Multiphase systems, computational fluid dynamics

Physics mathematics engineering chemistry suspension mechanics hydrodynamics computational fluid dynamics microfluidic systems coating flows multiphase flows viscous flows. 

The effects deriving from both nonideal mixing and the presence of multiphase systems are considered, in order to develop an adequate mathematical modeling. Computational fluid dynamics models and zone models are briefly discussed and compared to simpler approaches, based on physical models made out of a few ideal reactors conveniently connected. 

The primary objective in catalyst layer development is to obtain highest possible rates of desired reactions with a minimum amount of the expensive Pt (DOE target for 2010 0.2g Pt per kW). This requires a huge electroche-mically active catalyst area and small barriers to transport and reaction processes. At present, random multiphase composites comply best with these competing demands. Since a number of vital processes interact in a nonlinear way in these structures, they form inhospitable systems for systematic theoretical treatment. Not surprisingly then, most cell and stack models, in particular those employing computational fluid dynamics, treat catalyst layers as infinitesimally thin interfaces without structural resolution. 

Computational fluid dynamics (CFD) approach has become a standard tool for analyzing various situations where fluid flow has an effect on the studied processes. Numerous studies using CFD for chemical process industry have also been reported. Mostly, they have been simple cases as the system is non-reacting, contains only one phase (liquid or gas), or physical properties are assumed constant. When we are dealing with multiphase systems like gas-liquid or liquid-liquid systems we must take into account some phenomena which are not of importance for one-phase systems. The vapor-liquid or liquid-liquid equilibrium is one of these that are needed in order to model the system. In addition to that, mass and heat transfer between the phases must generally be taken into account. Also, the two-phase characteristics of fluid flow need to be taken into consideration in the CFD models. 

Computational fluid dynamics (CFD) is now quite well established as a tool for modeling mixing processes with single-phase systems, but its success in predicting multiphase coalescing or dispersing flows has hitherto been limited. A brief overview in the context of the modeling of gas-liquid systems has been included in Section 11-3.1. 

Computational fluid dynamics (CDF) presents a relatively new method of computer-aided mathematical tools for process simulation. The description of multiphase systems in the CFD is a promising new field. This is so because only recently sufficiently extensive and accurate spatially resolved measurements in multiphase systems to create mathematical models and to validate them became possible. Often, the modeling is not perceived as a part of the CFD, especially when working with commercial programs, in which the equations are already available. Since mathematical models are developed independently of the CFD, they are seen as outside to the CFD to be supplied accessories. The CFD models build on model developments, which are also common in other techniques, but have their own structure. 

The lattice Boltzmann method is a mesoscopic simulation method for complex fluid systems. The fluid is modeled as fictitious particles, and they propagate and coUide over a discrete lattice domain at discrete time steps. Macroscopic continuum equations can be obtained from this propagation-colhsion dynamics through a mathematical analysis. The particulate nature and local d3mamics also provide advantages for complex boundaries, multiphase/multicomponent flows, and parallel computation. 

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