Computational fluid dynamics FLUENT

It has become quite popular to optimize the manifold design using computational fluid dynamic codes, ie, FID AP, Phoenix, Fluent, etc, which solve the full Navier-Stokes equations for Newtonian fluids. The effect of the area ratio, on the flow distribution has been studied numerically and the flow distribution was reported to improve with decreasing yiR. 

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. 

In computational fluid dynamics only the last two methods have been extensively implemented into commercial flow solvers. Especially for CFD problems the FVM has proven robust and stable, and as a conservative discretization scheme it has some built-in mechanism of error avoidance. For this reason, many of the leading commercially available CFD tools, such as CFX4/5, Fluent and Star-CD, are based on the FVM. The oufline on CFD given in this book wiU be based on this method however, certain parts of the discussion also apply to the other two methods. 

Individual process steps identified in a conceptual design (reactors or separation/purification units) are studied experimentally in the laboratory and/or by computer simulation (see simulation programs as given in SOFTWARE DIRECTORY or Computational Fluid Dynamics (CFD) programs for studying fluid dynamics, such as PHOENIX, FLUENT, and FIDAP). 

Computational fluid dynamics (CFD) programs are more specialized, and most have been designed to solve sets of equations that are appropriate to specific industries. They can then include approximations and correlations for some features that would be difficult to solve for directly. Four major packages widely used are Fluent (http //www.fluent.com/), CFX (now part of ANSYS), Comsol Multiphysics (formerly FEMLAB) (http //www.comsol.com/), and ANSYS (http //www.ansys.com/). Of these, Comsol Multiphysics is particularly useful because it has a convenient graphical-user interface, permits easy mesh generation and refinement (including adaptive mesh refinement), allows the user to add phenomena and equations easily, permits solution by continuation methods (thus enhancing... 

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... 

Takeuchi et al. 7 reported a membrane reactor as a reaction system that provides higher productivity and lower separation cost in chemical reaction processes. In this paper, packed bed catalytic membrane reactor with palladium membrane for SMR reaction has been discussed. The numerical model consists of a full set of partial differential equations derived from conservation of mass, momentum, heat, and chemical species, respectively, with chemical kinetics and appropriate boundary conditions for the problem. The solution of this system was obtained by computational fluid dynamics (CFD). To perform CFD calculations, a commercial solver FLUENT has been used, and the selective permeation through the membrane has been modeled by user-defined functions. The CFD simulation results exhibited the flow distribution in the reactor by inserting a membrane protection tube, in addition to the temperature and concentration distribution in the axial and radial directions in the reactor, as reported in the membrane reactor numerical simulation. On the basis of the simulation results, effects of the flow distribution, concentration polarization, and mass transfer in the packed bed have been evaluated to design a membrane reactor system. 

Significant progress is being made in fundamental approaches. The current powerful computational fluid dynamics (CFD) tools (e.g., FLUENT and CFX software)—based on the solution of differential mass and momentum balances—have made it possible to allow simulations of the flow patterns within the crystallizer. Both physical and mathematical modeling add to our knowledge and understanding of the nature of high-concentration suspension flows. 

This partial differential equation is deterministic by nature. In practice, however, many hydrodynamic phenomena (e.g., transition from laminar to turbulent flow) have chaotic features (deterministic chaos [Stewart 1993]). The reason for this is that the Navier-Stokes equation assumes a homogeneous ideal fluid, whereas a real fluid consists of atoms and molecules. Today highly developed numerical flow simulators (computational fluid dynamics, CFD) are available for solving the Navier-Stokes equation under certain boundary conditions (e.g.. Fluent Deutschland GmbH). These even allow complex flow conditions, including particle, droplet, bubble, plug, and free surface flow, as well as multiphase flow such as that foundin fluidized-bed reactors and bubble columns, to be treated numerically [Fluent 1998]. 

FLUENT Deutschland GmbH Computational Fluid Dynamics Darmstadt, http //www.fluent.de, 1998... 

Omran et al. have proposed a 3D, single phase steady-state model of a liquid feed DMFC [181]. Their model is implemented into the commercial computational fluid dynamics (CFD) software package FLUENT . The continuity, momentum, and species conservation equations are coupled with mathematical descriptions of the electrochemical kinetics in the anode and cathode channel and MEA. For electrochemical kinetics, the Tafel equation is used at both the anode and cathode sides. Results are validated against DMFC experimental data with reasonable agreement and used to explore the effects of cell temperature, channel depth, and channel width on polarization curve, power density and crossover rate. The results show that the power density peak and crossover increase as the operational temperature increases. It is also shown that the increasing of the channel width improves the cell performance at a methanol concentration below 1 M. 

Computational fluid dynamics (CFD) based on the continuum Navier-Stokes equations Eq. 2 has long been successfully used in fundamental research and engineering design in different fluid related areas. Namrally, it becomes the first choice for the simulation of microfluidic phenomena in Lab-on-a-Chip devices and is still the most popular simulation model to date. Due to the nonlinearity arising from the convention term, Eq. 2 must be solved numerically by different discretization schemes, such as finite element method, finite difference method, finite volume method, or boundary element method. Besides, there are a variety of commercially available CFD packages that can be less or more adapted to model microfluidic processes (e.g., COMSOL (http //www.femlab.com), CFD-ACE+ (http // www.cfdrc.com), Coventor (http //www. coventor.com), Fluent (http //www.fluent.com), and Ansys CFX (http //www.ansys.com). For majority of the microfluidic flows, Re number is... 

Increased power of present computers and progress in numerical methods and programming enables application of more sophisticated computer codes to some industrial problems. Before such aplication is made, the computer codes must be validated, especially when solving the problems of nuclear safety. Also the NRI therefore started validation and application of Computational Fluid Dynamics (CFD) codes to some selected problems encountered in NPP safety analyses. The commercial code FLUENT 5 was the first code undergoing such validation. 

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. 

Nowadays, the use of numerical methods associated with different algorithms of Computational Fluid Dynamics (CFD) to determine the concentration of the vapor cloud of hazardous substances released into the atmosphere, in space and time, has grown considerably (Cornier 2008, Middha 2009, Dharmavaram, 2005). CFD is found in some commercial software tools such as CFX, FLACS and FLUENT. The CFD tools transform... 

Figs. A4.11 and A4.12 show a comparison of Eq. [A4.2] with the experimental data by Kirillov et al. (2003). Fig. A4.13 shows a comparison between experimentally obtained HTC and wall temperature values and those calculated with the computational fluid dynamics (CFD) code FLUENT and Eq. [A4.2]. It is worth noting that in CFD codes, not all mrbulent models are applicable to heat transfer at supercritical pressures. These models need to be tuned on the basis of experimental data prior their use in similar conditions (Miletic et al., 2015). 

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