Probabilistic Main Bearing Performance for an Internal Combustion Engine

[+] Author and Article Information
Zissimos P. Mourelatos

Mechanical Engineering Department, Oakland University, Rochester, MI 48309mourelat@oakland.edu

Nickolas Vlahopoulos

Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48109nickvl@engin.umich.edu

Omidreza Ebrat

 Federal-Mogul Technical Center, 47001 Port St., Plymouth, MI 48170oebrat@fmo.com

Jinghong Liang

Mechanical Engineering Department, Oakland University, Rochester, MI 48309jliang@oakland.edu

Jin Wang

Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48109jinw@engin.umich.edu

J. Tribol 127(4), 784-792 (Aug 09, 2004) (9 pages) doi:10.1115/1.2000268 History: Received February 23, 2004; Revised August 09, 2004

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.

Copyright © 2005 by American Society of Mechanical Engineers
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Figure 1

Journal bearing notation

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Figure 2

Crankshaft and block finite element mesh and cylinder pressure for an automotive V6 engine

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Figure 3

PDF of performance measure press3

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Figure 4

RBDO progress for β=1.28

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Figure 5

RBDO progress for β=1.52



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