Evaluating simulation techniques for lubricant distribution in gearboxes

TL;DR

Addressing the challenge of predicting lubricant distribution in gearboxes, the paper conducts a cross-solver benchmark across two numerical paradigms (VoF and SPH) and a mesh-free approach (FPM), against experimental oil- and grease-distribution data. It evaluates OpenFOAM, Ansys-Fluent, PreonLab, and MESHFREE on oil distribution with qualitative and quantitative validation, and then applies a Herschel-Bulkley SPH model in PreonLab to grease, assessing deposition under varying gear speed and sump filling. The study finds OpenFOAM and Fluent to be accurate yet computationally intensive, PreonLab to offer the best efficiency and strong accuracy (especially for high-viscosity fluids), and MESHFREE to have larger deviations overall. The results provide practical guidance for solver selection in gearbox lubrication studies and extend to grease behaviour under rotating conditions, including a threshold speed concept and filling-volume effects that influence deposition and torque.

Abstract

Efficient lubrication is crucial for the performance and durability of high-speed gearboxes, particularly under varying load conditions. Excess lubrication leads to increased churning losses, while insufficient lubrication accelerates wear on contact surfaces. Due to the high rotational speeds involved, direct experimental visualization of lubricant distribution within gearboxes is challenging, making numerical simulations indispensable. Although various modelling approaches exist, a direct comparison that jointly evaluates accuracy and computational efficiency is missing. Furthermore, studies on the computational modelling of highly viscous lubricants such as grease in gearboxes are limited. This study addresses these gaps by comparing two mesh-based Eulerian solvers (OpenFOAM and Ansys-Fluent) and two Lagrangian particle-based solvers (PreonLab and MESHFREE) for oil distribution in gearboxes. Two benchmark cases are considered: one for qualitative assessment and another for quantitative evaluation. OpenFOAM and Ansys-Fluent show good agreement with the experiment data in selected cases, but incur a significant computational cost. PreonLab performs well qualitatively, yet exhibits greater deviation in quantitative predictions. These comparisons provide information for selecting the suitable solver according to specific simulation requirements. Furthermore, the study extends to grease distribution by first validating the solver and then investigating the influence of filling volume and gear speed on the amount of grease deposited on gears. The benchmark cases presented provide a reference framework for evaluating additional solvers in future gearbox lubrication studies.

Figures (21)

• Figure 1: Schematic illustration of domain decomposition and mesh rotation approaches tested with Volume of Fluid method. Note: The white region in the domain represents air phase, while the yellow region represents the oil phase.
• Figure 2: Schematic representation of domain decomposition and particle distribution in Smoothed Particle Hydrodynamics: Left inset shows reference particle '$i_r$' (red) has its properties influenced by the neighbour particles '$i_j$' within the domain of influence 'kh'. Right inset illustrates the interaction for a boundary particle '$i_b$' (green).
• Figure 3: Experimental oil distribution used as a reference for each solver. This picture is taken from the work of Hua Liu et al. lubricants6020047.
• Figure 4: Illustration of FZG setup. Left: FZG setup used in the experiments. This picture is taken from the work of Hua Liu et al. lubricants6020047. Right: Simulation setup with only pinion gear.
• Figure 5: Visualization of mesh and particle resolution for intermediate discretization level across different solvers. Top: Pinion gear employed in this study, with the red box highlighting the area where the mesh or particles are illustrated in the subsequent images. Bottom: Mesh or particle resolution in each solver.