Computational fluid dynamics issues

A young scientist said, I have never seen a complex scientific area such as industrial ventilation, where so little scientific research and brain power has been applied. This is one of the major reasons activities in the industrial ventilation field at the global level were started. The young scientist was right. The challenges faced by designers and practitioners in the industrial ventilation field, compared to comfort ventilation, are much more complex. In industrial ventilation, it is essential to have an in-depth knowledge of modern computational fluid dynamics (CFD), three-dimensional heat flow, complex fluid flows, steady state and transient conditions, operator issues, contaminants inside and outside the facility, etc. 

The computational fluid dynamics investigations listed here are all based on the so-called volume-of-fluid method (VOF) used to follow the dynamics of the disperse/ continuous phase interface. The VOF method is a technique that represents the interface between two fluids defining an F function. This function is chosen with a value of unity at any cell occupied by disperse phase and zero elsewhere. A unit value of F corresponds to a cell full of disperse phase, whereas a zero value indicates that the cell contains only continuous phase. Cells with F values between zero and one contain the liquid/liquid interface. In addition to the above continuity and Navier-Stokes equation solved by the finite-volume method, an equation governing the time dependence of the F function therefore has to be solved. A constant value of the interfacial tension is implemented in the summarized algorithm, however, the diffusion of emulsifier from continuous phase toward the droplet interface and its adsorption remains still an important issue and challenge in the computational fluid-dynamic framework. 

Nancy Jackson I think that it is both. Within the chemical industry, certainly in the areas in which there are more precompetitive issues, we have seen in the past five or six years, certainly with Dow, that they have worked closely with the national labs in these precompetitive issues. The computational fluid dynamics consortium is a very good example. Separations is another good example often there is a lot of precompetitive work in that area. There are large consortia in that area, not with Sandia, but with other national labs. But Sandia is doing a lot of work with industry on separations too. Yes, there are areas other then catalysis that are farther along with the chemical industry, but I am unsure if they are as far along as microelectronics. 

Slurry bubble column reactor for methanol and other hydrocarbons productions from synthesis gas is an issue of interest to the energy industries throughout the world. Computational fluid dynamics (CFD) is a recently developed tool which can help in the scale up. We have developed an algorithm for computing the optimum process of fluidized bed reactors. The mathematical technique can be applied to gas solid, liquid-solid, and gas-liquid-solid fluidized bed reactors, as well as the LaPorte slurry bubble column reactor. Our computations for the optimum particle size show that there is a factor of about two differences between 20 and 60 pm size with maximum granular-like temperature (turbulent kinetic energy) near the 60 pm size particles. 

It is important to know how mixing can influence the selectivity of chemical reactions, and computational fluid dynamics (CFD) simulations are quite helpful in providing a deeper insight into this issue. The calculations are based on a laminar flow model where mixing takes place only by molecular diffusion (Figure 6.9). Let us focus on the competitive... 

Utilize computational fluid dynamics (CFD) models to rmderstand and address heat and mass transfer issues and reactor performance for steady-state and transient analysis. 

Other critical issues include the flame stabilization location and acoustic issues that could be high or low frequency. A flare test facility can also be used to validate computational fluid dynamics (CFD see Chapter 11)... 

Thompson D.S., Machiraju R., Dusi V.S., Jiang M., Nair J., Thampy S., and Craciun G., Physics-based Feature Mining of Computational Fluid Dynamics Data Sets, Special issue of IEEE... 

Computational fluid dynamics models were developed over the years that include the effects of leaflet motion and its interaction with the flowing blood (Bellhouse et al., 1973 Mazumdar, 1992). Several finite-element structural models for heart valves were also developed in which issues such as material and geometric nonlinearities, leaflet structural dynamics, stent deformation, and leaflet coaptation for closed valve configurations were effectively dealt with (Bluestein and Einav, 1993 1994). More recently, fluid-structure interaction models, based on the immersed boundary technique. 

Accurate CFD (computational fluid dynamic) simulation of the flow in stirred tanks requires correct specification of both the geometry and the physical conditions of the flow. While specification of the geometry, the gridding, and the solution algorithm is relatively straightforward, some other issues remain difficult. The most challenging problem is definition of a physically accurate, computationally tractable impeller or impeller model which incorporates the effect of the tank geometry. This... 

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). 

The National Energy Technology Laboratory (NETL) developed a 3-dimensional computational fluid dynamics (CFD) model to allow stack developers to reduce time-consuming build-and-test efforts. As opposed to systems models, 3-dimensional CFD models can address critical issues such as temperature profiles and fuel utilization important considerations in fuel cell development. 

A thorough treatment of the mathematics needed for model development and analysis is beyond the scope of this volume, and is presented in numerous sources [ 1,2]. Herein, the goal is to provide a physical understanding of the important issues relevant to hemodynamic flow and transport. Solution methods are summarized, and the benefits associated with use of computational fluid dynamics (CFD) packages... 

Until this point we have considered only inelastic liquids in both the analytical and numerical treatments of polymer processing. The viscoelasticity of polymer melts sometimes plays a major role in the mechanics of processing behavior, and we take up this important issue in the next and subsequent chapters. Numerical problems are greatly compounded by the presence of fluid elasticity, but the overall approach is unchanged. We will return to the use of computational fluid dynamics with complex rheology after taking up the subject of viscoelasticity. 

Appropriate design and layout of a bioreactor system for production scale are a key issue for the successful production of biopharmaceuticals. Here, techniques such as characterization of the reactors with respect to power input, mass transfer, and mixing are applied, often coupled with computational fluid dynamic simulations [34,113-119]. 

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