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On the tilting‐pad thrust bearings hydrodynamic lubrication under combined numerical and machine learning techniques
Lubrication Science ( IF 1.8 ) Pub Date : 2021-01-24 , DOI: 10.1002/ls.1535
Konstantinos P. Katsaros 1 , Pantelis G. Nikolakopoulos 1
Affiliation  

Thrust bearings are machine elements designed to support axial loads in rotating machinery. The hydrodynamic lubrication analysis of such bearings has been a major subject for many studies over the years, leading to important conclusions for design parameters that affect their optimal operating conditions. Furthermore, the last few years, the influence of the industry 4.0 concept has brought new tools that can revolutionise bearings' design. The aim of this study is to combine numerical analysis and machine learning techniques in order to identify optimal thrust bearing's hydrodynamic designs. For this purpose, the Reynolds equations are solved using the finite difference technique on a 2‐D grid of a tilting pivoted bearing's pad. The bearing pressure distribution; load carrying capacity and friction are calculated for multiple operating conditions. The data produced are used as input for the training of regression models that predict the behaviour of the thrust bearing for a wide range of loads and rotating speeds. Simple and multi‐variable, linear, polynomial and SVM regression models are compared for their accuracy to predicting the bearing's operating conditions. The major findings related with three different lubricants, a monograde SAE 30, a multi‐grade SAE 10W40 and a bio‐lubricant AWS 100 that are compared and their optimal operating conditions are suggested in terms of minimum friction force and maximum load carrying capacity for the bearing pad. AWS 100 is found to be the most suitable lubricant that provides the bearing with low operating friction and high load carrying capacity in all studied cases.

中文翻译:

数值和机器学习相结合的斜垫推力轴承流体动力润滑

推力轴承是旨在支撑旋转机械中的轴向载荷的机械元件。多年来,此类轴承的流体动力润滑分析一直是许多研究的主要课题,从而得出了影响其最佳运行条件的设计参数的重要结论。此外,最近几年,工业4.0概念的影响带来了可以彻底改变轴承设计的新工具。这项研究的目的是将数值分析和机器学习技术相结合,以确定最佳的推力轴承的流体力学设计。为此,使用有限差分技术在倾斜的枢转轴承垫的二维网格上求解雷诺方程。轴承压力分布;计算了多种操作条件下的承载能力和摩擦力。产生的数据用作训练回归模型的输入,这些回归模型可预测各种载荷和转速下推力轴承的性能。比较了简单多元变量,线性,多项式和SVM回归模型的准确性,以预测轴承的工作条件。与三种不同的润滑剂(单级SAE 30,多级SAE 10W40和生物润滑剂AWS 100)相关的主要发现进行了比较,并就其最小摩擦力和最大负载能力提出了最佳操作条件。轴承垫。
更新日期:2021-03-03
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