Computational Fluid Dynamics and combustion modelling is one of the engineer's major tools for improvements of combustion devices such as gas turbines, engines and furnaces. Improved combustion efficiency is a key to reducing environmentally harmful emissions. Most flows of practical interest are turbulent and the turbulence will strongly affect the combustion process in general and the formation of pollutants such as soot and nitrogen oxides in particular. Furthermore, the development offuel efficient (low C02 emission) technologies typically requires operation close to stability limits where interactions of turbulence and chemistry come to the fore. The computational costs for a detailed description of the full range of turbulent motion is prohibitive and models need to be applied to account for turbulence effects on unresolved quantities. With the advent of High Performance Computing more complex models can be applied to flames of practical interest and stochastic methods are among the most promising methods to describe the mixing and chemical reactions in turbulent flows. However, stochastic methods are still expensive and essentially limit the number of reactive species that can be treated explicitly. Multiple Mapping Closure (MMC) offers a new framework that combines stochastic methods with -computationally less expensive- deterministic methods and the feasibility of this approach has been demonstrated in simplified flow geometries of non-reacting flows. If MMC can be extended to reacting flows, the new model can account for detailed chemical kinetics that are so vital for the accrate prediction of nitrogen oxide formation and soot. The aim of the proposed research is to validate MMC in reacting flows with multi-step chemistry. The methods will subsequently be applied to a variety of flames of practical interest. The latter will be the first application of MMC to real turbulent hydrocarbon flames where predictions of emissions of pollutants such as NOx and soot can be compared with experimental data. In particular, improvements to predictions of soot emissions can be expected since the approach enables the accurate resolution of all important turbulent fluctuations and the inclusion of a large number of reactive scalars which will be necessary for accurate predictions of soot precursors such as aromatics and acetylene
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01/01/07
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