Together with my colleagues Katie McCallum, Jing Zhao, Michael Workman, Dr. Mariano Kappes, Prof. Joe Payer, Prof. Curt Clemons, Dr. Sandeep Chawla, Prof. Kevin Kreider, Prof. Nao Mimoto, and Prof. Jerry Young we recently published a paper on localized corrosion assessment of aluminum alloys using Markov analysis.
K. McCallum, J. Zhao, M. Workman, M. Iannuzzi, M. Kappes, J. Payer, C.B. Clemons, S. Chawla, K.I. Kreider, N. Mimoto, and G.W. Young (2014) Localized Corrosion Risk Assessment Using Markov Analysis. Corrosion: November 2014, Vol. 70, No. 11, pp. 1114–1127. doi: 10.5006/1184 ISSN 0010–9312 (print), 1938–159X (online).
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The objective of this work was to develop the foundation for an interactive corrosion risk management tool for assessing the probability of failure of equipment/infrastructure as a function of threats (such as pitting corrosion and coating degradation) and mitigation schemes (such as inhibitors and coatings). The application of this work was to assist with corrosion management and maintenance planning of equipment/infrastructure given dynamic changes in environmental conditions. Markov models are developed to estimate pitting damage accumulation density distributions as a function of input parameters for pit nucleation and growth rates. The input parameters are selected based upon characterization with experimental or field observations over a sufficiently long period of time. Model predictions are benchmarked against laboratory pitting corrosion tests and long-term atmospheric exposure data for aluminum alloys, obtained from the literature. The models are also used to examine hypothetical scenarios for the probability of failure in pipeline systems subject to sudden, gradual, and episodic events that change the corrosive conditions.
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