Full resolution of the Monte Carlo time scale demonstrated through the modelling of two-amorphous-particles sintering

2008 ◽  
Vol 43 (4) ◽  
pp. 1074-1080 ◽  
Author(s):  
S. Bordère ◽  
D. Bernard
Keyword(s):  
Monte Carlo ◽  
Time Scale ◽  
Full Resolution ◽  
Amorphous Particles

scholarly journals Uncertainty Analysis of Hydrological Drought due to Record Length, Time Scale, and Probability Distribution Functions Using Monte-Carlo Simulation Method

2021 ◽  
Author(s):  
Faezeh Ghasemnezhad ◽  
Ommolbanin Bazrafshan ◽  
Mehdi Fazeli ◽  
Mohammad Parvinnia ◽  
Vijay Singh
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Confidence Interval ◽  
Time Scale ◽  
Statistical Data ◽  
Distribution Functions ◽  
Hydrological Drought ◽  
Discharge Data ◽  
Probability Distribution Functions ◽  
Record Length

Abstract Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30 and 49 year length from Minab dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3, 6. 9, 12, 24 and 48 month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte-Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to normal and lognormal distributions and the minimum uncertainty to gamma and Weibull distributions. Further, the increase in both time scale and record length led to the decrease in uncertainty.

scholarly journals Simulations of long time scale dynamics using the dimer method

2001 ◽  
Vol 677 ◽  
Author(s):  
Graeme Henkelman ◽  
Hannes Jónsson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Transition State ◽  
Time Scale ◽  
Transition State Theory ◽  
Kinetic Monte Carlo ◽  
Chem Phys ◽  
State Theory ◽  
Kinetic Monte Carlo Method ◽  
Long Time

We have carried out long time scale simulations where the “dimer method” [G. Henkelman and H. Jónsson, J. Chem. Phys. 111, 7010 (1999)] is used to find the mechanism and estimate the rate of transitions within harmonic transition state theory and time is evolved by using the kinetic Monte Carlo method. Unlike traditional applications of kinetic Monte Carlo, the atoms are not assigned to lattice sites and a list of all possible transitions does not need to be specified beforehand. Rather, the relevant transitions are found on the y during the simulation. An application to the diffusion and island formation of Al adatoms on an Al(100) surface is presented.

scholarly journals Uniform-acceptance force-bias Monte Carlo method with time scale to study solid-state diffusion

2012 ◽  
Vol 85 (13) ◽  
Author(s):  
Maarten J. Mees ◽  
Geoffrey Pourtois ◽  
Erik C. Neyts ◽  
Barend J. Thijsse ◽  
André Stesmans
Keyword(s):  
Monte Carlo ◽  
Solid State ◽  
Monte Carlo Method ◽  
Time Scale ◽  
Solid State Diffusion ◽  
State Diffusion

Simulation of Atomistic Crystal Growth from the Vapor Phase

1986 ◽  
Vol 77 ◽  
Author(s):  
Paul A. Taylor ◽  
Brian W. Dodson
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Crystal Growth ◽  
Monte Carlo Method ◽  
Vapor Phase ◽  
Time Scale ◽  
Transient Dynamics ◽  
Strained Layer ◽  
The Monte Carlo Method ◽  
Long Time

ABSTRACTWe are in the process of studying strained-layer growth of two-dimensional Lennard-Jones lattices. To do so, we have developed three techniques, based on the Monte Carlo method and molecular dynamics, of simulating atomistic crystal growth from the vapor phase. The Monte Carlo method efficiently simulates the effects of long time-scale processes on the growth of strained-layer systems, but omits the transient dynamics of particle adsorption. The second technique, using molecular dynamics, gives results suggesting that epitaxial growth of strained-layer systems can occur on the picosecond timescales. However, this technique cannot capture the influence of the long time-scale processes on the growth process. In view of the shortcomings of the previous two techniques, A hybrid technique incorporating both the Monte Carlo method and molecular dynamics, has been developed. In principle, this technique models the transient dynamics of adsorption as well as the long term evolution of the system. This technique, however, is limited by artifacts that may only be eliminated by use of unwarrented amounts of supercomputer time.

scholarly journals Sensitivity Study on the Influence of Cloud Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase Partitioning in CAM5

2016 ◽  
Vol 73 (2) ◽  
pp. 709-728 ◽  
Author(s):  
Ivy Tan ◽  
Trude Storelvmo
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Time Scale ◽  
Monte Carlo Sampling ◽  
Sensitivity Study ◽  
Mixed Phase ◽  
Phase Partitioning ◽  
Quasi Monte Carlo ◽  
Microphysical Parameters ◽  
Mixed Phase Clouds

Abstract The influence of six CAM5.1 cloud microphysical parameters on the variance of phase partitioning in mixed-phase clouds is determined by application of a variance-based sensitivity analysis. The sensitivity analysis is based on a generalized linear model that assumes a polynomial relationship between the six parameters and the two-way interactions between them. The parameters, bounded such that they yield realistic cloud phase values, were selected by adopting a quasi–Monte Carlo sampling approach. The sensitivity analysis is applied globally, and to 20°-latitude-wide bands, and over the Southern Ocean at various mixed-phase cloud isotherms and reveals that the Wegener–Bergeron–Findeisen (WBF) time scale for the growth of ice crystals single-handedly accounts for the vast majority of the variance in cloud phase partitioning in mixed-phase clouds, while its interaction with the WBF time scale for the growth of snowflakes plays a secondary role. The fraction of dust aerosols active as ice nuclei in latitude bands, and the parameter related to the ice crystal fall speed and their interactions with the WBF time scale for ice are also significant. All other investigated parameters and their interactions with each other are negligible (<3%). Further analysis comparing three of the quasi–Monte Carlo–sampled simulations with spaceborne lidar observations by CALIOP suggests that the WBF process in CAM5.1 is currently parameterized such that it occurs too rapidly due to failure to account for subgrid-scale variability of liquid and ice partitioning in mixed-phase clouds.

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