Development of the semi-empirical equation of state for square-well chain fluid based on the Statistical Associating Fluid Theory (SAFT)

A semi-empirical equation of state for the freely jointed square-well chain fluid is developed. This equation of state is based on Wertheim’s thermodynamic perturbation theory (TPT) and the statistical associating fluid theory (SAFT). The compressibility factor and radial distribution function of square-well monomer are obtained from Monte Carlo simulations. These results are correlated using density expansion. In developing the equation of state the exact analytical expressions are adopted for the second and third virial coefficients for the compressibility factor and the first two terms of the radial distribution function, while the higher order coefficients are determined from regression using the simulation data. In the limit of infinite temperature, the present equation of state and the expression for the radial distribution function are represented by the Carnahan-Starling equation of state. This semi-empirical equation of state gives at least comparable accuracy with other empirical equation of state for the square-well monomer fluid. With the new SAFT equation of state from the accurate expressions for the monomer reference and covalent terms, we compare the prediction of the equation of state to the simulation results for the compressibility factor and radial distribution function of the square-well monomer and chain fluids. The predicted compressibility factors for square well chains are found to be in a good agreement with simulation data. The high accuracy of the present equation of state is ascribed to the fact that rigorous simulation results for the reference fluid are used, especially at low temperatures and low densities. This paper was presented at the 8th APCChE (Asia Pacific Confederation of Chemical Engineering) Congress held at Seoul between August 16 and 19, 1999.     

Application of an improved control-variate scheme to local neoclassical transport simulations

A new radially local neoclassical transport code is developed based on the radially global two-weight δf$\delta f$ Monte Carlo code, FORTEC-3D (Satake et al., 2008). In the collisional two-weight δf$\delta f$ method, the variance of weight increases in time due to the so-called weight spreading, which leads to an increasing numerical noise in long time simulations. A new improved control-variate scheme was proposed by Kleiber et al. (2011) to reduce the variance. We investigate the effectiveness and validity of the new control-variate scheme for a practical collisional transport problem in a plasma described by the drift kinetic equation. It is demonstrated that the new scheme reduces the variance of weight by approximately 75% in an axisymmetric magnetic field configuration. The burst-like behavior of a transport observable caused by a large numerical noise can be avoided. As a result, the time evolution of a transport observable can be successfully smoothed.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

基于Monte Carlo随机模拟的综合交通网络布局评价与决策方法

针对综合交通网络评价指标权重及属性值具有主观性和随机性的特点,提出了基于模拟运算的布局规划方案排序选优的群体随机决策方法.仿真生成满足集结的多个专家对指标重要性偏好排序统计分布的权重,同时考虑交通需求的不确定性对指标值的影响,结合客观熵权计算方案的综合评价值,由多次模拟得到的排序优势度确定方案的优劣差异.根据设计的仿真流程通过算例说明了方法应用的有效性,评价中考虑了主客观因素及随机性特征,可以为网络布局提供科学决策依据.     

SIMULATION AND ESTIMATION OF LONG MEMORY CONTINUOUS TIME MODELS

Abstract. Some general properties of long memory continuous time processes are recalled or proved. Methods of simulation are studied. A comparison with the usual discrete time autoregressive fractionally integrated moving-average filter is made and illustrations are provided. Then, two methods of estimation of the parameters of such a model from a discrete sample are studied, both theoretically and empirically, with Monte Carlo experiments.     

Unsteady radiative heat transfer within a suspension of ZnO particles undergoing thermal dissociation

An emitting, absorbing, and anisotropically scattering plain medium containing a suspension of ZnO particles is considered, in which the particles are directly exposed to high-flux irradiation and undergo shrinkage during their endothermic dissociation into Zn(g) and O₂ at above 2100 K. The unsteady energy equation that links the rate of radiative heat transfer to the rate of the chemical reaction is formulated and solved numerically by the finite volume technique and the explicit Euler time-integration scheme. The path-length Monte Carlo method is applied for modeling the radiative transfer within the suspension using the absorption/scattering coefficients and the scattering phase function obtained from the Mie theory. It is found that the particle suspension can be heated rapidly from its initial 300 K to over 1800 K in less than 0.1 s, resulting in a more uniform temperature profile as the reaction progresses, particles shrink, and the suspension becomes optically thinner. The chemical conversion increases with decreasing initial particle diameter and volume fraction due to the efficient radiative absorption.     

