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Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation

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  • Yeratapally, Saikumar R.
  • Glavicic, Michael G.
  • Argyrakis, Christos
  • Sangid, Michael D.

Abstract

A microstructure and deformation mechanism based fatigue crack initiation and life prediction model, which links microstructure variability of a polycrystalline material to the scatter in fatigue life, is validated using an uncertainty quantification and propagation framework. First, global sensitivity analysis (GSA) is used to identify the set of most influential parameters in the fatigue life prediction model. Following GSA, the posterior distributions of all influential parameters are calculated using a Bayesian inference framework, which is built based on a Markov chain Monte Carlo (MCMC) algorithm. The quantified uncertainties thus obtained, are propagated through the model using Monte Carlo sampling technique to make robust predictions of fatigue life. The model is validated by comparing the predictions to experimental fatigue life data.

Suggested Citation

  • Yeratapally, Saikumar R. & Glavicic, Michael G. & Argyrakis, Christos & Sangid, Michael D., 2017. "Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 110-123.
    • Handle: RePEc:eee:reensy:v:164:y:2017:i:c:p:110-123
    DOI: 10.1016/j.ress.2017.03.006
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    Cited by:

    1. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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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