RESEARCH PAPER
Fatigue strength reliability assessment of turbofan blades subjected to intake disturbances based on the improved kriging model
1
School of Mechatronic Engineering and Automation, Shanghai University, China
2
School of Mechanical Engineering, North University of China, China
These authors had equal contribution to this work
Submission date: 2024-07-29
Final revision date: 2024-08-29
Acceptance date: 2024-10-06
Online publication date: 2024-10-09
Publication date: 2024-10-09
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194175
HIGHLIGHTS
- The influence of intake disturbance on the vibration of turbofan blades was discussed.
- Dynamic analysis of structures subjected to multiple sinusoidal loads simultaneously.
- Calculate the reliability of fatigue strength for different vibration modes of blades.
- DCGAK improves the numerical and reliability prediction accuracy of complex systems.
KEYWORDS
TOPICS
ABSTRACT
This paper aims to develop an efficient and precise reliability analysis method to enhance the numerical prediction accuracy for complex structures. Kriging, an implicit surrogate model, used to address highly nonlinear and complex problems. In this study, genetic algorithms (GA) are utilized to optimize the parameters of the Kriging model, which is then integrated with a distributed collaborative strategy to introduce the Genetic Algorithm Optimized Distributed Collaborative Kriging Model (DCGAK). Using the CFM56-fan blade as a case study, the impact of intake disturbances at the engine inlet is evaluated to assess the fatigue strength reliability of the blade. Comparison with different mathematical models demonstrates that the prediction accuracy of DCGAK closely aligns with the Monte Carlo sampling results, suggesting promising prospects for its application in numerical prediction and reliability analysis. This approach enriches the current methods for structural reliability analysis of complex mechanical systems.
ACKNOWLEDGEMENTS
This paper is co-supported by the National Natural Science Foundation of China (Grant no. 51705309), and the China Postdoctoral Science Foundation (Grant no. 2017M621481). The authors would like to thank them.
REFERENCES (36)
1.
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....
2.
Gao H, Bai G. 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....
3.
Gao H F, Wang A, Zio E, et al. Fatigue strength reliability assessment of turbo-fan blades by Kriging-based distributed collaborative response surface method. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21(3): 530-538, https://doi.org/10.17531/ein.2....
4.
Xu D, He C, Sun D, et al. Stall inception prediction of axial compressors with radial inlet distortions. Aerospace Science and Technology 2021; 109: 106433, https://doi.org/10.1016/j.ast.....
5.
Gu B, Xu D, Dong X, et al. A modified small perturbation stability prediction model for axial compressors with circumferential inlet distortion. Aerospace Science and Technology 2023; 132: 108079, https://doi.org/10.1016/j.ast.....
6.
Su G, Sun D, Li Y, et al. Aeroelastic stability of labyrinth seal ring under steady and dynamic total pressure intake distortion conditions. Mechanical Systems and Signal Processing 2023; 204: 110776, https://doi.org/10.1016/j.ymss....
7.
Doll U, Migliorini M, Baikie J, et al. Non-intrusive flow diagnostics for unsteady inlet flow distortion measurements in novel aircraft architectures. Progress in Aerospace Sciences 2022; 130: 100810, https://doi.org/10.1016/j.paer....
8.
Ren L H, Ye Z F, Zhao Y P. A modeling method for aero-engine by combining stochastic gradient descent with support vector regression. Aerospace Science and Technology 2020; 99: 105775, https://doi.org/10.1016/j.ast.....
9.
Lu C, Teng D, Keshtegar B, et al. Extremum hybrid intelligent-inspired models for accurate predicting mechanical performances of turbine blisk. Mechanical Systems and Signal Processing 2023; 190: 110136, https://doi.org/10.1016/j.ymss....
10.
Lu C, Fei C W, Liu H T, et al. Moving extremum surrogate modeling strategy for dynamic reliability estimation of turbine blisk with multi-physics fields. Aerospace Science and Technology 2020; 106: 106112, https://doi.org/10.1016/j.ast.....
11.
Teng D, Feng Y W, Lu C, et al. Generative adversarial surrogate modeling framework for aerospace engineering structural system reliability design. Aerospace Science and Technology 2024; 144: 108781, https://doi.org/10.1016/j.ast.....
12.
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....
13.
Chen Z, He J, Li G, et al. Fast convergence strategy for adaptive structural reliability analysis based on kriging believer criterion and importance sampling. Reliability Engineering & System Safety 2024; 242: 109730, https://doi.org/10.1016/j.ress....
14.
Michael S L. Statistical interpolation of spatial data: some theory for Kriging. 1999.
15.
Cui D, Wang G, Lu Y, et al. Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model. Reliability Engineering & System Safety 2020; 203: 107074, https://doi.org/10.1016/j.ress....
16.
