Representing localized corrosion processes through cellular automata

Abstract

This research illustrates a spatially distributed cellular automaton aimed at simulating the pitting corrosion process in stainless steels. The cellular automaton model is based on the metal surface discretization into square cells; in any instant, each cell is found to be in one of the following states: (i) passivity (the passive fi lm on the cell surface is stable and protective), (ii) metastability (the passive fi lm is unstable and can either break, causing pitting initiation, or repassivate), and (iii) pitting (the cell has turned into a stable attack area, with no repassivation chance). At the very beginning, all cells are in passivity state. Subsequently, a stochastic process drives the changes in the state of cells, following rules drawn from the kinetics of pitting localized corrosion. In particular, the transition probabilities of each single cell from one state to another are infl uenced by the adjacent cells. The comparison between simulations run with the proposed model and laboratory experimental results demonstrates that the model is suitable for describing pitting corrosion processes.

Improved texture image classification through the use of a corrosion-inspired cellular automaton

“…Therefore, modeling and simulation could allow for predicting more accurately the corrosion process in time. CA-based models are excellent candidates for modeling corrosion due to their intrinsic simplicity and therefore, since the beginning of the new millennium, attempts are being made to employ these models in the field of corrosion engineering [14][15][16][17][18]. Corrosion is present in a wide range of metals and environments, which points to the universality of this phenomenon.…”

Cellular automata approach to corrosion and passivity phenomena

“…In this paper, we concentrate mainly on our model for anodic and cathodic zone separation [25][26][27] for the corrosion in view of a considerable interest in this subject [31,32]. We show how a characteristic length appears in this model and present some so far unpublished results on the surface development and pit generation.…”

Modeling pitting corrosion by means of a 3D discrete stochastic model

“…The number of cells is chosen as such to obtain a small enough grid resolution to prevent overly sensitive model parameters that result in very fluctuating solutions from simulation to simulation, while still not resulting in excessive computation times. Along the j-and k-axes periodic boundary conditions apply, meaning that the cells of the first row (column) are considered to be adjacent to those of the last row (column) to avoid border effects [2]. Along the i-axis on the other hand, fixed boundary conditions are employed since the top layer (aqueous solution) and bottom layer (metal) of G do not physically form an interface and so through these boundaries no movement is possible.…”

“…This form of localized corrosion is restricted to small areas and can be recognized by the appearance of small holes on the metal surface as shown in Fig. 1 [2,3].…”

“…The use of CAs in the field of corrosion is relatively new [36,37], but the number of researchers employing CAs as well as the knowledge on the subject is growing. Although many different aspects that influence corrosion, such as film formation, pH, potential differences and heterogeneous composition of alloys, are being explored and are incorporated in CAbased models, most of them only establish a qualitative resemblance between the simulated model output and the real-world phenomenon, ignoring the importance of a sound model validation that is a prerequisite to have a model with predictive value [2,17,19,[38][39][40][41]. Only very recently, a few authors validated their CA-based model using time series of data, but with modest success [42].…”