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dc.contributor.authorKorsunovs, Aleksandrs*
dc.contributor.authorCampean, Felician*
dc.contributor.authorPant, G.*
dc.contributor.authorGarcia-Afonso, O.*
dc.contributor.authorTunc, E.*
dc.date.accessioned2019-03-26T12:25:09Z
dc.date.available2019-03-26T12:25:09Z
dc.date.issued2020-04
dc.identifier.citationKorsunovs A, Campean F, Pant G et al (2020) Evaluation of zero-dimensional stochastic reactor modelling for a diesel engine application. International Journal of Engine Research. 21(4): 592-609.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16918
dc.descriptionYesen_US
dc.description.abstractPrediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This paper contributes to this fast evolving body of knowledge by focusing on the evaluation of NOx emissions predictions capability of a Probability Density Function (PDF) based Stochastic Reactor Engine Models (SRM), for a Diesel engine. The research implements a systematic approach to the study of the SRM engine model performance, based on a detailed space-filling design of experiments based sensitivity analysis of both external and internal parameters, evaluating their effects on the accuracy in matching physical measurements of in-cylinder conditions, and NOx emissions output. The approach proposed in this paper introduces an automatic SRM model calibration methodology across the engine operating envelope, based on a multi-objective optimization approach. This aims to exploit opportunities for internal SRM parameters tuning to achieve good overall modelling performance as a trade-off between physical in-cylinder measurements accuracy and the output NOx emissions predictions error. The results from the case study provide a valuable insight into the effectiveness of the SRM model, showing good capability for NOx emissions prediction and trends, while pointing out the critical sensitivity to the external input parameters and modelling conditions.en_US
dc.description.sponsorship41043/R00836 Jaguar Land Rover funded research “MULTI-PHYSICS ENGINE SIMULATION FRAMEWORK: RESEARCH INTO ADVANCED CAE CAPABILITY FOR MULTI-PHYSICS SIMULATION FRAMEWORK TO GENERATE HIGH FIDELITY PREDICTION OF ENGINE-OUT EMISSIONS”, 2016 – 2019.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1177/1468087419845823
dc.rightsThe final, definitive version of this paper has been published in International Journal of Engine Research, vol 21/issue 4 by SAGE Publications Ltd, All rights reserved. © 2019 SAGE Publications Ltd.
dc.subjectThermodynamic modelsen_US
dc.subjectStochastic Reaction Modelen_US
dc.subjectPDFen_US
dc.subjectNOx emissionsen_US
dc.subjectOLH experimentsen_US
dc.subjectResearch Development Fund Publication Prize Award
dc.titleEvaluation of zero-dimensional stochastic reactor modelling for a diesel engine applicationen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-03-20
dc.date.application2019-04-29
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesResearch Development Fund Publication Prize Award winner, March 2019.
refterms.dateFOA2019-03-26T12:25:09Z


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