A computational methodology for formulating gasoline surrogate fuels with accurate physical and chemical kinetic properties

Handle URI:
http://hdl.handle.net/10754/564073
Title:
A computational methodology for formulating gasoline surrogate fuels with accurate physical and chemical kinetic properties
Authors:
Ahmed, Ahfaz ( 0000-0001-5982-3464 ) ; Goteng, Gokop; Shankar, Vijai; Al-Qurashi, Khalid; Roberts, William L. ( 0000-0003-1999-2831 ) ; Sarathy, Mani ( 0000-0002-3975-6206 )
Abstract:
Gasoline is the most widely used fuel for light duty automobile transportation, but its molecular complexity makes it intractable to experimentally and computationally study the fundamental combustion properties. Therefore, surrogate fuels with a simpler molecular composition that represent real fuel behavior in one or more aspects are needed to enable repeatable experimental and computational combustion investigations. This study presents a novel computational methodology for formulating surrogates for FACE (fuels for advanced combustion engines) gasolines A and C by combining regression modeling with physical and chemical kinetics simulations. The computational methodology integrates simulation tools executed across different software platforms. Initially, the palette of surrogate species and carbon types for the target fuels were determined from a detailed hydrocarbon analysis (DHA). A regression algorithm implemented in MATLAB was linked to REFPROP for simulation of distillation curves and calculation of physical properties of surrogate compositions. The MATLAB code generates a surrogate composition at each iteration, which is then used to automatically generate CHEMKIN input files that are submitted to homogeneous batch reactor simulations for prediction of research octane number (RON). The regression algorithm determines the optimal surrogate composition to match the fuel properties of FACE A and C gasoline, specifically hydrogen/carbon (H/C) ratio, density, distillation characteristics, carbon types, and RON. The optimal surrogate fuel compositions obtained using the present computational approach was compared to the real fuel properties, as well as with surrogate compositions available in the literature. Experiments were conducted within a Cooperative Fuels Research (CFR) engine operating under controlled autoignition (CAI) mode to compare the formulated surrogates against the real fuels. Carbon monoxide measurements indicated that the proposed surrogates accurately reproduced the global reactivity of the real fuels across various combustion regimes.
KAUST Department:
Clean Combustion Research Center; Physical Sciences and Engineering (PSE) Division; Chemical and Biological Engineering Program; Mechanical Engineering Program
Publisher:
Elsevier BV
Journal:
Fuel
Issue Date:
Mar-2015
DOI:
10.1016/j.fuel.2014.11.022
Type:
Article
ISSN:
00162361
Sponsors:
The authors acknowledge Dr. Marcia Huber at NIST Boulder Colorado, USA for her comments and suggestions regarding the ADC simulations. The authors thank Mr. Adrian I. Ichim from the KAUST CCRC for preparing the engine test cell. The authors acknowledge funding support from the Clean Combustion Research Center and from Saudi Aramco under the FUELCOM program.
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Chemical and Biological Engineering Program; Mechanical Engineering Program; Clean Combustion Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorAhmed, Ahfazen
dc.contributor.authorGoteng, Gokopen
dc.contributor.authorShankar, Vijaien
dc.contributor.authorAl-Qurashi, Khaliden
dc.contributor.authorRoberts, William L.en
dc.contributor.authorSarathy, Manien
dc.date.accessioned2015-08-03T12:30:54Zen
dc.date.available2015-08-03T12:30:54Zen
dc.date.issued2015-03en
dc.identifier.issn00162361en
dc.identifier.doi10.1016/j.fuel.2014.11.022en
dc.identifier.urihttp://hdl.handle.net/10754/564073en
dc.description.abstractGasoline is the most widely used fuel for light duty automobile transportation, but its molecular complexity makes it intractable to experimentally and computationally study the fundamental combustion properties. Therefore, surrogate fuels with a simpler molecular composition that represent real fuel behavior in one or more aspects are needed to enable repeatable experimental and computational combustion investigations. This study presents a novel computational methodology for formulating surrogates for FACE (fuels for advanced combustion engines) gasolines A and C by combining regression modeling with physical and chemical kinetics simulations. The computational methodology integrates simulation tools executed across different software platforms. Initially, the palette of surrogate species and carbon types for the target fuels were determined from a detailed hydrocarbon analysis (DHA). A regression algorithm implemented in MATLAB was linked to REFPROP for simulation of distillation curves and calculation of physical properties of surrogate compositions. The MATLAB code generates a surrogate composition at each iteration, which is then used to automatically generate CHEMKIN input files that are submitted to homogeneous batch reactor simulations for prediction of research octane number (RON). The regression algorithm determines the optimal surrogate composition to match the fuel properties of FACE A and C gasoline, specifically hydrogen/carbon (H/C) ratio, density, distillation characteristics, carbon types, and RON. The optimal surrogate fuel compositions obtained using the present computational approach was compared to the real fuel properties, as well as with surrogate compositions available in the literature. Experiments were conducted within a Cooperative Fuels Research (CFR) engine operating under controlled autoignition (CAI) mode to compare the formulated surrogates against the real fuels. Carbon monoxide measurements indicated that the proposed surrogates accurately reproduced the global reactivity of the real fuels across various combustion regimes.en
dc.description.sponsorshipThe authors acknowledge Dr. Marcia Huber at NIST Boulder Colorado, USA for her comments and suggestions regarding the ADC simulations. The authors thank Mr. Adrian I. Ichim from the KAUST CCRC for preparing the engine test cell. The authors acknowledge funding support from the Clean Combustion Research Center and from Saudi Aramco under the FUELCOM program.en
dc.publisherElsevier BVen
dc.subjectChemical kinetic simulationen
dc.subjectCombustionen
dc.subjectFormulationen
dc.subjectGasolineen
dc.subjectSurrogate fuelen
dc.titleA computational methodology for formulating gasoline surrogate fuels with accurate physical and chemical kinetic propertiesen
dc.typeArticleen
dc.contributor.departmentClean Combustion Research Centeren
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentChemical and Biological Engineering Programen
dc.contributor.departmentMechanical Engineering Programen
dc.identifier.journalFuelen
kaust.authorGoteng, Gokopen
kaust.authorAl-Qurashi, Khaliden
kaust.authorRoberts, William L.en
kaust.authorSarathy, Manien
kaust.authorAhmed, Ahfazen
kaust.authorShankar, Vijaien
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