Genetic algorithm optimization of a chemical kinetic mechanism for propane at engine relevant conditions

DelVescovo, Dan A.; Li, Jiaqi; Splitter, Derek A.; Chuahy, Flavio Dal Forno; Zhao, Peng

doi:10.1016/j.fuel.2022.127371

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Genetic algorithm optimization of a chemical kinetic mechanism for propane at engine relevant conditions

DelVescovo, Dan A.·Li, Jiaqi·Splitter, Derek A.·Chuahy, Flavio Dal Forno·Zhao, Peng

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Abstract

Propane has demonstrated significant potential for reductions in greenhouse gas and pollutant emissions in medium- and heavy-duty engine applications, but further improvements require accurate, compact, and scalable chemical kinetic mechanisms to design the next generation of propane fueled engines, particularly at the boosted operating conditions necessary to meet the power density demand of medium- and heavy-duty applications. In this work, six key chemical reactions were identified in a reduced mechanism with 70 species and 352 reactions through a sensitivity analysis performed at conditions typical of thermodynamic trajectories observed in a high compression ratio, long stroke engine operated on propane from throttled to boosted operating conditions. While the original mechanism was validated against rapid compression machine (RCM) data, it was found to overpredict experimental autoignition tendencies in 2-zone, 0-D SI engine simulations performed in Chemkin Pro. Subsequently, a genetic algorithm approach was used to optimize the six reaction rate parameters within established uncertainty bounds by performing RCM simulations and comparing to two independent sets of literature ignition delay times for propane, thus generating two new kinetic mechanisms. The first optimization achieved a mean absolute percent error (MPE) reduction in 2nd stage ignition delay of 61.4% in seven generations, while the second optimization utilized a newer experimental RCM dataset, and achieved MPE reduction of 56.7% in seven generations, and further marginal improvement to 57.8% reduction in 34 generations. Finally, the two mechanisms were then evaluated again in the 2-zone 0-D SI engine model in Chemkin Pro comparing typical mean and knocking cycle trajectories, and it was found that the second optimized mechanism provided better prediction of knock onset at the representative conditions evaluated in this work, particularly for higher load operating conditions.

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DelVescovo, Dan A., Li, Jiaqi, Splitter, Derek A., Chuahy, Flavio Dal Forno, Zhao, Peng. 2023-01-10. Genetic algorithm optimization of a chemical kinetic mechanism for propane at engine relevant conditions. https://doi.org/10.1016/j.fuel.2022.127371

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