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Experimental and kinetic modeling study of tetralin: A naphtheno-aromatic fuel for gasoline,jet and diesel surrogates
Authors:Gani Issayev  Khalil Djebbi  Goutham Kukkadapu  Marco Mehl  Scott W Wagnon  William J Pitz  Aamir Farooq
Institution:1. Clean Combustion Research Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia;2. Lawrence Livermore National Laboratory, Livermore 94551, CA, USA;3. Department of Chemistry, Material and Chemical Engineering, Politecnico di Milano, Milano 20133, Italy
Abstract:Distillate fuels contain significant proportions of naphtheno-aromatic components and tetralin is a suitable surrogate component to represent this molecular moiety. The presence of aromatic and naphthyl rings makes kinetic modeling of tetralin very challenging. Primary radicals formed during the oxidation of tetralin can be aryl, benzylic or paraffinic in nature. Using available information on reaction paths and rate constants of naphthenes and alkyl-aromatics, a kinetic model of tetralin has been developed in the current study with emphasis on low-temperature chemistry and high-pressure conditions. Due to the lack of high-level quantum chemical calculations on reaction pathways of tetralin, analogous rates from ab-initio studies on benzylic and paraffinic radicals have been adopted here. Some modifications to the reaction rate rules are incorporated to account for the unique characteristics of tetralin's molecular structure. Important reaction channels have been identified using reaction path and brute force sensitivity analyses. In order to investigate the model performance at low temperatures, new experiments are carried out in a rapid compression machine on blends of tetralin and 3-methylpentane. Blending of low-reactivity tetralin with a high-reactivity alkane allowed the investigation of tetralin ignition at very low temperatures (665 – 856 K). The kinetic model developed in the current study is found to predict the current experiments and literature data adequately. The new model will aid in high-fidelity surrogate predictions at engine-relevant conditions.
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