A multi-element micro-coupling self-adaptive selection corrosion dynamic expansion simulation method

Abstract

A multi-element microcouple adaptive selective corrosion dynamic expansion simulation method, belonging to the field of electro-corrosion simulation technology, is disclosed. The method includes: constructing a geometric model of compound particles and meshing the model; constructing the compound particle transport control equations and establishing the coupling relationship between potential, particle concentration, current density, boundary coverage, and reaction rate; adaptively selecting the anode and cathode among the multi-element microcouples of the compound particles and performing fixed-value processing on the geometric model; setting boundary conditions and initial values, including setting insulating boundaries, electrode surface boundaries, and initial particle concentrations; re-meshing the mesh, calculating the movement velocity of the corrosion interface, and constraining and controlling boundaries that do not corrode or undergo geometric deformation to complete the construction of the finite element model; solving the finite element model and outputting relevant physicochemical parameters during the corrosion process. This application clarifies the driving mechanism of pitting corrosion under open-circuit conditions and the existence state of corrosion products.

Description

Technical Field

[0001] This application belongs to the field of electro-corrosion simulation technology, specifically relating to a multi-element microcouple adaptive selective corrosion dynamic extension simulation method. Background Technology

[0002] Aluminum alloys, as lightweight and high-strength materials, are widely used in the aerospace industry. However, due to their non-uniform microstructure, they are prone to pitting corrosion. The size of the pits is usually tens to hundreds of micrometers, and they are highly concealed by coatings and corrosion products. The corrosion area will generate significant stress concentration, which can easily lead to sudden structural catastrophic failures under load, seriously threatening the safety of aerospace structures.

[0003] In existing technologies, the main approaches to studying the electrochemical performance of intermetallic compound particles are ultra-microelectrodes combined with micro-liquid pools and microscopic observation, or the synthesis of large particles for macroscopic measurement. However, these methods can only obtain the electrochemical parameters of individual particles and cannot accurately detect the dynamic changes of the micron-scale electrolyte environment, making it difficult to quantitatively characterize pitting corrosion-related processes. Numerical simulation provides an effective research tool for this problem, but early models were too simplified and did not consider the dynamic changes of homogeneous reactions within the electrolyte. Subsequently, eight improved homogeneous reaction models were introduced, but they were limited to fixed pit geometry. Although the dynamic extended model solved the dynamic interface problem under single-phase inclusions, the inclusions in the actual microstructure of aluminum alloys are mostly multi-phase coexistences (such as θ phase and S phase), and the corrosion competition and selection of multi-phase inclusions makes it difficult for existing models to adapt.

[0004] Therefore, there is an urgent need for a dynamic extension simulation method for corrosion that is suitable for multiphase mixed scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-element microcouple adaptive selective corrosion dynamic expansion simulation method to solve the above problems.

[0006] The method includes the following steps: S1. Construct a geometric model of intermetallic compound particles and divide the geometric model into mesh units; S2. Using the Nernst-Planck equation as the governing equation for particle transport in compounds, establish the coupling relationship between potential, particle concentration, current density, boundary coverage, and reaction rate; S3. Adaptive selection of anode and cathode between multiple electric dipoles of compound particles is performed through level set structure function, and the four different phases on the same interface of the geometric model are fixed to distinguish different particles. S4. Use interpolation functions to characterize the relationship between potential and current density, set boundary conditions and initial values, and set insulation boundaries, electrode surface boundaries and initial particle concentrations; S5. The mesh is re-divided based on the Laplace smoothing mesh update method. The moving speed of the corrosion dynamic interface is calculated by Faraday's law. The boundary without corrosion and geometric deformation is constrained and controlled to complete the construction of single-phase and multi-phase coupled corrosion finite element models. S6. Solve the finite element model and output the relevant physicochemical parameters of the corrosion process.

[0007] Furthermore, in step S1, the intermetallic compound particles are a single-phase or multi-phase inclusion system composed of cathodic particles, anodic particles and aluminum matrix in aluminum alloy. The cathodic particles include Al3Fe, Al6Mn, and Al7Cu2Fe, the anodic particles are MgZn2, and the multiphase inclusion system is a system in which Al3Fe and MgZn2 coexist and are combined with an aluminum matrix.

[0008] Furthermore, in step S1, the geometric model generation method is either an image processing-based method or a Voronoi polygon-based method; The microgalvanic corrosion geometric model is simplified using a semi-circular intermetallic compound particle structure.

[0009] Furthermore, the image processing-based method specifically involves: determining the composition of intermetallic compound particles using EDS based on the scanning electron microscope image, and then using binarization to assign different values ​​to different intermetallic compound particles and the aluminum matrix for differentiation.

[0010] Furthermore, the method based on Voronoi polygons specifically involves: using cells to simulate grains / pores in a material, with seed points corresponding to grain nucleation points / pore centers, and cell boundaries corresponding to grain boundaries / pore walls. Random perturbations with coordinate offsets not exceeding a preset percentage are added to the boundaries to reproduce the characteristics of uneven particle size and irregular boundaries in intermetallic compounds.

