Pitting corrosion modelling by means of a stochastic cellular automata-based model
Résumé
We use stochastic Cellular Automata (CA) based modelling for corrosion studies at a mesoscopic scale. Physico-chemical phenomena that cannot be satisfactorily described by standard deterministic and macroscopic methods are represented. Materials are described by a 3D lattice where each cell has a state. Phenomena are represented as simple transition rules defining the evolution of the cells with given probabilities. Diffusion in the electrolyte is modelled as a random walk. Charge transfer corresponds to simultaneous anodic and cathodic reactions with special emphasis on their electric connection ensuring electric balance. We study evolution of pitting corrosion in terms of pit size morphology and acidity of the electrolyte. Starting from metal covered by a passive layer with a single defect, a cavity starts growing inside the metal and notably the exterior basic environment delays the dissolution of the protective passive layer. Results are in accordance with published experimental results on pitting corrosion.