Method for predicting pitting corrosion damage of aluminum alloy in high-temperature water environment and related equipment

Abstract

The invention provides a method and related equipment for predicting pitting damage of an aluminum alloy in a high-temperature water environment, according to the method, a multi-physics-field coupled pitting damage simulation model is constructed, a two-dimensional axial symmetry design is adopted, the model can be rotatably expanded to three dimensions, and influences of an electrochemical field, a chemical reaction field, a substance transfer field and interface evolution and corrosion precipitation are integrated. A Stokes-Einstein equation is introduced into the model to correct an ion diffusion coefficient, a Van't Hoff equation based on constant enthalpy deltaH0 is used to correct an equilibrium reaction constant, an electrochemical workstation is used to measure an aluminum alloy polarization curve so as to obtain accurate model electrochemical boundary conditions, and through a progressive process of electrochemical experiment-parameter correction-multi-field coupling simulation, a multi-field coupling simulation model is obtained. And dynamic prediction of pitting expansion of the aluminum alloy in the high-temperature water environment is realized. According to the method, the pitting damage behavior of the aluminum alloy in the high-temperature water environment can be simulated with high precision, a scientific basis is provided for corrosion protection, and the method is suitable for pitting protection design of high-temperature equipment in the fields of nuclear energy and the like, assists in optimizing material selection and has important engineering application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of corrosion simulation, more particularly, it relates to a method for predicting pitting corrosion damage of aluminum alloy under high-temperature water environment and related equipment. BACKGROUND

[0002] Aluminum alloy, as a kind of light-weight, high-strength and corrosion-resistant material, has shown a wide application prospect and great development potential in many fields such as aviation, aerospace, transportation, construction and electronics since its advent, especially in the key components of nuclear reactors. However, under the operating conditions of the reactor, aluminum alloy is prone to pitting corrosion, which poses a serious challenge to the safety and reliability of the key components of the nuclear reactor.

[0003] Traditional pitting corrosion research methods mainly rely on experimental methods, which can provide intuitive corrosion results, but it is difficult to reveal the micro-mechanism in the corrosion process. In addition, experimental methods are usually time-consuming, costly, and difficult to simulate complex working conditions. Multi-physics simulation has become a promising method for studying pitting corrosion. Compared with traditional experimental methods, multi-physics simulation can simulate the complete process of pitting corrosion in a shorter time, significantly shortening the research period. In addition, multi-physics simulation can provide rich visualization results, including potential distribution, concentration distribution and morphology evolution, which helps to intuitively understand the complex phenomena of pitting corrosion, thereby more deeply revealing its inherent mechanism, and ultimately making corresponding prevention and control measures to reduce pitting corrosion of aluminum alloy in nuclear power environment. Therefore, in order to deeply understand the occurrence mechanism, development process and behavior of aluminum alloy pitting corrosion under different environmental conditions, it is of great significance to establish a scientific and reasonable long-period pitting corrosion model of aluminum alloy.

[0004] However, according to the existing pitting corrosion model research, most traditional methods only simulate pitting corrosion in normal temperature water solution, and the acquisition of electrochemical kinetic parameters is limited to theoretical values, which cannot consider the influence of temperature on electrochemistry, particle diffusion and chemical reaction constant, limiting the promotion of aluminum alloy pitting corrosion model simulation to high-temperature environment. SUMMARY

[0005] In view of the defects in the prior art, the purpose of the present application is to provide a method for predicting pitting corrosion damage of aluminum alloy under high-temperature water environment and related equipment to overcome the above-mentioned defects.

[0006] The above technical purpose of the present application is achieved by the following technical scheme: in a first aspect, a method for predicting pitting corrosion damage of aluminum alloy under high-temperature water environment, comprising: S1, constructing an initial pitting corrosion morphology based on pitting corrosion parameters; the initial pitting corrosion morphology is specifically a two-dimensional morphology; S2. Applying boundary conditions to the initial pitting morphology based on a high-temperature composite field model, dividing the mesh and solving it with a transient solver to generate a simulated pitting morphology; the simulated pitting morphology is specifically a two-dimensional morphology; wherein the high-temperature composite field model includes at least: an electrochemical reaction field, a chemical reaction field, and a material transport field; The electrochemical reaction field is generated by measuring polarization curves under high-temperature liquid conditions and fitting parameters; the equilibrium reaction constant in the chemical reaction field is corrected based on the high-temperature water environment; and the particle diffusion coefficient in the material transport field is corrected based on the high-temperature water environment. S3. Rotate the simulated pitting morphology to generate a three-dimensional pitting model.

