Multiphase computational fluid dynamics

Multiscale descriptions of particle-droplet interactions in spray processing of composite particles are realized based on Multiphase Computational Fluid Dynamics (M-CFD) models, in which processes such as liquid atomization and particle-droplet mixing spray (macro-scale), particle-droplet collision (mesoscale), and particle penetration into droplet (micro-scale) are taken into account as shown in Fig. 18.52. Thereby, the incorporation efficiency and sticking efficiency of solid particles in matrix particles are correlated with the operatiOTi conditions and material properties. 

There are many nonintrusive experimental tools available that can help scientists to develop a good picture of fluid dynamics and transport in chemical reactors. Laser Doppler velocimetry (LDV), particle image velocimetry (PIV) and sonar Doppler for velocity measurement, planar laser induced fluorescence (PLIF) for mixing studies, and high-speed cameras and tomography are very useful for multiphase studies. These experimental methods combined with computational fluid dynamics (CFDs) provide very good tools to understand what is happening in chemical reactors. 

Pareek, V., M.P. Brungs, and A.A. Adesina, Photocausticization of Spent Bayer liquor A Pilot-Scale Study. Advances in Environmental Research, 2003. 7(2) p. 411-420. Bertola, F., M. Vanni, and G. Baldi, Application of Computational Fluid Dynamics to Multiphase Flow in Bubble Columns. International journal of Chemical Reactor Engineering, 2003. 1 p. A3. 

Computational fluid dynamics (CFD) is rapidly becoming a standard tool for the analysis of chemically reacting flows. For single-phase reactors, such as stirred tanks and empty tubes, it is already well-established. For multiphase reactors such as fixed beds, bubble columns, trickle beds and fluidized beds, its use is relatively new, and methods are still under development. The aim of this chapter is to present the application of CFD to the simulation of three-dimensional interstitial flow in packed tubes, with and without catalytic reaction. Although the use of... 

Scheuerer G. Solution algorithms and implementations strategies for Eulerian-Eulerian multiphase-flow models. Proceedings of ACFD 2000 International Conference on Applied Computational Fluid Dynamics, Beijing, 2000. 

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. 

Care is needed when modeling compressible gas flows, flows of vapor-liquid mixtures, slurry flows, and flows of non-Newtonian liquids. Some simulators use different pipe models for compressible flow. The prediction of pressure drop in multiphase flow is inexact at best and can be subject to very large errors if the extent of vaporization is unknown. In most of these cases, the simulation model should be replaced by a computational fluid dynamics (CFD) model of the important parts of the plant. 

In solid-liquid mixing design problems, the main features to be determined are the flow patterns in the vessel, the impeller power draw, and the solid concentration profile versus the solid concentration. In principle, they could be readily obtained by resorting to the CFD (computational fluid dynamics) resolution of the appropriate multiphase fluid mechanics equations. Historically, simplified methods have first been proposed in the literature, which do not use numerical intensive computation. The most common approach is the dispersion-sedimentation phenomenological model. It postulates equilibrium between the particle flux due to sedimentation and the particle flux resuspended by the turbulent diffusion created by the rotating impeller. 

Kolev NI (2002) Multiphase Flow Dynamics 1 Fundamentals. Springer, Berlin Kuipers JAM, van Swaaij WPM (1997) Application of Computational Fluid Dynamics to Chemical Reaction Engineering. Reviews in Chemical Engineering 13 (3) 1-118. 

Caia C, Minev P (2004) A finite element method for an averaged multiphase flow model. Int J Computational Fluid Dynamics 18(2) 111-123... 

The answer to this question is mainly driven by the computational cost of solving the kinetic equation due to the large number of independent variables. In the simplest example of a 3D velocity-distribution function n t, x, v) the number of independent variables is 1 + 3 + 3 = 1. However, for polydisperse multiphase flows the number of mesoscale variables can be much larger than three. In comparison, the moment-transport equations involve four independent variables (physical space and time). Furthermore, the form of the moment-transport equations is such that they can be easily integrated into standard computational-fluid-dynamics (CFD) codes. Direct solvers for the kinetic equation are much more difficult to construct and require specialized numerical methods if accurate results are to be obtained (Filbet Russo, 2003). For example, with a direct solver it is necessary to discretize all of phase space since a priori the location of nonzero values of n is unknown, which can be very costly when phase space is not bounded. 

This partial differential equation is deterministic by nature. In practice, however, many hydrodynamic phenomena (e.g., transition from laminar to turbulent flow) have chaotic features (deterministic chaos [Stewart 1993]). The reason for this is that the Navier-Stokes equation assumes a homogeneous ideal fluid, whereas a real fluid consists of atoms and molecules. Today highly developed numerical flow simulators (computational fluid dynamics, CFD) are available for solving the Navier-Stokes equation under certain boundary conditions (e.g.. Fluent Deutschland GmbH). These even allow complex flow conditions, including particle, droplet, bubble, plug, and free surface flow, as well as multiphase flow such as that foundin fluidized-bed reactors and bubble columns, to be treated numerically [Fluent 1998]. 

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