Enhancing practical modeling: A neural network approach for locally-resolved prediction of elastohydrodynamic line contacts

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dc.catalogadorjlo
dc.contributor.authorKelley, Josephine
dc.contributor.authorSchneider, Volker
dc.contributor.authorPoll, Gerhard
dc.contributor.authorMarian, Max
dc.date.accessioned2024-07-18T14:57:06Z
dc.date.available2024-07-18T14:57:06Z
dc.date.issued2024
dc.description.abstractWhen modeling bearings in the context of entire transmissions or drivetrains, there are practical limits to the calculation resources available to calculate single bearings or even contacts. In settings such as these, curve-fitting methods have historically been deployed to estimate the elastohydrodynamic lubrication conditions. Machine learning methods have the potential to enable more sophisticated physical modeling in the context of larger computation environments, as the evaluation time of a trained model is typically negligible. We present a neural network that accurately evaluates the locally variable elastohydrodynamic film pressure and film thickness distributions and explore its application to (e.g.) cylindrical roller bearings. Employing a neural network for the EHL film thickness calculations rather than the curve-fitted, simplified methods that are today’s standard can enable a more physically precise modeling strategy at almost no additional computational cost.
dc.fechaingreso.objetodigital2024-09-05
dc.format.extent20 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.triboint.2024.109988
dc.identifier.issn0301-679X
dc.identifier.urihttps://doi.org/10.1016/j.triboint.2024.109988
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/87110
dc.identifier.wosidWOS:001284925700001
dc.information.autorucEscuela de Ingeniería; Marian, Max; 0000-0003-2045-6649; 1247429
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final17
dc.pagina.inicio1
dc.revistaTribology International
dc.rightsacceso restringido
dc.subjectElastohydrodynamic lubrication
dc.subjectMachine learning
dc.subjectEHL Sliding friction
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleEnhancing practical modeling: A neural network approach for locally-resolved prediction of elastohydrodynamic line contacts
dc.typepreprint
sipa.codpersvinculados1247429
sipa.trazabilidadORCID;2024-07-15
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