Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation
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DOI: 10.1016/j.ress.2017.03.006
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Cited by:
- Chen, Jian & Yuan, Shenfang & Sbarufatti, Claudio & Jin, Xin, 2021. "Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
- Ruiz Muñoz, G.A. & Sørensen, J.D., 2020. "Probabilistic inspection planning of offshore welds subject to the transition from protected to corrosive environment," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
- Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
- Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
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Keywords
Fatigue crack initiation; Global sensitivity analysis; Uncertainty quantification; Uncertainty propagation; Bayesian inference; Markov chain Monte Carlo;All these keywords.
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