Thermal Conductance Correlations of Static Lubricated Ball Bearings
Indronil Ghosh, John P. McHale, Yoshimi R. Takeuchi, Peter P. Frantz,, Payton J. Batliner, and Timothy S. Fisher

TL;DR
This paper develops advanced numerical models to accurately predict thermal conductance in static lubricated ball bearings, improving existing models and providing a reference for spacecraft bearing applications.
Contribution
It introduces a hierarchical modeling approach combining first-principles, FEM, and multiphysics simulations to enhance thermal conductance predictions in lubricated ball bearings.
Findings
Thermal conductance varies with lubricant volume and load.
The new models outperform classic simplified models in accuracy.
Hierarchical approach improves tribo-thermo-mechanical predictions.
Abstract
Ball bearings are commonly used to reduce the friction in rotating mechanical components. The present work reports improved numerical approaches to model bearing thermal conductance in the absence of convection. We start by modeling the thermal pathway across a single ball-to-race pathway for a simplified geometry, an azimuthally symmetric ball in contact with a flat surface (ball-on-flat). A first-principles approach is used to calculate the static lubricant meniscus shape using a custom-developed Python code. We apply the finite element method (FEM) to extract total thermal conductance of the lubricated ball-on-flat system. Using similar methods, we also present a three-dimensional numerical model of the lubricant meniscus in a static angular contact ball bearing section (ball-on-race). By generating thermal conductance correlations for Yovanovich's classic lubricated model, our…
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Taxonomy
TopicsAdhesion, Friction, and Surface Interactions · Gear and Bearing Dynamics Analysis · Tribology and Lubrication Engineering
