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.

The hydrodynamic lubrication and thermal analysis of tilting pad thrust bearings has been a major subject for many studies in the field of tribology. There is only a limited number of studies regarding thrust bearings with coated surfaces. The purpose of this study is to build a parametric, iterative algorithm in order to perform a complete thermal and hydrodynamic lubrication analysis for pivoted pad thrust bearings with coatings. The analytical model is mainly based on the energy, continuity and Navier-Stokes equations, which are solved numerically with the Semi-Implicit Method for Pressure Linked Equations Consistent (SIMPLEC) method. The analysis focuses on a single pivoted pad of the thrust bearing. The thermal properties of the coating material are taken into account and the resulting thermal and flow fields are solved. The basic hydrodynamic and tribological characteristics are calculated for an uncoated, a Babbitt coated, a PTFE coated and a diamond like carbon (DLC) coated pivoted pad thrust bearing. The pressure and the film thickness distribution, as well as the load capacity and the frictional forces, are determined for several pad positions and velocities of the rotor. A mineral oil lubricant is used to estimate the shear thinning or thickening effects on the pad tribological performance. The results indicate that pads coated with PTFE and DLC show lower friction forces compared to the common steel and Babbitt applications. At the same time, the DLC coating seems to affect the bearing's flow and thermal fields less than the PTFE, making it more suitable for thrust bearings applications.

The purpose of this study is to build a parametric algorithm combining analytical results and Machine Learning in order to improve the tribological performance of coated piston rings and thrust bearings in mixed lubrication using different synthetic lubricants. The friction models for piston ring conjunction and pivoted pad thrust bearing consider the basic lubrication theory, the detailed contact geometry and the complete lubricant action for a wide range of speeds. The data produced from the analytical solutions are used as input for the training of regression models. The effect of TiN, TiAlN, CrN and DLC coatings on friction coefficient are investigated through multi-variable quadratic regression and support vector machine models. The optimum selection is considered when the minimum friction coefficient is predicted. Smooth TiN2 and TiAlN coatings seem to affect better the ring friction coefficient than rougher steel, TiN1 and CrN coatings using an uncoated or coated Nickel Nanocomposite (NNC) cylinder. Using an NNC cylinder for better durability, the friction coefficients were found to be higher by 31.3−58.8% for all the studied rings due to the rougher surface morphology. On the other hand, the results indicate that pads coated with DLC show lower friction coefficients compared to the common steel and TiAlN, CrN, and TiN applications. The multi-variable second-order polynomial regression models were demonstrated to be 1−6% more accurate than the quadratic support vector machine models in both tribological contacts.

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.

Hydrodynamic thrust bearings are machine elements used in many rotating machinery in order to support axial loads. The investigation of the lubrication in such mechanisms using numerical analysis methods has been the major subject of many studies over the years. Furthermore, the evolution of technology in the last decade brought the concept of industry 4.0 and machine learning techniques have started to play important role in the operational optimization of such assemblies. The aim of this study is to examine optimal designs of tilting pad thrust bearings by combining numerical analysis and machine learning techniques.