scholarly journals Phase Diagram of a Lattice Model for Ternary Mixtures of Water, Oil, and Surfactants

1991 ◽  
Vol 248 ◽  
Author(s):  
Mohamed Laradji ◽  
Hong Guo ◽  
Martin Grant ◽  
Martin J. Zuchkermann
Keyword(s):  
Phase Diagram ◽  
Monte Carlo ◽  
Phase Behavior ◽  
Lattice Model ◽  
Large Scale ◽  
Ternary Mixtures ◽  
Rich Phase ◽  
Component System ◽  
Chemical Potentials ◽  
Wide Range

AbstractLarge scale Monte-Carlo simulations have been performed on a lattice model for a three component system of water, oil, and surfactants to obtain the phase equilibria and scattering behavior for a wide range of temperatures and chemical potentials. We observed that this model has a rich phase behavior, namely a water-oil phase coexistence, a microemulsion phase, a lamellar phase, and a square phase. This phase diagram is consistent with experiments, and is in qualitative agreement with a model of Gompper and Schick [ Phys. Rev. Lett. 62, 1647 (1989)].

scholarly journals LATTICE MODEL OF ADSORPTION OF MULTISITE TRIANGLES WITH ATTRACTIVE VERTEXES: PARAMETRIZATION FOR 1, 3, 5-TRIS(PYRIDYL)BENZENE AND CU ON OXIDATED TITANIUM CARBIDE SURFACE

2020 ◽  
Vol 7 (4) ◽  
pp. 004-011
Author(s):  
O. S. Solovyeva ◽  
◽  
V. A. Gorbunov ◽  
A. V. Myshlyavtsev ◽  
◽  
...  
Keyword(s):  
Phase Diagram ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ground State ◽  
Lattice Model ◽  
Adsorption Layer ◽  
Ground State Phase Diagram ◽  
Component System ◽  
Simple Lattice ◽  
Metal Organic

In this paper, a simple lattice model of the metal-organic adsorption layer of 1,3,5- tris(pyridyl)benzene and copper on the surface of Ti2CO2 was proposed. In this model, the selfassembly of the organometallic layer is considered as a one-component system that implicitly includes metal adatoms. The ground state phase diagram is calculated. A Monte Carlo simulation is performed using the Metropolis algorithm and the parallel temperature technique. The isotherm of the metal-organic is calculated at T = 300 K. All the results indicate the possibility of the formation of stable metal-organic phases on the Ti2CO2 surface.

PHASE DIAGRAM OF DOUBLE EXCHANGE MODEL WITH ANTIFERROMAGNETIC COUPLING FOR LOCALIZED t2g SPINS IN ONE DIMENSION REVISITED

2005 ◽  
Vol 19 (24) ◽  
pp. 3731-3743 ◽  
Author(s):  
Q. L. ZHANG
Keyword(s):  
Phase Diagram ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Large Scale ◽  
Double Exchange ◽  
Exchange Model ◽  
One Dimension ◽  
Antiferromagnetic Coupling ◽  
Single Orbit ◽  
Superexchange Coupling

The phase diagram of the single-orbit double exchange model for manganites with ferromagnetic Hund coupling between mobile eg electrons and spins of localized t2g electrons as well as antiferromagnetic superexchange coupling between t2g electrons is investigated with a large scale Monte Carlo simulation in one dimension. The phase boundary is determined based on the internal energy, the electron density and the structure factor. In particular, low-temperature properties at quarter filling are studied in detail.

scholarly journals Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

2021 ◽  
pp. 1-36
Author(s):  
Oskar Elek ◽  
Joseph N. Burchett ◽  
J. Xavier Prochaska ◽  
Angus G. Forbes
Keyword(s):  
Monte Carlo ◽  
Large Scale ◽  
Data Sets ◽  
Transport Networks ◽  
Domain Specific ◽  
3D Data ◽  
Stochastic Transport ◽  
Density Maps ◽  
Wide Range ◽  
Cosmological Data

Abstract We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.

scholarly journals Phase diagram of a lattice model for ternary mixtures of water, oil, and surfactants

1991 ◽  
Vol 44 (12) ◽  
pp. 8184-8188 ◽  
Author(s):  
Mohamed Laradji ◽  
Hong Guo ◽  
Martin Grant ◽  
Martin J. Zuckermann
Keyword(s):  
Phase Diagram ◽  
Lattice Model ◽  
Ternary Mixtures

scholarly journals Large-Scale Monte Carlo Study of a Realistic Lattice Model forGa1−xMnxAs

2007 ◽  
Vol 99 (5) ◽  
Author(s):  
Yucel Yildirim ◽  
Gonzalo Alvarez ◽  
Adriana Moreo ◽  
Elbio Dagotto
Keyword(s):  
Monte Carlo ◽  
Lattice Model ◽  
Large Scale ◽  
Monte Carlo Study

HMCtomo: A framework for Hamiltonian Monte Carlo sampling of Bayesian geophysical inverse problems

2020 ◽  
Author(s):  
Lars Gebraad ◽  
Andrea Zunino ◽  
Andreas Fichtner ◽  
Klaus Mosegaard
Keyword(s):  
Monte Carlo ◽  
Bayesian Inference ◽  
Inverse Problems ◽  
Large Scale ◽  
Monte Carlo Sampling ◽  
Hamiltonian Monte Carlo ◽  
Forward Models ◽  
Inference Problems ◽  
Wide Range ◽  
Gradient Based

