Simulation method for predicting morphology of corrosion pit by considering grain orientation

Abstract

The invention relates to a simulation method for predicting the morphology of a corrosion pit by considering grain orientation, and belongs to the technical field of metal material calculation and simulation. The method comprises the following steps: firstly, constructing a model that an initial corrosion pit exists on the surface of a material and importing EBSD data of the material, secondly, setting a boundary condition and a control equation, introducing a relationship between crystal orientation and corrosion potential, and finally simulating movement of a corrosion pit interface in a corrosion process to predict the morphology of the corrosion pit. According to the method, by considering the influence of the actual crystal structure of the material on the pitting behavior, the obtained simulation result can be used for predicting the morphology of the pitting pit, and reference is provided for researching more complex corrosion phenomena involving a series of microstructure characteristics and various electrochemical mechanisms.

Description

Technical Field

[0001] The invention belongs to the technical field of metal material calculation and simulation, and in particular relates to a simulation method for predicting corrosion pit morphology by considering grain orientation. Background Art

[0002] Metal materials are widely used in various industrial applications, but the corrosion phenomena they experience in corrosive environments not only cause huge economic losses, but may also cause safety accidents. The grain orientation of metals has a significant effect on their corrosion behavior, and regions with different grain orientations may have different corrosion rates and morphologies. Although previous studies have explored the effect of grain orientation on corrosion behavior, most of them focus on experimental observations and theoretical analysis, and lack effective simulation tools to predict the morphology of corrosion pits, especially the development of corrosion pits under different grain orientations. Therefore, in order to better understand and control the corrosion process, it is necessary to develop methods that can simulate and predict the morphology of corrosion pits. Summary of the invention

[0003] The purpose of the present invention is to provide a simulation method for predicting the morphology of corrosion pits by considering the grain orientation. By establishing a pitting model that considers the actual microstructure of the material, the relationship between crystal orientation and corrosion potential is introduced, and the movement of the corrosion pit interface during the corrosion process is simulated to predict the corrosion pit morphology. The method of the present invention provides a reference for more accurately predicting and controlling the corrosion process.

[0004] To achieve the above-mentioned purpose, the technical solution of the present invention is: a simulation method for predicting the morphology of corrosion pits considering grain orientation, by establishing a pitting corrosion model considering the actual microstructure of the material, introducing the relationship between crystal orientation and corrosion potential, simulating the movement of the corrosion pit interface during the corrosion process to predict the corrosion pit morphology.

[0005] In one embodiment of the present invention, the method comprises the following steps:

[0006] Step 1: Build a model with initial corrosion pits on the material surface and import material EBSD data;

[0007] Step 2: Set material parameters and control equations;

[0008] Step 3: Introduce the relationship between crystal orientation and corrosion potential;

[0009] Step 4: Calculate and solve to obtain the movement result of the pit interface, that is, the pit morphology.

[0010] In one embodiment of the present invention, in step 1, the model of the initial corrosion pits on the surface of the material includes: the microstructure of the material, the electrolyte solution and the initial pits.

[0011] In an embodiment of the present invention, step 2 is specifically implemented as follows:

[0012] The physics of transport of diluted species describes the reaction process for a given electrolyte via the mass balance equation:

[0013]

[0014] Among them, c i is the concentration of substance i, t is the time, N i represents the material flux, R i is the rate at which the substance is produced during the reaction, described by the Nernst-Planck equation:

[0015]

[0016] in, is the gradient operator, F is the Faraday constant, D i and z i is the diffusion coefficient and charge number of substance i, u i is the mass mobility, is the potential gradient of the electrolyte solution, v is the solvent diffusion rate, and it is assumed that the electrolyte solution remains static, so the convection term is not considered, that is, v = 0;

[0017] Assuming that the ion concentration in the electrolyte solution is a constant, the electrolyte solution is an incompressible liquid and is electrically neutral, and assuming that the substance concentration in the electrolyte is lower than saturation, the net output of the reactant is zero, we can get:

[0018]

[0019] The function of current density is described by the log-linear Butler-Volmer relationship, namely the Tafel equation:

[0020]

[0021] Among them, exp function represents the natural exponential function, α is the anode charge transfer coefficient, dimensionless; R is the gas constant; T is the temperature; η α is the peak current; z is the average charge; the exchange current density i corr for:

[0022]

[0023] Where A is the solubility, E corr is the corrosion potential.

