Computational fluid dynamics turbulence modeling

With an effective thermal model of the cells, modules and overall system, an analysis of the performance under different situations and load conditions can be evaluated. This proves to be a very useful tool in the development of the pack as these thermal models can be input into computational fluid dynamic (CFD) models to determine how the cells will heat during operation. A good CFD model can be used to determine flow rates, turbulence, and heat transfer within a pack. In addition, it is possible to use a lumped parameter model to develop a simplified model where the external parameters are basically ignored and the model is designed using fully adjustable parameters to do high-level evaluations of the thermal effectiveness of a system. 

A number of commercially available computational fluid dynamics (CFD) models could be used for the prediction of squat. At the core of any CFD problem is a computational grid or mesh where the solution is divided into thousands of elements. These elements are usually 2D quadrilaterals or triangles and three-dimensional (3D) hexahedral, tetrahedral, or prisms. Mathematical equations are solved for each element by the numerical model. For hydrodynamics the Navier-Stokes equations (NSEs) can be solved to include viscosity and turbulence. The NSEs provide detailed prediction (vortices) of the flow field, but require very thin meshes, high central processing unit (CPU) time, and memory storage. Its resolution is also quite difficult with numerical instabilities. Examples of commercial CFD models include Fluent and Fidap. 

Recently, robust developments in the capabilities of computers have led to the modeling of transient turbulent flows becoming much less challenging. The experimental analysis of PCD, which requires sophisticated measurements (e.g., LDA, PDA, pressure, temperature, noise, etc.) is difficult, hostile (e.g., high noise level, around 110-130 dB) and expensive to carry out in comparison with numerical simulations (Zbicinski, 2002). Computational fluid dynamics (CFD) models for a steady or transient flow, for example, as generated by the pulse combustor, differ... 

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. 

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 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. 

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. 

Madnia, C.K., S.H. Frankel, and P. Givi. 1992. Reactant conversion in homogeneous turbulence Mathematical modeling, computational validations and practical applications. Theoretical Computational Fluid Dynamics 4 79-93. 

Using these methods, the elementary reaction steps that define a fuel s overall combustion can be compiled, generating an overall combustion mechanism. Combustion simulation software, like CHEMKIN, takes as input a fuel s combustion mechanism and other system parameters, along with a reactor model, and simulates a complex combustion environment (Fig. 4). For instance, one of CHEMKIN s applications can simulate the behavior of a flame in a given fuel, providing a wealth of information about flame speed, key intermediates, and dominant reactions. Computational fluid dynamics can be combined with detailed chemical kinetic models to also be able to simulate turbulent flames and macroscopic combustion environments. 

Computational fluid dynamics were used to describe the flow which undergoes a fast transition from laminar (at the fluid outlets) to turbulent (in the large mixing chamber) [41]. Using the commercial tool FLUENT, the following different turbulence models were applied a ke model, an RNC-ki model and a Reynolds-stress model. For the last model, each stream is solved by a separate equation for the two first models, two-equation models are applied. To have the simulations at... 

The interaction of dispersing clouds with vapor fences is a complex physical process. When a flow meets an obstruction, turbulence levels are increased downstream because of vorticities introduced into the flow field, and increased velocity gradients are induced by flow momentum losses. Detailed modeling of such a process is very difficult and requires a combination of small-scale experiments and computational fluid dynamics. 

Bai, X. S., and Fuchs, L., Numerical model for turbulent diffusion flames with applications. In Computational Fluid Dynamics (C. Hirsch, J. Periaux and W. Kordulla, eds.). Elsevier, Amsterdam, 1992, vol. l,p. 169. 

Simonin, O., Modelling turbulent reactive dispersed two-phase flows in industrial equipments. Proc. Third World Conf. Applied Computational Fluid Dynamics, May 19-23, Freiburg, Germany, Workshop E, 17.9,1996. 

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