[0011] Furthermore, the specific steps of step S2 are as follows: S21. Assuming that the electrolyte solution has sufficient oxygen at the electrode interface after thorough stirring, the main reactions at the anode and cathode in the electrolyte solution are oxygen reduction and active dissolution of aluminum. The driving force for microcouple corrosion in the neutral solution comes from the potential difference between the anode and cathode. Furthermore, as the corrosion time increases, the anode and cathode areas will change over time, and the coupling potential will change. S22. Particle transport throughout the entire electrolyte domain is governed by the Nernst-Planck equations in the form of mass conservation:

[0012] in, F Let be the Faraday constant, and u be the flow velocity vector of the electrolyte solution.c i For different particle concentrations, z i The number of charges carried by the particle. D i Represents the diffusion coefficient. m i The electromobility coefficient represents the potential gradient in the solution. Powering particle migration R i In solution i The net reaction rate of particles due to homogeneous reaction, that is i The sum of the differences between the forward and reverse reactions in each homogeneous reaction is the net flux caused by the reaction. Electromobility coefficient m i Calculated using the Nernst-Einstein equation:

[0013] R i The calculation formula is:

[0014] In the formula, , Representing the first j The rate constants for the forward and reverse reactions, m , n Representing the first j The number of particles in the forward and reverse reactions of each reaction. Representing the j Each reaction i The stoichiometric coefficient of particles N r The number of homogeneous reactions. x , y Represents the horizontal and vertical coordinates; By combining the Nernst-Planck equations using the electroneutrality equation, R i The calculation formula is used to calculate the changes in the concentration of various particles and the electric field over time in the finite element model. The electroneutrality equation is:

[0015] in, z i and c i Represent i The number of charges and concentration of particles; According to Faraday's law, the vector current density in an electrolyte solution is...i l The calculation formula is:

[0016] S23. Assuming Al(OH)3 deposition only occurs at the boundary, calculate the boundary coverage. θ Considered as a thickness-free boundary, its variation with time is determined by the deposition rate, and the ordinary differential equation is:

[0017] in, and Let the molar mass (kg / mol) and density (kg / m³) of Al(OH)₃ be represented respectively. 3 , This indicates the thickness of Al(OH)3 deposited when no reaction occurs at the electrode interface. Take 1μm, This means that the volume of sedimentary products generated in each grid cell can be converted into the thickness of the sedimentary layer, which is the grid size; By using a time function of coverage to continuously describe the coverage process, the boundary coverage rate can be calculated. θ :

[0018] The anode and cathode reaction rates on the electrode surface are corrected using an if function: ; in, The anode current density, Cathode current density.

[0019] Further, in step S3, the level set structure function is:

[0020] The level set structure function defines the anode and cathode electrode reactions simultaneously on the same active boundary through the factor (1-micro (x,y)) in the anode reaction and the factor micro(x,y) in the cathode reaction. The value setting process uses four constants—-1, 0, 1, and 2—to correspond to the cathodic particles, anodic particles, aluminum substrate, and passivation film, respectively.

[0021] Adaptive selection of multi-element microcouples is achieved by correcting the current density using micro parameters. .

[0022] Further, in step S4, the interpolation function is used to establish the numerical relationship between the electrode potential and the current density, and the interpolation function is:

[0023] The thickness of the electrolyte domain is set to 10 μm. The initial concentration of each particle on the upper surface of the domain is consistent with the corresponding particle concentration in the electrolyte solution. The two side boundaries are set as insulating boundaries, and the lower surface is used as the electrode surface.

[0024] Furthermore, the specific steps of step S5 are as follows: S51. The ALE adaptive mesh is re-divided using Laplace smoothing, and the governing equation for mesh displacement is:

[0025]

[0026] S52. According to Faraday's law, the formula for calculating the normal component of the velocity vector v of the moving interface during corrosion is:

[0027] Where M is the molar mass of the metal, z It is the charge number, and F is the Faraday constant. ρ It is the density of the metal; S53. Boundaries that do not corrode or undergo geometric deformation are controlled by the following equation: .

[0028] Further, in step S6, the relevant physicochemical parameters include: Electric field distribution, pH distribution, O2 concentration distribution, Al(OH)2Cl concentration distribution, Al(OH)3 concentration distribution, OH - Concentration distribution and corrosion product coverage data.

[0029] As can be seen from the above technical solutions, the present invention has the following advantages: By constructing an ALE-based dynamic interface tracking finite element model, comprehensively incorporating dynamic changes in anode and cathode areas, steric hindrance effects of corrosion products, and various chemical and electrochemical reactions, this model can dynamically output physicochemical parameters in a micron-scale electrolyte environment, capture particle transport patterns within pits, and achieve quantitative characterization of the initiation and propagation processes of pitting corrosion in aluminum alloys. Through level set structure functions, adaptive selection of multiple phases is achieved, fully considering the interactions and corrosion competition between Al3Fe, MgZn2, and the aluminum matrix, overcoming the shortcomings of existing models that only adapt to single-phase inclusions and do not consider multiphase coupling. Simultaneously, interpolation functions characterize polarization curves, and Laplace smoothing controls dynamic interface tracking, significantly improving the realism and accuracy of the simulation. This application clarifies the driving mechanism of pitting corrosion under open-circuit conditions and the state of corrosion products, providing a reliable tool for understanding the essential laws of pitting corrosion induced by multiphase inclusions, and also providing scientific support for corrosion protection design and structural safety prediction of aluminum alloys used in the aerospace industry. Attached Figure Description

[0030] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the adaptive selection corrosion dynamic extension simulation method of the multivariate microcouple in this application.

