Polynomial neural network based probabilistic hydrodynamic analysis of two-lobe bearings with stochasticity in surface roughness

Abstract

This paper investigates the probabilistic response of two-lobe bearings considering the uncertainty in eccentricity ratio, preload value, bearing clearance, supply pressure, oil viscosity and surface roughness. To simulate stochasticity in input variables, Monte Carlo simulation (MCS) is carried out in conjunction with the Reynolds equation using finite difference method. Polynomial neural network based machine learning model is used as a surrogate model to increase the efficiency of MCS. To assess the relative importance of the stochastic input parameters, a sensitivity analysis is carried out. The physically insightful new probabilistic results, presented here covering a wide spectrum of uncertainty sources including surface roughness, make it evident that different forms of source-uncertainties have a significant effect on the critical performance of bearings.