FLUENT computer code

Later on, containment codes MELCOR, DRASYS and RALOC, and advanced CFD computer codes (FLUENT, TRIO). 

Computer code FLUENT, ISP-43 (Muhlbauer International Standard Problem ISP-43. Comparison of Pretest Calculations with Experimental Results. Report NRI 11 464, November 2000)... 

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. 

The NRI group selected very simple input model since the ISP-43 represented our first larger application of the FLUENT 5 code and mainly of the GAMBIT pre-processor. After several attempts we decided not to model the flaps in the lower part of the downcomer, the perforated bottom of the core barrel, lower support plate, and the heater rods, and spent the capacity of the computer on the rest of the domain. Also the outlet plane was situated at the position of the support plate, quite near the downcomer outlet. Hexahedral control volumes were used throughout the domain with the exception of the region of cold leg nozzles, where unstructured tetrahedral mesh was generated. 

There is growing interest in CFD computer codes that are capable of modelling 2-D and 3-D fields, and which have the potential to simulate complex geometries. However, more developments and validation is necessary. The FLUENT code will be described below as an example of a CFD code. 

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. 

Venneker et al. (2002) used as many as 20 bubble size classes in the bubble size range from 0.25 to some 20 mm. Just like GHOST , their in-house code named DA WN builds upon a liquid-only velocity field obtained with FLUENT, now with an anisotropic Reynolds Stress Model (RSM) for the turbulent momentum transport. To allow for the drastic increase in computational burden associated with using 20 population balance equations, the 3-D FLUENT flow field is averaged azimuthally into a 2-D flow field (Venneker, 1999, used a less elegant simplification )... 

The Fluent code with the RSM turbulence model, predict very well the pressure drop in cyclones and can be used in cyclone design for any operational conditions (Figs. 3, 5, 7 and 8). In the CFD numerical calculations a very small pressme drop deviation were observed, with less than 3% of deviation at different inlet velocity which probably in the same magnitude of the experimental error. The CFD simulations with RNG k-e turbulence model still yield a reasonably good prediction (Figs. 3, 5, 7 and 8) with the deviation about 14-20% of an experimental data. It considerably tolerable since the RNG k-e model is much less on computational time required compared to the complicated RSM tmbulence model. In all cases of the simulation the RNG k-< model considerably underestimates the cyclone pressme drop as revealed by Griffiths and Boysan [8], However under extreme temperature (>850 K) there is no significant difference between RNG k-< and RSM model prediction. 

Typically, the numerical solutions techniques used are very specific to the problem. Particularly challenging problems include moving front problems where concentration profiles, for example, may vary widely over a short distance but may not change much at other spatial locations. The spatial discretization must be small close to the front for accuracy and numerical stability, but must be larger at other locations to reduce computation time. Various adaptive grid techniques to change the spatial step sizes have been developed for these problems. One of the more common codes to solve fluid-flow-related problems is FLUENT. 

Various hypotheses can be introduced to lighten the computational effort required. For example, Kolaczkowski et al. (2007) approached the simulation of an isolated spherical pellet using the commercial code ANSYS Fluent, adopting a standard Arrhenius expression for the definition of the heterogeneous reaction... 

Memory requirement for an unstmctured CFD code such as Fluent is approximately 1 GB RAM per IM cells. Nowadays, a total RAM of the order of 5-10 GB is standard for a single off-the-shelf PC configuration, making it possible in principle to fit the entire simulation into such a machine. However, to obtain practical turnover times, a parallel computer platform would be needed to solve virtual mannequin problems. In the form of Beowulf Linux clusters, such systems are quite inexpensive nowadays and can be bought virtually off-the-shelf. The development of a reliable, full-scale virtual matmequin seems a very realistic and feasible goal for the next few years. 

For the simulations, the governing equations described in section 4.4 are solved using the commercial CFD code FLUENT. The computational procedure is described in the following section. 

Our process simulation tool ANGTANK (Mota et al., 1997,2001) and FLUENT address different regions of the adsorption tank, i.e., the computational domains employed by the two codes do not overlap. ANGTANK models the nonisothermal adsorption dynamics in a cylindrical packed bed of adsorbent, whereas FLUENT models the hydrodynamics and heat transfer of the exhaust gas flowing in the annular space of the jacket and also takes into account heat transfer in the cylinder wall. Both codes employ two-dimensional axially-symmetric cylindrical coordinates. The different regions of the computational domain are depicted in Figure 3. 

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