Numerical simulations of corrosion processes. Properties of the corrosion front and formation of islands

Using a one-parameter model and a cellular automaton description we investigate the formation of a cavity in a material for which a small area has been deprotected and put in contact with an aggressive environment. Corrosion processes take place in a medium where the diffusion is assumed to be v...

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Збережено в:
Бібліографічні деталі
Видавець:Інститут фізики конденсованих систем НАН України
Дата:2004
Автори: Vautrin-Ul, C., Chausse, A., Stafiej, J., Badiali, J.P.
Формат: Стаття
Мова:English
Опубліковано: Інститут фізики конденсованих систем НАН України 2004
Назва видання:Condensed Matter Physics
Онлайн доступ:http://dspace.nbuv.gov.ua/handle/123456789/119014
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Цитувати:Numerical simulations of corrosion processes. Properties of the corrosion front and formation of islands / C. Vautrin-Ul, A. Chausse, J. Stafiej, J.P. Badiali // Condensed Matter Physics. — 2004. — Т. 7, № 4(40). — С. 813–828. — Бібліогр.: 16 назв. — англ.

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
Опис
Резюме:Using a one-parameter model and a cellular automaton description we investigate the formation of a cavity in a material for which a small area has been deprotected and put in contact with an aggressive environment. Corrosion processes take place in a medium where the diffusion is assumed to be very rapid. We investigate the mean size of the cavity, the roughness of the corrosion front, the chemical composition of the front and the formation of islands resulting from the detachment of pieces of material. For the simulation times that we have investigated, a stationary regime is observed in which the relative composition of the front and the roughness become constant. An increase of the roughness produces the islands which leads to a smoothing of the front, and thus the roughness exhibits a maximum value. A mean field approach quite satisfactoryly reproduces the combination of processes involved in the corrosion model. Although we consider a one-parameter model, some results such as the value of roughness and its maximum are difficult to predict in a quantitative way.