Reviewed Software Methodology to Stress Corrosion Prediction

Author(s): Omar Fernandes Aly, Miguel Mattar Neto

Journal Name: Innovations in Corrosion and Materials Science
Formerly Recent Patents on Corrosion Science

Volume 6 , Issue 1 , 2016

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Abstract:

Background: Stress Corrosion Cracking (SCC) is a sudden and difficult-to-predict severe degradation mode of failure of nuclear, petrochemical, and other industries. This is a review of a development proposal for methodological software for modeling SCC based on: the failure propensity plus a kinetic model link which better describes its evolution.

Methods: The basic issues of this methodology are: a) A fixed combination of material-environmental condition is plotted on a potential-pH (Pourbaix) diagram marked with corrosion submodes – which can be originated from literature and/or experimental data. This forms a Knowledge Base (KB) for SCC-Propensity. Fuzzy Logic- a form of multiple valued logic where uncertainties can be considered - can be used to determine the SCC-Propensity zones; b) When the actual corrosion submode of the concerning material-environment is marked, based on new experiments, a feedback should be sent to the KB with the purpose to check the original submode border; c) Over the determined point (or region) in a SCC submode, a proper kinetic model should be chosen (departing for example from a kinetic library model-KB) to adjust the experimental data from the concerning material-environment. Alternatively a new empiric or numeric model can be adjusted; d) The regression quality of the model adjusted should be properly and statistically evaluated, and a feedback should be “fuzzylogically” retrofit its adequacy.

Results: The main result is prediction with an adequate statistical regression.

Conclusion: In this article the methodology is reviewed with an improving concerning the Pourbaix diagram construction for multielement systems, and at high temperatures.

Keywords: Light water nuclear reactors, modeling software, Pourbaix diagram, stress corrosion prediction.

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Article Details

VOLUME: 6
ISSUE: 1
Year: 2016
Page: [27 - 32]
Pages: 6
DOI: 10.2174/235209490601160428145657

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