An efficient surrogate fuel formulation methodology, which directly uses the chemical structure information from nuclear magnetic resonance (NMR) spectroscopy analysis, has been proposed. Five functional groups, paraffinic CH2, paraffinic CH3, aromatic C-CH, olefinic CH-CH2, and cycloparaffin CH2, have been selected to show the basic molecular structure of the fuels for the advanced combustion engines (FACE) fuels. A palette that contains six candidate components, n-heptane, iso-octane, toluene, 2,5-dimethylhexane, methylcyclohexane, and 1-hexene, is chosen for different FACE fuels, based on the consideration that surrogate mixtures should provide the representative functional groups and comparable molecular sizes. The kinetic mechanisms of these six candidate components are chosen to assemble a detailed mechanism of each surrogate fuel for FACE gasoline. Whereafter, the accuracy of FACE A and F surrogate models was demonstrated by comparing the model predictions against experimental data in homogeneous ignition, jet stirred reactor oxidation, and premixed flame.
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April 2019
Research-Article
Surrogate Fuels Formulation for FACE Gasoline Using the Nuclear Magnetic Resonance Spectroscopy
Jin Yu,
Jin Yu
School of Power Engineering,
Chongqing University,
Chongqing 400044, China
Chongqing University,
Chongqing 400044, China
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Xiaolong Gou
Xiaolong Gou
Search for other works by this author on:
Jin Yu
School of Power Engineering,
Chongqing University,
Chongqing 400044, China
Chongqing University,
Chongqing 400044, China
Xiaolong Gou
1Corresponding author.
Manuscript received August 7, 2017; final manuscript received July 5, 2018; published online December 4, 2018. Assoc. Editor: Eric Petersen.
J. Eng. Gas Turbines Power. Apr 2019, 141(4): 041019 (8 pages)
Published Online: December 4, 2018
Article history
Received:
August 7, 2017
Revised:
July 5, 2018
Citation
Yu, J., and Gou, X. (December 4, 2018). "Surrogate Fuels Formulation for FACE Gasoline Using the Nuclear Magnetic Resonance Spectroscopy." ASME. J. Eng. Gas Turbines Power. April 2019; 141(4): 041019. https://doi.org/10.1115/1.4040808
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