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dc.contributor.authorRanade, Rishikesh
dc.contributor.authorAlqahtani, Sultan
dc.contributor.authorFarooq, Aamir
dc.contributor.authorEchekki, Tarek
dc.date.accessioned2019-01-06T06:50:25Z
dc.date.available2019-01-06T06:50:25Z
dc.date.issued2018-12-22
dc.identifier.citationRanade R, Alqahtani S, Farooq A, Echekki T (2019) An ANN based hybrid chemistry framework for complex fuels. Fuel 241: 625–636. Available: http://dx.doi.org/10.1016/j.fuel.2018.12.082.
dc.identifier.issn0016-2361
dc.identifier.doi10.1016/j.fuel.2018.12.082
dc.identifier.urihttp://hdl.handle.net/10754/630723
dc.description.abstractThe oxidation chemistry of complex hydrocarbons involves large mechanisms with hundreds or thousands of chemical species and reactions. For practical applications and computational ease, it is desirable to reduce their chemistry. To this end, high-temperature fuel oxidation for large carbon number fuels may be described as comprising two steps, fuel pyrolysis and small species oxidation. Such an approach has recently been adopted as ‘hybrid chemistry’ or HyChem to handle high-temperature chemistry of jet fuels by utilizing time-series measurements of pyrolysis products. In the approach proposed here, a shallow Artificial Neural Network (ANN) is used to fit temporal profiles of fuel fragments to directly extract chemical reaction rate information. This information is then correlated with the species concentrations to build an ANN-based model for the fragments’ chemistry during the pyrolysis stage. Finally, this model is combined with a C0-C4 chemical mechanism to model high-temperature fuel oxidation. This new hybrid chemistry approach is demonstrated using homogeneous chemistry calculations of n-dodecane (n-C12H26) oxidation. The experimental uncertainty is simulated by introducing realistic noise in the data. The comparison shows a good agreement between the proposed ANN hybrid chemistry approach and detailed chemistry results.
dc.description.sponsorshipDr. Aamir Farooq would like to thank the Office of Sponsored Research at the King Abdullah University of Science and Technology (KAUST) for financial support. Sultan Alqahtani would like to acknowledge the support of King Khalid University in Abha, Saudi Arabia.
dc.publisherElsevier BV
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0016236118321483
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Fuel. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Fuel, [, , (2018-12-22)] DOI: 10.1016/j.fuel.2018.12.082 . © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectChemistry reduction
dc.subjectArtificial neural networks
dc.subjectHydrocarbon oxidation
dc.subjectPyrolysis
dc.titleAn ANN based hybrid chemistry framework for complex fuels
dc.typeArticle
dc.contributor.departmentChemical Kinetics & Laser Sensors Laboratory
dc.contributor.departmentClean Combustion Research Center
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalFuel
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695-7910, USA.
kaust.personFarooq, Aamir
refterms.dateFOA2020-12-22T00:00:00Z
dc.date.published-online2018-12-22
dc.date.published-print2019-04


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