A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures

Run-Zi Wang, Hang-Hang Gu, Shun-Peng Zhu, Kai-Shang Li, Ji Wang, Xiao-Wei Wang, Miura Hideo, Xian-Cheng Zhang and Shan-Tung Tu

Reliability Engineering and System Safety, 2022, vol. 225, issue C

Abstract: High-reliability life design process not only can ensure system safety in service, but also can provide scientific life management during the maintenance period. The objective of the present work is to develop a roadmap for creep-fatigue reliability assessment. Material-level data accumulations and theoretical foundations of creep-fatigue including creep-fatigue constitutive and multi-axial damage models are introduced. Afterwards, a low-pressure turbine disk under a typical creep-fatigue load waveform is applied as a case study to demonstrate how to perform creep-fatigue reliability assessment by using this roadmap in practice. Precise weakness hotspots are identified at the mortise joint of turbine disk. Based on hotspot-based strategy, it is found that the surrogate model assisted by an optimized machine learning method enhances significantly the computational efficiency. Accordingly, the probabilistic creep-fatigue life with considering multi-sources uncertainty obeys lognormal distributions. In the aspect of failure probability analysis, the current probabilistic damage interaction diagram method with creep-fatigue interaction gives conservative reliability assessments and excellent universality as compared to traditional ones mainly used in the low cycle fatigue field. Last but not least, joint failure evaluation of the turbine disk is discussed to comprehensively consider potential failure occurrence in an averaged hot region instead of a single hotspot.

Keywords: Creep-fatigue; Reliability assessment; Turbine disk; Surrogate model; Probabilistic safety design (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022001788

DOI: 10.1016/j.ress.2022.108523

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