[0032] Figure 2 The Voronoi polygon used in the adaptive selection of corrosion dynamic propagation simulation method for multivariate microcouples in this application is given by, where, Figure 2 a) in the example refers to Voronoi polygon generation. Figure 2 b) in the example assigns a value to the Voronoi polygon. Figure 2 c) in the equation represents a simplified particle structure shape using a semi-circle / circle.

[0033] Figure 3 This is a schematic diagram of the electric field distribution simulation results of the adaptive selection corrosion dynamic propagation simulation method of the multi-element microcouple in this application, wherein, Figure 3 a) in the figure represents the electric field distribution at t=0h. Figure 3 b) represents the electric field distribution at t=1h. Figure 3 c) represents the electric field distribution at t=2h. Figure 3 In the diagram, d) represents the electric field distribution at t=25h. Figure 3 e) in the figure represents the electric field distribution at t=50h. Figure 3 f) in the figure represents the electric field distribution at t=100h.

[0034] Figure 4 This is a schematic diagram illustrating the pH distribution simulation results of the adaptive selection corrosion dynamic propagation simulation method using multi-element microcouples in this application. Figure 4 a) in the figure represents the pH distribution at t=3h. Figure 4 b) represents the pH distribution at t=25h. Figure 4 c) in the figure represents the pH distribution at t=50h. Figure 4 In the figure, d) represents the pH distribution at t=100h.

[0035] Figure 5 This is a schematic diagram of the O2 concentration distribution simulation results using the adaptive selection corrosion dynamic propagation simulation method of the multi-element microcouple in this application. Figure 5 a) in the figure represents the O2 concentration distribution at t=3h. Figure 5 b) in the figure represents the O2 concentration distribution at t=25h. Figure 5 c) in the figure represents the O2 concentration distribution at t=50h. Figure 5 In the figure, d) represents the O2 concentration distribution at t=100h.

[0036] Figure 6 This is a schematic diagram of the Al(OH)₂Cl distribution simulation results using the adaptive selective corrosion propagation simulation method of the multi-element microcouple in this application. Figure 6 a) represents the distribution of Al(OH)₂Cl at t=3h. Figure 6 b) in the figure represents the Al(OH)2Cl distribution at t=100h.

[0037] Figure 7 This is a schematic diagram of the Al(OH)3 distribution simulation results using the adaptive selective corrosion dynamic propagation simulation method of the multi-element microcouple in this application. Figure 7 a) in the figure represents the distribution of Al(OH)3 at t=3h. Figure 7 b) represents the Al(OH)3 distribution at t=25h. Figure 7 c) represents the Al(OH)3 distribution at t=50h. Figure 7 In the figure, d) represents the Al(OH)3 distribution at t=100h.

[0038] Figure 8 The adaptive selection corrosion dynamic propagation simulation method of multivariate microcouples in this application (OH) - A schematic diagram of the distribution simulation results, in which, Figure 8 a) in the equation is t=3h OH - distributed, Figure 8 b) in the equation refers to OH at t=100h. - distributed.

[0039] Figure 9This is a schematic diagram illustrating the simulation results of the corrosion product deposition electric field distribution considering the adaptive selection of corrosion dynamic propagation simulation method using multivariate microcouples in this application. Figure 9 a) in the figure represents the electric field distribution at t=0h. Figure 9 b) in the figure represents the electric field distribution at t=2h. Figure 9 c) represents the electric field distribution at t=25h. Figure 9 In the equation d), the electric field distribution is defined as t ≥ 50h.

[0040] Figure 10 This is a schematic diagram illustrating the simulation results of the adaptive selection of corrosion dynamic propagation simulation method using multivariate microcouples, considering corrosion product deposition and steric hindrance effects. Figure 10 In the figure, a) represents the coverage of corrosion products at t=3h. θ )distributed, Figure 10 b) in the figure represents the coverage of corrosion products at t=25h. θ )distributed, Figure 10 c) in the figure represents the coverage of corrosion products at t≥50h. θ )distributed.

[0041] Figure 11 This is a schematic diagram illustrating the simulation results of the particle distribution in the corrosion product deposition concentration field, considering the adaptive selection of corrosion dynamic propagation simulation method using multivariate microcouples in this application. Figure 11 a) represents the pH distribution at t=3h. Figure 11 b) represents the pH distribution over t ≥ 50 h. Figure 11 c) in the figure represents the O2 distribution at t=3h. Figure 11 In the figure, d) represents the O2 distribution at t ≥ 50h. Figure 11 e) in the equation represents OH at t=3h. - distributed, Figure 11 f) in the context refers to t ≥ 50h OH - distributed, Figure 11 In the figure, g) represents the distribution of Al(OH)2Cl at t=3h. Figure 11 In the above, h) represents the Al(OH)2Cl distribution at t≥50h. Detailed Implementation

[0042] Various embodiments of the invention will be described more fully in the detailed steps of the adaptive selective corrosion dynamic extension simulation method for multi-element microcouples described below. The invention may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the invention to the specific embodiments disclosed herein, but rather the invention should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of the invention.

