Publication result detail

MODELLING GREASE RETENTIVITY FOR INTELLIGENT LUBRICATION SYSTEM

KVARDA, D.; OMASTA, M.; GALAS, R.; KŘUPKA, I.; HARTL, M.

Original Title

MODELLING GREASE RETENTIVITY FOR INTELLIGENT LUBRICATION SYSTEM

English Title

MODELLING GREASE RETENTIVITY FOR INTELLIGENT LUBRICATION SYSTEM

Type

Paper in proceedings outside WoS and Scopus

Original Abstract

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.

English abstract

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.

Keywords

Wheel-rail contact; grease retentivity; coefficient of adhesion; friction reduction; flange lubrication.

Key words in English

Wheel-rail contact; grease retentivity; coefficient of adhesion; friction reduction; flange lubrication.

Authors

KVARDA, D.; OMASTA, M.; GALAS, R.; KŘUPKA, I.; HARTL, M.

Released

22.09.2025

Publisher

Japanese Society of Contact Mechanics on Railway

Location

Tokyo, Japan

Pages count

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"
}