Stochastic Field Models for Aircraft Jet Engine Applications
Publication: Journal of Aerospace Engineering
Volume 14, Issue 4
Abstract
This paper addresses the stochastic modeling for advanced jet engine applications and also presents an overview of the basic theoretical aspects of different useful stochastic field models and then illustrates their application to practical problems. It is shown that stochastic fields adequately capture key random aspects of engine environment and behavior. The illustrated applications address stochastic modeling issues that are typical for engine structural analysis, such as time-varying operating speed conditions, manufacturing geometry deviations, and engine vibration, that produce highly nonlinear responses. This paper discusses stochastic modeling for two situations often met in practice: (1) stochastic process/field models with known statistics; and (2) stochastic process/field models with unknown statistics. Illustrative examples are used to highlight features of different stochastic field modeling techniques.
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References
1.
Chen, Z., Gu, C., and Wahba, G. ( 1989). “Comment on linear smoothers and additive models.” The Ann. of Statistics, 17.
2.
Chin, S. L. ( 1994). “Identification based on cluster estimation.” J. Intelligent and Fuzzy Sys., 2.
3.
Cressie, J. ( 1991). Spatial statistics, Prentice-Hall, Englewood Cliffs, N.J.
4.
Ghanem, R., and Ghiocel, D. M. ( 1996). “Limitations of FORM/SORM (MPP expansion methods) for solving practical engineering reliability problems.” Proc., 7th ASCE EMD/STD Joint Spec. Conf. on Probabilistic Mech. and Struct. Reliability, ASCE, New York.
5.
Ghanem, R., and Spanos, P. ( 1991). Stochastic finite elements, Springer, New York.
6.
Ghiocel, D. M. ( 1999). “Factorable stochastic field models for jet engine vibration response.” Proc., 13th ASCE Spec. Conf., ASCE, Reston, Va.
7.
Ghiocel, D. M. ( 2000a). “Probabilistic fatigue life prediction for jet engine components: stochastic modeling issues.” Proc., ECOMASS 2000.
8.
Ghiocel, D. M. ( 2000b). “Stochastic models for jet engine condition assessment and life prediction.” Proc., USAF Aircraft Struct. Integrity Program Conf.
9.
Ghiocel, D. M., and Altmann, J. ( 2001). “Critical modeling issues for prediction of turbine performance degradation: Use of a stochastic-neuro-fuzzy inference system.” Proc., 42th AIAAIASME/ASCE/AHS/ASC Struct., Struct. Dyn., and Mat. Conf., AIAA/ASCE/ASME Non-Deterministic Approaches Forum.
10.
Ghiocel, D. M., and Rieger, N. F. ( 1998). “Specific probabilistic issues for gas turbine HCF life prediction.” Proc., 3rd FAA/AFRL Workshop on Application of Probabilistic Des. Methodologies to Gas Turbine Rotating Components.
11.
Grigoriu, M. ( 1995). Applied non-Gaussian processes, Prentice-Hall, Englewood Cliffs, N.J.
12.
Loeve, J. ( 1977). Probability theory, 4th Ed., Springer, New York.
13.
Patrick, E. ( 1972). Fundamental of pattern recognition, Electrical Engrg. Ser., Prentice-Hall, New York.
14.
Yager, R. R., and Filev, D. P. ( 1992). “Approximate clustering via the mountain method.” Tech. Rep. MII 1305, Iowa College, La Rochelle, N.Y.
Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 14 • Issue 4 • October 2001
Pages: 127 - 139
History
Received: Feb 13, 2001
Published online: Oct 1, 2001
Published in print: Oct 2001
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