Azhar M. Memon, U. Salman, Abdulhammed K. Hamzat, L. Alhems
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Neural network method for the modeling of SS 316L elbow corrosion based on electric field mapping
Abstract Stainless steel is known for its superior corrosion resistance in industrial applications. In this work, corrosion modeling of stainless steel 316L is presented using artificial neural networks. The experimental setup consists of a loop containing stainless steel elbow with simulated seawater of known concentration continuously flowing at a specific flow rate, thus allowing to study the effect of flow dynamics and salt concentration on corrosion. Electric field mapping setup is used to collect the voltage and current information along with the temperature of the elbow section. In addition to modeling, characteristics of the observed scale deposits are also studied in-depth and briefly reported in this work.
期刊介绍：
Corrosion Reviews is an international bimonthly journal devoted to critical reviews and, to a lesser extent, outstanding original articles that are key to advancing the understanding and application of corrosion science and engineering in the service of society. Papers may be of a theoretical, experimental or practical nature, provided that they make a significant contribution to knowledge in the field.
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