Probabilistic Main Bearing Performance for an Internal Combustion Engine

A probabilistic analysis is presented for studying the variation effects on the main bearing performance of an I.C. engine system, under structural dynamic conditions. For computational efficiency, the probabilistic analysis is based on surrogate models (metamodels), which are developed using the kriging method. An optimum symmetric Latin hypercube algorithm is used for efficient “space-filling” sampling of the design space. The metamodels provide an efficient and accurate substitute to the actual engine bearing simulation models. The bearing performance is based on a comprehensive engine system dynamic analysis which couples the flexible crankshaft and block dynamics with a detailed main bearing elastohydrodynamic analysis. The clearance of all main bearings and the oil viscosity comprise the random variables in the probabilistic analysis. The maximum oil pressure and the percentage of time within each cycle that a bearing operates with oil film thickness below a threshold value of 0.27μm at each main bearing constitute the system performance measures. Probabilistic analyses are first performed to calculate the mean, standard deviation and probability density function of the bearing performance measures. Subsequently, a probabilistic sensitivity analysis is described for identifying the important random variables. Finally, a reliability-based design optimization study is conducted for optimizing the main bearing performance under uncertainty. Results from a V6 engine are presented.

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