Performance Prediction Model for Hydrodynamically Lubricated Tilting Pad Thrust Bearings Operating under Incomplete Oil Film with the Combination of Numerical and Machine-Learning Techniques

Abstract

Pivoted pad thrust bearings are common machine elements used in rotating mechanisms in order to support axial loads. The hydrodynamic lubrication of such bearings has been a major subject of many investigations over the years. However, the majority of these investigations are based on full film lubrication models, when, in fact, incomplete oil film profiles appear during various operating conditions, such as startups and shutdowns. The lack of lubricant during operations can have severe impact on the bearing’s performance, affecting its ability to carry the applied axial load. The scope of the current investigation is to combine numerical analysis and machine-learning techniques in order to create a model that predicts the thrust bearing’s performance in terms of the pad’s load-carrying capacity. For this purpose, the 2-D Reynolds equation is solved numerically for a variety of angular velocities and three different lubricants: SAE 20, SAE 30 and SAE 10W40. The position of the lack of lubricant within the oil film’s control volume is studied and evaluated, together with the percentage of oil film coverage in the inlet of the pad. The results of the numerical analysis are used as input, in order to train and evaluate three different machine-learning models: Quadratic Polynomial Regression, Quadratic SVM Regression and Regression Trees. The results showed that the position of the film incompleteness affects the ability of the bearing to carry the axial load. At the same time as less lubricant entered the domain, the pressure drop could reach lower values, up to 93%. From the studied lubricants, SAE 10W40 was the one that showed the best performance results during incomplete oil film operation. Finally, the Quadratic Polynomial Regression model showed the best fit and 99% accuracy in predicting the pad’s load-carrying capacity.

Experimental Study of the Rheology of Grease by the Example of CIATIM-221 and Identification of Its Behavior Model

“…Lubricants are an integral part of the actual tribology problem [20]. In this case, several works [21][22][23] point out the importance of conducting full-scale and numerical experiments to determine the properties and perform mathematical modeling of their behavior.…”

“…One of the actual problems is the study of the dynamic characteristics of the lubricant behavior [21][22][23][29][30][31], including the viscoelasticity, viscoplasticity, etc. Many studies [32,33] describe lubrication by Maxwell-type equations.…”

Failure diagnosis and physical interpretation of journal bearing for slurry liquid using long-term real vibration data

“…However, it does not deal with the diagnosis of wear nor peeling naturally generated from healthy journal bearings. Katsaros and Nikolakopoulos 23 investigated the tilting pad thrust bearing in the case of insufficient oil supply by a combination of numerical analysis and ML, considering the viscous changes caused by thermal effects. The validity of the analytical results was confirmed experimentally by comparing the load on the pad.…”