[0024] In one embodiment of the present invention, step three is specifically implemented as follows:

[0025] The effect of the metal on pit growth is described using the experimentally measured anodic current density:

[0026]

[0027] in, is the corrosion potential of the i-th crystal orientation S, and n is the total number of crystal faces; The sum of the experimentally measured polycrystalline corrosion potential

[0028] Further assume that:

[0029]

[0030] in, is the corrosion potential in the

[001] orientation, X represents E corr The magnitude of change;

[0031] Assume that the potential in any crystal direction is The projection on

[001] changes linearly, from which we can get:

[0032]

[0033] Among them, S i is the vector of the crystallographic direction, m 1 and m 2 is a constant that is linearly related to the equation.

[0034] In one embodiment of the present invention, m 1 and m 2 Depend on and Sure.

[0035] In one embodiment of the present invention, the material is 2205 duplex stainless steel, and COMSOL is used for modeling.

[0036] In one embodiment of the present invention, the material is 316L stainless steel, and COMSOL is used for modeling.

[0037] The present invention also provides a simulation system for predicting corrosion pit morphology by considering grain orientation, comprising a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0038] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0039] Compared with the prior art, the present invention has the following beneficial effects: The method of the present invention aims to combine the experimental data of grain orientation and computational simulation technology to predict the morphology of corrosion pits on the surface of materials under different grain orientations, providing a scientific basis for optimizing the microstructure of materials, improving the corrosion resistance of materials, and reducing the losses caused by corrosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the simulation model of this method.

[0041] Figure 2 This is the IPF diagram of 2205 duplex stainless steel in Example 1 of the present method, showing different grain orientations.

[0042] Figure 3 This is the geometric model in Examples 1 and 2 of this method.

[0043] Figure 4 This is the grain orientation distribution under the initial etch pit in Example 1 of this method.

[0044] Figure 5 This is the corrosion potential of the corrosion pit simulated after 240s in Example 1 of this method.

[0045] Figure 6 The corrosion current density of the corrosion pit simulated after 240s in Example 1 of the present method.

[0046] Figure 7 The three-dimensional morphology of the etch pits on the material surface and the pit depth curve after the electrochemical experiment in Example 1 of the present method.

[0047] Figure 8 This is the IPF diagram of 316 stainless steel in Example 2 of the present method, showing different grain orientations.

[0048] Fig. 9 This is the grain orientation distribution under the initial etch pit in Example 2 of this method.

[0049] Fig.10 This is the corrosion potential of the corrosion pit simulated after 240s in Example 2 of this method.

[0050] Fig.11 The corrosion current density of the corrosion pit simulated after 240s in Example 2 of this method.

[0051] Fig.12 The three-dimensional morphology of the surface etch pits and the pit depth curve after the electrochemical experiment in Example 2 of this method. DETAILED DESCRIPTION

[0052] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0053] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0054] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0055] The present invention provides a simulation method for predicting the morphology of corrosion pits by considering the grain orientation. By establishing a pitting model considering the actual microstructure of the material, the relationship between crystal orientation and corrosion potential is introduced, and the movement of the corrosion pit interface during the corrosion process is simulated to predict the morphology of the corrosion pit. The method specifically comprises the following steps:

[0056] Step 1: Construct a model with initial corrosion pits on the material surface (see Figure 1 ) and import material EBSD data;

[0057] Step 2: Set material parameters and control equations;

[0058] Step 3: Introduce the relationship between crystal orientation and corrosion potential;

[0059] Step 4: Calculate and solve to obtain the movement result of the pit interface, that is, the pit morphology.

[0060] The following are specific implementation examples of the present invention.

[0061] Example 1: A simulation method for predicting corrosion pit morphology considering grain orientation of 2205 duplex stainless steel.

