Kinetic Monte Carlo method applied to micrometric particle detachment mechanisms by aerodynamic forces

2021 ◽  
Author(s):  
Marcela C Villagrán Olivares ◽  
Jesica G Benito ◽  
Rodolfo O Uñac ◽  
Ana M Vidales
Keyword(s):  
Monte Carlo ◽  
Transition Probabilities ◽  
Kinetic Monte Carlo ◽  
Critical Conditions ◽  
Relative Role ◽  
Particle Removal ◽  
Transition Rates ◽  
Aerodynamic Forces ◽  
Movement Initiation ◽  
Desorption Process

Abstract The formulation of a Kinetic Monte Carlo simulation to account for the different possible mechanisms present in the problem of resuspension of aerosol particles is addressed as an extension of a former model [1]. The re-entrainment of micrometer particles to airflow when detached from a surface by aerodynamic forces is modeled using the similitude of the problem with the desorption process from heterogeneous surfaces. Depending on the relative role of the intervening forces, three main mechanisms for movement initiation can be present: rolling, sliding and lifting-off. Three different transition probabilities are defined for each mechanism and the corresponding transition rates calculated for the kinetic process to be simulated. The decisive factor for the development of the model is to set an appropriate dynamical hierarchy to simulate correctly the evolution of the transition rates as the airflow velocity increases, reflecting the stochastic nature of the process, not always fully captured by other Monte Carlo approaches. The model is applied to spherical and elongated particles on a flat surface, reproducing qualitatively well the experimental trends found by other authors for the case of particles with different shapes. It is also demonstrated that, for elongated particles, the main mechanism assisting the detachment is not rolling but sliding, underscoring the need for an adequate choice of the particles shape and detachment mechanism when looking for the critical conditions for particle removal from surfaces.

scholarly journals Self-learning kinetic Monte Carlo model for arbitrary surface orientations

2013 ◽  
Vol 1559 ◽  
Author(s):  
Andreas Latz ◽  
Lothar Brendel ◽  
Dietrich E. Wolf
Keyword(s):  
Monte Carlo ◽  
Specific Surface ◽  
Kinetic Monte Carlo ◽  
Monte Carlo Model ◽  
Three Dimensional ◽  
The Self ◽  
Transition Rates ◽  
Local Orientation ◽  
Realistic Potential ◽  
Self Learning

ABSTRACTWhile the self-learning kinetic Monte Carlo (SLKMC) method enables the calculation of transition rates from a realistic potential, implementations of it were usually limited to one specific surface orientation. An example is the fcc (111) surface in Latz et al. 2012, J. Phys.: Condens. Matter 24, 485005. This work provides an extension by means of detecting the local orientation, and thus allows for the accurate simulation of arbitrarily shaped surfaces. We applied the model to the diffusion of Ag monolayer islands and voids on a Ag(111) and Ag(001) surface, as well as the relaxation of a three-dimensional spherical particle.

Combined DFT and kinetic Monte Carlo study of a bridging-spillover mechanism on fluorine-decorating graphene

2020 ◽  
Author(s):  
Jing-hua Guo ◽  
Jin-Xiang Liu ◽  
Hongbo Wang ◽  
Haiying Liu ◽  
Gang Chen
Keyword(s):  
Monte Carlo ◽  
First Principles ◽  
Kinetic Monte Carlo ◽  
Monte Carlo Study ◽  
First Principles Calculations ◽  
Graphene Surface

In this work, combining the first-principles calculations with kinetic Monte Carlo (KMC) simulations, we constructed an irregular carbon bridge on the graphene surface and explored the process of H migration...

A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations

2021 ◽  
pp. 096228022199750
Author(s):  
Zvifadzo Matsena Zingoni ◽  
Tobias F Chirwa ◽  
Jim Todd ◽  
Eustasius Musenge
Keyword(s):  
Bayesian Estimation ◽  
Transition Probabilities ◽  
Markov Property ◽  
Likelihood Estimation ◽  
Single Individual ◽  
Transition Rates ◽  
Multistate Models ◽  
Time To Event ◽  
Modeling Framework ◽  
Forward Equations

There are numerous fields of science in which multistate models are used, including biomedical research and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. The multistate framework can describe more than one possible time-to-event outcome for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations from the Bayesian estimation perspective. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated, an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimation and data features compatible with fitting multistate models. We highlight the contrast between the frequentist (maximum likelihood estimation) and the Bayesian estimation approaches in the multistate modeling framework and point out where the latter is advantageous. A partially observed and aggregated dataset from the Zimbabwe national ART program was used to illustrate the use of Kolmogorov-Chapman forward equations. The transition rates from a three-stage reversible multistate model based on viral load measurements in WinBUGS were reported.

scholarly journals A semi-grand canonical kinetic Monte Carlo study of single-walled carbon nanotube growth

AIP Advances ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 045306
Author(s):  
Georg Daniel Förster ◽  
Thomas D. Swinburne ◽  
Hua Jiang ◽  
Esko Kauppinen ◽  
Christophe Bichara
Keyword(s):  
Monte Carlo ◽  
Carbon Nanotube ◽  
Kinetic Monte Carlo ◽  
Monte Carlo Study ◽  
Single Walled Carbon Nanotube ◽  
Single Walled Carbon ◽  
Carbon Nanotube Growth ◽  
Nanotube Growth ◽  
Grand Canonical

scholarly journals A Novel Kinetic Modeling Framework for the Polycondensation of Sugars Using Monte Carlo and the Method of Moments

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 745
Author(s):  
Dimitrios Meimaroglou ◽  
Sandrine Hoppe ◽  
Baptiste Boit
Keyword(s):  
Monte Carlo ◽  
Kinetic Modeling ◽  
Method Of Moments ◽  
Kinetic Monte Carlo ◽  
Kinetic Scheme ◽  
Modeling Framework ◽  
Industrial Sectors ◽  
The Monte Carlo Method ◽  
Reacting Systems ◽  
High Degree

The kinetics of the hydrolysis and polycondensation reactions of saccharides have made the subject of numerous studies, due to their importance in several industrial sectors. The present work, presents a novel kinetic modeling framework that is specifically well-suited to reacting systems under strict moisture control that favor the polycondensation reactions towards the formation of high-degree polysaccharides. The proposed model is based on an extended and generalized kinetic scheme, including also the presence of polyols, and is formulated using two different numerical approaches, namely a deterministic one in terms of the method of moments and a stochastic kinetic Monte Carlo approach. Accordingly, the most significant advantages and drawbacks of each technique are clearly demonstrated and the most fitted one (i.e., the Monte Carlo method) is implemented for the modeling of the system under different conditions, for which experimental data were available. Through these comparisons it is shown that the model can successfully follow the evolution of the reactions up to the formation of polysaccharides of very high degrees of polymerization.

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