Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 109988 |
| Seitenumfang | 13 |
| Fachzeitschrift | Tribology international |
| Jahrgang | 199 |
| Frühes Online-Datum | 14 Juli 2024 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 14 Juli 2024 |
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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Werkstoffmechanik
- Ingenieurwesen (insg.)
- Maschinenbau
- Physik und Astronomie (insg.)
- Oberflächen und Grenzflächen
- Werkstoffwissenschaften (insg.)
- Oberflächen, Beschichtungen und Folien
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in: Tribology international, Jahrgang 199, 109988, 11.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Enhancing practical modeling
T2 - A neural network approach for locally-resolved prediction of elastohydrodynamic line contacts
AU - Kelley, Josephine
AU - Schneider, Volker
AU - Poll, Gerhard
AU - Marian, Max
N1 - Publisher Copyright: © 2024 The Author(s)
PY - 2024/7/14
Y1 - 2024/7/14
N2 - 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.
AB - 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.
KW - EHL rolling friction
KW - EHL sliding friction
KW - Elastohydrodynamic lubrication
KW - Electrical capacitance
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85199689332&partnerID=8YFLogxK
U2 - 10.1016/j.triboint.2024.109988
DO - 10.1016/j.triboint.2024.109988
M3 - Article
AN - SCOPUS:85199689332
VL - 199
JO - Tribology international
JF - Tribology international
SN - 0301-679X
M1 - 109988
ER -