[0007] In one embodiment, the electrochemical reaction field is constructed by the following steps, specifically including: S211. Measure the open circuit potential, AC impedance, and potentiodynamic polarization curve of aluminum alloy in a high-temperature solution environment; S212, calculating the solution resistance based on the AC impedance , using the solution resistance Correcting the potentiodynamic polarization curve to generate a post-IR drop polarization curve; S213 , estimating electrode kinetic parameters based on the post-IR drop polarization curve, and calculating and obtaining boundary condition assignments of the electrochemical reaction field.

[0008] In one embodiment, the solution resistance is calculated based on the AC impedance. , using the solution resistance The potentiodynamic polarization curve is corrected to generate a post-IR drop polarization curve, specifically comprising: S2121, calculating the solution resistance based on the AC impedance , get the test current , using the solution resistance And the test current , calculate the pressure drop ,include: ; S2122, calculating the potentiodynamic polarization curve and the voltage drop The difference between them is used to generate the IR drop polarization curve.

[0009] In one embodiment, the electrode kinetic parameter estimation based on the post-IR drop polarization curve and the calculation of the boundary condition assignment of the electrochemical reaction field specifically include: S2131. Constructing the electrode kinetic model mechanism function, including: ; in, is the corrosion current density, is the passivation current density, is the corrosion potential, is the anodic Tafel slope, is the cathode Tafel slope, is the electrode potential; S2132. Calculating a set of optimal electrode kinetic parameters using a least squares method as boundary condition assignments for the electrochemical reaction field, including: ; ; in, is the predicted value of the electrode kinetic model, represents the set of electrode kinetic parameters, represents the current density value of the experimental polarization curve.

[0010] In one embodiment, the particle diffusion coefficient is corrected based on the following steps, specifically including: S221, obtaining the particle diffusion coefficient equation, specifically including: ; in, is the particle diffusion coefficient, is the Boltzmann constant, is the target temperature for the reaction, is the solvent viscosity, is the radius of the diffusing particle; S222. Determine solvent viscosity and temperature The relationships between ; S223. Determine the expression for the particle diffusion coefficient at any temperature, specifically: ; in, is the standard temperature, is the particle diffusion coefficient at standard temperature, is the solvent viscosity at standard temperature.

[0011] In one embodiment, the equilibrium reaction constant is corrected based on the following steps, specifically comprising: S231, obtaining the particle diffusion coefficient equation: ; in, Indicates absolute temperature; represents the gas constant; represents the standard Gibbs free energy change; S232, determining the relationship between the absolute temperature , specifically: ; wherein, represents the standard reaction enthalpy variable; represents the standard entropy variable; S233, based on the particle diffusion coefficient equation and the standard Gibbs free energy variable, determining the expression form of the particle diffusion coefficient equation under the condition of any standard temperature , including: ; wherein, represents the standard temperature, specifically 298.15K; represents the target temperature of the reaction.

[0012] In one embodiment, the high-temperature composite field model further includes: an interface evolution field, which describes the position change of the metal interface of the initial pitting morphology based on any Lagrange-Euler method.

[0013] In one embodiment, the high-temperature composite field model further includes: a corrosion deposition field, which describes the position change of the metal interface of the initial pitting morphology based on a level set method.

[0014] The device for predicting aluminum alloy pitting corrosion damage under high-temperature water environment comprises: A construction unit is configured to construct an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional morphology. A simulation unit is configured to apply boundary conditions to the initial pitting morphology based on a high-temperature composite field model, divide the grid, and then solve it with a transient solver to generate a simulated pitting morphology; the simulated pitting morphology is specifically a two-dimensional morphology; wherein the high-temperature composite field model at least includes: an electrochemical reaction field, a chemical reaction field, and a mass transfer field. The electrochemical reaction field is generated by measuring the polarization curve under high-temperature liquid conditions and fitting parameters; the equilibrium reaction constant in the chemical reaction field is modified based on the high-temperature water environment; the particle diffusion coefficient in the mass transfer field is modified based on the high-temperature water environment. A rotating unit is configured to rotate the simulated pitting morphology to generate a three-dimensional pitting model.

