Modeling fluid dynamics model

Computer modelling provides powerful and convenient tools for the quantitative analysis of fluid dynamics and heat transfer in non-Newtonian polymer flow systems. Therefore these techniques arc routmely used in the modern polymer industry to design and develop better and more efficient process equipment and operations. The main steps in the development of a computer model for a physical process, such as the flow and deformation of polymeric materials, can be summarized as ... 

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). 

Spray characteristics are those fluid dynamic parameters that can be observed or measured during Hquid breakup and dispersal. They are used to identify and quantify the features of sprays for the purpose of evaluating atomizer and system performance, for estabHshing practical correlations, and for verifying computer model predictions. Spray characteristics provide information that is of value in understanding the fundamental physical laws that govern Hquid atomization. 

A principal purpose of ladle metallurgy is to produce a weU-stirred and homogeneous bath. Thus considerable effort has been spent on the fluid dynamics necessary to ensure this. Water has been a valuable tool as a modeling agent. 

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. 

Despite their popularity, these methods normally have an inherent limitation—the fluid dynamics information they generate is usually described in global parametric form. Such information conceals local turbulence and mixing behavior that can significantly affect vessel performance. And because the parameters of these models are necessarily obtained and fine-tuned from a given set of experimental data, the validity of the models tends to extend over only the range studied in that experimental program. 

Concluding, it is essential to represent complex, real-life flow situations by computationally tractable models that retain adequate details. As an example, a computational snapshot approach that simulates the flow in stirred reactors or other vessels for any arbitrary impeller has been developed [5]. This approach lets the engineer simulate the detailed fluid dynamics around the impeller blades with much less computations that would otherwise be required. Improvements in CFD technique are likely to encourage further work along these lines. 

Skiiret, E. 1993. Advanced design of ventilation systems Ventilation models. Lecture Series, Von Karman Institute for Fluid Dynamics. 

Another detailed method of determining pressures is computational fluid dynamics (CFD), which uses a numerical solution of simplified equations of motion over a dense grid of points around the building. Murakami et al. and Zhoy and Stathopoulos found less agreement with computational fluid dynamics methods using the k-e turbulence model typically used in current commercial codes. More advanced turbulence models such as large eddy simulation were more successful but much more costly. ... 

Computational fluid dynamics (CFD) is becoming more popular, as discussed above for building pressures. However, a recent paper found difficulties in the practical use of current commercial codes due to the wide range of user inputs and decisions. - Other papers are exploring alternatives to the standard k- e model typically used in commercial codes today. -" ... 

In theory it should be possible to calculate the capture efficiency without measurements. Some attempts have used computational fluid dynamics (CFD) models, but difficulty modeling air movement and source characteristics have shown that it will be a long time before it will be possible to calculate the capture efficiency in advance. ... 

Computational fluid dynamics methods may allow for more accurate predictions. These models account for turbulence and other parameters such as thermal effects. A description of these methods is included in Chapter 11. 

Four methods for industrial air technology design are presented in this chapter computational fluid dynamics (CFD), thermal building-dynamics simulation, multizone airflow models, and integrated airflow and thermal modeling. In addition to the basic physics of the problem, the methods, purpose, recommended applications, limitations, cost and effort, and examples are pro vided. 

Davidson, L. Turbulence modelling and calculation of ventilation parameters in ventilated rooms. Lie. thesis. Report 86/10, Dept, of Thermo and Fluid Dynamics, Chalmers Universirv of Technology, Gothenburg, 1986. 

The highest level of integration would be to establish one large set of equations and to apply one solution process to both thermal and airflow-related variables. Nevertheless, a very sparse matrix must be solved, and one cannot use the reliable and well-proven solvers of the present codes anymore. Therefore, a separate solution process for thermal and airflow parameters respectively remains the most promising approach. This seems to be appropriate also for the coupling of computational fluid dynamics (CFD) with a thermal model. ... 

P. V. Nielsen. Airflow in a world exposition pavilion studied by scale-model experiments and computational fluid dynamics. ASHRAE Transactions, 101(2), 1118-1126, 1995. 

Particle trajectories can be calculated by utilizing the modern CFD (computational fluid dynamics) methods. In these calculations, the flow field is determined with numerical means, and particle motion is modeled by combining a deterministic component with a stochastic component caused by the air turbulence. This technique is probably an effective means for solving particle collection in complicated cleaning systems. Computers and computational techniques are being developed at a fast pace, and one can expect that practical computer programs for solving particle collection in electrostatic precipitators will become available in the future. 

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. 

The model is able to predict the influence of mixing on particle properties and kinetic rates on different scales for a continuously operated reactor and a semibatch reactor with different types of impellers and under a wide range of operational conditions. From laboratory-scale experiments, the precipitation kinetics for nucleation, growth, agglomeration and disruption have to be determined (Zauner and Jones, 2000a). The fluid dynamic parameters, i.e. the local specific energy dissipation around the feed point, can be obtained either from CFD or from FDA measurements. In the compartmental SFM, the population balance is solved and the particle properties of the final product are predicted. As the model contains only physical and no phenomenological parameters, it can be used for scale-up. 

Several reported chemical systems of gas-liquid precipitation are first reviewed from the viewpoints of both experimental study and industrial application. The characteristic feature of gas-liquid mass transfer in terms of its effects on the crystallization process is then discussed theoretically together with a summary of experimental results. The secondary processes of particle agglomeration and disruption are then modelled and discussed in respect of the effect of reactor fluid dynamics. Finally, different types of gas-liquid contacting reactor and their respective design considerations are overviewed for application to controlled precipitate particle formation. 

Computational Fluid Dynamics modelling predictions (Al-Rashed etal., 1996) indicate that such velocity gradients can vary considerably throughout a vessel, as illustrated in Figure 8.28. 

Schreiner etal. (2001) modelled the precipitation process of CaC03 in the SFTR via direct solution of the coupled mass and population balances and CFD in order to predict flow regimes, induction times and powder quality. The fluid dynamic conditions in the mixer-segmenter were predicted using CFX 4.3 (Flarwell, UK). 

W. Shyy, H. S. Udaykumar, M. M. Rao, R. W. Smith. Computational Fluid Dynamics with Moving Boundaries in Series in Computational and Physical Processes in Mechanics and Thermal Sciences. Washington, DC Taylor Francis, 1995 W. Shyy. Computational Modeling for Fluid Flow and Interfacial Transport. Amsterdam Elsevier, 1994. 

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