[0062] (1) The crystal structure data of 2205 duplex stainless steel was obtained by EBSD and modeled using COMSOL, as shown in Figure 3 As shown. The size of the upper electrolyte solution is set to be 4×10 -4 m, width 2×10 -4 m. The initial corrosion pit radius is 0.4×10 -4 m. Figure 2 IPF diagram of 2205 duplex stainless steel, showing different grain orientations. Figure 4 This is the grain orientation distribution under the initial etch pit.

[0063] (2) Set the material parameters in the software as shown in Table 1:

[0064] Table 1

[0065] name value unit Electrolyte conductivity 2.5 S / m Density of 2205 duplex stainless steel 7.8 <![CDATA[g / cm 3 ]]> Molar mass of 2205 duplex stainless steel 0.056 kg / mol Exchange current density 2500 <![CDATA[A / m 2 ]]> Limiting current density 5000 <![CDATA[A / m 2 ]]> Equilibrium potential -0.58 V

[0066] (3) Add deformed geometry and set the model mesh smoothing type to hyperelastic. Add secondary current distribution and enter the governing equations. Enter the equation for the relationship between crystal orientation and current density into the electrode reaction. Add multiphysics and The boundary of is set as the non-deformed boundary, and the remaining boundaries are set as the deformed electrode surface. Assume that the pitting potential of 2205 duplex stainless steel in the

[001] and

[111] orientations is the minimum and maximum, respectively, with a deviation of 6%.

[0067] (4) Add transient study and select t = 240s as the transient state to be studied. The electrolyte potential and current density at the pit interface are as follows: Figure 5 , 6 shown.

[0068] (5) In order to obtain surface pits, 2205 duplex stainless steel was immersed in 1M H 2 SO 4 +1M NaCl solution for 12h, then rinsed with deionized water and dried. Figure 7 shown.

[0069] Example 2: A simulation method for predicting corrosion pit morphology considering the grain orientation of 316L stainless steel.

[0070] (1) The crystal structure data of 316L stainless steel was obtained by EBSD and modeled using COMSOL, such as Figure 3 As shown. The size of the upper electrolyte solution is set to be 4×10 -4 m, width 2×10 -4 m. The initial corrosion pit radius is 0.4×10 -4 m. Figure 8 IPF diagram of 316 stainless steel, showing different grain orientations. Fig. 9 is the grain orientation distribution under the initial etch pit.

[0071] (2) Set the material parameters in the software, as shown in Table 2:

[0072] Table 2

[0073] name value unit Electrolyte conductivity 2.5 S / m Density of 316L stainless steel 7.93 <![CDATA[g / cm 3 ]]> Molar mass of 316L stainless steel 0.056 kg / mol Exchange current density 2300 <![CDATA[A / m 2 ]]> Limiting current density 4700 <![CDATA[A / m 2 ]]> Equilibrium potential -0.63 V

[0074] (3) Add deformed geometry and set the model mesh smoothing type to hyperelastic. Add secondary current distribution and enter the governing equations. Enter the equation for the relationship between crystal orientation and current density into the electrode reaction. Add multiphysics and The boundaries of the electrode are set as non-deformed boundaries, and the remaining boundaries are set as deformed electrode surfaces.

[0075] (4) Add transient study and select t = 240s as the transient state to be studied. The electrolyte potential and current density at the pit interface are as follows: Fig.10 , 11 shown.

[0076] (5) In order to obtain surface pits, 2205 duplex stainless steel was immersed in 1M H 2 SO 4 +1M NaCl solution for 12h, then rinsed with deionized water and dried. Fig.12 shown.

[0077] Comparing the simulation results with the pits after the experiment, it is found that the pits on the surface of 2205 duplex stainless steel are shallower and larger in size. The profile curve of the pits presents an undulating step shape, which is similar to the internal morphology of the pits observed by SEM. Figure 5 As shown in the figure, during the initial pit expansion process, when the pit encounters the

[001] orientation on the expansion path, the growth rate at this location is significantly reduced relative to the rest. The pit growth front tends to expand in the

[111] direction, that is, the closer the orientation in the growth direction is to

[111] , the more obvious the pit expansion at this location. The pits on the surface of 316L stainless steel mainly extend in the longitudinal direction, and the overall contour of the pits tends to be more hemispherical. The simulation results are in good agreement with the pit morphology.

