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dc.contributor.authorGkantonas, Savvas
dc.contributor.authorJella, Sandeep
dc.contributor.authorIavarone, Salvatore
dc.contributor.authorVersailles, Philippe
dc.contributor.authorMastorakos, Epaminondas
dc.contributor.authorBourque, Gilles
dc.date.accessioned2022-04-01T23:30:41Z
dc.date.available2022-04-01T23:30:41Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335697
dc.description.abstractThe study of autoignition propensity in premixers for gas turbines is critical for their safe operation and design. Although premixers can be analysed using reacting Computational Fluid Dynamics (CFD) coupled with detailed autoignition chemical kinetics, it is essential to also develop methods with lower computational cost to be able to explore more geometries and operating conditions during the design process. This paper presents such an approach based on Incompletely Stirred Reactor Network (ISRN) modelling. This method uses a CFD solution of a non-reacting flow and subsequently estimates the spatial evolution of reacting scalars such as autoignition precursors and temperature conditioned on the mixture fraction, which are used to quantify autoignition propensity. The approach is intended as a "post-processing'" step, enabling the use of very complex chemical mechanisms and the study of many operating conditions. For a representative premixer of an aeroderivative gas turbine, results show that autoignition propensity can be reproduced with ISRN at highly reactive operating conditions featuring multi-stage autoignition of a dual fuel mixture. The ISRN computations are consequently analysed to explore the evolution of reacting scalars and propose some autoignition metrics that combine mixing and chemical reaction to assist the design of premixers.
dc.description.sponsorshipSiemens Energy Canada Limited
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleEstimation of Autoignition Propensity in Aeroderivative Gas Turbine Premixers Using Incompletely Stirred Reactor Network Modelling
dc.typeConference Object
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-04-01T11:34:51Z
prism.publicationNameProceedings of the ASME Turbo Expo
dc.identifier.doi10.17863/CAM.83133
dcterms.dateAccepted2022-03-12
rioxxterms.versionofrecord10.17863/CAM.83133
rioxxterms.versionAM
dc.contributor.orcidGkantonas, Savvas [0000-0002-5354-578X]
dc.contributor.orcidMastorakos, Epaminondas [0000-0001-8245-5188]
pubs.conference-nameASME Turbo Expo 2022
pubs.conference-start-date2022-06-13
cam.orpheus.counter7*
cam.depositDate2022-04-01
pubs.conference-finish-date2022-06-17
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-04-01


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