LAUSR

The conventional design process of journal bearings is completely based on deterministic theoretical predictions. The deterministic approach ignores the probabilistic and random nature of input variables and consequently, poses an arbitrary tolerance or safety factor on the performance parameters. However, such arbitrary safety factors result in performance loss when the factor is very high or failure of the system when the factor is low. This paper presents a probabilistic approach for the design of a three-lobe journal bearing considering the uncertainty in geometric parameters such as clearance, preload, and offset factor. The Monte Carlo simulation (MCS) algorithm is used for the propagation of input uncertainties to the systems’ output. To increase the efficiency of computationally expensive MCS, the moving least square (MLS) method is used as a surrogate model. The training data of the surrogate model is obtained by solving the Reynolds equation, at selective design points, using the finite difference method. To explain the probabilistic response of the bearing for various applications and different operating conditions, the results are presented for long, short, and finite bearings at three different supply pressures considering three different eccentricity ratios. The results of the probabilistic analysis show a significant deviation of the system response from its deterministically predicted values and suggest safe design values for the reliable operation of a three-lobe bearing.