Abstract Performing isothermal-isochoric Monte Carlo simulations, I examine the types of clusters that dumbbell-like one–patch particles form in thin space between two parallel walls, assuming that each particle is synthesized through the merging of two particles, one non-attracting and the other attracting for which, for example, the inter-particle interaction is approximated by the DLVO model. The shape of these dumbbell-like particles is controlled by the ratio of the diameters q of the two spherical particles and by the dimensionless distance l between them. Using a modified Kern–Frenkel potential, I examine the dependence of the cluster shape on l and q. Large island-like clusters are created when q < 1. With increasing q, the clusters become chain-like. When q increases further, elongated clusters and regular polygonal clusters are created. In hte simulations, the cluster shape becomes three-dimensional with increasing l because the thickness of the thin system increases proportionally to l.

scholarly journals Clusters formed by dumbbell-like one-patch particles confined in thin systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masahide Sato
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Particle Interaction ◽  
Three Dimensional ◽  
The Other ◽  
Spherical Particles ◽  
Dimensionless Distance ◽  
Large Island ◽  
Cluster Shape

AbstractPerforming isothermal-isochoric Monte Carlo simulations, I examine the types of clusters that dumbbell-like one–patch particles form in thin space between two parallel walls, assuming that each particle is synthesized through the merging of two particles, one non-attracting and the other attracting for which, for example, the inter-particle interaction is approximated by the DLVO model . The shape of these dumbbell-like particles is controlled by the ratio of the diameters q of the two spherical particles and by the dimensionless distance l between these centers. Using a modified Kern–Frenkel potential, I examine the dependence of the cluster shape on l and q. Large island-like clusters are created when $$q<1$$ q < 1 . With increasing q, the clusters become chain-like . When q increases further, elongated clusters and regular polygonal clusters are created. In the simulations, the cluster shape becomes three-dimensional with increasing l because the thickness of the thin system increases proportionally to l.

Small-angle scattering from collections of interacting hard ellipsoids of revolution studied by Monte Carlo simulations and other methods of statistical thermodynamics

1999 ◽  
Vol 32 (5) ◽  
pp. 917-923 ◽  
Author(s):  
Bo Sjöberg
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Small Angle ◽  
Chemical Potential ◽  
Compressibility Factor ◽  
Particle Method ◽  
Spherical Particles ◽  
Angle Scattering ◽  
Approximation Formulas ◽  
Experimental Values

Computer simulations using Monte Carlo methods are used to investigate the effects of interparticle correlations on small-angle X-ray and neutron scattering from moderate or highly concentrated systems of ellipsoids of revolution. Both oblate and prolate ellipsoids, of varying eccentricities and concentrations, are considered. The advantage with Monte Carlo simulation is that completely general models, both regarding particle shapes and interaction potentials, can be considered. Equations are also given that relate the nonideal part of the chemical potential, βμni, with the scattering at zero angle,I(0), and the compressibility factor,z. The quantity βμnican be obtained during the Monte Carlo simulations by using Widom's test-particle method. For spherical particles, the simulations are compared with approximation formulas based on the Percus–Yevick equation. A method is also suggested for the calculation of both βμniandzfrom experimental values ofI(0) recorded as a function of concentration.

Methods of simulating random patterns of non-spherical objects and their application

1996 ◽  
Vol 28 (02) ◽  
pp. 342-343
Author(s):  
Masaharu Tanemura
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Spatial Patterns ◽  
Hard Core ◽  
Spherical Particles ◽  
Finite Region ◽  
Uniform Sampling ◽  
Regular Manner ◽  
Random Patterns

We consider two mechanisms for simulating spatial patterns of hard-core non-spherical particles, namely the random sequential packing (RSP) and the Markov chain Monte Carlo (MCMC) procedures. The former is described as follows: we put a particle one-by-one into a finite region by sampling its location x and direction θ uniformly at random; if it does not overlap with other particles put before, it is put successfully, otherwise, we discard it and try another uniform sampling of (x, θ); by repeating the above, we can obtain a set of non-overlapping particles. The MCMC procedure is the following: we first give a certain non-overlapping pattern of non-spherical particles prepared in a random or a regular manner; then we select a particle and sample its new trial location x and direction θ at random; if the new sample (x, θ) is accepted, i.e. it does not overlap with other particles, the selected particle is moved to the new ‘position’, otherwise the particle is retained at the old position; by repeating the above, a series of a set of non-overlapping particles is generated.

Monte Carlo simulation of fluorescence correlation spectroscopy data

2011 ◽  
Vol 76 (3) ◽  
pp. 207-222 ◽  
Author(s):  
Peter Košovan ◽  
Filip Uhlík ◽  
Jitka Kuldová ◽  
Miroslav Štěpánek ◽  
Zuzana Limpouchová ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Diffusion Coefficients ◽  
Fluorescence Correlation Spectroscopy ◽  
Rotational Diffusion ◽  
Correlation Spectroscopy ◽  
Analytical Models ◽  
Spherical Particles ◽  
Fluorescence Signal ◽  
Fluorescence Correlation

