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Specialized Reduced Models of Dynamic Flows in 2-Stroke Engines

dc.rights.licenseopenen_US
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorCAGIN, Stephanie
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorFISCHER, Xavier
dc.contributor.authorDELACOURT, Eric
hal.structure.identifierENSIAME
dc.contributor.authorBOURABAA, Nachida
dc.contributor.authorMORIN, Céline
dc.contributor.authorCOUTELLIER, Daniel
dc.contributor.authorCARRE, Bertrand
dc.contributor.authorLOUMÉ, Sylvain
dc.date.accessioned2023-06-13T12:38:06Z
dc.date.available2023-06-13T12:38:06Z
dc.date.created2015
dc.date.issued2015
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/182659
dc.description.abstractEnThe complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).
dc.language.isoENen_US
dc.subject.enDiesel engine
dc.subject.enDesign optimization
dc.subject.enModel reduction
dc.subject.enNeural network
dc.subject.enNTF algorithm
dc.subject.enScavenging
dc.title.enSpecialized Reduced Models of Dynamic Flows in 2-Stroke Engines
dc.typeArticle de revueen_US
dc.identifier.doi10.5281/zenodo.1124187en_US
dc.subject.halPhysique [physics]/Physique [physics]/Dynamique des Fluides [physics.flu-dyn]en_US
dc.subject.halPhysique [physics]/Mécanique [physics]/Mécanique des fluides [physics.class-ph]en_US
dc.subject.halInformatique [cs]/Modélisation et simulationen_US
bordeaux.journalInternational Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineeringen_US
bordeaux.page1684 - 1693en_US
bordeaux.volume9en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.issue9en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-01330965
hal.version1
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=International%20Journal%20of%20Mechanical,%20Aerospace,%20Industrial,%20Mechatronic%20and%20Manufacturing%20Engineering&rft.date=2015&rft.volume=9&rft.issue=9&rft.spage=1684%20-%201693&rft.epage=1684%20-%201693&rft.au=CAGIN,%20Stephanie&FISCHER,%20Xavier&DELACOURT,%20Eric&BOURABAA,%20Nachida&MORIN,%20C%C3%A9line&rft.genre=article


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