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Multi-state Markov modeling of pitting corrosion in stainless steel exposed to chloride-containing environment

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  • Xie, Yi
  • Zhang, Jinsuo
  • Aldemir, Tunc
  • Denning, Richard

Abstract

Although stainless steels (SSs) have excellent general corrosion resistance, they are nevertheless susceptible to pitting corrosion. The variation of pit depth and density is significant for the prediction of likelihood of corrosion damage occurring in service. Among the available pitting corrosion models, it is difficult to identify a specific model capable of characterizing all the pit formation processes observed and one that can be used for estimating the evolution of pit density distribution with time. A physics-based multi-state Markov model giving a full description of pitting corrosion states is presented. The transition rates used in the model are determined by fitting the model to experimental data. The variation of pit depth and density is simulated. The simulation is verified by experimental scenarios of SS exposed to chloride-containing environments.

Suggested Citation

  • Xie, Yi & Zhang, Jinsuo & Aldemir, Tunc & Denning, Richard, 2018. "Multi-state Markov modeling of pitting corrosion in stainless steel exposed to chloride-containing environment," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 239-248.
    • Handle: RePEc:eee:reensy:v:172:y:2018:i:c:p:239-248
    DOI: 10.1016/j.ress.2017.12.015
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    References listed on IDEAS

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    1. Stewart, Mark G. & Al-Harthy, Ali, 2008. "Pitting corrosion and structural reliability of corroding RC structures: Experimental data and probabilistic analysis," Reliability Engineering and System Safety, Elsevier, vol. 93(3), pages 373-382.
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    Cited by:

    1. Bansal, Parth & Zheng, Zhuoyuan & Shao, Chenhui & Li, Jingjing & Banu, Mihaela & Carlson, Blair E & Li, Yumeng, 2022. "Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Ruiz-Castro, Juan Eloy & Dawabsha, Mohammed & Alonso, Francisco Javier, 2018. "Discrete-time Markovian arrival processes to model multi-state complex systems with loss of units and an indeterminate variable number of repairpersons," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 114-127.
    3. Sidum Adumene & Rabiul Islam & Ibitoru Festus Dick & Esmaeil Zarei & Morrison Inegiyemiema & Ming Yang, 2022. "Influence-Based Consequence Assessment of Subsea Pipeline Failure under Stochastic Degradation," Energies, MDPI, vol. 15(20), pages 1-10, October.

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