Corrosion behavior of zirconium alloys in the aqueous environment. Phenomenological aspects. Overview

被引:3
|
作者
Stoll, Uwe [1 ]
Slavinskaya, Nadezda [1 ]
机构
[1] Forschungszentrum Julich, Gesell Anlagen & Reaktorsicherheit GRS gGmbH, Munich, Germany
关键词
word; Zr alloy; corrosion; polarisation curve; passivation; transpassivation; coolant chemistry; hydrating; irradiation; TRANSPASSIVE DISSOLUTION MECHANISM; TRANSMISSION ELECTRON-MICROSCOPY; HIGH-TEMPERATURE WATER; OXIDE LAYERS; SYNCHROTRON-RADIATION; DISSOLVED HYDROGEN; OXIDATION-KINETICS; PASSIVE ZIRCONIUM; CRYSTAL-STRUCTURE; PURE WATER;
D O I
10.1080/00223131.2022.2127954
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
The present work highlights opportunities and open research issues about the phenomena accompanying the Zr corrosion and describes that with both, the generalization of established opinions and the presentation of individual investigations. The overview aimed to lay out the phenomena accompanying Zr corrosion to better understand the restrictions and assumptions of modern process models and reveal perspectives of simulations. The formation and destruction of the protective oxidized cladding were analyzed and summarized in the following groups: chemical composition of the alloy; kinetics of oxide layer formation; electrochemical processes; chemistry of the coolant; hydrogen absorption; irradiation. The physicochemical, electrochemical, and crystallographic processes driving the multi-scale, multi-phase, multi-parametric Zr corrosion are strongly correlated, barely distinguishable, and have combined, and competing impacts that are very comprehensive to the numerical modeling. Despite a boundless number of studies, mostly empirical, Zr corrosion does not still have fundamental analytical descriptions. The noticeable improvement could be achieved by applying modern machine learning technologies allowing the analysis and optimization of multi-parametrical processes. The described groups of phenomena can be included in the clustering of the neural network of the deep learning approach in the integrated expert system for the Zr alloy corrosion.
引用
收藏
页码:573 / 602
页数:30
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