CFD codes

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 ... 

The numerical solution of the energy balance and momentum balance equations can be combined with flow equations to describe heat transfer and chemical reactions in flow situations. The simulation results can be in various forms numerical, graphical, or pictorial. CFD codes are structured around the numerical algorithms and, to provide easy assess to their solving power, CFD commercial packages incorporate user interfaces to input parameters and observe the results. CFD... 

The commercial CFD codes use the finite volume method, which was originally developed as a special finite difference formulation. The numerical algorithm consists of the following steps ... 

Almost all modern CFD codes have a k - model. Advanced models like algebraic stress models or Reynolds stress model are provided FLUENT, PHOENICS and FLOW3D. Table 10-3 summarizes the capabilities of some widely used commercial CFD codes. Other commercially CFD codes can be readily assessed on the web from hptt//www.cfd-online.com This is largest CFD site on the net that provides various facilities such as a comprehensive link section and discussion forum. 

Particular features of some commercial CFD codes—A comparison... 

Calculate maximum air velocity, airflow rate, and excessive temperature (relative to the ambient air temperature equal to 20 °C) in thermal plume above the heated cube (0.66 m x 0.66 m x 0.66 m) with convective heat production = 225 W, at heights of 2.0 m and 4.0 m above the floor level. Neglect temperature gradient along the room height. Compare the results with predictions made for the same case using CFD code (Fig. 7.80). 

It should also be remembered that the discretization scheme influences the accuracy of the results. In most CFD codes, different discretization schemes can be chosen for the convective terms. Usually, one can choose between first-order schemes (e.g., the first-order upwind scheme or the hybrid scheme) or second-order schemes (e.g., a second-order upwind scheme or some modified QUICK scheme). Second-order schemes are, as the name implies, more accurate than first-order schemes. However, it should also be remembered that the second-order schemes are numerically more unstable than the first-order schemes. Usually, it is a good idea to start the computations using a first-order scheme. Then, when a converged solution has been obtained, the user can continue the calculations with a second-order scheme. 

This section deals mainly with the interaction of thermal models as outlined in Section J 1.3 and airflow models as described in Section 11.4 for the purpose of integrated modeling of thermally induced (stack-driven) natural ventilation, governed by the thermal behavior of the building. For the integrated analysis ol air velocity fields and radiative and thermal effects in the building using CFD codes, see also Section 11.2 and Ott and Schild.-... 

Determination of high-resolution, accurate flow data providing a basis for boundary conditions used in CFD, for the verification of CFD results, and for improvement in CFD codes... 

The highly resolved velocity profile can be mapped in the vicinity of solid boundaries such as the walls of a room and in the entire enclosure, providing relevant data for CFD boundary conditions. These data form a basis for verification of CFD results and for improvement of CFD codes. 

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. 

Commercially available CFD codes have three main elements in common. Firstly, the pre-processor enables the user to define the geometry, which is often referred to as the computational domain or flow domain of the problem, for... 

Theoretical research is then discussed. Most theoretical research has concentrated on blast generation as a function of flame speed. Models of flame-acceleration processes and subsequent pressure generation (CFD-codes) are described as well, but in less detail. 

We first explain the setting of reactors for all CFD simulations. We used Fluent 6.2 as a CFD code. Each reactant fluid is split into laminated fluid segments at the reactor inlet. The flow in reactors was assumed to be laminar flow. Thus, the reactants mix only by molecular diffusion, and reactions take place fi om the interface between each reactant fluid. The reaction formulas and the rate equations of multiple reactions proceeding in reactors were as follows A + B R, ri = A iCaCb B + R S, t2 = CbCr, where R was the desired product and S was the by-product. The other assumptions were as follows the diffusion coefficient of every component was 10" m /s the reactants reacted isothermally, that is, k was fixed at... 

Generally, the behavior of a gas (or vapor) cloud during dispersion can be either buoyant or gravity driven. The former is regarded with heavier-than-air gases and the later with lighter-than-air gases. Currently, dispersion estimations can be performed via the use of semiempirical one-dimensional models (the so-called box models) and the CFD codes. 

In practical applications, dispersion calculations need to be performed in topographically complex environment. Thus, solid obstacles intervening in the area dispersion should be counting in the computations. In previous validation works, it has been proved that CFD codes constitute powerful tools for complex terrain dispersion simulation providing high accuracy results with excellent visualization capabilities, which can be helpful in quantitative risk analysis applications [55]. The dominating mixing mechanism between... 

The Euler Lagrangian approach is very common in the field of dilute dispersed two-phase flow. Already in the mid 1980s, a particle tracking routine was available in the commercial CFD-code FLUENT. In the Euler-Lagrangian approach, the dispersed phase is conceived as a collection of individual particles (solid particles, droplets, bubbles) for which the equations of motion can be solved individually. The particles are conceived as point particles which move... 

Most commercial CFD-codes are based upon the ideas and numerical methods developed back in the 1970s at Imperial College London by Spalding, Patankar, Gosman, and others ... 

The difference in speed between a LB code and a FV code in the above studies partly originates from their different character the FV code in a general-purpose commercial CFD code should be robust and suited in many applications, while the LB codes used are of a research type and usually strongly dedicated and geared to a specific job. 

Whenever a free surface is present at some (mean) fixed position, most CFD codes assume it to be strictly flat, while in the direction normal to the free surface velocities and gradients of most variables are taken zero. Usually, this is accomplished by defining mirror cells at the free surface. It is not clear what the effect is of the use of such mirror cells on the flow field in the upper part of the vessel in comparison with real life where the surface is not necessarily flat. 

Despite the many advances, the users of CFD codes must keep in mind that the underlying transport equations are based on models, which may or may not be valid for a particular application. This fact often escapes the minds of newcomers to the field who are typically overwhelmed by the numerical issues associated with convergence, grid-independence, and post-processing. Even... 

As mentioned before in Eq. (3), the most common source of SGS phenomena is turbulence due to the Reynolds number of the flow. It is thus important to understand what the principal length and time scales in turbulent flow are, and how they depend on Reynolds number. In a CFD code, a turbulence model will provide the local values of the turbulent kinetic energy k and the turbulent dissipation rate s. These quantities, combined with the kinematic viscosity of the fluid v, define the length and time scales given in Table I. Moreover, they define the local turbulent Reynolds number ReL also given in the table. 

Note that although the density is constant, we have included it in the transport equations to be consistent with the formulation used in commercial CFD codes. 

The next step would be to implement the CFD transport equation for the state variables in a CFD code. This is a little more difficult for the two-environment model (due to the gradient terms on the right-hand sides of Eqs. 37 and 38) than for the moment closure. Nevertheless, if done correctly both models will... 

Finally, we can also mention that laminar-flow systems with non-Newtonian fluids often require special numerical algorithms that are usually not available in CFD codes designed mainly for turbulent flows. 

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