[0015] A computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the above method.

[0016] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.

[0017] In summary, the present application has the following advantages: the present application provides a high hydrological environment aluminum alloy pitting corrosion damage prediction method, using the method of the present application, first, based on the high temperature water environment, the multiple physical fields are corrected, then the multiple physical fields are coupled with each other and then act on the initial pitting corrosion morphology, the initial pitting corrosion morphology is simulated, the corrosion process of the pitting corrosion defect under the high temperature water environment can be accurately obtained, and in the present application, the two-dimensional pitting corrosion morphology is rotated to generate a three-dimensional pitting corrosion model, which can effectively reduce the calculation burden of simulation and improve the simulation calculation speed. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 Flow chart of the high temperature water environment aluminum alloy pitting corrosion damage prediction method of the present application; Figure 2 Structure diagram of the high temperature water environment aluminum alloy pitting corrosion damage prediction device of the present application; Figure 3 Internal structure diagram of the computer device in the embodiment of the present application; Figure 4 Aluminum alloy pitting corrosion anode and cathode relationship schematic diagram in the specific embodiment of the present application; Figure 5 Model geometry structure and mesh division situation schematic diagram in the specific embodiment of the present application; Figure 6 Aluminum alloy polarization curve and electrode kinetics model fitting diagram in embodiment 5 of the present application; Figure 7 Particle type and equilibrium reaction schematic diagram contained in the model in embodiment 5 of the present application; Figure 8 Model pit bottom pH change with time simulation result diagram in embodiment 5 of the present application; Figure 9 Model pit mouth and pit bottom corrosion depth change with time simulation result diagram in embodiment 5 of the present application; Figure 10 Model pit mouth and pit bottom corrosion depth change with time simulation result diagram in embodiment 5 of the present application; 1 / 2 Figure 11 Initial corrosion morphology diagram after the two-dimensional axisymmetric model is rotated and expanded into a three-dimensional model in embodiment 5 of the present application; Figure 12 Simulated pitting corrosion morphology diagram after the two-dimensional axisymmetric model is rotated and expanded into a three-dimensional model in embodiment 5 of the present application; ​​​Figure 13 A method according to the present application.

[0019] In the figure: 1, construction unit; 2, simulation unit; 3, rotating unit. DETAILED DESCRIPTION

[0020] In order to make the objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. Several embodiments of the present application are given in the drawings. However, the present application can be realized in many different forms, and is not limited to the embodiments described herein.

[0021] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, and c can be single or multiple.

[0022] Those of ordinary skill in the art can realize that each unit and algorithm step described in the embodiments disclosed herein can be realized by electronic hardware, computer software and a combination of electronic hardware and computer software. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0023] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0024] In several embodiments provided in the present application, any function, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts of the technical solutions that make contributions to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing an apparatus (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0025] The above description is merely a specific implementation of the present application. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application. The protection scope of the present application should be subject to the protection scope of the claims.

[0026] The present application will be described in detail below in combination with the accompanying drawings and embodiments.

[0027] Embodiment one To solve the above problems, the present application provides a high-temperature water environment aluminum alloy pitting corrosion damage prediction method. The present application takes aluminum alloy as an example, the environment is 50-150℃, the normal water chemical condition (ultra-pure water) / abnormal water chemical condition (containing Cl-, Fe3+), COMSOL multi-physical field simulation software is selected to carry out simulation calculation, the corrosion relationship between a single pitting pit and the surrounding aluminum alloy is studied, the oxidation reaction of aluminum occurs in the pitting pit ( ), and the oxygen reduction reaction occurs on the surface of the surrounding aluminum alloy ( ), which drives the pitting process. The aluminum alloy pitting anode and cathode relationship diagram is shown in Figure 4 .

