Computational fluid dynamics defined

The advantages of this type of system are obvious the pore space is of sufficient complexity to represent any natural or technical pore network. As the model objects are based on computer generated clusters, the pore spaces are well defined so that point-by-point data sets describing the pore space are available. Because these data sets are known, they can be fed directly into finite element or finite volume computational fluid dynamics (CFD) programs in order to simulate transport properties [7]. The percolation model objects are taken as a transport paradigm for any pore network of major complexity. 

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. 

The equations implemented are those defined in Sections 3.2-3.4, i.e. in a partial differential form, for each cell component. This approach is also referred to as Computational Fluid Dynamic (CFD). In order to illustrate the capabilities of the model, in terms of assessment of particular phenomena taking place within the fuel cell, one particular problem is analyzed for each geometry. In particular, for the disk-shaped cell, emphasis is put on the effect of the gas channel configuration on the gas distribution, and, ultimately, on the resulting performance. For the tubular geometry, three different options for the current collector layouts are analyzed. 

Computational fluid dynamics enables us to investigate the time-dependent behavior of what happens inside a reactor with spatial resolution from the micro to the reactor scale. That is to say, CFD in itself allows a multi-scale description of chemical reactors. To this end, for single-phase flow, the space resolution of the CFD model should go down to the scales of the smallest dissipative eddies (Kolmogorov scales) (Pope, 2000), which is inversely proportional to Re-3/4 and of the orders of magnitude of microns to millimeters for typical reactors. On such scales, the Navier-Stokes (NS) equations can be expected to apply directly to predict the hydrodynamics of well-defined system, resolving all the meso-scale structures. That is the merit of the so-called DNS. 

Software tools are applied in every step of process development. Tools for individual reactor simulations such as computational fluid dynamic simulations are not the topic in this chapter. These tools supply only numerical data for specific defined reactor geometry and defined specific process conditions. A change of parameter would demand a complete recalculation, which is often a very time-consuming process and not applicable to a parameter screening. Methods for reactor optimization by CFD are described in detail in the first volume of this series. Tools for process simulation allow the early selection of feasible process routes from a large... 

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. 

A commercial Computational Fluid Dynamics package (FIDAP Version 7.6, Fluid Dynamics International, Evanston, IL) based on the finite element method was used to solve the governing continuity, momentum and heat transport equations. A mesh was defined with more nodes near the wall and the entrance of the tubular heat exchanger to resolve the larger variations of temperature and velocities near the wall and the entrance. 

Industrial use of hydrogen has experienced a few accidents, which subsequently have been useful in defining norms and procedures. One occurred in a narrow Stockholm street in 1983, where 13.5 kg of H2 escaped from a set of 20-MPa pressure tanks with defect cormections and exploded (Fig. 5.4a) 16 people were injured, and 10 cars and the adjacent building were heavily damaged. This accident has recently been modelled by computational fluid dynamics methods, giving the distribution of H2 velocities and concentra-... 

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. 

Carry out heat and mass balances, airflow rate for drying, heater capacity. Computational fluid dynamics (CFD) simulations for such a spray dryer may be performed to determine trends as accuracy of CID models are still not well defined. 

Computational Fluid Dynamics (CFD) Results The data trend of the above presented analytical study has been verified by computational fluid dynamics (CFD) analysis. Moreover, some microscale-specific effects could be seen for the initial part of the process, the pressure drop is confined over a short distance (between stations 2 and 3 in Fig. 5a) as the shockwave travels further firom the left to the right, the pressure gradient dissipates more and more continuously over a longer range. Instead of a well-defined shockwave, a set of compression waves can... 

The heart of the eompander is the design of the impellers. A high performance aerodynamic shape for eaeh expander and compressor impeller is defined using Computational Fluid Dynamics (CFD). Finite Element Analysis (FEA) verified acceptable stresses due to speed and blade loading, and defined an operating zone free from destructive natural frequencies. This work was verified by outside consultants. 

Stiffness can be defined as the resistance of an elastic body to deflect or distort upon application of a force. The definition in computational fluid dynamics corresponds to how hard is to solve a system of equations. The efficiency loss can be seen as a problem of stiffness, because it is a direct consequence of the flow. 

This paper reviews the detailed hydrodynamics of Outokumpu flotation cells by using Computational Fluid Dynamic (CFD) modelling. This includes different computational grid type dependency defining in the CFD model and examining the flow pattern induced in the cell as well as validating the model. 

Classical computational fluid dynamics (CFD) deals with a well-established system of equations. Typically, transport parameters and kinetic constants (if any) are also well defined. Scientific CFD problems are complicated by the presence of turbulence in the system. The spectrum of turbulence covers many orders of magnitude, which requires exhausting computing resources to resolve all the space scale and timescales. 

Shear rate, with reciprocal time as the unit, can be viewed as a time constant. If a process has a shear rate of 1000 s the events in the flow occur on the order of 1 ms. Such high shear rates are generated in the immediate vicinity of the impeller. However, the volume of this region is relatively small and, therefore, a very small amount of the material experiences these shear rates. The conditions in the vortices are similar, with high shear rate but small volume. The overall mixing process is defined by the combination of shear rate and the volume. Detailed information on the distribution of shear rates and respective volumes is difficult to obtain experimentally. Computational fluid dynamics can be used to extract such information for given mixing conditions. 

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