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- W4323317236 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." @default.
- W4323317236 created "2023-03-07" @default.
- W4323317236 creator A5024088947 @default.
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- W4323317236 date "2023-03-04" @default.
- W4323317236 modified "2023-09-26" @default.
- W4323317236 title "Performance Prediction Model for Hydrodynamically Lubricated Tilting Pad Thrust Bearings Operating under Incomplete Oil Film with the Combination of Numerical and Machine-Learning Techniques" @default.
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- W4323317236 doi "https://doi.org/10.3390/lubricants11030113" @default.
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