0
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.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.

References

Voutchkov, I. I., and Keane, A. J., 2010, “Multiobjective Optimization Using Surrogates,” Adaptation, Learning, and Optimization, Vol. 7, Computational Intelligence in Optimization, 1st ed., Y.Tenne, and C.-K.Goh, eds., Springer, New York, pp. 155–175.
Zitzler, E., Deb, K., and Thiele, L., 2000, “Comparison of Multiobjective Evolutionary Algorithms: Empirical Results,” Evol. Comput., 8(2), pp. 173–195. [CrossRef] [PubMed]
Forrester, A. I. J., and Keane, A. J., 2009, “Recent Advances in Surrogate-Based Optimization,” Prog. Aerosp. Sci., 45, pp. 50–79. [CrossRef]
Goel, T., Vaidyanathan, R., Haftka, R., Shyy, W., Queipo, N., and Tucker, K., 2007, “Response Surface Approximation of Pareto Optimal Front in Multi-Objective Optimization,” Comput. Methods Appl. Mech. Eng., 196(4–6), pp. 879–893. [CrossRef]
Boedo, S., and Eshkabilov, S. L., 2003, “Optimal Shape Design of Steadily Loaded Journal Bearings using Genetic Algorithms,” Tribol. Trans., 46(1), pp. 134–143. [CrossRef]
Papadopoulos, C. I., Efstathiou, E. E., Nikolakopoulos, P. G., and Kaiktsis, L., 2011, “Geometry Optimization of Textured Three-Dimensional Micro-Thrust Bearings,” ASME J. Tribol., 133(4), p. 041702. [CrossRef]
Papadopoulos, C. I., Nikolakopoulos, P. G., and Kaiktsis, L., 2011, “Evolutionary Optimization of Micro-Thrust Bearings With Periodic Partial Trapezoidal Surface Texturing,” J. Eng. Gas Turbines Power, 133(1), p. 012301. [CrossRef]
Hirani, H., 2005, “Multiobjective Optimization of Journal Bearing Using Mass Conserving and Genetic Algorithms,” Proc. Inst. Mech. Eng., Part J: J. Eng. Tribol., 219(3), pp. 235–248. [CrossRef]
Bhat, N., and Barrans, S. M., 2008, “Design and Test of a Pareto Optimal Flat Pad Aerostatic Bearing,” Tribol. Int., 41(3), pp. 181–188. [CrossRef]
Bhat, N., Barrans, S. M., and Kumar, A. S., 2010, “Performance Analysis of Pareto Optimal Bearings Subject to Surface Error Variations,” Tribol. Int., 43(11), pp. 2240–2249. [CrossRef]
Booker, J. F., Boedo, S., and Bonneau, D., 2010, “Conformal Elastohydrodynamic Lubrication Analysis for Engine Bearing Design: A Brief Review,” Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sc., 224(12), pp. 2648–2653. [CrossRef]
Bonneau, D., Guines, D., Frêne, J., and Toplosky, J., 1995, “EHD Analysis, Including Structural Inertia Effects and a Mass Conserving Cavitation Model,” ASME J. Tribol., 117(3), pp. 540–547. [CrossRef]
Piffeteau, S., Souchet, D., and Bonneau, D., 2000, “Influence of Thermal and Elastic Deformations on Connecting Rod Big-End Bearing Lubrication Under Dynamic Loading,” ASME J. Tribol., 122(1), pp. 181–191. [CrossRef]
Patir, N., and Cheng, H. S., 1978, “An Average Flow Model for Determining Effects of Three-Dimensional Roughness on Partial Hydrodynamic Lubrication,” ASME J. Lubr. Technol., 100, pp. 12–17. [CrossRef]
Patir, N., and Cheng, H. S., 1979, “Application of Average Flow Model to Lubrication Between Rough Sliding Surfaces,” ASME J. Lubr. Technol., 101, pp. 220–230. [CrossRef]
Robbe-Valloire, F., Paffoni, B., and Progri, R., 2001, “Load Transmission by Elastic, Elasto-Plastic or Fully Plastic Deformation of Rough Interface Asperities,” Mech. Mater., 33, pp. 617–633. [CrossRef]
ISO 12085, 1996, Geometrical Product Specifications (GPS), Surface texture: Profile method - Motif parameters, International Organization for Standardization.
Bonneau, D., Fatu, A., and Souchet, D., 2011, Paliers Hydrodynamiques 1: Equations, Modèles Numériques Isothermes et Lubrification Mixte, Hermes Science Publications–Lavoisier, Paris
Francisco, A., Fatu, A., and Bonneau, D., 2009, “Using Design of Experiments to Analyze the Connecting Rod Big- End Bearing Behavior,” ASME J. Tribol., 131(1), pp. 1–13. [CrossRef]
Eriksson, L., 2008, Design of Experiments: Principles and Applications, MKS Umetrics AB, Umeå, Sweden,
Montgomery, D. C., 2005, Design and Analysis of Experiments, 6th ed., John Wiley & Sons Inc., New York.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., 2000, “A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II,” IEEE Trans. Evol. Comput., 6, pp. 182–197. [CrossRef]
Harnoy, A., 2002, Bearing Design in Machinery: Engineering Tribology and Lubrication, 1st ed., CRC Press, Boca Raton, FL.
Lavie, T., 2012, “Optimisation de la Lubrification des Paliers de Tete de Bielle: Demarche Methodologique,” Ph.D. thesis, Université de Poitiers, Poitiers, France.

Figures

Grahic Jump Location
Fig. 1

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

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

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

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
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.

Grahic Jump Location
Fig. 6

HDI 1.6 L 110 CV connecting rod

Grahic Jump Location
Fig. 7

Load diagram, 2000 rpm, full load

Grahic Jump Location
Fig. 8

Ellipsis shape input parameter λ

Grahic Jump Location
Fig. 9

Shell bore relief input parameters, d and l

Grahic Jump Location
Fig. 10

Effect of the barrel shape parameter on the bearing pressure

Grahic Jump Location
Fig. 11

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

Grahic Jump Location
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

Grahic Jump Location
Fig. 13

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

Grahic Jump Location
Fig. 14

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

Grahic Jump Location
Fig. 15

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

Grahic Jump Location
Fig. 16

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

Grahic Jump Location
Fig. 17

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

Grahic Jump Location
Fig. 18

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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In