Optimized reaction mechanism rate rules for ignition of normal alkanes

The increasing demand for cleaner combustion and reduced greenhouse gas emissions motivates research on the combustion of hydrocarbon fuels and their surrogates. Accurate detailed chemical kinetic models are an important prerequisite for high fidelity reacting flow simulations capable of improving combustor design and operation. The development of such models for many new fuel components and/or surrogate molecules is greatly facilitated by the application of reaction classes and rate rules. Accurate and versatile rate rules are desirable to improve the predictive accuracy of kinetic models. A major contribution in the literature is the recent work by Bugler et al. (2015), which has significantly improved rate rules and thermochemical parameters used in kinetic modeling of alkanes. In the present study, it is demonstrated that rate rules can be used and consistently optimized for a set of normal alkanes including n-heptane, n-octane, n-nonane, n-decane, and n-undecane, thereby improving the predictive accuracy for all the considered fuels. A Bayesian framework is applied in the calibration of the rate rules. The optimized rate rules are subsequently applied to generate a mechanism for n-dodecane, which was not part of the training set for the optimized rate rules. The developed mechanism shows accurate predictions compared with published well-validated mechanisms for a wide range of conditions. (C) 2016 The Combustion Institute. Published by Elsevier Inc. All rights reserved.This work was performed within the Cluster of Excellence “Tailor-Made Fuels from Biomass”, which is funded by the Excellence Initiative of the German federal state governments to promote science and research at German universities. The authors also acknowledge funding support from the Clean Combustion Research Center and Saudi Aramco under the FUELCOM program. VR was supported by SERDP Grant WP-2151 with Dr. Robin Nissan as Program Manager. NUI Galway would like to acknowledge the support of the Irish Research Council in funding this work.2018-08-1