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Development of a reduced four-component (toluene/n-heptane/iso-octane/ethanol) gasoline surrogate model

Li, Yang; Alfazazi, Adamu; Mohan, Balaji; Alexandros Tingas, Efstathios; Badra, Jihad; Im, Hong G.; Mani Sarathy, S.

Authors

Yang Li

Adamu Alfazazi

Balaji Mohan

Jihad Badra

Hong G. Im

S. Mani Sarathy



Abstract

The prospect of blending gasoline fuel with ethanol is being investigated as a potential way to improve the knock residence of the base gasoline. However, one of the drawbacks is a lack of proper understanding of the reason for the non-linear response of blending ethanol and gasoline. This non-linearity could be better understood by an improved knowledge of the interactions of these fuel components at a molecular level. This study proposed a highly reduced four-component (toluene/n-heptane/iso-octane/ethanol) gasoline surrogate model containing 59 species and 270 reactions. The model was reduced using the direct relation graph with expert knowledge (DRG-X) (Lu and Law, 20015; Lu et al., 2011) and isomer lumping method. The computational singular perturbation (CSP) analysis were performed to reduce the potential stiffness issues by accordingly adjusting the Arrhenius coefficients of the proper reactions. The model has been comprehensively validated against wide range of ignition delay times (IDT) and flame speed (FS) measurement data as well as compared against two representative literature models from Liu et al. (2013) and Wang et al. (2015). Overall, good agreements were observed between model predictions and experimental data across the entire research octane number (RON), equivalence ratio, pressure and temperature range. In addition, the model has also been coupled with the computational fluid dynamic (CFD) models to simulate the experimental data of constant volume reacting spray of a low-octane gasoline (Haltermann straight-run naphtha), and in-cylinder pressures and temperatures of a high-octane gasoline (Haltermann Gasoline) combustion in a heavy duty compression ignition engine. The coupled model can qualitatively predict the experimentally obtained data with an improved performance for PRF, TPRF, and TPRF-ethanol surrogates.

Journal Article Type Article
Acceptance Date Mar 8, 2019
Online Publication Date Mar 13, 2019
Publication Date 2019-07
Deposit Date Jun 26, 2020
Journal Fuel
Print ISSN 0016-2361
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 247
Pages 164-178
DOI https://doi.org/10.1016/j.fuel.2019.03.052
Keywords Fuel Technology; Organic Chemistry; Energy Engineering and Power Technology; General Chemical Engineering
Public URL http://researchrepository.napier.ac.uk/Output/2672531