Skip to content
2000
Volume 6, Issue 1
  • ISSN: 2352-0949
  • E-ISSN: 2352-0957

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.

Loading

Article metrics loading...

/content/journals/icms/10.2174/235209490601160428145657
2016-04-01
2025-10-24
Loading full text...

Full text loading...

/content/journals/icms/10.2174/235209490601160428145657
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test