A Neural Network for Fast Modeling of Elastohydrodynamic Line Contacts

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dc.catalogadorpva
dc.contributor.authorKelley, Josephine
dc.contributor.authorSchneider, Volker
dc.contributor.authorMarian, Max
dc.contributor.authorPoll, Gerhard
dc.date.accessioned2025-03-05T20:25:39Z
dc.date.available2025-03-05T20:25:39Z
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 elastohydrodynamic film pressure and film thickness and explore its applications. Employing a neural network for the EHL film thickness calculations can enable a more physically precise modeling strategy at almost no additional computational cost.
dc.format.extent17 páginas
dc.fuente.origenORCID
dc.identifier.doi10.2139/ssrn.4823524
dc.identifier.issn1556-5068
dc.identifier.scopusidSCOPUS_ID:2-s2.0-85192926871
dc.identifier.urihttp://doi.org/10.2139/ssrn.4823524
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-85192926871&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102383
dc.information.autorucEscuela de Ingeniería; Marian, Max; 0000-0003-2045-6649; 1247429
dc.language.isoen
dc.nota.accesocontenido parcial
dc.publisherSSRN
dc.rightsacceso restringido
dc.subjectElastohydrodynamic lubrication
dc.subjectNeural networks
dc.subjectRolling element bearing modeling
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleA Neural Network for Fast Modeling of Elastohydrodynamic Line Contacts
dc.typepreprint
sipa.codpersvinculados1247429
sipa.trazabilidadORCID;2025-03-03
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