[0078] In summary, this method has the following beneficial effects: This method establishes a pitting model that takes into account the actual microstructure of the material, is not limited to a single simulation software, introduces the relationship between crystal orientation and corrosion potential, predicts the final pit morphology based on the simulation results, and the simulation results are well consistent with the actual pits. This method can contribute to optimizing material processing technology and reducing the risk of material failure.

[0079] The present invention also provides a simulation system for predicting corrosion pit morphology by considering grain orientation, comprising a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0080] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0085] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A simulation method for predicting corrosion pit morphology considering grain orientation, characterized in that: By establishing a pitting corrosion model that takes into account the actual microstructure of the material, the relationship between crystal orientation and corrosion potential is introduced, and the movement of the pit interface during the corrosion process is simulated to predict the pit morphology.

2. A simulation method for predicting corrosion pit morphology considering grain orientation according to claim 1, characterized in that: The steps include: Step 1: Build a model with initial corrosion pits on the material surface and import material EBSD data; Step 2: Set material parameters and control equations; Step 3: Introduce the relationship between crystal orientation and corrosion potential; Step 4: Calculate and solve to obtain the movement result of the pit interface, that is, the pit morphology.

3. A simulation method for predicting corrosion pit morphology considering grain orientation according to claim 2, characterized in that: In step 1, the model of the initial corrosion pits on the surface of the material includes: the microstructure of the material, the electrolyte solution and the initial pits.

4. The simulation method for predicting corrosion pit morphology by considering grain orientation according to claim 2, characterized in that: Step 2 is implemented as follows: The physics of transport of diluted species describes the reaction process for a given electrolyte via the mass balance equation: Among them, c i is the concentration of substance i, t is the time, N i represents the material flux, R i is the rate at which the substance is produced during the reaction, described by the Nernst-Planck equation: in, is the gradient operator, F is the Faraday constant, D i and z i is the diffusion coefficient and charge number of substance i, u i is the mass mobility, is the potential gradient of the electrolyte solution, v is the solvent diffusion rate, and it is assumed that the electrolyte solution remains static, so the convection term is not considered, that is, v = 0; Assuming that the ion concentration in the electrolyte solution is a constant, the electrolyte solution is an incompressible liquid and is electrically neutral, and assuming that the substance concentration in the electrolyte is lower than saturation, the net output of the reactant is zero, we can get: The function of current density is described by the log-linear Butler-Volmer relationship, namely the Tafel equation: Among them, exp function represents the natural exponential function, α is the anode charge transfer coefficient, dimensionless; R is the gas constant; T is the temperature; η α is the peak current; z is the average charge; the exchange current density i corr for: Where A is the solubility, E corr is the corrosion potential.

5. The simulation method for predicting corrosion pit morphology by considering grain orientation according to claim 2, characterized in that: Step 3 is implemented as follows: The effect of the metal on pit growth is described using the experimentally measured anodic current density: in, is the corrosion potential of the i-th crystal orientation S, and n is the total number of crystal faces; The sum of the experimentally measured polycrystalline corrosion potential Further assume that: in, is the corrosion potential in the [001] orientation, X represents E corr The magnitude of change; Assume that the potential in any crystal direction is The projection on [001] changes linearly, from which we can get: Among them, S i is the vector of the crystallographic direction, m1 and m2 are constants linearly related to the equation.

6. A simulation method for predicting corrosion pit morphology considering grain orientation according to claim 5, characterized in that: m1 and m2 are and Sure.

7. The simulation method for predicting corrosion pit morphology by considering grain orientation according to claim 2, characterized in that: The material is 2205 duplex stainless steel, and COMSOL is used for modeling.

8. The simulation method for predicting corrosion pit morphology by considering grain orientation according to claim 2, characterized in that: The material is 316L stainless steel, and COMSOL is used for modeling.

9. A simulation system for predicting corrosion pit morphology considering grain orientation, characterized in that: The method comprises a memory, a processor and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, the method steps as claimed in any one of claims 1 to 8 can be implemented.

10. A computer-readable storage medium storing computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 8 can be implemented.

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