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Surrogate-modeling-assisted creep-fatigue reliability assessment in a low-pressure turbine disc considering multi-source uncertainty

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  • Wang, Run-Zi
  • Gu, Hang-Hang
  • Liu, Yu
  • Miura, Hideo
  • Zhang, Xian-Cheng
  • Tu, Shan-Tung

Abstract

This paper proposes a surrogate modeling approach based on XGboost machine learning technique, in order to establish a data-driven mapping relationship between input and output abstracted from practical finite element analysis (FEA) results. It facilitates novel insights into an efficient application of creep-fatigue reliability assessment in low-pressure turbine disk without a large amount of high-fidelity FEA cases. In detail, a general technical route is proposed for the probabilistic estimations of creep-fatigue lifetimes, where the multi-source uncertainties in the sequenced levels are synchronously considered. Subjected to typical creep-fatigue load spectrum, precise weakness hotspot is identified at the 1st bottom fir-tree groove of the turbine disk. Based on hotspot-based strategy, it is found that XGboost-involved surrogate modeling approach significantly improves the computational efficiency. The common results show that logarithmic creep-fatigue lifetimes roughly obey the normal distributions with the present of uncertainty sources, regardless of the multi-source combinations. Specifically, geometric tolerance plays an important role in reliability assessment results, which not only makes conservative gap but also shows high sensitivity in the reliability assessments.

Suggested Citation

  • Wang, Run-Zi & Gu, Hang-Hang & Liu, Yu & Miura, Hideo & Zhang, Xian-Cheng & Tu, Shan-Tung, 2023. "Surrogate-modeling-assisted creep-fatigue reliability assessment in a low-pressure turbine disc considering multi-source uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004647
    DOI: 10.1016/j.ress.2023.109550
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    References listed on IDEAS

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    1. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
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    Cited by:

    1. Wang, Haijie & Li, Bo & Lei, Liming & Xuan, Fuzhen, 2024. "Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Wang, Yifei & Xie, Mingjiang & Su, Chun, 2024. "Multi-objective maintenance strategy for corroded pipelines considering the correlation of different failure modes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Gao, Hai-Feng & Wang, Yu-Hang & Li, Yang & Zio, Enrico, 2024. "Distributed-collaborative surrogate modeling approach for creep-fatigue reliability assessment of turbine blades considering multi-source uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    4. Gu, Hang-Hang & Wang, Run-Zi & Zhang, Kun & Li, Kai-Shang & Sun, Li & Zhang, Xian-Cheng & Tu, Shan-Tung, 2025. "Damage-driven framework for reliability assessment of steam turbine rotors operating under flexible conditions," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    5. Deyin, Jiang & Zhixuan, Gao & Keke, Wang & Senke, Jiang & Weimin, Cui & Song, Bifeng, 2025. "A reliability analysis method for evaluating performance degradation considering the coupling of multiple progressive damage factors and multiple stochastic factors," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).

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