Simulations of Corrosion and Passivation Phenomena:  Diffusion Feedback on the Corrosion Rate

Taleb, Abdelhafed; Chaussé, A.; Dymitrowska, Magdalena; Stafiej, J.; Badiali, J.P.

doi:10.1021/jp035377g

Cited by 34 publications

(29 citation statements)

References 14 publications

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“…Consequently, surface peaks will be more easily subject to corrosion than the valleys of the surface, which favors the production of surfaces with low roughness, as observed in previous numerical works for high values of p [13,14]. These features indicate that the long time regime is equivalent to that of the DLE model [16].…”

Section: Scaling Of Surface Roughnesssupporting

confidence: 51%

Crossover of interface growth dynamics during corrosion and passivation

Reis

Stafiej

2007

J. Phys.: Condens. Matter

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We study a model of corrosion and passivation of a metalic surface in contact with a solution using scaling arguments and simulation. The passive layer is porous so that the metal surface is in contact with the solution. The volume excess of the products may suppress the access of the solution to the metal surface, but it is then restored by a diffusion mechanism. A metalic site in contact with the solution or with the porous layer can be passivated with rate p and volume excess diffuses with rate D. At small times, the corrosion front linearly grows in time, but the growth velocity shows a t −1/2 decrease after a crossover time of order t c ∼ D/p 2 , where the average front height is of order h c ∼ D/p. A universal scaling relation between h/h c and t/t c is proposed and confirmed by simulation for 0.00005 ≤ p ≤ 0.5 in square lattices. The roughness of the corrosion front shows a crossover from Kardar-Parisi-Zhang scaling to Laplacian growth (diffusion-limited erosion -DLE) at t c . The amplitudes of roughness scaling are obtained by the same kind of arguments as previously applied to the other competitive growth models. The simulation results confirm their validity. Since the proposed model captures the essential ingredients of different corrosion processes, we also expect these universal features to appear in real systems.

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Section: Scaling Of Surface Roughnesssupporting

confidence: 51%

Crossover of interface growth dynamics during corrosion and passivation

Reis

Stafiej

2007

J. Phys.: Condens. Matter

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“…There have already been several attempts to employ such models in the domain of corrosion [9][10][11][12] and in the description of the growth and the morphology of layers formed on corroding surfaces [13,14].…”

Section: Introductionmentioning

confidence: 99%

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

Vautrin-Ul¹,

Chaussé²,

Stafiej³

et al. 2004

Condens. Matter Phys.

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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.

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“…The equations of motion are replaced by rules that govern the discrete evolution of the lattice configuration, and in many cases, this method has provided an excellent description of complex phenomena. Famous examples include lattice gas automata (17-19), which were used for the description of complex phenomena in flow dynamics, and models for diffusion (20)(21)(22) and percolation (23) processes. AB modeling can be considered a generalization of CA where the model system is (in general) not required to be on a lattice and the rules can take any form including adaptive elements and goals-directed behavior.…”

mentioning

confidence: 99%

An agent-based approach for modeling molecular self-organization

Troisi

Wong

Ratner

2004

Proc. Natl. Acad. Sci. U.S.A.

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Agent-based modeling is a technique currently used to simulate complex systems in computer science and social science. Here, we propose its application to the problem of molecular self-assembly. A system is allowed to evolve from a separated to an aggregated state following a combination of stochastic, deterministic, and adaptive rules. We consider the problem of packing rigid shapes on a lattice to verify that this algorithm produces more nearly optimal aggregates with less computational effort than comparable Monte Carlo simulations.agent-based simulation ͉ molecular self-assembly ͉ nanostructure prediction S elf-organization is one of the most fascinating phenomena in nature. It appears in such apparently disparate arenas as crystal growth, the regulation of metabolism, and dynamics of animal and human behavior (1-3). One of the great challenges in the field of complexity is the definition of the common patterns that make possible the emergence of order from apparently disordered systems. Although it is not proven that selforganization in apparently unrelated systems can be studied within a unique framework, many researchers are trying to identify new methods for interpretation of this aspect of complexity that allow a unified view. One example is given by the scale invariant networks that appear to offer a good perspective for many complex systems (4). Another possibility is the study of emergent phenomena through agent-based (AB) modeling. This developed, almost in parallel, in computer science (5-7) and social science (8-10) and has proved to be an excellent tool for the study of self-organizing computer programs, robots, and individuals. In this paper, after defining briefly the principles of AB modeling, we explore the possibility that such a modeling paradigm could be useful for the study of self-organizing chemical systems, complementing the currently used stochastic (Monte Carlo) or deterministic (molecular dynamics) methods. Agent-Based Model of Molecular Self-AssemblyBackground. An agent is a computer system that decides for itself (11,12). After sensing the environment it takes decisions based on some rules. A typical example is a thermostat that switches on and off the heating after sensing the temperature. An intelligent agent is capable of ''flexible'' autonomous actions: (i) It interacts with other agents and its environment; (ii) its actions (rules) might change in time as a result of this interaction; and (iii) the agent shows goal-directed behavior (i.e., it takes the initiative to satisfy a goal). An AB simulation is a simulation with many intelligent agents interacting among themselves and with the environment. In a typical AB simulation of social behavior, the agents are the individuals that take rational decision based on their neighbors' decisions. Very interesting social phenomena have been recently investigated for example by Axelrod (13) (cooperation), Epstein (14) (social instability), and Helbing (15) (crowd modeling).The great advantage of this modeling technique is that the emerg...

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Simulations of Corrosion and Passivation Phenomena: Diffusion Feedback on the Corrosion Rate

Cited by 34 publications

References 14 publications

Crossover of interface growth dynamics during corrosion and passivation

Crossover of interface growth dynamics during corrosion and passivation

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

An agent-based approach for modeling molecular self-organization

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