Session: PTG-06-01: Lubrication and Efficiency

Paper Number: 148107

148107 - An Equivalent Lubrication Model to Speed Up High-Fidelity Lubricated Transmission Models 

Performance, efficiency and durability are highly improved by optimal lubrication in transmission systems. An ElastoHydrodynamic Lubrication (EHL) model enables an accurate computation of lubricant forces and shear stresses, yet renders impractical for system level simulations with multiple contact interactions between different gear stages. This work aims to speed up high-fidelity lubricated transmission models by lumping the EHL-model in an equivalent normal stiffness and damping, tangential contact forces will be accounted for with an equivalent friction coefficient. The equivalent stiffness is obtained from steady-state simulations of the high-fidelity lubricated transmission model at pre-defined angular positions within one gear pitch. On the other hand, this steady-state equilibrium provides the corresponding global deformation of the gear pair, which are used in an interpolating reduced order model of the gear pair itself, called the interpolated global line contact shapes. Both the reduced dimensions of the lubricant as the structural domain result into an efficient high-fidelity lubricated transmission model, which provides accurate results for the steady-state transmission error. The equivalent lubricant damping for transient simulations is computed from a transient EHL-simulation with the steady-state contact forces as an input. The performance of this equivalent damping will be validated shortly in a dynamic simulation to unlock the full potential of the method.

Presenting Author: Sander Neeckx KU Leuven

Presenting Author Biography: Sander Neeckx is a PhD student at the KU Leuven, who obtained his Master's degree in Mechanical Engineering from the same university in 2019. His expertise lies in drivetrain modelling, finite element analysis, flexible multibody dynamics, multi-physical simulation, (thermo) elastohydrodynamic lubrication, and model reduction.

Authors:

Sander Neeckx KU Leuven
Bart Blockmans KU Leuven
Maxime Dujardin KU Leuven
Frank Naets KU Leuven