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.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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