Advanced Lubrication Simulations of an Entire Test Rig: Optimization of the Nozzle Orientation to Maximize the Lubrication Capability

Abstract

In the framework of the H2020 project IDERPLANE, aimed at providing innovative, effective, and validated criteria for the design and assessment of more reliable planet bearings for aerospace application analyzing the problem from a damage tolerance perspective, the present paper presents the numerical study and optimization of a test rig specifically designed for the experiments on the full-test article. Specifically, for the first time ever, an entire system including shafts, gears and bearings with all the rolling elements have been studied with a Finite Volume Computational Fluid Dynamics approach. This ambitious challenge was addressed with the implementation of a new mesh handling technique, namely the Global Remeshing Approach with Mesh Clustering (GRAMC). The aim was to optimize the lubrication of the test article to avoid unexpected failures during the experimental campaign. Three different oil jet directions have been studied and the most effective one, namely the axial one, was selected for the final test rig design.

Computational Fluid Dynamics Methodology to Estimate the Drag Coefficient of Balls in Rolling Element Bearings

“…This relative difference is less when Δ 𝑏 = D 𝑏 21 ⁄ (relative difference equals 2%). Once again, this highlights the sensitivity of the solution to the mesh size and using a mesh so that the size of the cells on the ball equal to D 𝑏 21 ⁄ is satisfying as it was the case when no moving walls are around the balls. In the following parts of the article, three elements in the gap and cells on the balls that have size equal to D 𝑏 21 ⁄ will be employed for the meshes.…”

“…The maximum value of the edge size of the first cells on the balls, Δ 𝑏 (Figure 1) will vary later in order to see its influence on the drag coefficient value. In previous investigations, the authors showed that using an edge size equal at maximum to ∆ 𝑏 = 𝐷 𝑏 21 ⁄ is sufficient to reach a correct drag coefficient [30]. Beyond this first structured mesh, a homogeneous unstructured mesh with a mesh size of 0.8 × ∆ 𝑏 is built inside the bearing and to a certain axial extension outside the bearing.…”

“…Here the influence of both the number of the cells in the gap between the ball and the ring, and the cell size on the ball, Δ 𝑏 , on the drag force value is investigated. For the first study, five different meshes are used based on different numbers of element in the gap between the rings and the ball while the edge size of the first cells on the ball equals 𝐷 𝑏 21 ⁄ as proposed previously by the author for one isolated ball [30]. This test has been made with one ball as in the example of Figure 2.…”

“…The consequence is a 5% difference on the drag coefficient values in comparison with the simplified case based on only three layers and without any structured mesh in the gap between the ring and the ball. 21 ⁄ when considering only balls without any moving solids in their vicinity (i.e., rings or cage). It remains to be seen if this refinement is sufficient when the two rings and the cage are present this time in the computation.…”

“…Concli et al [18] employed the volume of fluid method [19] for the analysis of power losses in a planetary speed reducer, and Hildebrand et al [20] for investigating the gearbox housing geometry and oil guide plates influence on churning power losses. Recently Concli and Mastrone [21] have optimized numerically the lubrication of an entire system including shafts, gears and bearings with all the rolling elements. When specifically considering bearings this time, Hu et al [22] and Wu et al [23] have simulated the oil volume fraction in the complete ball bearing in order to investigate the temperature distribution inside the bearing.…”