Detail publikace
MODELLING GREASE RETENTIVITY FOR INTELLIGENT LUBRICATION SYSTEM
KVARDA, D. OMASTA, M. GALAS, R. KŘUPKA, I. HARTL, M.
Anglický název
MODELLING GREASE RETENTIVITY FOR INTELLIGENT LUBRICATION SYSTEM
Typ
Stať ve sborníku mimo WoS a Scopus
Jazyk
en
Originální abstrakt
This study investigates the retentivity of grease lubricants for wheel-rail flange contacts, focusing on the effects of load, slide-to-roll ratio, speed and grease quantity. Experiments were conducted using a Mini-Traction Machine in a ball-on-disk configuration. Material selection of bearing steel ensured stable contact conditions and minimized wear, enabling reproducible measurements. The results reveal that both load and slide-to-roll ratio reduce grease retentivity following a decaying power-law relationship, with similar decay exponents indicating that mechanical energy input governs lubricant depletion. Speed exhibits a dual effect: increasing speed decreases time-based retentivity but enhances sliding distance-based retentivity, consistent with elastohydrodynamic lubrication film thickness assumption. Additionally, grease quantity positively correlates with retentivity, where insufficient lubricant leads to rapid loss of friction-reducing performance. These findings provide critical insights into lubricant behavior and will be integrated with lubricant redistribution models and train line simulations to develop a digital twin for predicting lubricant effectiveness across rail networks. This combined experimental and modelling approach advances the optimization of flange lubrication strategies, ultimately improving rail system reliability, wear resistance, and energy efficiency.
Klíčová slova anglicky
Wheel-rail contact; grease retentivity; coefficient of adhesion; friction reduction; flange lubrication.
Vydáno
2025-09-22
Nakladatel
Japanese Society of Contact Mechanics on Railway
Místo
Tokyo, Japan
Počet stran
8
BIBTEX
@inproceedings{BUT201014,
author="Daniel {Kvarda} and Milan {Omasta} and Radovan {Galas} and Ivan {Křupka} and Martin {Hartl}",
title="MODELLING GREASE RETENTIVITY FOR INTELLIGENT LUBRICATION SYSTEM",
year="2025",
pages="8",
publisher="Japanese Society of Contact Mechanics on Railway",
address="Tokyo, Japan"
}