Utilization of basic multi-layer perceptron artificial neural networks to resolve turbulent fine structure chemical kinetics applied to a CFD model of a methane/air piloted jet flame

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dc.contributor.authorLaubscher, R.en_ZA
dc.contributor.authorHoffmann, J. H.en_ZA
dc.date.accessioned2020-04-29T16:42:26Z
dc.date.available2020-04-29T16:42:26Z
dc.date.issued2018
dc.descriptionCITATION: Laubscher, R. & Hoffmann, J. H. 2018. Utilization of basic multi-layer perceptron artificial neural networks to resolve turbulent fine structure chemical kinetics applied to a CFD model of a methane/air piloted jet flame. Journal of Thermal Engineering, 4(2):1828-1846.
dc.descriptionThe original publication is available at http://dergipark.org.tr/en/download/article-file/408300
dc.description.abstractENGLISH ABSTRACT: This work investigates and proposes an alternative chemistry integration approach to be used with the eddy dissipation concept (EDC) advanced combustion model. The approach uses basic multi-layer perceptron (MLP) artificial neural networks (ANNs) as a chemistry integrator for the reactions that take place in the fine structure regions created by the turbulence field. The ANNs are therefore utilised to predict the incremental species changes that occur in these fine structure regions as a function of the initial species composition, temperature and the residence time of the mixture in the fine structure regions. The chemistry integration approach for the EDC model was implemented to model a piloted methane/air turbulent jet diffusion flame (Sandia Flame D) at a Reynolds number of 22400. To prove the concept, a five-step methane combustion mechanism was used to model the chemical reactions of the experimental flame. The results of the new approach were benchmarked against experimental data and the simulation results using the standard integration approaches in Fluent. It was shown that once the ANNs are well-trained (in-sample error minimised as best possible), it can predict the species mass fractions with relative accuracy in a manner that is both time and computer-memory efficient compared with using traditional integration procedures.en_ZA
dc.description.urihttp://static.dergipark.org.tr/article-download/391c/abdd/f3ec/5a6314bc8c477.pdf
dc.description.versionPublisher's version
dc.format.extent19 pagesen_ZA
dc.identifier.citationLaubscher, R. & Hoffmann, J. H. 2018. Utilization of basic multi-layer perceptron artificial neural networks to resolve turbulent fine structure chemical kinetics applied to a CFD model of a methane/air piloted jet flame. Journal of Thermal Engineering, 4(2):1828-1846
dc.identifier.issn2148-7847 (online)
dc.identifier.urihttp://hdl.handle.net/10019.1/108497
dc.language.isoen_ZAen_ZA
dc.publisherYildiz Technical Universityen_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectComputational Fluid Dynamicsen_ZA
dc.subjectMachine learningen_ ZA
dc.subjectArtificial neural networksen_ZA
dc.subjectChemical kineticsen_ZA
dc.subjectCombustionen_ZA
dc.titleUtilization of basic multi-layer perceptron artificial neural networks to resolve turbulent fine structure chemical kinetics applied to a CFD model of a methane/air piloted jet flameen_ZA
dc.typeArticleen_ZA
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