Computational fluid dynamics based models

In practical combustion systems, such as CO boilers, the flue gas experiences spatial and temporal variations. Constituent concentration, streamline residence time, and temperature are critical to determining an efficient process design. Computational fluid dynamics (CFD) modeling and chemical kinetic modeling are used to achieve accurate design assessments and NO, reduction predictions based on these parameters. The critical parameters affecting SNCR and eSNCR design are listed in Table 17.4. 

Some other studies were performed relating human exposure in urban areas based on ambient PM air concentrations determined with computational fluid dynamics (CFD) modelling applications. The three-dimensional CFD model MISKAM has been successfully implemented to provide better assessment of exposure to traffic-related air pollutants in urban areas [33]. 

Computational fluid dynamics based flow models were then developed to simulate flow and mixing in the loop reactor. Even here, instead of developing a single CFD model to simulate complex flows in the loop reactor (gas dispersed in liquid phase in the heater section and liquid dispersed in gas phase in the vapor space of the vapor-liquid separator), four separate flow models were developed. In the first, the bottom portion of the reactor, in which liquid is a continuous phase, was modeled using a Eulerian-Eulerian approach. Instead of actually simulating reactions in the CFD model, results obtained from the simplified reactor model were used to specify vapor generation rate along the heater. Initially some preliminary simulations were carried out for the whole reactor. However, it was noticed that the presence of the gas-liquid interface within the solution domain and inversion of the continuous phase. 

Papadakis and King (1988a,b) used this PSI-Cell model to simulate a spray dryer and compare their predicted results with limited experimental results associated with a lab-scale spray dryer. They have shown that the measured air temperatures at various levels below the roof of the spray drying chamber were well predicted by the computational fluid dynamics (CFD) model. Negiz et al. (1995) developed a program to simulate a cocurrent spray dryer based on the PSI-Cell model. Straatsma et al. (1999) developed a drying model, named NIZO-DrySim, to simulate aspects of... 

Kashid et al. studied the fiow patterns within the slugs and mass transfer between two consecutive slugs in liquid-liquid slug flow using a finite element-based computational fluid dynamics (CFD) model [51]. The model equations are implemented in the open-source software FEATFLOW (www.featflow.de). Figure 12.18 shows snapshots of the concentration profiles of the extract (acetic acid). These results are compared with experimental results and are consistent with them. 

DTR experiments were carried out in an argon atmosphere at ten ratures from 400 C to 17(K) C (750TP to 3090 F) in order to determine the devolatilization reactivity of both sawdust and urban wood waste. Using the equaticms shown in Giapter 2, Arrhenius equations were then determined. It slmuld be noted that these are reactivity measurements based upon bulk furnace temperatures rather than particle temperatures. These measurements provide tiie data necessary for computational fluid dynamics (CFD) modeling. 

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. 

The effects deriving from both nonideal mixing and the presence of multiphase systems are considered, in order to develop an adequate mathematical modeling. Computational fluid dynamics models and zone models are briefly discussed and compared to simpler approaches, based on physical models made out of a few ideal reactors conveniently connected. 

Frank, R. 1980. S02-particulate interactions recent observations. Am. J. Ind. Med. l(3-4) 427-434. Frederick, C.B., M.L. Bush, L.G. Lomax, K.A. Black, L. Finch, J.S. Kimbell, K.T. Morgan, R.P. Subramaniam, J.B. Morris, and J.S. Ultman. 1998. Apphcation of a hybrid computational fluid dynamics and physiologically based inhalation model for interspecies dosimetry extrapolation of acidic vapors in the upper airways. Toxicol. Appl. Pharmacol. 152 (1) 211-231. 

Sweeney LM, Andersen ME, and Gargas ML (2004) Ethyl acrylate risk assessment with a hybrid computational fluid dynamics and physiologically based nasal dosimetry model. Toxicological Sciences. 

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