Simulation parameters, equilibrium phase

A typical molecular dynamics simulation comprises an equflibration and a production phase. The former is necessary, as the name imphes, to ensure that the system is in equilibrium before data acquisition starts. It is useful to check the time evolution of several simulation parameters such as temperature (which is directly connected to the kinetic energy), potential energy, total energy, density (when periodic boundary conditions with constant pressure are apphed), and their root-mean-square deviations. Having these and other variables constant at the end of the equilibration phase is the prerequisite for the statistically meaningful sampling of data in the following production phase. 

The method developed in this book is also used to provide input parameters for composite models which can be used to predict the thermoelastic and transport properties of multiphase materials. The prediction of the morphologies and properties of such materials is a very active area of research at the frontiers of materials modeling. The prediction of morphology will be discussed in Chapter 19, with emphasis on the rapidly improving advanced methods to predict thermodynamic equilibrium phase diagrams (such as self-consistent mean field theory) and to predict the dynamic pathway by which the morphology evolves (such as mesoscale simulation methods). Chapter 20 will focus on both analytical (closed-form) equations and numerical simulation methods to predict the thermoelastic properties, mechanical properties under large deformation, and transport properties of multiphase polymeric systems. 

The complexity of the equilibrium phases and nonequilibrium phenomena exhibited by multicomponent oil-water-surfactant systems is amply demonstrated in numerous contributions in this volume. Therefore, the need for theoretical (and computational) methods that make the interpretation of experimental observations easier and serve as predictive tools is readily apparent. Excellent treatments of the current status of theoretical advances in dealing with microemulsions are available in recent monographs and compendia (see, e.g., Refs. 1-3 and references therein). These references deal with systems consisting of significant fractions of oil and water and focus on the different phases and intricate microstructures that develop in such systems as the surfactant and salt concentrations are varied. In contrast, the present chapter focuses exclusively on simulations, particularly on a first level introduction to the use of lattice Monte Carlo methods for modeling self-association and phase equilibria in surfactant solutions with and without an oil phase. Although results on phase equilibria are presented, we spend a substantial portion of the review on micellization in surfactant-water mixtures, as this forms the necessary first step in the eventual identification of the most essential parameters needed in computer models of surfactant-water-oil systems. 

The Kohonen neural networks were chosen to prepare a relevant model for fast selection of the most suitable phase equilibrium method(s) to be used in efficient vapor-liquid chemical process design and simulation. They were trained to classify the objects of the study (the known physical properties and parameters of samples) into none, one or more possible classes (possible methods of phase equilibrium) and to estimate the reliability of the proposed classes (adequacy of different methods of phase equilibrium). Out of the several ones the Kohonen network architecture yielding the best separation of clusters was chosen. Besides the main Kohonen map, maps of physical properties and parameters, and phase equilibrium probability maps wo e obtained with horizontal intersections of the neural network. A proposition of phase equilibrium methods is represented with the trained neural network. 

The original GB parametrisation with p = 2 and v = 1 is probably the most thoroughly studied one [5,11-16] and both Monte Carlo and molecular (tynamics methods have been employed [11-20,64] to obtain the equilibrium phases generated under a variety of thermodynamic conditions and to construct, at least in part, its phase diagram [14-16]. Isotropic, nematic and smectic B phases have been found and their order and molecular organisation have be determined. Curiously, direct simulation of the 4 site L uiard-Jones potratial which was originally fitted to yield this choice of GB parameters does not... 

By virtue of their simple stnicture, some properties of continuum models can be solved analytically in a mean field approxunation. The phase behaviour interfacial properties and the wetting properties have been explored. The effect of fluctuations is hrvestigated in Monte Carlo simulations as well as non-equilibrium phenomena (e.g., phase separation kinetics). Extensions of this one-order-parameter model are described in the review by Gompper and Schick [76]. A very interesting feature of tiiese models is that effective quantities of the interface—like the interfacial tension and the bending moduli—can be expressed as a fiinctional of the order parameter profiles across an interface [78]. These quantities can then be used as input for an even more coarse-grained description. 

A sequence of successive con figurations from a Mon te Carlo simulation constitutes a trajectory in phase space with IlypcrC hem. this trajectory in ay be saved and played back in the same way as a dynamics trajectory. With appropriate choices of setup parameters, the Mon te Carlo m ethod m ay ach leve ec nilibration more rapidly than molecular dynamics. Tor some systems, then. Monte C arlo provides a more direct route to equilibrium sinictural and thermodynamic properties. However, these calculations can be quite long, depentiing upon the system studied. 

A question of practical interest is the amount of electrolyte adsorbed into nanostructures and how this depends on various surface and solution parameters. The equilibrium concentration of ions inside porous structures will affect the applications, such as ion exchange resins and membranes, containment of nuclear wastes [67], and battery materials [68]. Experimental studies of electrosorption studies on a single planar electrode were reported [69]. Studies on porous structures are difficult, since most structures are ill defined with a wide distribution of pore sizes and surface charges. Only rough estimates of the average number of fixed charges and pore sizes were reported [70-73]. Molecular simulations of nonelectrolyte adsorption into nanopores were widely reported [58]. The confinement effect can lead to abnormalities of lowered critical points and compressed two-phase envelope [74]. 

An accurate representation of the phase equilibrium behavior is required to design or simulate any separation process. Equilibrium data for salt-free systems are usually correlated by one of a number of possible equations, such as those of Wilson, Van Laar, Margules, Redlich-Kister, etc. These correlations can then be used in the appropriate process model. It has become common to utilize parameters from such correlations to obtain insight into the fundamentals underlying the behavior of solutions and to predict the behavior of other solutions. This has been particularly true of the Wilson equation, which is shown below for a binary system. 

Basically, DESIGNER can use different physical property packages that are easy to interchange with commercial flowsheet simulators. For the case considered, the vapor-liquid equilibrium description is based on the UNIQUAC model. The liquid-phase binary diffusivities are determined using the method of Tyn and Calus (see Ref. 72) for the diluted mixtures, corrected by the Vignes equation (57), to account for finite concentrations. The vapor-phase diffusion coefficients are assumed constant. The reaction kinetics parameters taken from Ref. 202 are implemented directly in the DESIGNER code. 

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