Research Papers: Hydrodynamic Lubrication

Metamodel-Assisted Optimization of Connecting Rod Big-End Bearings

[+] Author and Article Information
Bernard Villechaise

IUT Angoulême,
Institut PPRIME–UPR 3346,
Department Génie Mécanique et Systèmes Complexes,
Angoulême, 16021, France

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received November 25, 2012; final manuscript received May 2, 2013; published online June 24, 2013. Assoc. Editor: Daniel Nélias.

J. Tribol 135(4), 041704 (Jun 24, 2013) (10 pages) Paper No: TRIB-12-1214; doi: 10.1115/1.4024555 History: Received November 25, 2012; Revised May 02, 2013

From a very general point of view, optimization involves numerous calculations and therefore a high computational cost. In the fields where a single calculation is long and the optimization is crucial, specific techniques, devoted to this task, have been developed. First, the surrogate-based models are introduced and a short review of optimization in tribology is presented. The aim of the present work is to combine both. To demonstrate the power of the methodology on a lubricated bearing, the theoretical background is first outlined. Then, the two aforementioned processes are described: the construction of the surrogate, based on the Finite Element Method well-chosen computations, and the Multiobjective Optimization, thanks to a Nondominated Sorting Genetic Algorithm. Both are utilized on a connecting rod big-end bearing. As a result, the power loss and the functioning severity are simultaneously minimized upon a set of ten input parameters. The user is then provided with simple analytical expressions of the input variables, for which the bearing behavior is optimal.

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Fig. 1

Meshed quarter structure (right), shell mesh (middle) and TEHD mesh (left)

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Fig. 2

Typical case. From left to right: elastic deformation, contact pressure, and hydrodynamic pressure

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Fig. 3

CCC design (left) and CCF design (right), with four star points (diamond points)

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Fig. 4

Example of a Pareto Optimal Front; circled solutions are not dominated and called “Pareto Optimal”

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Fig. 5

Optimization flow chart: a surrogate is built and used for optimization. If some predicted optimal responses do not match the corresponding full-model calculations, the surrogate is improved.

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Fig. 6

HDI 1.6 L 110 CV connecting rod

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Fig. 7

Load diagram, 2000 rpm, full load

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Fig. 8

Ellipsis shape input parameter λ

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Fig. 9

Shell bore relief input parameters, d and l

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Fig. 10

Effect of the barrel shape parameter on the bearing pressure

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Fig. 11

The 10 parameter genotype: low value (dark gray), middle value (gray), and high value (light gray)

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Fig. 12

Main input effects on PV (left) and on PL (right): noninfluent factors have been removed so that the cumulated effects exhibit no plateau

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Fig. 13

Coefficient removal: confidence interval too high (dashed ellipsis) or value too low (continuous ellipsis)

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Fig. 14

Contradictory response illustration: what is gained on the one hand, is lost on the other

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Fig. 15

Resulting Pareto Optimal Front (1000th generation) with 1st and 50th generations

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Fig. 16

Comparison fullscale/metamodel and gain between non- and optimized points

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Fig. 17

Differences between non- and optimal cases on the deformations (resp. a1 and a2) and the pressures (resp. b1 and b2)

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Fig. 18

Work area approximated Pareto Front with the simplified main input expressions and the 149 DoE points



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