Stochastic Analysis of Lubrication in Misaligned Journal Bearings

Abstract Misalignment is common in hydrodynamic journal bearings and the causes of it can be diversified, making lubrication performance exhibits stochasticity. Lubricant viscosity often heavily depends on temperature, which may vary during service result unexpected deviations. This article analyzes stochastic lubrications a cylindrical bearing with misalignment under uncertainties. The Reynolds equation governing misaligned discretized by polynomial chaos expansion (PCE), an efficient uncertainty tracking tool, then solved finite difference method to obtain sampled lubrication. crude Monte Carlo simulation used verify PCE frame. Various critical parameters are studied comprehensively ensemble mean, standard deviation, probability density function, cumulative distribution function. Insightful inspections provided results, found that different cause significant effects performance. new findings present study will guide robust design analysis general bearings.