Data-driven optimization model customization for atmospheric corrosion on low-alloy steel: incorporating the dynamic evolution of the surface rust layer
Publication Name
Corrosion Science
Abstract
This study utilized a state-of-the-art sensor to gather a big-data set of corrosion on low-alloy steel under six distinct meteorological conditions. Through modeling and calculations, we discovered that the effectiveness of the rust layer is a dynamic process that can be influenced by changes in weather, resulting in unpredictable levels of protection. We determined that prolonged periods of moisture have the most detrimental impact, while higher temperatures have a positive effect. To enhance the accuracy of corrosion assessment, We digitized and incorporated this dynamic process into the model that demonstrates promising results, and emphasized the significance of considering rust layer evolution in corrosion modeling.
Open Access Status
This publication is not available as open access
Volume
221
Article Number
111349
Funding Number
SQ2022FY010060
Funding Sponsor
National Natural Science Foundation of China