[0028] The present application mainly focuses on the prediction of pitting corrosion damage of aluminum alloy in high-temperature water environment. First, the factors affecting the pitting corrosion damage process in high-temperature water environment need to be determined. Then, these factors are corrected based on the high-temperature water environment. Secondly, the high-temperature composite field model comprehensively considers the coupling relationship of multiple factors such as electrochemical reaction, chemical reaction, mass transfer, interface evolution and corrosion deposition, introduces anodic dissolution reaction, cathodic oxygen reduction reaction, aluminum ion hydrolysis and complex reaction with chloride ion, describes the ion transport process based on Nernst-Planck equation, and dynamically simulates the influence of corrosion product deposition on the electrode surface and its feedback effect through ALE and level set method, so as to fully reflect the pitting corrosion damage mechanism of aluminum alloy under complex working conditions. Finally, the pitting corrosion model is simplified, a two-dimensional axisymmetric design is constructed, and the pitting corrosion model is simplified by taking the pitting corrosion pit as the center to reduce the complexity while retaining the core features, and the two-dimensional model can generate a three-dimensional model by rotation. Ultimately, a pitting corrosion model prediction result combining multiple field coupling and located in high-temperature water environment can be obtained.

[0029] Based on the above, the prediction steps for an aluminum alloy battery defect can be summarized as follows: S1, constructing an initial pitting corrosion morphology based on pitting corrosion parameters; the initial pitting corrosion morphology is specifically a two-dimensional morphology; wherein the formation of the initial pitting corrosion defect is a local corrosion initiation point caused by local micro-heterogeneity, environmental factors or physical damage of the metal material in the early service or manufacturing process; the initial pitting corrosion morphology can be simplified from a three-dimensional model to a two-dimensional central axisymmetric model; the two-dimensional model can effectively reduce the burden of the computing device, allowing the device to maintain high-precision calculation for a long time; S2, applying boundary conditions to the initial pitting corrosion morphology based on the high-temperature composite field model, and dividing the grid to solve with a transient solver to generate a simulated pitting corrosion morphology; the simulated pitting corrosion morphology is specifically a two-dimensional morphology; wherein the high-temperature composite field model at least includes: an electrochemical reaction field, a chemical reaction field and a mass transfer field; specifically, the fields that are more affected by the high-temperature water environment in the high-temperature composite field model mainly include: the electrochemical reaction field, the chemical reaction field and the mass transfer field, so it is necessary to correct these three fields under high-temperature water environment, so that the model can accurately reflect the mechanism difference of pitting corrosion behavior with temperature change. This embodiment first corrects the above three field models under high-temperature: the electrochemical reaction field is generated by measuring the polarization curve under high-temperature liquid conditions and fitting the parameters; the equilibrium reaction constant in the chemical reaction field is corrected based on the high-temperature water environment; the particle diffusion coefficient in the mass transfer field is corrected based on the high-temperature water environment; S3, rotating the simulated pitting corrosion morphology to generate a three-dimensional pitting corrosion model.

[0030] The correction steps of the electrochemical reaction field include: Experimental preparation: cutting aluminum alloy, grinding to 1000#-2000# sandpaper, ultrasonic cleaning with alcohol for 5-20min, exposing the area to 1cm 2 Ultrapure water / containing Cl - / Fe 3+ Solution, high temperature environment, pH value is adjusted to 4-7 by NaOH / HCl. Three-electrode system, including: (1) working electrode: aluminum alloy; (2) reference electrode: Ag / AgCl (saturated KCl); (3) auxiliary electrode: platinum sheet, high temperature reactor, temperature control precision , electrochemical workstation, model: Gamry Reference 600+.

[0031] S211. Measure the open circuit potential, AC impedance, and potentiodynamic polarization curve of aluminum alloy in a high-temperature solution environment; the open circuit potential is 30 min-60 min, the AC impedance starting frequency is 10,000 Hz-20,000 Hz, the ending frequency is 100 Hz-1,000 Hz, the potentiodynamic polarization starting potential is 200 mV-600 mV lower than the open circuit potential, the ending potential is 200 mV-600 mV higher than the open circuit potential, and the scanning speed is 0.166 mV / s-1 mV / s.

