RESEARCH PAPER
Fatigue strength reliability assessment of turbo-fan blades by Kriging-based distributed collaborative response surface method
1
School of Mechanical Engineering Shanghai Jiao Tong University Shanghai, China Energy Department, Politecnico di Milano, Italy
2
School of Mechanical Engineering Shanghai Jiao Tong University Shanghai, China.
3
Energy Department, Politecnico di Milano Milano, Italy MINES ParisTech, PSL Research University, CRC Sophia Antipolis, France
4
School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai, China
Publication date: 2019-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(3):530-538
KEYWORDS
turbo-fan bladefatigue strengthreliability assessmentKrigingdistributed collaborative responsesurface method
ABSTRACT
Fatigue crack propagation affects the operational reliability of engine turbo-fan blades. In this article, we integrate a Kriging
regression model and a distributed collaborative response surface method (DCRSM) for the reliability assessment of turbo-fan
blades, considering the relevant uncertainty. Following a series of deterministic analyses, such as steady-state aerodynamic
analysis, harmonic response analysis and Campbell diagram, and based on the assumption that vibration stress is mainly from
aerodynamic load, the fatigue strength is calculated for turbo-fan blades under coupling aerodynamic forces, according to a
modified Goodman curve of titanium-alloy. Giving consideration to the uncertainty of the resonance frequencies and material
properties, the fatigue strength of the turbo-fan blade is evaluated, including probabilistic analysis and sensitivity analysis. In the
case study analyzed, the conclusions are that the fatigue strength reliability reaches 96.808% with confidence level of 0.95 for the
turbo-fan blade under the coupling aerodynamic forces, and the first three-order resonant frequencies are found to have important
influence on the fatigue performance of turbo-fan blades.
REFERENCES (22)
1.
Beck J L, Au S K. Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation. Journal of Engineering Mechanics 2002; 128(4): 380-391, https://doi.org/10.1061/(ASCE)...).
2.
Billinton R, Wang P. Teaching distribution system reliability evaluation using Monte Carlo simulation. IEEE Transactions on Power Systems 1999; 14(2): 397-403, https://doi.org/10.1109/59.761....
3.
Bucher C G, Bourgund U. A fast and efficient response surface approach for structural reliability problems. Structural Safety 1990; 7(1):57-66, https://doi.org/10.1016/0167-4....
4.
Cornell C A. A first order reliability theory of structural designs. Structural Reliability arid Codified Design, 1970.
5.
Das P K, Zheng Y. Cumulative formation of response surface and its use in reliability analysis. Probabilistic Engineering Mechanics 2000;15(4): 309-315, https://doi.org/10.1016/S0266-....
6.
Der Kiureghian A, Lin H Z, Hwang S J. Second-order reliability approximations. Journal of Engineering mechanics 1987; 113(8): 1208-1225, https://doi.org/10.1061/(ASCE)...).
7.
Gao H F, Bai G C, Gao Y, Bao T W. Reliability analysis for aeroengine turbine disc fatigue life with multiple random variables based on distributed collaborative response surface method. Jo-rnal of Central South University 2015; 22(12): 4693-4701, https://doi.org/10.1007/s11771....
8.
Gao H, Bai G. Reliability analysis on resonance for low-pressure compressor rotor blade based on least squares support vector machine with leave-one-out cross-validation. Advances in Mechanical Engineering 2015; 7(4): 1687814015578351, https://doi.org/10.1177/168781....
9.
Gao H F, Bai G C. Vibration reliability analysis for aeroengine compressor blade based on support vector machine response surface method. Journal of Central South University 2015; 22(5): 1685-1694, https://doi.org/10.1007/s11771....
10.
Gao H, Fei C, Bai G, Ding L. Reliability-based low-cycle fatigue damage analysis for turbine blade with thermo-structural interaction. Aerospace Science and Technology 2016; 49:289-300, https://doi.org/10.1016/j.ast.....
11.
Gao H, Wang A, Bai G, Wei C, Fei C. Substructure-based distributed collaborative probabilistic analysis method for low-cycle fatigue damage assessment of turbine blade-disk. Aerospace Science and Technology 2018; 79: 636-646, https://doi.org/10.1016/j.ast.....
13.
Hasofer A M, Lind N C. Exact and invariant second-moment code format. Journal of the Engineering Mechanics Division 1974; 100(1): 111-121.
14.
Isaaks E H, Srivastava R M. An introduction to applied geostatistics (No. BOOK). Oxford University Press, 1989.
15.
Lophaven S N, Nielsen H B, Sondergaard J, DACE-A M K T. Informatics and mathematical modelling. Technical University of Denmark, 2002.
16.
Poursaeidi E, Babaei A, Arhani M M, Arablu M. Effects of natural frequencies on the failure of R1 compressor blades. Engineering Failure Analysis 2012; 25: 304-315, https://doi.org/10.1016/j.engf....
17.
Rackwitz R, Flessler B. Structural reliability under combined random load sequences. Computers & Structures 1978; 9(5): 489-494, https://doi.org/10.1016/0045-7....
18.
Rajashekhar M R, Ellingwood B R. A new look at the response surface approach for reliability analysis. Structural Safety 1993; 12(3): 205-220, https://doi.org/10.1016/0167-4....
19.
Sacks J, Welch W J, Mitchell T J, Wynn H P. Design and analysis of computer experiments. Statistical Science 1989; 409-423, https://doi.org/10.1214/ss/117....
20.
Srinivasan A V. Flutter and resonant vibration characteristics of engine blades. Journal of Engineering for Gas Turbines and Power 1997;119(4): 742-775, https://doi.org/10.1115/1.2817....
21.
Subrahmanyam K B, Kulkarni S V, Rao J S. Coupled bending-bending vibrations of pre-twisted cantilever blading allowing for shear deflection and rotary inertia by the Reissner method. International Journal of Mechanical Sciences 1981; 23(9): 517-530, https://doi.org/10.1016/0020-7....
22.
Tvedt L. Distribution of quadratic forms in normal space-application to structural reliability. Journal of Engineering Mechanics 1990;116(6): 1183-1197, https://doi.org/10.1061/(ASCE)...).
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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