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

11.2024

Publikationsstatus

Veröffentlicht

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)