2025 - Sustainable Industrial Processing Summit
SIPS2025 Volume 13. Intl. Symp on Solid State Chemistry, Physical Chemistry, Corrosion and Coating
| Editors: | F. Kongoli, I. Chung, H. Kageyama, M.G. Kanatzidis, F. Marquis, A. Navrotsky, A. Tressaud, J. Atwood, G. Duca, R. Kuroda, A. Legocki, J. Lipkowski, M. Zaworotko, R. Singh, R. Gupta, M. Halama, D. Macdonald, F. Wang |
| Publisher: | Flogen Star OUTREACH |
| Publication Year: | 2025 |
| Pages: | 262 pages |
| ISBN: | 978-1-998384-62-4 (CD) |
| ISSN: | 2291-1227 (Metals and Materials Processing in a Clean Environment Series) |
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PREDICTING MACROSCOPIC CORROSION OF STEEL USING PROBABILISTIC MODELING AND EXPERIMENTAL VALIDATION
Ramana Pidaparti1;1UNIVERSITY OF GEORGIA, Athens, United States;
Type of Paper: Regular
Id Paper: 54
Topic: 66
Abstract:
Corrosion is a natural phenomenon that degrades the properties of materials when they are exposed to environmental elements. This issue is especially prevalent in steel structures, where it can result in substantial economic losses, structural failures, and even pose risks to human safety. The corrosion of steel can be triggered by various factors, including environmental conditions, mechanical stress, and the presence of impurities. This study investigates the macroscopic corrosion of steel under potentiostatic conditions through a combination of electrochemical experiments and probabilistic modeling. A probabilistic cellular automata (PCA) model was developed in MATLAB to predict the propagation and penetration of corrosive material in steel. The model was refined using experimental data obtained from a three-electrode corrosion cell. Various steel specimens were subjected to corrosion under different environmental conditions, and their mechanical strengths were assessed. The refined model's predictions were validated using finite element analysis (FEA) and tensile testing of the corroded specimens. The FEA results showed a strong correlation with the tensile testing outcomes across three different specimen designs. This thesis enhances the understanding of steel corrosion under potentiostatic conditions and offers a predictive tool for assessing the corrosion behavior and mechanical properties of steel in such environments.