Atmospheric corrosion prediction: a review
Abstract: The atmospheric corrosion of metallic materials causes great economic loss every year worldwide. Thus, it is meaningful to predict the corrosion loss in different field environments. Generally, the corrosion prediction method includes three parts of work: the modelling of the corrosive environment, the calibration of the corrosion effects, and the establishment of the corrosion kinetics. This paper gives an overview of the existing methods as well as promising tools and technologies which can be used in corrosion prediction. The basic corrosion kinetic model is the power function model and it is accurate for short-term corrosion process. As for the long-term corrosion process, the general linear models are more appropriate as they consider the protective effect of the corrosion products. Most corrosion effect models correlate the environmental variables, which are characterized by the annual average value in most cases, with corrosion parameters by linear equations which is known as the dose-response function. Apart from these conventional methods, some mathematical and numerical methods are also appropriate for corrosion prediction. The corrosive environment can be described by statistical distributions, time-varying functions and even geographic information system (GIS), while the corrosion effect can be captured via response surface models and statistical learning methods.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Atmospheric corrosion prediction: a review ; volume:38 ; number:4 ; year:2020 ; pages:299-321 ; extent:23
Corrosion reviews ; 38, Heft 4 (2020), 299-321 (gesamt 23)
- Creator
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Cai, Yikun
Xu, Yuanming
Zhao, Yu
Ma, Xiaobing
- DOI
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10.1515/corrrev-2019-0100
- URN
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urn:nbn:de:101:1-2410301755398.633424876227
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:31 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Cai, Yikun
- Xu, Yuanming
- Zhao, Yu
- Ma, Xiaobing
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