Computational fluid dynamics applications, examples

Some Computational fluid dynamics Application Examples... 

Computational fluid dynamics (CFD) is the analysis of systems involving fluid flow, energy transfer, and associated phenomena such as combustion and chemical reactions by means of computer-based simulation. CFD codes numerically solve the mass-continuity equation over a specific domain set by the user. The technique is very powerful and covers a wide range of industrial applications. Examples in the field of chemical engineering are ... 

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. 

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 two examples, deliberately chosen for their simplicity, show that computational fluid dynamics facilitate a more in-depth examination of the local flow behavior of twin screw extruders. Local peaks in the mechanical and thermal stresses can be easily identified. By changing the geometry, stresses can be reduced and the quality of the polymer can thereby be optimized. Another application focus is the rapid determination of the dimensionless axis intercepts for the pressure build-up A, and A2 and for the power requirement B, and B2. The significance of these parameters has already been discussed in detail in the two previous chapters. 

If the system geometry is too complex, for example, a detailed car engine, or a number of phenomena are important simultaneously, we may have to resort to numerical techniques. At present, the development and application of numerical methods have led to a new technology known as computational fluid dynamics (CFD). 

The majority of models for treating fireballs is based on correlations for its diameter and duration [2, 40]. More fundamental models are discussed in [2] and the application of methods of computational fluid dynamics ( CFD ) to fireballs is treated, for example, in [41]. 

To illustrate application of the more complex systems approach of integrating computational fluid dynamics (CFD) with intracellular kinetics we again take as a first example Sacchararomyces cerevisiae for which a dynamic model for the glycolysis based upon measurements of intracellular metabolites has been presented earlier [64,70]. To reduce the complexity of this model, the simplified version for anaerobic growth will be used. Measured data and model structure have been discussed above [71]. Simulations have been performed for a production... 

Computational fluid dynamics (CFD) approaches are emerging as alternative detailed tools for examining polymerization systems with complex mixing and reactor components. Recent examples on LDPE cases include Kolhapure and Fox [118], micromixing effects in tubular reactors Zhou etal. [119], tubular (and autoclave) reactors Wells and Ray [120], analysis of imperfect mixing effects applicable to many reactive flow systems, including LDPE autoclaves and Buchelli etal. [121], fouling effects. 

Standard texts including the application of computational fluid dynamics to chemically reacting flow situations are available see, for example, Gupta and... 

Many specihc examples of heat transfer may need to be considered in board design and, of course, most examples involve both conductive and convective transfer. For example, the air gap between the bottom of a standard SMD and the board affects the thermal resistance from the case to ambient, . A wider gap will result in a higher resistance, due to poorer convective transfer, whereas filling the gap with a thermal-conductive epoxy will lower the resistance by increasing conductive heat transfer. Thermal-modeling software is the best way to deal with these types of issues, due to the rigorous application of computational fluid dynamics (CFD) (Lee, 1994). 

In addition, just as employee participation is the key element of process safety management systems, worker involvement is crucial to the effective application of Safety Cases. Unfortunately, the perception among many that a Safety Case is a lengthy, highly technical document that can only be understood by specialists, mitigates effective employee participation. For example the Computational Fluid Dynamics (CFD) technique used to model explosion over-pressure uses very sophisticated mathematics. This sophistication makes communication with nonspecialists a challenge. In addition the sheer size and complexity of a Safety Case may serve as a barrier to the involvement of nontechnical personnel. 

At present, computational fluid dynamics methods are finding many new and diverse applications in bioengineering and biomimetics. For example, CFD techniques can be used to predict (1) velocity and stress distribution maps in complex reactor performance studies as well as in vascular and bronchial models (2) strength of adhesion and dynamics of detachment for mammalian cells (3) transport properties for nonhomogeneous materials and nonideal interfaces (4) multicomponent diffusion rates using the Maxwell-Stefan transport model, as opposed to the limited traditional Fickian approach. 

Unstructured grids have found widespread application in many areas of computational fluid dynamics and elasticity theory but have not been used very much in the geosciences. See [66] for a selective example in the geosciences. Unstructured grids have, however, found application in the context of 2.5D unstructured grid generation as explained next. 

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