[0043] It should be understood that, when used in this specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0044] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0045] To facilitate a clear description of the technical solutions of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0046] To make the objectives, features, and advantages of this invention more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solutions protected by this invention. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0047] Please see Figure 1 The diagram shows a flowchart of a multi-element microcouple adaptive selective corrosion dynamic extension simulation method, which includes the following steps: S1. Construct a geometric model of intermetallic compound particles and divide the geometric model into mesh units; S2. Using the Nernst-Planck equation as the governing equation for particle transport in compounds, establish the coupling relationship between potential, particle concentration, current density, boundary coverage, and reaction rate; S3. Adaptive selection of anode and cathode between multiple electric dipoles of compound particles is performed through level set structure function, and the four different phases on the same interface of the geometric model are fixed to distinguish different particles. S4. Use interpolation functions to characterize the relationship between potential and current density, set boundary conditions and initial values, and set insulation boundaries, electrode surface boundaries and initial particle concentrations; S5. The mesh is re-divided based on the Laplace smoothing mesh update method. The moving speed of the corrosion dynamic interface is calculated by Faraday's law. The boundary without corrosion and geometric deformation is constrained and controlled to complete the construction of single-phase and multi-phase coupled corrosion finite element models. S6. Solve the finite element model and output the relevant physicochemical parameters of the corrosion process.

[0048] As a refinement and extension of the specific implementation methods described above, and to fully illustrate the specific implementation process in this embodiment, another multi-element microcouple adaptive selective corrosion dynamic extension simulation method is provided, which includes: S1. Construct a geometric model of intermetallic compound particles and divide the geometric model into mesh units; In step S1, the intermetallic compound particles are a single-phase or multi-phase inclusion system composed of cathodic particles, anodic particles and aluminum matrix in aluminum alloy. The cathodic particles include Al3Fe, Al6Mn, and Al7Cu2Fe, the anodic particles are MgZn2, and the multiphase inclusion system is a system in which Al3Fe and MgZn2 coexist and are combined with an aluminum matrix.

[0049] In step S1, the geometric model is generated using either an image processing-based method or a Voronoi polygon-based method. The microgalvanic corrosion geometric model is simplified using a semi-circular intermetallic compound particle structure.

[0050] like Figure 2 As shown, Figure 2 a) in the example refers to Voronoi polygon generation. Figure 2 b) in the example assigns a value to the Voronoi polygon. Figure 2 c) in the text refers to the simplified particle structure shape using a semi-circular / circular shape. The microgalvanic corrosion geometric model is constructed using a simplified semi-circular intermetallic compound particle structure.

[0051] The image processing-based method specifically involves: determining the composition of intermetallic compound particles using EDS based on the scanning electron microscope image, and then using binarization to assign different values ​​to different intermetallic compound particles and the aluminum matrix for differentiation.

[0052] The method based on Voronoi polygons specifically involves: using cells to simulate grains / pores in a material, with seed points corresponding to grain nucleation points / pore centers, and cell boundaries corresponding to grain boundaries / pore walls. Random perturbations with coordinate offsets not exceeding a preset percentage are added to the boundaries to reproduce the characteristics of uneven particle size and irregular boundaries in intermetallic compounds.

[0053] S2. Using the Nernst-Planck equation as the governing equation for particle transport in the compound, establish the coupling relationship between potential, particle concentration, current density, boundary coverage, and reaction rate; the specific steps of step S2 are as follows: S21. Assuming that the electrolyte solution has sufficient oxygen at the electrode interface after thorough stirring, the main reactions at the anode and cathode in the electrolyte solution are oxygen reduction and active dissolution of aluminum. The driving force for microcouple corrosion in the neutral solution comes from the potential difference between the anode and cathode. Furthermore, as the corrosion time increases, the anode and cathode areas will change over time, and the coupling potential will change.

[0054]

[0055] S22. Particle transport throughout the entire electrolyte domain is governed by the Nernst-Planck equations in the form of mass conservation:

[0056] in, F Let be the Faraday constant, and u be the flow velocity vector of the electrolyte solution. c i For different particle concentrations, z i The number of charges carried by the particle. D i Represents the diffusion coefficient. m i The electromobility coefficient represents the potential gradient in the solution. Powering particle migration R i In solution i The net reaction rate of particles due to homogeneous reaction, that is i The sum of the differences between the forward and reverse reactions in each homogeneous reaction is the net flux caused by the reaction. Electromobility coefficient m i Calculated using the Nernst-Einstein equation:

[0057] R i The calculation formula is:

[0058] In the formula, , Representing the first j The rate constants for the forward and reverse reactions, m , n Representing the first j The number of particles in the forward and reverse reactions of each reaction. Representing the j Each reaction i The stoichiometric coefficient of particles N r The number of homogeneous reactions. x , y Represents the horizontal and vertical coordinates; By combining the Nernst-Planck equations using the electroneutrality equation, R i The calculation formula is used to calculate the changes in the concentration of various particles and the electric field over time in the finite element model. The electroneutrality equation is:

[0059] in, z i and c i Represent i The number of charges and concentration of particles; According to Faraday's law, the vector current density in an electrolyte solution is... i l The calculation formula is:

[0060] It should be noted that each particle i in the model can be expressed using the Nernst-Planck equation, but it can be observed that, except for the particle concentration... c i In addition, electric potential is also an independent variable. Therefore, to obtain a unique solution, it is necessary to introduce the electron neutrality equation and solve the Nernst-Planck equations simultaneously. R i The calculation formulas and the electroneutrality equations can be used to obtain the changes in the concentration of various particles and the electric field over time in the model.

