Fluidization, computation fluid dynamics

So far, some researchers have analyzed particle fluidization behaviors in a RFB, however, they have not well studied yet, since particle fluidization behaviors are very complicated. In this study, fundamental particle fluidization behaviors of Geldart s group B particle in a RFB were numerically analyzed by using a Discrete Element Method (DEM)- Computational Fluid Dynamics (CFD) coupling model [3]. First of all, visualization of particle fluidization behaviors in a RFB was conducted. Relationship between bed pressure drop and gas velocity was also investigated by the numerical simulation. In addition, fluctuations of bed pressure drop and particle mixing behaviors of radial direction were numerically analyzed. 

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... 

Xu, B.H. and Yu, A.B. (1997), Numerical simulation of gas solid flow in a fluidized bed by combining discrete particle method with computational fluid dynamics, Chem. Eng. Sci., 52, 2785. 

In recent years, there has been considerable effort to develop computational fluid dynamic (CFD) models to predict the hydrodynamics and performance of fluidized beds. While this approach will no doubt yield valuable tools in the future, CFD models are not yet at the point where they can be used with confidence for design and scale-up of fluidized bed processes. 

Slurry bubble column reactor for methanol and other hydrocarbons productions from synthesis gas is an issue of interest to the energy industries throughout the world. Computational fluid dynamics (CFD) is a recently developed tool which can help in the scale up. We have developed an algorithm for computing the optimum process of fluidized bed reactors. The mathematical technique can be applied to gas solid, liquid-solid, and gas-liquid-solid fluidized bed reactors, as well as the LaPorte slurry bubble column reactor. Our computations for the optimum particle size show that there is a factor of about two differences between 20 and 60 pm size with maximum granular-like temperature (turbulent kinetic energy) near the 60 pm size particles. 

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]. 

How much did computational fluid dynamics (CFD) enter into the actual design of the fluidized-bed reactors Perform a thorough literature survey and find a solid evidence of the use of CFD tools in fluidized-bed reactor design. 

Two basic approaches are often used for fluidized bed reactor modeling. One approach is based on computational fluid dynamics developed on the basis of the mass, momentum, and energy balance or the first principle coupled with reaction kinetics (see Chapter 9). Another approach is based on phenomenological models that capture the main features of the flow with simplifications by assumption. The flow patterns of plug flow, CSTR (continuous-stirred tank reactor). 

Kunii and Levenspiel, 1990) and computational fluid dynamics (CFD) modeling (Gidaspow, 1994), have been extensively studied in the hterature. Considerable effort remains to be made in regard to CFD modeling as a predictive tool for fluidized bed performance in terms of conversion and selectivity. 

Kashiwa BA, Yang WC. Computational fluid dynamics for the CFBR Challenges that lie ahead. In Grace JR, Zhu J, deLasa H, eds. Circulating Fluidized Bed Technology VII. Ottawa CSChE, 2002, p. 27-39. 

The fundamental characteristics of three-phase fluidization including bubble characteristics, hydrodynamics, and heat and mass transfer properties along with many industrial processes have been extensively reported in Fan s (1989) book as well as its companion book on bubble wake dynamics (Fan and Tsuchiya, 1990). As both books are widely referenced in the field of three-phase fluidization, this chapter is presented mainly as an update to these two books. The chapter will cover the continued research progress made over the past ten years on the fundamentals of three-phase fluidization. Major findings on fluidization and bubble dynamics under ambient conditions and the relevant literature reported earlier will be covered. Furthermore, new research on the high-pressure and high-temperature three-phase fluidization will be highlighted as well as computational fluid dynamics. 

An understanding of three-phase flows is still limited because of complicated phenomena underlying interactions between phases, such as the particle-bubble interaction and the liquid interstitial effect during particle-particle collision. Recently, several computational fluid dynamics models were reported to simulate three-phase fluidization behavior (Gidaspow et al., 1994 Grevskott et al., 1996 Mitra-Majumdar et al., 1997). These models are based on the multifluid... 

To date, the design of three-phase fluidized beds still relies heavily on experimental observations, empirical correlations, and engineering models. With increasing computer power, the employment of the computational fluid dynamics approach has gained consider-... 

The model species, total mass, momentum, and energy continuity equations are similar to those presented in Section 13.7 on fluidized bed reactors. Constant values of the gas and liquid phase densities, viscosities, and diffusivities were assumed, as well as constant values of the interphase mass transfer coefficient and the reaction rate coefficient. The interphase momentum transfer was modelled in terms of the Eotvos number as in Clift et al. [1978]. The Reynolds-Averaged Navier-Stokes approach was taken and a standard Computational Fluid Dynamics solver was used. In the continuous liquid phase, turbulence, that is, fluctuations in the flow field at the micro-scale, was accounted for using a standard single phase k-e model (see Chapter 12). Its applicability has been considered in detail by Sokolichin and Eigenberger [1999]. No turbulence model was used for the dispersed gas phase. Meso-scale fluctuations around the statistically stationary state occur and were explicitly calculated. This requires a transient simulation and sufficiently fine spatial and temporal grids. 

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