Song L K, Bai G C, Fei C W. Multi-failure probabilistic design for turbine bladed disks using neural network regression with distributed collaborative strategy. Aerospace Science and Technology 2019; 92: 464-477, https://doi.org/10.1016/j.ast.....
17.
Gao H F, Wang Y H, Li Y, et al. Distributed-collaborative surrogate modeling approach for creep-fatigue reliability assessment of turbine blades considering multi-source uncertainty. Reliability Engineering & System Safety 2024; 250: 110316, https://doi.org/10.1016/j.ress....
18.
Huang H M, Jiao W. Experiment and FEM analysis for fracture of fan blades. Advanced Materials Research, 2011; 143: 819-823, https://doi.org/10.4028/www.sc....
19.
Longley J P, Shin H W, Plumley R E, et al. Effects of rotating inlet distortion on multistage compressor stability. Journal of Turbomachinery 1996; 118: 181–188, https://doi.org/10.1115/1.2836....
20.
Dong X, Sun D, Li F, et al. Stall margin enhancement of a novel casing treatment subjected to circumferential pressure distortion. Aerospace Science and Technology 2018; 73: 239-255, https://doi.org/10.1016/j.ast.....
21.
Peters T, Bürgener T, Fottner L. Effects of rotating inlet distortion on a 5-Stage HP-Compressor, American Society of Mechanical Engineers Digital Collection 2014, https://doi.org/10.1115/2001-G....
22.
Reid C. The response of axial flow compressors to intake flow distortion. American society of Mechanical Engineers 1969, https://doi.org/10.1115/69-GT-....
23.
Fang M, Wang Y. Efficient numerical prediction of blade forced response under inlet distortion. Aerospace Science and Technology 2023; 142: 108612, https://doi.org/10.1016/j.ast.....
24.
Li X, Li W, Imran Lashari M, et al. Fatigue failure behavior and strength prediction of nickel-based superalloy for turbine blade at elevated temperature. Engineering Failure Analysis 2022; 136: 106191, https://doi.org/10.1016/j.engf....
25.
Wang Y, Song L, Li X, Bai G. Probabilistic fatigue estimation framework for aeroengine bladed discs with multiple fuzziness modeling. Journal of Materials Research and Technology 2023; 24: 2812–2827, https://doi.org/10.1016/j.jmrt....
26.
Zhu S, Liu Q, Peng W, Zhang X. Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks. International Journal of Mechanical Sciences 2018; 142: 502-517, https://doi.org/10.1016/j.ijme....
27.
Guo J, Zan X, Wang L, et al. A random forest regression with bayesian optimization-based method for fatigue strength prediction of ferrous alloys. Engineering Fracture Mechanics 2023; 293: 109714, https://doi.org/10.1016/j.engf....
28.
Ma Y, Zhang D, Hong J, Chen L. Prediction on high cycle life of blades using probability method. Journal of Propulsion Technology 2009; 30(4): 462-467, https://doi.org/10.13675/j.cnk....
29.
Liu Y, Lao D, Yang C, et al. Harmonic resonance and high cycle fatigue of a radial turbine in pressure fluctuation. Transactions of Beijing Institute of Technology 2014; 34(11): 1120-1124, https://doi.org/10.15918/j.tbi....
30.
Phan, H. M., and He, L. Investigation of structurally and aerodynamically mistuned oscillating cascade using fully coupled method. Journal of Engineering for Gas Turbines and Power 2022; 144(3): 031009, https://doi.org/10.1115/1.4052....
31.
Lin, J. Fatigue life assessment and reliability analysis of fan blades in turbofan engines. Tianjin University 2013.
32.
Zhang M, Hou A, He X, et al. Intensity analysis of blades in axial compressor with inlet distortion. Journal of Aerospace Power 2011; 26(1): 108-114, https://doi.org/10.13224/j.cnk....
33.
Du, X. Unified uncertainty analysis by the first order reliability method. Journal of Mechanical Design 2008; 130(9): 091401, https://doi.org/10.1115/1.2943....
34.
Li Y, Xu S, Chen H, et al. A general degradation process of useful life analysis under unreliable signals for accelerated degradation testing. IEEE Transactions on Industrial Informatics 2023; 19(6): 7742-7750, https://doi.org/10.1109/TII.20....
35.
Li Y, Gao H, Chen H, et al. Accelerated degradation testing for lifetime analysis considering random effects and the influence of stress and measurement errors. Reliability Engineering & System Safety 2024; 247: 110101, https://doi.org/10.1016/j.ress....
36.
Li Y, Fei M, Jia L, et al. Novel outlier-robust accelerated degradation testing model and lifetime analysis method considering time-stress-dependent factors. IEEE Transactions on Industrial Informatics 2024; 20(8): 9907-9917, https://doi.org/10.1109/TII.20....
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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