This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Probabilistic Analysis for the Performance Characteristics of Engine Bearings due to Variability in Bearing Properties
Technical Paper
2003-01-1733
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine without performing time consuming analyses. The metamodels are developed based on results from actual simulation solvers computed at a limited number of sample points, which sample the design space. A finite difference bearing solver 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 bearing performance solver over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses. The initial clearance, the length of the bearing, the radius of the journal, and the oil viscosity comprise the random variables in the probabilistic analysis. The maximum oil pressure and the minimum oil film thickness constitute the probabilistic response for which the analyses are performed. The availability of the metamodels allows to compare 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 its performance.
Recommended Content
| Technical Paper | MAJOR CAUSES OF BEARING FAILURES |
| Technical Paper | Probabilistic Computations for the Main Bearings of an Operating Engine Due to Variability in Bearing Properties |
| Technical Paper | ENGINE BEARING FAILURES |
Authors
Citation
Wang, J., Vlahopoulos, N., Mourelatos, Z., Ebrat, O. et al., "Probabilistic Analysis for the Performance Characteristics of Engine Bearings due to Variability in Bearing Properties," SAE Technical Paper 2003-01-1733, 2003, https://doi.org/10.4271/2003-01-1733.Also In
References
- Boedo, S., Booker, J. F. and Wilkie, M. J., “A Mass Conserving Modal Analysis for Elastohydrodynamic Lubrication”, Lubricants and Lubrication, Dowson, D., et al. (Editors), (1995).
- Booker, J. F., “Dynamically Loaded Journal Bearings: Mobility Method of Solution”, Trans ASME, J. Basic Engng, Vol. 87, pp 537-546, (1965).
- Booker, J. F., “Dynamically-Loaded Journal Bearings: Numerical Application of the Mobility Methods”, Trans. ASME, Journal of Lubrication Technology, Vol.93, n1, (1971).
- Craven, P., Wahba, G., “Smoothing Noisy Data with Spline Functions: Estimating the Correct Degree of Smoothing by the Methods of Generating Cross-Validation,” Numerical Mathematics, Vol.31, pp. 377-403, (1978).
- Cressie, N., “Spatial Prediction and Ordinary Kriging,” Mathematical Geology, Vol.20, n4, pp. 405-421, (1988).
- Curran, C., Mitchell, T., Morris, M., and Ylvisaker, D., “A Bayesian Approach to the Design and Analysis of Computer Experiments,” ORNL Technical Report 6498, available from the National Technical Information Service, Springfield, VA, 22161, (1988).
- Dyn, N., Levin, D., and Rippa, S., “Numerical Procedures for Surface Fitting of Scattered Data by Radial Functions,” SIAM Journal of Scientific and Statistical Computing, Vol.7, n2, pp. 639-659, (1986).
- Ebrat, O., “Detailed Main Bearing Hydrodynamic Characteristics for Crankshaft-Block Dynamic Interaction in Internal Combustion Engines,” Ph.D. Dissertation, The University of Michigan, (2002).
- Ellacott, S.W., Mason, J.C., and Anderson, I.J., “Mathematics of Neural Networks: Models, Algorithms, and Applications,” Kluver Academic Publishers, Boston, MA, (1997).
- Frene, J et al, Hydrodynamic Lubrication, Bearings and Thrust Bearings, Tribology Series, 33, Dowson, D. (Editor), Elsevier (1997).
- Goenka, P. K., “Dynamically Loaded Journal Bearings: Finite-Element Method Analysis”, Trans. ASME, Journal of Tribology, Series F, pp 429-439, (1984).
- Goenka, P. K., Paranjpe, R. S., “A Review of Engine Bearing Analysis Methods at General Motors”, SAE Publication, SP-919, on Engine Tribology, SAE Paper No. 920489, pp 67-75, (1992).
- Goenka, P. K., Oh, K. P., “An Optimum Short Bearing Theory for the Elastohydrodynamic Solution of Journal Bearings”, Trans. ASME, Journal of Tribology, Vol. 108, pp 294-299, (1986).
- Hirani, H., Athre, K., and Biswas, S., “Dynamically Loaded Finite Length Journal Bearings: Analytical Method of Solution,” Journal of Tribology, Transactions of the ASME, Vol.121 n4, Oct. 1999, pp. 844-852. (1999).
- Iman, R.L., and Conover, W.J., “Small Sampling Sensitivity Analysis Techniques for Computer Models, With an Application to Risk Assessment,” (with discussion), Communications in Statistics, Part A-Theory and Methods, Vol.9, pp. 1749-1874, (1980).
- Jansen, M., Maifait, M., and Bultheel, A., “Generalized Cross Validation for Wavelet Thresholding,” Signal Processing, Elsevier, n56, pp. 33-44, (1996).