[0061] S23. Assuming Al(OH)3 deposition only occurs at the boundary, calculate the boundary coverage. θ Considered as a thickness-free boundary, its variation with time is determined by the deposition rate, and the ordinary differential equation is:

[0062] in, and Let the molar mass (kg / mol) and density (kg / m³) of Al(OH)₃ be represented respectively. 3 , This indicates the thickness of Al(OH)3 deposited when no reaction occurs at the electrode interface. Take 1μm, This means that the volume of sedimentary products generated in each grid cell can be converted into the thickness of the sedimentary layer, which is the grid size; However, solving ordinary differential equations increases the nonlinearity of the model and reduces its computational efficiency. In reality, the process of θ changing from 0 to 1 is analogous to the gradual formation of a dense, porous deposition layer during deposition. The corrosion products, moving from the electrolyte to the corrosion interface surface in the normal direction, should logically represent a gradual process of coverage (θ) changing from 0 to 1. The assumption of a thickness-free interface is an oversimplification of the interface treatment, neglecting this process. By using a time function of coverage to continuously describe the coverage process, the boundary coverage rate can be calculated. θ :

[0063] The anode and cathode reaction rates on the electrode surface are corrected using an if function: ; in, The anode current density, Cathode current density.

[0064] It should be noted that due to the anode and cathode reactions on the electrode surface, a large amount of Al is generated at the solid-liquid interface. 3+ and OH - It moves within the electrolyte through diffusion and electromigration, undergoing a homogeneous reaction to form soluble Al(OH)3. When the concentration of Al(OH)3 exceeds its solubility... S Al(OH)3 When Al(OH)3 crystallizes, its concentration will not increase further. Depositing insoluble Al(OH)3 on electrochemically active surfaces reduces electrochemical activity by blocking the surface, thereby decreasing the reaction rates of the anodic and cathodic reactions. To quantitatively describe the blocking effect, the coverage of the deposited product is introduced (…). θ This quantitatively describes the coverage of Al(OH)3 precipitate on the electrode surface, where the coverage parameter (0 ≤ θ ≤1) Varies with deposition rate; when there is no deposit cover at the electrode interface. θ =0, when the interface is completely covered by Al(OH)3 θ =1. Assuming Al(OH)3 deposition only occurs at the boundary, the boundary coverage ( θIt is considered as a thickness-free boundary, and its change over time is determined by the deposition rate.

[0065] S3. Adaptive selection of anode and cathode between multiple electric couples of compound particles is performed through level set structure functions. The four different phases on the same interface of the geometric model are given fixed values ​​to distinguish different particles. Different particles include: cathodic particles, anodic particles, aluminum matrix and passivation film. In step S3, the level set structure function is:

[0066] The level set structure function defines the anode and cathode electrode reactions simultaneously on the same active boundary through the factor (1-micro (x,y)) in the anode reaction and the factor micro(x,y) in the cathode reaction. The value setting process uses four constants—-1, 0, 1, and 2—to correspond to the cathodic particles, anodic particles, aluminum substrate, and passivation film, respectively.

[0067] Adaptive selection of multi-element microcouples is achieved by correcting the current density using micro parameters. .

[0068] S4. An interpolation function is used to characterize the relationship between potential and current density. Boundary conditions and initial values ​​are set, including the setting of the insulating boundary, electrode surface boundary, and initial particle concentration. In step S4, the interpolation function is used to establish the numerical relationship between electrode potential and current density. The interpolation function is as follows:

[0069] The thickness of the electrolyte domain is set to 10 μm. The initial concentration of each particle on the upper surface of the domain is consistent with the corresponding particle concentration in the electrolyte solution. The two side boundaries are set as insulating boundaries, and the lower surface is used as the electrode surface.

[0070] The finite element model has no external applied potential and is used to reflect the potential difference between different materials. The net current on the electrode surface is 0, and the mixed potential is directly calculated based on the mixed potential theory.

[0071] It should be noted that, without an external potential, the reaction is driven by the potential difference between different materials. According to the mixed potential theory and the Evans plot, the net current at the electrode surface is zero. If solution resistance is not considered, under this constraint, the electrode surfaces should be polarized to the same potential, i.e., the mixed potential. Theoretically, selecting an appropriate initial mixed potential based on the self-corrosion potentials of the anode and cathode can save computational efficiency and improve convergence. However, this embodiment involves multiphase coupling. The electrodes with the highest and lowest potentials can be identified as the cathode and anode, but the polarity of the anode and cathode in between is difficult to determine. Therefore, the initial mixed potential cannot be predetermined. However, verification shows that appropriately increasing the smoothing range of the Heaviside function can effectively improve convergence. The mixed potential can be directly calculated based on the mixed potential theory without needing to set a corresponding initial value.

