Detailansicht

Enhancing practical modeling

A neural network approach for locally-resolved prediction of elastohydrodynamic line contacts

verfasst von
Josephine Kelley, Volker Schneider, Gerhard Poll, Max Marian
Abstract

When 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.

Organisationseinheit(en)
Institut für Maschinenkonstruktion und Tribologie
Externe Organisation(en)
Pontificia Universidad Catolica de Chile
Typ
Artikel
Journal
Tribology international
Band
199
Anzahl der Seiten
13
ISSN
0301-679X
Publikationsdatum
14.07.2024
Publikationsstatus
Elektronisch veröffentlicht (E-Pub)
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Werkstoffmechanik, Maschinenbau, Oberflächen und Grenzflächen, Oberflächen, Beschichtungen und Folien
Elektronische Version(en)
https://doi.org/10.1016/j.triboint.2024.109988 (Zugang: Offen)