A numerical framework for the modeling of corrosive dissolution

Sarkar, Swarnavo; Warner, James E.; Aquino, Wilkins

doi:10.1016/j.corsci.2012.08.059

Cited by 44 publications

(23 citation statements)

References 24 publications

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“…Most of these modeling efforts have used a sharp interface to represent the corroding surface, which requires the matching mesh at each time step, 11,29 thus increasing the errors associated with the violation of mass conservation laws and increasing the computation cost. The finite volume method models overcome this problem by creating a matching mesh as a function of the concentration of ions, but they are still unable to model complex microstructures.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] Pitting corrosion of stainless steel usually occurs in two different stages: (1) pit initiation from passive film breakage [4][5][6] and (2) pit growth. 2,3,[7][8][9][10][11][12] In this study, we focused on the development of a phase-field modeling capability to study pit growth by considering both anodic and cathodic reactions.…”

Section: Introductionmentioning

confidence: 99%

“…1-3 Pitting corrosion of stainless steel usually occurs in two different stages: (1) pit initiation from passive film breakage 4-6 and (2) pit growth. 2,3,[7][8][9][10][11][12] In this study, we focused on the development of a phase-field modeling capability to study pit growth by considering both anodic and cathodic reactions.In the past few decades, great efforts have been made to develop numerical models for pitting corrosion. The moving interface and the electrical double layer at the metal/electrolyte interface are the two challenging problems faced by most of these numerical models.…”

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confidence: 99%

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Phase-field model of pitting corrosion kinetics in metallic materials

Ansari

Xiao

et al. 2018

npj Comput Mater

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Pitting corrosion is one of the most destructive forms of corrosion that can lead to catastrophic failure of structures. This study presents a thermodynamically consistent phase field model for the quantitative prediction of the pitting corrosion kinetics in metallic materials. An order parameter is introduced to represent the local physical state of the metal within a metal-electrolyte system. The free energy of the system is described in terms of its metal ion concentration and the order parameter. Both the ion transport in the electrolyte and the electrochemical reactions at the electrolyte/metal interface are explicitly taken into consideration. The temporal evolution of ion concentration profile and the order parameter field is driven by the reduction in the total free energy of the system and is obtained by numerically solving the governing equations. A calibration study is performed to couple the kinetic interface parameter with the corrosion current density to obtain a direct relationship between overpotential and the kinetic interface parameter. The phase field model is validated against the experimental results, and several examples are presented for applications of the phase-field model to understand the corrosion behavior of closely located pits, stressed material, ceramic particles-reinforced steel, and their crystallographic orientation dependence. INTRODUCTIONCorrosion is the gradual destruction of materials (usually metallic materials) via chemical and/or electrochemical reaction with their environment. It costs about 3.1% of the gross domestic product (GDP) in the United States, which is much more than the cost of all natural disasters combined. Localized corrosion, such as pitting corrosion, is one of the most destructive forms of corrosion; it leads to the catastrophic failure of structures and raises both human safety and financial concerns. 1-3 Pitting corrosion of stainless steel usually occurs in two different stages: (1) pit initiation from passive film breakage 4-6 and (2) pit growth. 2,3,[7][8][9][10][11][12] In this study, we focused on the development of a phase-field modeling capability to study pit growth by considering both anodic and cathodic reactions.In the past few decades, great efforts have been made to develop numerical models for pitting corrosion. The moving interface and the electrical double layer at the metal/electrolyte interface are the two challenging problems faced by most of these numerical models. These numerical models can be divided according to the method in which a moving interface is incorporated in their models. Several steady state 9,10,13-18 and transient state 19-28 models have been developed over the years that did not allow for changes in the shape and dimensions of the pits/crevices as corrosion proceeds.Recent advances in numerical techniques, such as the finite element method, the finite volume method, the arbitrary Lagrangian-Eulerian method, the mesh-free method, and the level set method have encouraged researchers to model the evolving morphology...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Phase-field model of pitting corrosion kinetics in metallic materials

Ansari

Xiao

et al. 2018

npj Comput Mater

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“…For moving boundary problems, (2) indicates that the velocity of each boundary point is proportional to the normal derivative of the field at that point. After evaluating coefficients c i , the normal component of the velocity at the current time step can be computed as…”

Section: Green's Discrete Transformation Methods Approximationmentioning

confidence: 99%

“…, where a and b are diameters of the ellipse and u eq D 5 mol/L. According to (2), the velocity of the moving boundary is given by…”

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confidence: 99%

A Green's discrete transformation meshfree method for simulating transient diffusion problems

Mai

Soghrati

Buchheit

2016

Numerical Meth Engineering

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SUMMARYThis manuscript presents the formulation and application of the Green's discrete transformation method (GDTM) for the meshfree simulation of transient diffusion problems, including those with moving boundaries. The GDTM implements a linear combination of time-dependent Green's basis functions defined on a set of source points to approximate the field in the form of a solution series. A discrete transformation is implemented to evaluate unknown coefficients of this series, which eliminates the need to use time integration schemes. We will study the optimal number and location of the GDTM source points that yield the highest level of accuracy, while maintaining a manageable condition number for the resulting linear system of equations. The optimal values of these parameters, which are inherently independent of the domain geometry, are determined such that the basis functions have appropriate features for approximating the field. A comprehensive convergence study is presented to show the precision and convergence rate of the GDTM for modeling various diffusion problems. We also demonstrate the application of this method for simulating three diffusion problems with complex and evolving morphologies: heat transfer in a turbine blade, thermal response of a porous material, and localized (pitting) corrosion in stainless steel.

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