[0072] S5. The mesh is re-divided based on the Laplace smoothing mesh update method. The moving velocity of the corroded dynamic interface is calculated using Faraday's law. Constraints are applied to the boundaries that do not corrode or undergo geometric deformation to complete the construction of single-phase and multi-phase coupled corrosion finite element models. The specific steps of step S5 are as follows: S51. The ALE adaptive mesh is re-divided using Laplace smoothing, and the governing equation for mesh displacement is:

[0073]

[0074] S52. According to Faraday's law, the formula for calculating the normal component of the velocity vector v of the moving interface during corrosion is:

[0075] Where M is the molar mass of the metal, z It is the charge number, and F is the Faraday constant. ρ It is the density of the metal; S53. Boundaries that do not corrode or undergo geometric deformation are controlled by the following equation: .

[0076] S6. Solve the finite element model and output the relevant physicochemical parameters of the corrosion process.

[0077] In step S6, the relevant physicochemical parameters include: Electric field distribution, pH distribution, O2 concentration distribution, Al(OH)2Cl concentration distribution, Al(OH)3 concentration distribution, OH - Concentration distribution and corrosion product coverage data.

[0078] In some embodiments, taking a semi-circular intermetallic compound particle structure as an example, and using cathodic particles Al3Fe and anodic particles MgZn2 as research objects, the simulation results of adaptive selection of multi-element microcouples are discussed and analyzed: (1) Simulation results and discussion of corrosion product deposition 1) Simulation results and discussion of electric field distribution Without considering the influence of corrosion product deposition, the simulated potential distribution is as follows: Figure 3 As shown, when Al3Fe and MgZn2 coexist, they form a multi-electrode system with the Al matrix. When all three phases coexist, only MgZn2 acts as the anode of the corrosion galvanic cell, preferentially undergoing active dissolution, while Al3Fe and the Al matrix both act as cathodes. This can be seen from its coupling potential of approximately -981 mV, which falls between the self-corrosion potentials of MgZn2 and Al3Fe / Al. When the corrosion time is ≥2 h, MgZn2 is completely corroded, and the coupling potential in the solution domain rapidly decreases to approximately -813 mV, falling between the self-corrosion potentials of Al3Fe and Al. Furthermore, the current direction within the domain changes, and the Al matrix, which was originally the cathode, reverses its polarity, acting as the anode of the Al matrix / Al3Fe electrode pair, undergoing corrosion dissolution.

[0079] It is worth noting that when multiphase inclusions are present, after 100 hours of corrosion, the Al matrix surrounding Al3Fe produces significantly smaller pits compared to when Al3Fe exists alone. The "cavities" left after the complete corrosion of MgZn2 further corrode, starting to wedge between the passivation film and the Al matrix after 50 hours, and continuously expanding laterally, thus peeling off the passivation film.

[0080] 2) pH distribution simulation results and discussion The simulated pH distribution is as follows Figure 4 As shown. During the dissolution of MgZn2, the pH within the electrolyte domain remained essentially unchanged, therefore, no simulation calculation of the pH distribution during the MgZn2 dissolution process was performed. After 2 hours, the pH distribution within the solution domain was approximately in the range of (6.2, 8.1), and the pH around the "cavities" generated by the MgZn2 dissolution was all below 7, indicating a slightly acidic environment. The pH inside the corrosion pits formed by the Al matrix around Al3Fe was slightly higher than that around the "cavities," but still satisfied pH < 7. This is because the exposed area of ​​the Al matrix within the "cavities" is larger, resulting in more intense anodic dissolution and the generation of more Al. 3+ It undergoes hydrolysis, generating a large amount of H. + This causes the pH to decrease, while the Al matrix surrounding Al3Fe is less exposed, resulting in the generation of H2O. + The number is significantly smaller than that around the "cavity", and H is present in some areas around Al3Fe. + OH generated by the cathodic reduction reaction of Al3Fe -Neutralization, therefore the pH must be higher than inside the "cavity".

[0081] 3) Simulation results and discussion of O2 concentration distribution The simulation yielded the O2 concentration distribution as follows: Figure 5 As shown in the figure, the O2 concentration in the electrolyte domain does not change significantly, with a variation range of approximately 0.001 mol / m. 3 The lowest values ​​all appeared around the Al3Fe phase, which is related to Al3Fe acting as the cathode of the corrosion cell and undergoing the reduction reaction of O2. Compared with the O2 concentration distribution around the single-phase Al3Fe corrosion pits, the O2 concentration distribution range in the electrolyte domain is smaller when Al3Fe and MgZn2 coexist. This is because the "cavities" left by the dissolution of MgZn2 have larger openings, and oxygen in the "cavities" can be replenished in time through diffusion.

[0082] 4) Simulation results and discussion of Al(OH)2Cl concentration distribution The simulation yielded the following Al(OH)₂Cl distribution: Figure 6 As shown in the figure, it can be seen that the concentration of Al(OH)₂Cl is lower than its solubility (S) within 100 h. Al(OH)2Cl =3×10 3 mol / m 3 Furthermore, the maximum value is concentrated in the "cavities" left after MgZn2 corrosion, so free Al(OH)2Cl will not precipitate, meaning that the effect of Al(OH)2Cl deposition on corrosion does not need to be considered.