We employed the Monte Carlo simulation methodology to emulate the diffusion of fluorescently labeled particles and understand the source of differences between values of diffusion coefficients (and consequently hydrodynamic radii) of fluorescently labeled nanoparticles measured by fluorescence correlation spectroscopy (FCS) and dynamic light scattering (DLS). We used the simulation program developed in our laboratory and studied the diffusion of spherical particles of different sizes, which are labeled on their surface. In this study, we focused on two complicating effects: (i) multiple labeling and (ii) rotational diffusion which affect the fluorescence signal from large particles and hinder the analysis of autocorrelation functions according to simple analytical models. We have shown that the fluorescence fluctuations can be well fitted using the analytical model for small point-like particles, but the obtained parameters deviate in some cases significantly from the real ones. It means that the current data treatment yields apparent values of diffusion coefficients and other parameters only and the interpretation of experimental results for systems of particles with sizes comparable to the size of the active illuminated volume requires great care and precaution.

scholarly journals Computer Simulation of Volume Shrinkage after Mixing Container Media Components

1993 ◽  
Vol 118 (6) ◽  
pp. 757-761 ◽  
Author(s):  
Silvia Burés ◽  
Franklin A. Pokorny ◽  
David P. Landau ◽  
Alan M. Ferrenberg
Keyword(s):  
Experimental Data ◽  
Computer Simulation ◽  
Particle Size ◽  
Monte Carlo ◽  
Particle Size Distribution ◽  
Experimental System ◽  
Fine Sand ◽  
Spherical Particles ◽  
Particle Sizes ◽  
Coarse Particles

A FORTRAN computer program was developed to simulate packing of spherical particles via a Monte Carlo procedure. Shrinkage in volume upon mixing different particle sizes was studied and simulated results were compared with experimental data. Maximum experimental shrinkage was obtained when the proportion of coarse particles of pine bark and sand mixtures ranged from 50% to 70% of the volume. Experimental shrinkage of a mixture of coarse and fine sand was closely reproduced by means of simulation. Particle size distribution appears to be the most important factor in relation to shrinkage and also in the establishment of relationships between the simulated and the experimental system.

Modeling the transfer of ionizing radiation flux in multilayered barriers

2018 ◽  
pp. 143-157
Author(s):  
A. V. Ostrik ◽  
I. V. Bugay
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ionizing Radiation ◽  
Analytical Method ◽  
Radiation Flux ◽  
Numerical Realization ◽  
Method Of Calculation

The variant of Monte Carlo method for calculation of transfer of ionizing radiation having quanta energy of Eph=1-200keV is considered. The hybrid numerical and analytical method of calculation is offered. At numerical realization of each trajectory photo-absorption of quanta is considered analytically and other types of interactions (Compton and Relay scatterings, fluorescence) are realized in a random way. Calculations results for transfer of radiation having Plank spectrum are given for flat multilayered barriers.

scholarly journals Monte Carlo simulation of coagulation in discrete particle-size distributions. Part 2. Interparticle forces and the quasi-stationary equilibrium hypothesis

1984 ◽  
Vol 143 ◽  
pp. 387-411 ◽  
Author(s):  
I. A. Valioulis ◽  
E. J. List ◽  
H. J. Pearson
Keyword(s):  
Monte Carlo Simulation ◽  
Particle Size ◽  
Monte Carlo ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Dimensional Analysis ◽  
Spherical Particles ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Coagulation Rate

Hunt (1982) and Friedlander (1960a, b) used dimensional analysis to derive expressions for the steady-state particle-size distribution in aerosols and hydrosols. Their results were supported by the Monte Carlo simulation of a non-interacting coagulating population of suspended spherical particles developed by Pearson, Valioulis & List (1984). Here the realism of the Monte Carlo simulation is improved by accounting for the modification to the coagulation rate caused by van der Waals', electrostatic and hydrodynamic forces acting between particles. The results indicate that the major hypothesis underlying the dimensional reasoning, that is, collisions between particles of similar size are most important in determining the shape of the particle size distribution, is valid only for shear-induced coagulation. It is shown that dimensional analysis cannot, in general, be used to predict equilibrium particle-size distributions, mainly because of the strong dependence of the interparticle force on the absolute and relative size of the interacting particles.

Methods of simulating random patterns of non-spherical objects and their application

1996 ◽  
Vol 28 (2) ◽  
pp. 342-343
Author(s):  
Masaharu Tanemura
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Spatial Patterns ◽  
Hard Core ◽  
Spherical Particles ◽  
Finite Region ◽  
Uniform Sampling ◽  
Regular Manner ◽  
Random Patterns

We consider two mechanisms for simulating spatial patterns of hard-core non-spherical particles, namely the random sequential packing (RSP) and the Markov chain Monte Carlo (MCMC) procedures. The former is described as follows: we put a particle one-by-one into a finite region by sampling its location x and direction θ uniformly at random; if it does not overlap with other particles put before, it is put successfully, otherwise, we discard it and try another uniform sampling of (x, θ); by repeating the above, we can obtain a set of non-overlapping particles. The MCMC procedure is the following: we first give a certain non-overlapping pattern of non-spherical particles prepared in a random or a regular manner; then we select a particle and sample its new trial location x and direction θ at random; if the new sample (x, θ) is accepted, i.e. it does not overlap with other particles, the selected particle is moved to the new ‘position’, otherwise the particle is retained at the old position; by repeating the above, a series of a set of non-overlapping particles is generated.

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