Radial density distribution of fractal clusters

The radial density distributions of fractal clusters generated in both DLCA and RLCA conditions by Monte-Carlo off-lattice cluster-cluster aggregation have been investigated. It has been computed by averaging a large number of clusters of same mass to form an average cluster, which is then considered as spherically symmetric. It is found that the radial density distribution, calculated using the mass center of the cluster as the center point, does not follow the fractal scaling, as sometimes assumed in the literature. An empirical model has been proposed to describe the dependence of the radial density distribution on the number of particles in the cluster. The obtained radial density distribution is used to compute permeability profile of the fractal cluster using several literature models, which is then applied to estimate the cluster hydrodynamic radius, R_h, by considering the cluster as a porous permeable object and using the solution of Brinkman equations for the fluid flow inside the cluster. The so obtained R_h values are compared to those in the literature, computed using the Kirkwood-Riseman (KR) theory. It has been found that, among the five permeability models examined, only the model proposed by Davis (Proceedings of the Institution of Mechanical Engineers Part B 1, 185) provides results in good agreement with those obtained using the KR model. Furthermore, it has been verified that the R_h values are insensitive to the model used for the density distribution.     

Analysis of four Monte Carlo methods for the solution of population balances in dispersed systems

Monte Carlo (MC) constitutes an important class of methods for the numerical solution of the general dynamic equation (GDE) in particulate systems. We compare four such methods in a series of seven test cases that cover typical particulate mechanisms. The four MC methods studied are: time-driven direct simulation Monte Carlo (DSMC), stepwise constant-volume Monte Carlo, constant number Monte Carlo, and multi-Monte Carlo (MMC) method. These MC's are introduced briefly and applied numerically to simulate pure coagulation, breakage, condensation/evaporation (surface growth/dissolution), nucleation, and settling (deposition). We find that when run with comparable number of particles, all methods compute the size distribution within comparable levels of error. Because each method uses different approaches for advancing time, a wider margin of error is observed in the time evolution of the number and mass concentration, with event-driven methods generally providing better accuracy than time-driven methods. The computational cost depends on algorithmic details but generally, event-driven methods perform faster than time-driven methods. Overall, very good accuracy can be achieved using reasonably small numbers of simulation particles, O(10³), requiring computational times of the order 10²−10³ s on a typical desktop computer.     

Partitioning of polymers into pores with surface interactions at dilute solution limit

The partitioning of a single polymer chain into a slit in a good solvent with different surface interactions is examined through Monte Carlo simulations from subcritical regime to adsorptive regime. The chain conformation in the subcritical regime is not perturbed by the surface interactions significantly. In the adsorptive regime, the conformation of the chain is strongly perturbed by the surface interactions. The confinement free energy in the two regimes maybe written in a uniform formula, βΔμ_conf∼c₁N(a/D)^xε_w+c₂N(a/D)^1/ν with x∼2.0 or larger in the subcritical regime and ∼1.0 in the adsorptive regime, where ν is the Flory exponent, D is the slit width, N is the chain length, a is the monomer size, and ε_w is the surface interaction energy between the polymer beads and the slit. This formula is valid for a long chain in the narrow slit in the subcritical regime or when the adsorption layer h>D in the adsorptive regime. A critical behavior occurs when ε_w is at the critical adsorption point and x=1/ν, then Δμ_conf will have little dependence on N or D. Higher order terms that are neglected in the above equation, however, may be present that could lead to a weak dependence of K on N and D even in the critical adsorption point.