- Johnson, J., Moore, L., Ylvisaker, D., “Minimum and Maximum Distance Designs,” J. Statist. Plann. Inference, Vol.2, pp. 131-148, (1990).
- Knoll, G., Lang, J. and Reinaecker, A., “Transient EHD Connecting Rod Analysis: Full Dynamic versus Quasi-static Deformation”, Presented at Jt. ASME/STLE Tribology Conference, Orlando, FL., October 8-11, (1995).
- Knoll, G., Lang, J. and Reinaecker, A., “Transient EHD Connecting Rod Analysis: Full Dynamic versus Quasi-static Deformation”, Presented at Jt. ASME/STLE Tribology Conference, Orlando, FL., October 8-11, (1995).
- Kumar, A., Goenka, P.K., and Booker, J.B., “Modal Analysis Elastohydrodynamic Lubrication, A Connecting Rod Application,” J. Tribol. Trans., ASME, Vol.112, n3, pp. 524-534, (1990).
- Kumar, A., Booker, J. B., “A Mass and Energy Conserving Finite Element Lubrication Algorithm”, Trans. ASME, Journal of Tribology, Vol.116, pp 667-671, (1994).
- McIvor, J. D. and Fenner, D. N., “Finite Element Analysis of Dynamically Loaded Flexible Journal Bearings: A Fast Newton-Raphson Method”, Transaction ASME, Journal of Tribology, Vol. 111, pp 597-604, (1989).
- McKay, T., Beckman, R., Conover, W., “A Comparison of three methods for selecting values of input variables in the analysis of output from a computer code,” Technometrics 21, pp. 239-246, (1979).
- Madsen, H.O., Krenk, S., and Lind, N.C., “Methods of Structural Safety,” Prentice Hall Inc., Englewood Cliffs, New Jersey, (1986).
- Millwater, H.R., Wu, Y.-T., and Cardinal, J., “Probabilistic Structural Analysis of Fatigue and Fracture,” AIAA, Journal, Vol.15, n7, pp. 1506-1513, (1994).
- Millwater, H.R., Wu, Y.-T., Torng, Y., Thacker, B, Riha, D., and Leung, C., “Recent Development of the NESSUS Probabilistic Structural Analysis Computer Program,” AIAA, Journal, Vol.24, n11, pp. 614-624, (1992).
- Morris, M., Mitchell, T., “Exploratory Design for Computer Experiments,” J. Statist. Plann. Inference, Vol.43, pp. 381-402, (1995).
- Mourelatos, Z. P., “An Efficient Journal Bearing Lubrication Analysis for Engine Crankshafts”, Tribology Transactions Vol.44, n3, pp 351-358, (2001).
- “NESSUS /FPI Theoretical Manual”, version 2. 4, Southwest Research Institute, (1998).
- Park, J.-S., “Optimal Latin-Hypercube Designs for Computer Experiments,” J. Statist. Plann. Inference, Vol.39, pp. 95-111, (1994).
- Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., “Numerical Recipes in Fortran, The Art of Scientific Computing”, Cambridge University Press, (1996).
- Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P., “Design and Analysis of Computer Experiments,” Statistical Science, Vol.4, n4, pp.409-435,(1989)(a).
- Sacks, J., Welch, W.J., and Schiller, S.B., “Designs for Computer Experiments,” Technometrics, Vol.31, n1, pp.41-47,(1989)(b).
- Shewy, M., Wynn, H., “Maximum Entropy Design,” Journal of Applied Statistics, Vol.14, n2, pp. 165-170, (1987).
- Stein, M., “Large Sample Properties of Simulation Using Latin Hypercube Sampling,” Technometrics, Vol.29, pp.143-151, (1987).
- Thacker, B., Harren, S., and Millwater, H.R., “Combined Stress and Resistance Modeling with the NESSUS Software System,” ASME, Design Engineering Division, DE Vol.30, pp. 49-54, New York.
- Wu, Y.-T., “Demonstration of a New, Fast Probability Integration Method for Reliability Analysis,” Transaction of ASME, Vol.109, pp. 24-28, (1987).
- Wu, Y.-T., Millwater, H.R., and Cruse,T.A., “An Advanced Probabilistic Structural Analysis Method for Implicit Performance Functions,” AIAA Journal, Vol.28, n9, pp. 1663-1669, (1990).
- Wu, Y.-T., and Wirsching, P. H., “New Algorithm for Structural Reliability Estimation,” Journal of Engineering Mechanics, Vol.113, n9, pp. 1319-36, (1987).
- Ye, K.Q., Li, W., and Sudjianto, A., “Algorithm Construction of Optimal Symmetric Latin Hypercube Designs”, Journal of Statistical Planning and Inference, Vol.90, pp.145-159, (2000).