Analysis of a hydrodynamic thrust bearing with elastic deformation using a recurrent neural network

Abstract

A theoretical analysis on the general behaviour of a thrust bearing is presented in this paper. The model programme using a method adaptation of finite differences was developed to solve the Reynolds equation for lubrication. The model in the theoretical analysis uses a single one-dimensional grid. The altering of total lubrication load obtained in the result of under-cutting in the thrust bearing have been determined together with the parameters such as oil film thickness and pressure. Parameters such as the pressure and thickness of the oil film were determined. The hydrodynamic behaviour of thrust bearing was analysed by considering of different dimensionless system pressure, speed and geometry of the bearing. The effect of the elastic load due to elastic deflection is taken into account as on the load-bearing characteristics is included. Also, a proposed neural network predictor is utilised to analyse of the general behaviour of thrust bearing. The results of the proposed neural network predictor gives superior performance for analysing of the behaviour of a thrust bearing undergoing in elastic deformation.