Probabilistic Computations for the Main Bearings of an Operating Engine Due to Variability in Bearing Properties

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine. The metamodels are employed for performing probabilistic analyses for the engine bearings. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points, which sample the design space. An integrated system-level engine simulation model, consisting of a flexible crankshaft dynamics model and a flexible engine block model connected by a detailed hydrodynamic lubrication model, is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space. The development of the metamodels is validated by comparing results from the metamodels with results from the actual simulation models over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses. The initial clearance between the crankshaft and the bearing at each main bearing and the oil viscosity comprise the random variables in the probabilistic analyses. The maximum oil pressure and the percentage of time (the time ratio) within each cycle that a bearing operates with oil film thickness less than a user defined threshold value at each main bearing constitute the performance variables of the system. The availability of the metamodels allows comparing the performance of several probabilistic methods in terms of accuracy and computational efficiency. A useful insight is gained by the probabilistic analysis on how variability in the bearing characteristics affects the performance of the bearings.