Computer Simulation of Pitting Corrosion in Galvanostatic Conditions

Pitting corrosion of stainless steels has been the subject of substantial research over many years. The overall mechanism can be separated into nucleation and propagation stages, and the development of a reliable predictive model requires a robust treatment for both these processes. Over the years, several purely stochastic models have been developed, including those by Shibata and Takeyama [1], Williams et al. [2], Baroux [3] and Wu et al. [4]. Alternatively, Macdonald and co-workers [e.g., 5] have focused on a deterministic approach, based on the point defect model of passivity breakdown. Newman, Laycock and co-workers developed a deterministic model for the propagation of individual corrosion pits [6-8], which was then combined with a stochastic model of pit nucleation to enable simulation of pitting potential measurements [9]. Li, Scully and Frankel later published a series of papers based on a similar approach [e.g., 10-11]. The majority of the prior modelling work has focused on potential-controlled conditions, where the pitting outcomes are determined mainly by the pit propagation element; for example, a limiting lower bound distribution of the pitting potential can be calculated without any consideration of pit nucleation processes [9]. However, real corrosion does not occur under potential control; rather, there is a limited supply of cathodic current that must be shared between all simultaneously propagating pits [12,13]. This situation is closer to that of experiments under galvanostatic control [14]. Krouse et al [15] described simulations that included possible interactions between multiple simultaneously propagating pits under galvanostatic conditions, supporting earlier suggestions that pits compete for the available current, and that ‘champion pits’ will ultimately use all available resources (see, e.g., Figure 1). In more recent work [16], we have further developed the earlier propagation model [6-9] to include the interactions and possible mergers between two simultaneously propagating pits. Here we expand on the work of Krouse et al [15] to carry out simulations of galvanostatic experiments that now incorporate the possibility of mergers between propagating pits. References Shibata, T. Takeyama, Corrosion 33 (1997) 243. E. Williams, C. Westcott, M. Fleischmann, J. Electrochem. Soc. 132 (1985) 1796. Baroux, Corros. Sci. 28 (1988) 969. Wu, J.R. Scully, J.L. Hudson, A.S. Mikhailov, J. Electrochem. Soc. 144 (1997) 1614. Engelhardt, D.D. Macdonald, Corrosion 54 (1998) 469. Ernst, N.J. Laycock, M.H. Moayed, R.C. Newman, Corros. Sci. 39 (1997) 1133. J. Laycock, S.P. White, J.S. Noh, P.T. Wilson, R.C. Newman, J. Electrochem. Soc. 145 (1998) 1101. J. Laycock, S.P. White, J. Electrochem. Soc. 148 (2001) B264. J. Laycock, J.S. Noh, S.P. White and D.P. Krouse, Corros. Sci, 47, 3140 (2005). S. Frankel, T. Li, and J. R. Scully, Journal of the Electrochemical Society, 164, C180 (2017). Li, J. R. Scully, and G. S. Frankel, Journal of The Electrochemical Society, 165, C484 (2018). Y. Chen, F. Cui and R.G. Kelly, J. Electrochem. Soc., 155, C360-C368 (2008). Y. Chen and R.G. Kelly, J. Electrochem. Soc., 157, C69 (2010). I. Suleiman and R. C. Newman, Corros. Sci., 36, 1657 (1994). Krouse, P. McGavin and N. Laycock, in Proceedings of Corrosion & Prevention 2008, Paper # 97, ACA, Wellington, 16-19 November (2008). A Nguyen, R.C. Newman and N.J. Laycock, J. Electrochem. Soc., 169, 081503 (2022) Figure 1

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.