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Dynamic Monte Carlo method
About: Dynamic Monte Carlo method is a research topic. Over the lifetime, 13294 publications have been published within this topic receiving 371256 citations.
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TL;DR: Grand-canonical transition-matrix Monte Carlo is combined with configurational-bias and expanded ensemble Monte Carlo techniques to obtain saturated densities and vapor pressures of select n-alkanes and finds that a broad range of trial conformation numbers give reasonable performance and the optimal value increasing with decreasing temperature for a fixed chain length.
Abstract: Grand-canonical transition-matrix Monte Carlo is combined with configurational-bias and expanded ensemble Monte Carlo techniques to obtain saturated densities and vapor pressures of select n-alkanes. Surface tension values for butane, hexane, and octane are also computed via the finite-size scaling method of Binder. The exponential-6 model of Errington and Panagiotopoulos is used to describe the molecular interactions. The effect of the number of configurational-bias trial conformations on the efficiency of phase equilibra calculations is studied. We find that a broad range of trial conformation numbers give reasonable performance, with the optimal value increasing with decreasing temperature for a fixed chain length. Phase coexistence properties are in good agreement with literature values and are obtained with very reasonable computing resources. Similar to other recently developed n-alkane force fields, the exponential-6 model overestimates the surface tension relative to experimental values. Statistical uncertainties for coexistence properties obtained with the current approach are relatively small compared to existing methods.
89 citations
TL;DR: This article reviews the literature on steady-state and dynamic Monte Carlo methods in polymer reaction engineering and hopes to convince the readers that playing dice regularly can be a great asset to polymer reactors engineers.
Abstract: Monte Carlo methods are heuristic algorithms that use probabilities to select an outcome among several possible events in a given process. Monte Carlo methods are useful in polymer reaction engineering because they can predict the molecular architecture of polymers with details that cannot be easily captured by any other modeling technique. One of the advantages of Monte Carlo simulation is that one does not need to solve differential or algebraic equations to predict the microstructures of polymers. This article reviews the literature on steady-state and dynamic Monte Carlo methods in polymer reaction engineering. We hope to convince the readers that playing dice regularly can be a great asset to polymer reactors engineers.
89 citations
TL;DR: In this paper, a simple model is presented to explain the irregular bouncing phenomena of particles in pneumatic conveying by introducing a random factor to the model, and a PNE transport process was simulated by Monte Carlo method.
Abstract: On the basis of wall roughness, a simple model is presented to explain the irregular bouncing phenomena of particles in pneumatic conveying. By introducing a random factor to the model, a pneumatic transport process was simulated by Monte Carlo method. The model could fairly well account for the concentration and velocity distribution and the rate of revolution of particles. Even very small degree of roughness was found to have a great influence upon the behaviour of particles.
88 citations
01 Oct 1962
TL;DR: In this paper, a Monte Carlo method for estimating statistical parameters of quantum-mechanical systems is described, in general terms, which is an extension of the method of Metropolis for classical systems, and may have equally wide application.
Abstract: This paper describes, in general terms, a Monte Carlo method for estimating statistical parameters of quantum-mechanical systems. In this method, we construct a Markov chain of transitions between finite sequences of indices, and obtain these parameters in terms of parameters of the limit distribution. This is an extension of the method of Metropolis for classical systems, and may have equally wide application.
88 citations
TL;DR: The equation of state for hard spheres in a short-range attractive square well has been calculated for several values of the well strength by the Monte Carlo method as discussed by the authors, and two van der Waals loops appear, one at a density higher than the density of the order-disorder transition and another at a lower density.
Abstract: The equation of state for hard spheres in a short‐range attractive square well has been calculated for several values of the well strength by the Monte Carlo method. For shallow wells, it has the same general form but lies everywhere below the equation of state for the simple hard‐core potential. As the well strength increases, two van der Waals loops appear, one at a density higher than the density of the order—disorder transition and one at a lower density.
88 citations