[0083] 5) Simulation results and discussion of Al(OH)3 distribution The simulation yielded the following Al(OH)3 distribution: Figure 7 As shown in the figure, it can be seen that the calculated Al(OH)3 concentration is much greater than the Al(OH)3 solubility (S). Al(OH)3 =1.86×10 -6 mol / m 3 The concentration of Al(OH)3 is highest, especially in the "cavities" left after the corrosion of MgZn2 and in the pits formed after the corrosion of the Al matrix surrounding Al3Fe. Figure 4 The pH distribution simulation results show that the pH inside and outside the corrosion pit is greater than 4 within 100h, and Al(OH)3 can exist stably. Therefore, it is necessary to consider the influence of Al(OH)3 deposition on the simulation of multiphase inclusion corrosion.

[0084] 6) OH - Distribution Simulation Results and Discussion Simulation yields OH - Distribution as Figure 8 As shown. OH in the electrolyte domain -The main sources include the reduction reaction of O2 at the cathode on the electrode surface and the dissociation of H2O within the electrolyte, with OH- in areas of low O2 concentration. - The higher concentration indicates that O2 on the electrode surface is reduced to OH. - It plays a dominant role in the influence of concentration distribution.

[0085] (2) Simulation results and discussion of corrosion product deposition 1) Simulation results and discussion of electric field distribution Taking into account the deposition of corrosion products and their impact on subsequent corrosion, the simulated potential distribution is as follows: Figure 9 As shown, it can be seen that, under the same corrosion time, the coupling potential considering the effect of corrosion product deposition is significantly lower than the coupling potential without considering the effect of corrosion products, and when t ≥ 50 h, the corrosion interface basically no longer changes. This is because the corrosion products hinder the transmission of corrosion reaction current. When the current passes through the corrosion product layer, a potential drop occurs, making the coupling potential lower when considering the effect of corrosion products than when not considering it. As the hindering effect of corrosion products continuously increases, corrosion stops after 50 h.

[0086] 2) Simulation results and discussion of steric hindrance effect of corrosion products The calculated corrosion product coverage ( θ Distribution as follows Figure 10 As shown, it should be noted that the amount of Mg and Zn hydroxides produced within 2 hours is very small, and their inhibitory effect on the corrosion reaction can be ignored. Therefore, the steric hindrance effect of corrosion products during the dissolution of MgZn2 was not simulated. After 2 hours, the Al matrix exposed around Al3Fe and after the dissolution of MgZn2 began to undergo active dissolution under the catalysis of Al3Fe, and the corrosion product Al(OH)3 was precipitated and deposited on the electrode surface. After 25 hours, the coverage of corrosion products ( θ The maximum value has reached 0.9, and the Al matrix interface exposed after 50 hours... θ They all basically reach level 1, meaning complete isolation and no further corrosion.

[0087] 3) Simulation results and discussion of particle distribution in the concentration field Taking into account the influence of corrosion products, the particle distribution of the concentration field in the solution domain obtained by simulation is as follows: Figure 11 As shown in the figure, it can be seen that, under the same corrosion time, the maximum pH value within the reaction domain considering the influence of corrosion products is lower than that without considering them, while the minimum pH value is higher than that without considering the influence of corrosion products. Figure 11 a) and Figure 11 (b) This is because the corrosion reaction rate is limited by the corrosion products, causing O2 on the cathode surface to be reduced to produce OH. - The quantity decreases, and Al is produced by anodic dissolution.3+ The amount of H in the electrolyte also decreases accordingly, while the amount of H in the electrolyte decreases. + Mainly from AI 3+ Hydrolysis and the dissociation of water, therefore H + The quantity produced has decreased. (By...) Figure 11 c) and Figure 11 As can be seen from d), the O2 concentration in the electrolyte domain does not change much, so there is no need to consider the impact of concentration cell on corrosion development due to different O2 concentrations. Figure 11 e) and Figure 11 In f), the region with low O2 concentration OH - The higher concentration indicates that O2 on the electrode surface is reduced to OH-. - It plays a dominant role in the influence of concentration distribution. Figure 11 g) and Figure 11 From h), we can see that at various points within the solution domain... c Al(OH)2Cl Al(OH)2Cl Therefore, the effect of Al(OH)2Cl deposition does not need to be considered.

[0088] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0089] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.​

Claims

1. A multi-element microcouple adaptive selective corrosion dynamic propagation simulation method, characterized in that, The method includes the following steps: S1. Construct a geometric model of intermetallic compound particles and divide the geometric model into mesh units; S2. Using the Nernst-Planck equation as the governing equation for particle transport in compounds, establish the coupling relationship between potential, particle concentration, current density, boundary coverage, and reaction rate; S3. Adaptive selection of anode and cathode between multiple electric dipoles of compound particles is performed through level set structure function, and the four different phases on the same interface of the geometric model are fixed to distinguish different particles. S4. Use interpolation functions to characterize the relationship between potential and current density, set boundary conditions and initial values, and set insulation boundaries, electrode surface boundaries and initial particle concentrations; S5. The mesh is re-divided based on the Laplace smoothing mesh update method. The moving speed of the corrosion dynamic interface is calculated by Faraday's law. The boundary without corrosion and geometric deformation is constrained and controlled to complete the construction of single-phase and multi-phase coupled corrosion finite element models. S6. Solve the finite element model and output the relevant physicochemical parameters of the corrosion process.

2. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 1, characterized in that, In step S1, the intermetallic compound particles are a single-phase or multi-phase inclusion system composed of cathodic particles, anodic particles and aluminum matrix in aluminum alloy. The cathodic particles include Al3Fe, Al6Mn, and Al7Cu2Fe, the anodic particles are MgZn2, and the multiphase inclusion system is a system in which Al3Fe and MgZn2 coexist and are combined with an aluminum matrix.

3. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 1, characterized in that, In step S1, the geometric model is generated using either an image processing-based method or a Voronoi polygon-based method. The microgalvanic corrosion geometric model is simplified using a semi-circular intermetallic compound particle structure.

4. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 3, characterized in that, The image processing-based method specifically involves: determining the composition of intermetallic compound particles using EDS based on the scanning electron microscope image, and then using binarization to assign different values ​​to different intermetallic compound particles and the aluminum matrix for differentiation.

5. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 3, characterized in that, The method based on Voronoi polygons specifically involves: using cells to simulate grains / pores in a material, with seed points corresponding to grain nucleation points / pore centers, and cell boundaries corresponding to grain boundaries / pore walls. Random perturbations with coordinate offsets not exceeding a preset percentage are added to the boundaries to reproduce the characteristics of uneven particle size and irregular boundaries in intermetallic compounds.

6. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 1, characterized in that, The specific steps of step S2 are as follows: S21. Assuming that the electrolyte solution has sufficient oxygen at the electrode interface after thorough stirring, the main reactions at the anode and cathode in the electrolyte solution are oxygen reduction and active dissolution of aluminum. The driving force for microcouple corrosion in the neutral solution comes from the potential difference between the anode and cathode. Furthermore, as the corrosion time increases, the anode and cathode areas will change over time, and the coupling potential will change. S22. Particle transport throughout the entire electrolyte domain is governed by the Nernst-Planck equations in the form of mass conservation: in, F Let be the Faraday constant, and u be the flow velocity vector of the electrolyte solution. c i For different particle concentrations, z i The number of charges carried by the particle. D i Represents the diffusion coefficient. m i The electromobility coefficient represents the potential gradient in the solution. Powering particle migration R i In solution i The net reaction rate of particles due to homogeneous reaction, that is i The sum of the differences between the forward and reverse reactions in each homogeneous reaction is the net flux caused by the reaction. Electromobility coefficient m i Calculated using the Nernst-Einstein equation: R i The calculation formula is: In the formula, , Representing the first j The rate constants for the forward and reverse reactions, m , n Representing the first j The number of particles in the forward and reverse reactions of each reaction. Representing the j Each reaction i The stoichiometric coefficient of particles N r The number of homogeneous reactions. x , y Represents the horizontal and vertical coordinates; By combining the Nernst-Planck equations using the electroneutrality equation, R i The calculation formula is used to calculate the changes in the concentration of various particles and the electric field over time in the finite element model. The electroneutrality equation is: in, z i and c i Represent i The number of charges and concentration of particles; According to Faraday's law, the vector current density in an electrolyte solution is... i l The calculation formula is: S23. Assuming Al(OH)3 deposition only occurs at the boundary, calculate the boundary coverage. θ Considered as a thickness-free boundary, its variation with time is determined by the deposition rate, and the ordinary differential equation is: in, and Let the molar mass (kg / mol) and density (kg / m³) of Al(OH)₃ be represented respectively. 3 , This indicates the thickness of Al(OH)3 deposited when no reaction occurs at the electrode interface. Take 1μm, This means that the volume of sedimentary products generated in each grid cell can be converted into the thickness of the sedimentary layer, which is the grid size; By using a time function of coverage to continuously describe the coverage process, the boundary coverage rate can be calculated. θ : The anode and cathode reaction rates on the electrode surface are corrected using an if function: ； in, The anode current density, Cathode current density.

7. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 1, characterized in that, In step S3, the level set structure function is: The level set structure function defines the anode and cathode electrode reactions simultaneously on the same active boundary through the factor (1-micro (x,y)) in the anode reaction and the factor micro(x,y) in the cathode reaction. The value setting process uses four constants—-1, 0, 1, and 2—to correspond to the cathodic particles, anodic particles, aluminum substrate, and passivation film, respectively. Adaptive selection of multi-element microcouples is achieved by correcting the current density using micro parameters. 。 8. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 1, characterized in that, In step S4, the interpolation function is used to establish the numerical relationship between electrode potential and current density. The interpolation function is: The thickness of the electrolyte domain is set to 10 μm. The initial concentration of each particle on the upper surface of the domain is consistent with the corresponding particle concentration in the electrolyte solution. The two side boundaries are set as insulating boundaries, and the lower surface is used as the electrode surface.

9. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 1, characterized in that, The specific steps of step S5 are as follows: S51. The ALE adaptive mesh is re-divided using Laplace smoothing, and the governing equation for mesh displacement is: S52. According to Faraday's law, the formula for calculating the normal component of the velocity vector v of the moving interface during corrosion is: Where M is the molar mass of the metal, z It is the charge number, and F is the Faraday constant. ρ It is the density of the metal; S53. Boundaries that do not corrode or undergo geometric deformation are controlled by the following equation: 。 10. The adaptive selective corrosion dynamic propagation simulation method for multi-element microcouples according to claim 1, characterized in that, In step S6, the relevant physicochemical parameters include: Electric field distribution, pH distribution, O2 concentration distribution, Al(OH)2Cl concentration distribution, Al(OH)3 concentration distribution, OH - Concentration distribution and corrosion product coverage data.

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