Combustion, computational fluid dynamics model

A popular small-scale thermal biomass conversion method is combustion in grate furnaces. To meet the emission regulations for such a furnace, the operating conditions and design of the furnace have to be chosen carefully. Numerical models, known as computational fluid dynamics (CFD), can support the making of these choices, provided that accurate sub-models for the phenomena occurring in the oven are available. 

Appendix B consists of a systematic classification and review of conceptual models (physical models) in the context of PBC technology and the three-step model. The overall aim is to present a systematic overview of the complex and the interdisciplinary physical models in the field of PBC. A second objective is to point out the practicability of developing an all-round bed model or CFSD (computational fluid-solid dynamics) code that can simulate thermochemical conversion process of an arbitrary conversion system. The idea of a CFSD code is analogue to the user-friendly CFD (computational fluid dynamics) codes on the market, which are very all-round and successful in simulating different kinds of fluid mechanic processes. A third objective of this appendix is to present interesting research topics in the field of packed-bed combustion in general and thermochemical conversion of biofuels in particular. 

Saastamoinen J.J., Huttunen M., and Kjaldman L., Modelling of Pyrolysis and Combustion of Biomass Particles , the fourth European Computational Fluid Dynamics Conference, 7-11 Sept, Athens, Greece, (1998)... 

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. 

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. 

Fortunately, numerical modeling despite its many limitations associated with grid resolution, choice of turbulence model, or assignment of boundary conditions is not intrinsically limited by similitude or scale constraints. Thus, in principle, it should be possible to numerically simulate all aspects of fires within canopies for which realistic models exist for combustion, radiation, fluid properties, ignition sources, pyrolysis, etc. In addition it should be possible to examine all interactions of fire properties individually, sequentially and combined to evaluate nonlinear effects. Thus, computational fluid dynamics may well provide a greater understanding of the behavior of small, medium, and mass fires in the future. 

Any notable accumulation of gas was unlikely since the two fans inside the chassis create a flow rate of approximately 180 CFM of air through the system. This corresponded to more than 95 complete air changes or turnovers every minute (Heck and Manning, 2000). The most likely zone of gas escape would be above the microreactor due to a membrane failure. If this occurs, the control system should have interlocked and shutoff the flow of combustible gas to that reaction channel. The flammable gas that does escape would have been immediately diluted by air flowing over the microreactor at an estimated rate of 120 ft min (Heck and Manning, 2000). To provide a more detailed analysis of gas mixing in the immediate vicinity of a microreactor die, a computational fluid dynamics (CFD) model was constructed to simulate the gas flow hydrodynamics. This simulation quantifies that there is a recirculation zone above the reactor with an airflow rate... 

From geological studies to aerospace engineering, physical modeling has been widely used in the industry to study complex fluid dynamics where engineering calculations or computational fluid dynamics are deemed either unreliable (the former) or uneconomical (the latter). In the field of combustion, physical modeling is employed in studying flow distribution involving combustion air, over-fire air (OFA), and flue gas recirculation (FGR) as well as isothermal flows in combustion chambers of furnaces, boilers, heat recovery and steam... 

Hoke, B. C., and Schill, P. "CFD Modeling in the Glass Industry." In Computational Fluid Dynamics in Industrial Combustion, edited by C. Baukal. Boca Raton, FL CRC Press, pp. 411-453,2001. 

Previously, attempts were made to model the middle IR band emission spectra (2 to 5.5 i,m) from the rocket fuel chemistry and the physical properties during combustion by making use of techniques such as quantum mechanics and computational fluid dynamics. These methods proved to be too time consuming and the accuracies of the predictions were not acceptable (Roodt, 1998). 

There are three kinds of models of fires, (1) based on computational fluid dynamics (CFD) of the combustion process of fire, (2) phenomenological models and (3) empirical or semi-empirical models based on full scale tests. 

In order to understand the flow patterns within the combustion chamber to allow selection of suitable Injection points and make initial evaluation of the likely effect, flow modelling was required. Both computational fluid dynamics (CFD) and acid/alkali physical modelling were used. 

Computational Fluid Dynamics in Combustion Processes -Examples of Problem Specific Modelling Approaches... 

Ronnie Andersson is an Assistant professor in Chemical Engineering at Chalmers University of Technology. He obtained his PhD at Chalmers in 2005, and from 2005 until 2010 he worked as consultant at Epsilon HighTech as a speciahst in computational fluid dynamic simulations of combustion and multiphase flows. His research projects involve physical modeling, fluid dynamic simulations, and experimental methods. 

Boemer A, Qi H, Renz U, Vasquez S, Boysan F (1995) Eulerian computation of fluidized bed hydrodynamics—a comparison of physical models. Fluidized bed combustion, vol 2. ASME Campbell CS (1990) Rapid granular flows. Atmu Rev Fluid Mech 22 57-92 Carlo AD, Bocci E, Zuccari F, DeU Era A (2010) Numerical investigation of sorption enhanced steam methane reforming process using computational fluid dynamics Eulerian-Eulerian code. Ind Eng Chem Res 49 1561-1576... 

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