Cellular Automata Simulation of Corrosion Process for Steel

doi:10.11902/1005.4537.2017.003

Journal of Chinese Society for Corrosion and protection

2018, Vol. 38

Issue (1): 68-73 DOI: 10.11902/1005.4537.2017.003

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Cellular Automata Simulation of Corrosion Process for Steel

Mengcheng CHEN(

), Qingqing WEN

School of Civil Engineering and Architecture, East China Jiao-tong University, Nanchang 330013, China

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Abstract

Cellular automata method was adopted to simulate the corrosion damage behavior of steel. According to the results of experimental study on steel in corrosive environments and the principle of cellular automata method, a local evolution rule of cellular automata was defined, and then the evolution process of corrosion morphology for a pit were simulated. By comparing the simulation results for different initial concentrations c and dissolution probability p, the real simulation condition for the corrosion pit was determined. At the same time, the influence of different initial solution concentration c and dissolution probability p on the morphology was discussed. It was shown that with the increasing initial concentration c and dissolution probability p, the equivalent radius or depth of corrosion pit presents an approximate power function of etching time t. Meanwhile, the simulation results of pit depth were compared with the theoretical prediction proposed by Komp, which showed that both results are agreeable and the proposed CA model is feasible and efficient.

Key words: steel corrosion pit morphology cellular automata simulation

Received: 06 January 2017

ZTFLH:

TU503

Fund: Supported by National Natural Science Foundation of China (51378206 and 51468017)

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Cite this article:

Mengcheng CHEN, Qingqing WEN. Cellular Automata Simulation of Corrosion Process for Steel. Journal of Chinese Society for Corrosion and protection, 2018, 38(1): 68-73.

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https://www.jcscp.org/EN/10.11902/1005.4537.2017.003 OR https://www.jcscp.org/EN/Y2018/V38/I1/68

Fig.1 Initial CA model of pit: (a) transverse CA model, (b) longitudinal CA model (O: solution cell, M: metal cell)

Fig.2 Evolution relationship between the central cell and neighbor cells

Fig.3 Morphologies of pits on the surface of Q235 steel after immersion in simulated acid rain solution at 40 ℃ for 10 d (a), 20 d (b) and 30 d (c)

Fig.4 Surface morphologies evolution of pit: (a) t =200; (b) t =400; (c) t =600; (d) t =800; (e) t =1000; (f) t =1200 (t is dimensionless, the dark areas indicate metal cells, the white parts indicate solution cells)

Fig.5 Morphology evolutions of pit in depth: (a) t =200; (b) t =400; (c) t =600; (d) t =800; (e) t =1000; (f) t =1200

Fig.6 Effect of dissolution probability on equivalent radius

Fig.7 Effect of initial solution concentration on equivalent radius

Fig.8 Effect of dissolution probability on equivalent depth

Fig.9 Effect of initial solution concentration on equivalent depth

Fig.10 Fitting curve under different solution concentration(p=0.7)

Fig.11 Fitting curve under different dissolution probability(c=0.5)

Table 1 Fitting values of various parameters

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