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Prediction of Soot Formation in Laminar Opposed Diffusion Flame with Detailed and Reduced Reaction Mechanisms

The present work focuses on a computational study of a simplified soot model to predict soot production and destruction in methane/oxidizer (O2 and N2) and ethylene/air flames using a one-dimensional laminar opposed diffusion flame setup. Two different detailed reaction mechanisms (361 reactions and 61 species for methane/oxidizer flame and 527 reactions and 99 species for ethylene/air flame) are used to validate the simplified soot model in each flame. The effects of strain rate and oxygen content on the soot production and destruction are studied, and the soot related properties such as soot volume fraction, particle number density and particle diameter are compared with published results. The results show reasonable agreement with data and that the soot volume fraction decreases with higher strain rate and lower oxygen content. The simplified soot model has also been used with two reduced reaction mechanisms (12-step, 16-species for methane flame
and 20-species for ethylene flame) since such reduced mechanisms are computationally more efficient for practical application. The profiles of the physical properties and the major species are in excellent agreement with the results using the detailed reaction mechanisms. However, minor hydrocarbon-species such as acetylene (C2H2) that is the primary pyrolysis species in the simplified soot model is significantly over predicted and this, in turn, results in an over-prediction of soot production. Finally, the reduced reaction mechanism is modified to get more accurate prediction of the minor hydrocarbon-species. The modified reduced reaction mechanism shows that the soot prediction can be improved by improving the predictions of the key minor species.

Identiferoai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/4922
Date01 December 2004
CreatorsChang, Hojoon
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Languageen_US
Detected LanguageEnglish
TypeThesis
Format597474 bytes, application/pdf

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