Flow Modelling using Computational Fluid Dynamics

Flow Modelling using Computational Fluid Dynamics 

Chemical engineers were not the pioneers in this field because chemical engineering flow problems can be very complex. Some of the first users of CFD were car, plane and boat designers. One of the reasons for this was that CFD could tell the designers exactly what they wanted to know, that is the flow patterns obtained while their new designs moved. Indeed, the possibility to use Euler s equations for flow description has been one of the major contributions to the development of these applications. These kinds of CFD techniques have also been projected and have been successfully used to analyze heat flow from a body immersed into the flowing fluid [3.29, 3.30]. 

The use of CFD models for gas-liquid bubble columns has also raised considerable interest only Euler-Euler and Euler-Lagrange frameworks have been employed for the description of the gas and liquid phase states [3.38-3.42]. Bubble trays, considered as particular kinds of bubble columns, have lately presented enormous interest for the flow description by CFD. The flow patterns on a sieve tray have been analyzed in the liquid phase, solving the time-averaged equations of continuity and momentum [3.43]. 

The jump to the fully two-phase flow on a sieve tray requires the acceptance of some conditions [3.44]  

Then the simulation of real chemical engineering flows concerns a number of important difficulties beyond the pattern of turbulent flows. One of these complex problems concerns the description of viscosity however, this can be resolved using rheological equations. Another difficulty is the so-called micromixing problem, which must be characterized at the level considering the integration of a very little unit. 

---

Flow Modelling using Computational Fluid Dynamics... 

FIGU RE 12.7 Typical velocity pattern for a three-dimensional model using computational fluid dynamics for an axial flow impeller (A310). 

If one wants to model a process unit that has significant flow variation, and possibly some concentration distributions as well, one can consider using computational fluid dynamics (CFD) to do so. These calculations are very time-consuming, though, so that they are often left until the mechanical design of the unit. The exception would occur when the flow variation and concentration distribution had a significant effect on the output of the unit so that mass and energy balances couldn t be made without it. 

Grochowicz et al. modeled the secondary flow in serpentine and coiled tubes as the measure of the radial mixing using computational fluid dynamics techniques. They showed that flow characteristics in serpentine tubes result in considerably less band broadening per unit length than in linear tubes of the same inner... 

Estimating the size of the smallest length scale is relatively simple. One could use computational fluid dynamic modeling techniques or estimate them based on the power input to the system (head loss) and the mass of the fluid being powered. For example, in pipe flow, the energy dissipation rate is a function of the total head loss in the flow h, the volumetric flow rate Q, the density of the solution p, and the mass of the solution m, which in this case is the mass of fluid contained within the pipe [Equation (4.1-3)]... 

As a result of the significant variation in thermodynamic properties near and at the critical point, it is difficult to use Computational Fluid Dynamics (CFD) when modeling supercritical flows. Also, since small changes in temperature and pressure can have large effects on the structure of a fluid near the critical point, local property values are very important. 

Our discrete-particle approach possesses the important properties of mesoscopic systems. It can model easily the heterogeneous nature of complex fluid suspension in the presence of fluctuations. This allows for simulating processes, which cannot be modeled by computational fluid dynamics codes. We showed that our microscopic blood model can be used for simulating microscopic, multi-component blood flow under extreme conditions in presence of high acceleration [100]. 

The effect of drag reducers on the turbulence is modelled with computational fluid dynamics (CFD) by using a two-layer turbulence model. In the laminar buffer layer, the one-equation model of Hassid and Poreh (1975) is used to describe the enhanced dissipation of turbulence caused by drag reducers. The standard k-e model is applied in the fully turbulent regions. The flow conditions necessary to elongate the polymer, the drag reduction efficiency of polymers of different apparent molar masses and their degradation kinetics have been measured. This data has been used in the model development. 

This paper reviews the detailed hydrodynamics of Outokumpu flotation cells by using Computational Fluid Dynamic (CFD) modelling. This includes different computational grid type dependency defining in the CFD model and examining the flow pattern induced in the cell as well as validating the model. 

Adsorption process has been widely used in many chemical and related industries, such as the separation of hydrocarbon mixtures, the desulfurization of natural gas, and the removal of trace impurities in fine chemical production. Most of the adsorption researches in the past are focused on the experimental measurement of the breakthrough curve for studying the dynamics. The conventional model used for the adsorption process is based on one-dimensional or two-dimensional dispersion, in which the adsorbate flow is either simplified or computed by using computational fluid dynamics (CFD), and the distribution of adsorbate concentration is obtained by adding dispersion term to the adsorption equation with unknown turbulent mass dififusivity D(. Nevertheless, the usual way to find the D, is either by employing empirical correlation obtained from inert tracer experiment or by guessing a Schmidt number applied to the whole process. As stated in Chap. 3, such empirical method is unreliable and lacking theoretical basis. 

The flow becomes highly complex in a spiral-wound module containing a feed-side spacer screen. Numerical solutions of the governing equations incorporating most of these complexities have been/are being implemented (Wiley and Fletcher, 2003) using computational fluid dynamics models (see Schwinge et al. (2003) for the complex flow patterns in a spacer-filled channel). 

Boemer A, Qi H, Renz U, Vasquez S, Boysan F (1995) Eulerian computation of fluidized bed hydrodynamics—a comparison of physical models. Fluidized bed combustion, vol 2. ASME Campbell CS (1990) Rapid granular flows. Atmu Rev Fluid Mech 22 57-92 Carlo AD, Bocci E, Zuccari F, DeU Era A (2010) Numerical investigation of sorption enhanced steam methane reforming process using computational fluid dynamics Eulerian-Eulerian code. Ind Eng Chem Res 49 1561-1576... 

Karst et al. used computational fluid dynamics (CFD) to model dye hquor flow in beam and package dyeing and to determine how certain parameters affect liquor flow through the fibrous assemblies. 

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. 

Computer Models, The actual residence time for waste destmction can be quite different from the superficial value calculated by dividing the chamber volume by the volumetric flow rate. The large activation energies for chemical reaction, and the sensitivity of reaction rates to oxidant concentration, mean that the presence of cold spots or oxidant deficient zones render such subvolumes ineffective. Poor flow patterns, ie, dead zones and bypassing, can also contribute to loss of effective volume. The tools of computational fluid dynamics (qv) are useful in assessing the extent to which the actual profiles of velocity, temperature, and oxidant concentration deviate from the ideal (40). 

The Prandtl mixing length concept is useful for shear flows parallel to walls, but is inadequate for more general three-dimensional flows. A more complicated semiempirical model commonly used in numerical computations, and found in most commercial software for computational fluid dynamics (CFD see the following subsection), is the A — model described by Launder and Spaulding (Lectures in Mathematical Models of Turbulence, Academic, London, 1972). In this model the eddy viscosity is assumed proportional to the ratio /cVe. 

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. 

---