Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques

Abstract

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.