Computational fluid dynamics predictions using

Fig. 10.10 Effects of tool profile and orientation on the predicted flow from a two-dimensional computational fluid dynamics ° model using interfacial slip, with a limiting shear stress of 40 MPa (6 ksi). (a, b) Velocity vectors and the boundary at...

Fig. 1 0.1 2 Predicted streamlines for a three-dimensional computational fluid dynamics model using interfacial slip for (a) a Tri-° flute tool and (b) a Trivex tool. Adapted from Ref 28...

Although the Arrhenius equation does not predict rate constants without parameters obtained from another source, it does predict the temperature dependence of reaction rates. The Arrhenius parameters are often obtained from experimental kinetics results since these are an easy way to compare reaction kinetics. The Arrhenius equation is also often used to describe chemical kinetics in computational fluid dynamics programs for the purposes of designing chemical manufacturing equipment, such as flow reactors. Many computational predictions are based on computing the Arrhenius parameters. 

The simplest case of fluid modeling is the technique known as computational fluid dynamics. These calculations model the fluid as a continuum that has various properties of viscosity, Reynolds number, and so on. The flow of that fluid is then modeled by using numerical techniques, such as a finite element calculation, to determine the properties of the system as predicted by the Navier-Stokes equation. These techniques are generally the realm of the engineering community and will not be discussed further here. 

Computational fluid dynamics (CFD) is the numerical analysis of systems involving transport processes and solution by computer simulation. An early application of CFD (FLUENT) to predict flow within cooling crystallizers was made by Brown and Boysan (1987). Elementary equations that describe the conservation of mass, momentum and energy for fluid flow or heat transfer are solved for a number of sub regions of the flow field (Versteeg and Malalase-kera, 1995). Various commercial concerns provide ready-to-use CFD codes to perform this task and usually offer a choice of solution methods, model equations (for example turbulence models of turbulent flow) and visualization tools, as reviewed by Zauner (1999) below. 

In practical combustion systems, such as CO boilers, the flue gas experiences spatial and temporal variations. Constituent concentration, streamline residence time, and temperature are critical to determining an efficient process design. Computational fluid dynamics (CFD) modeling and chemical kinetic modeling are used to achieve accurate design assessments and NO, reduction predictions based on these parameters. The critical parameters affecting SNCR and eSNCR design are listed in Table 17.4. 

Although much progress has been made in the last decade regarding operation, design and scale-up of spin-filters, in most works found in the literature either fouling or retention problems (or both) were observed. A better comprehension of the fluid and particle dynamics involved in spin-filter perfusion would improve this situation. In this context, a valuable tool that could be used is computational fluid dynamics (CFD), which has been recently employed for the design and performance prediction of other cell separation devices [46,114]. 

A major feature of the work was the analytical modelling of the inclined pipeline situation using computational fluid dynamics, and comparing the numerical predictions with the experimental results. Test results obtained with 3 mm polymer pellets are presented in Figs. 17 and 18. In Fig. 17, the results are presented in terms of the difference between the... 

Shaw, C. T., Predicting Vehicle Aerodynamics Using Computational Fluid Dynamics—A User s Perspective. Research in Automotive Aerodynamics, SAE Special Publication 747, Feb. 1988, p. 119. 

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 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. 

Computational fluid dynamics (CFD) has emerged as a very valuable tool in modeling the real flow patterns in chemical reactors. It represents a quantum leap from the idealized reactor models or their modifications, such as the tanks-in-series or axial-dispersion models to account for nonidealities. It has the potential to account for flow and reactions inside a reactor in their entirety. CFD has been used successfully to predict the flow patterns and reactor performance in the case of reactions involving macro-mixing effects. 

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