<div>We present a framework to solve geophysical inverse problems using the Hamiltonian Monte Carlo (HMC) method, with a focus on Bayesian tomography. Recent work in the geophysical community has shown the potential for gradient-based Monte Carlo sampling for a wide range of inverse problems across several fields.</div><div> </div><div>Many high-dimensional (non-linear) problems in geophysics have readily accessible gradient information which is unused in classical probabilistic inversions. Using HMC is a way to help improve traditional Monte Carlo sampling while increasing the scalability of inference problems, allowing access to uncertainty quantification for problems with many free parameters (>10'000). The result of HMC sampling is a collection of models representing the posterior probability density function, from which not only "best" models can be inferred, but also uncertainties and potentially different plausible scenarios, all compatible with the observed data. However, the amount of tuning parameters required by HMC, as well as the complexity of existing statistical modeling software, has limited the geophysical community in widely adopting a specific tool for performing efficient large-scale Bayesian inference.</div><div> </div><div>This work attempts to make a step towards filling that gap by providing an HMC sampler tailored for geophysical inverse problems (by e.g. supplying relevant priors and visualizations) combined with a set of different forward models, ranging from elastic and acoustic wave propagation to magnetic anomaly modeling, traveltimes, etc.. The framework is coded in the didactic but performant languages Julia and Python, with the possibility for the user to combine their own forward models, which are linked to the sampler routines by proper interfaces. In this way, we hope to illustrate the usefulness and potential of HMC in Bayesian inference. Tutorials featuring an array of physical experiments are written with the aim of both showcasing Bayesian inference and successful HMC usage. It additionally includes examples on how to speed up HMC e.g. with automated tuning techniques and GPU computations.</div>

scholarly journals Generic phase diagram of binary superlattices

2016 ◽  
Vol 113 (37) ◽  
pp. 10269-10274 ◽  
Author(s):  
Alexei V. Tkachenko
Keyword(s):  
Phase Diagram ◽  
Phase Behavior ◽  
Hard Spheres ◽  
General Procedure ◽  
Particle Size Ratio ◽  
System Composition ◽  
Wide Range ◽  
Self Assembled ◽  
Model Features ◽  
2D And 3D

Emergence of a large variety of self-assembled superlattices is a dramatic recent trend in the fields of nanoparticle and colloidal sciences. Motivated by this development, we propose a model that combines simplicity with a remarkably rich phase behavior applicable to a wide range of such self-assembled systems. Those systems include nanoparticle and colloidal assemblies driven by DNA-mediated interactions, electrostatics, and possibly, controlled drying. In our model, a binary system of large and small hard spheres (L and S, respectively) interacts via selective short-range (“sticky”) attraction. In its simplest version, this binary sticky sphere model features attraction only between S and L particles. We show that, in the limit when this attraction is sufficiently strong compared with kT, the problem becomes purely geometrical: the thermodynamically preferred state should maximize the number of LS contacts. A general procedure for constructing the phase diagram as a function of system composition f and particle size ratio r is outlined. In this way, the global phase behavior can be calculated very efficiently for a given set of plausible candidate phases. Furthermore, the geometric nature of the problem enables us to generate those candidate phases through a well-defined and intuitive construction. We calculate the phase diagrams for both 2D and 3D systems and compare the results with existing experiments. Most of the 3D superlattices observed to date are featured in our phase diagram, whereas several more are predicted for future discovery.

A Negative-Flash Tie-Simplex Approach for Multiphase Reservoir Simulation

SPE Journal ◽  
2013 ◽  
Vol 18 (06) ◽  
pp. 1140-1149 ◽  
Author(s):  
Alireza Iranshahr ◽  
Denis V. Voskov ◽  
Hamdi A. Tchelepi
Keyword(s):  
Phase Behavior ◽  
Oil Recovery ◽  
Large Scale ◽  
Flow Simulation ◽  
Steam Injection ◽  
Compositional Simulation ◽  
Reservoir Flow ◽  
Wide Range ◽  
Tie Lines ◽  
Eor Processes

Summary Enhanced Oil Recovery (EOR) processes usually involve complex phase behavior between the injected fluid (e.g., steam, hydrocarbon, CO2, sour gas) and the in-situ rock-fluid system. Several fundamental questions remain regarding Equation-of-State (EOS) computations for mixtures that can form three, or more, phases at equilibrium. In addition, numerical and computational issues related to the proper coupling of the thermodynamic phase behavior with multi-component transport must be resolved to accurately and efficiently model the behavior of large-scale EOR processes. Previous work has shown that the adaptive tabulation of tie-simplexes in the course of a compositional simulation is a reliable alternative to the conventional EOS-based compositional simulation. In this paper, we present the numerical results of reservoir flow simulation with adaptive tie-simplex parameterization of the compositional space. We study the behavior of thermal-compositional reservoir displacement processes across a wide range of fluid mixtures, pressures, and temperatures. We show that our approach rigorously accounts for tie-simplex degeneration across phase boundaries. We also focus on the complex behavior of the tie-triangles and tie-lines associated with three-phase, steam injection problems in heterogeneous formations. Our studies indicate that the tie-simplex based simulation is a potential approach for fast EOS modeling of complex EOR processes.

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