Probabilistic and sensitivity analyses for the performance characteristics of the main bearings in an operating engine due to variability in bearing properties

Abstract

Surrogate models (metamodels) are developed and used to evaluate an engine bearing performance and perform probabilistic sensitivity analyses. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points. An integrated system-level engine simulation model, consisting of flexible crankshaft and block dynamic models, connected by a detailed hydrodynamic lubrication model, is employed for constructing the metamodels. An optimal symmetric Latin hypercube sampling algorithm is utilised. The metamodels are employed for performing probabilistic and sensitivity analyses. The initial clearance between the crankshaft and each main bearing and the oil viscosity comprise the random variables. The maximum oil pressure and the percentage of time (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 system performance variables.