[0032] S212, calculating the solution resistance based on the AC impedance , using the solution resistance The potentiodynamic polarization curve is corrected to generate a post-IR drop polarization curve, specifically comprising: S2121, calculating the solution resistance based on the AC impedance In actual testing, the instrument measures the total potential difference between the reference electrode and the working electrode. In addition to the potential change caused by the electrode reaction itself, the current flowing in the solution will also produce voltage loss due to the resistance of the solution, making the data inaccurate. That is, the measured data includes the electrode reaction potential + the IR drop caused by the solution resistance. Therefore, the latter needs to be subtracted to restore the true polarization behavior of the electrode. In the electrochemical impedance spectroscopy (EIS), the solution resistance It is obtained by fitting the impedance values ​​in the high-frequency band of the impedance spectrum. In electrochemical tests, a small sinusoidal AC voltage disturbance (frequency range from mHz to MHz) is applied to the electrode system, and the response current is recorded to obtain the impedance spectrum of the system. At high frequencies, the interface capacitance is approximately short-circuited, and reaction polarization and diffusion resistance have not yet appeared. The impedance is mainly determined by the ohmic resistance of the solution. Therefore, the real part corresponding to the intersection of the leftmost end (high-frequency band) of the impedance spectrum and the horizontal axis is the solution resistance. .

[0033] obtaining a solution resistance then obtaining a test current , calculating a voltage drop using the solution resistance and the test current , including: ; S2122, calculating a difference between the dynamic potential polarization curve and the voltage drop to generate an IR-drop post-polarization curve; S213, performing electrode kinetics parameter estimation based on the IR-drop post-polarization curve to obtain boundary condition assignments of the electrochemical reaction field, specifically including: S2131, constructing an electrode kinetics model mechanism function, including: ; wherein, is a corrosion current density, is a passivation current density, is a corrosion potential, is an anode Tafel slope, is a cathode Tafel slope, is an electrode potential; in the above parameters, , , , , five parameters are all key parameters for describing the model electrochemical boundary conditions, and the above key parameters are denoted as an electrode kinetics parameter set .

[0034] S2133, calculating a set of optimal electrode kinetics parameters using a least square method as the boundary condition assignments of the electrochemical reaction field, including: ; ; wherein, is a predicted value of the electrode kinetics model when the electrode potential is , denotes the electrode kinetics parameter set, denotes an experimental polarization curve current density value.

[0035] The least square method is a model fitting method that finds the optimal parameters by minimizing the sum of squares of errors between predicted values and true experimental values. Because the electrode kinetics model is a nonlinear function, it is difficult to directly derive, and there can be multiple local minima, so the parameters can be solved by algorithms, including but not limited to genetic algorithms, particle swarm optimization, grey wolf optimization, and Levenberg-Marquardt algorithms.

[0036] In one embodiment, the particle diffusion coefficient is corrected based on the following steps, specifically including: S221, obtaining the particle diffusion coefficient equation, for particles in a viscous fluid (liquid solution), its diffusion coefficient follows the Stokes-Einstein equation: specifically: ; wherein, is the particle diffusion coefficient, is the Boltzmann constant, is the target temperature at which the reaction occurs, is the solvent viscosity, is the radius of the diffusing particle; S222, the solvent viscosity itself strongly depends on the temperature, so it is necessary to further determine the relationship between the solvent viscosity and the temperature , including: ; S223, substituting the solvent viscosity expression into the Stokes-Einstein equation to determine the particle diffusion coefficient expression at any temperature, specifically: ; wherein, is the standard temperature, specifically 298.15 K, is the particle diffusion coefficient at the standard temperature, is the solvent viscosity at the standard temperature.

[0037] In one embodiment, the equilibrium reaction constant is corrected based on the following steps, specifically including: S231, obtaining the particle diffusion coefficient equation, for the equilibrium constant of the reaction at different temperatures follows the Van't Hoff equation: ; wherein, denotes the absolute temperature; denotes the gas constant; denotes the standard Gibbs free energy change; S232, determining The relationship between the absolute temperature and the standard Gibbs free energy change is specifically: ; wherein, the standard reaction enthalpy change represents the heat absorbed or released when the reaction occurs at a constant pressure, reflecting the energy involved in overcoming intermolecular forces (breaking and making chemical bonds); the standard entropy change represents the degree of disorder or chaos of the system, reflecting the change in the microscopic state of the system before and after the reaction; S233, based on the particle diffusion coefficient equation and the standard Gibbs free energy change, determining the expression form of the particle diffusion coefficient equation under the condition of any standard temperature , including: ; wherein, the standard temperature is specifically 298.15 K; the target temperature of the reaction.

[0038] In one embodiment, as shown in Figure 5 , the geometric design initiates a pitting pit: radius r0=10-1000μm (simulating early pitting), depth h0=10-1000μm, axial symmetry (z axis is the symmetry axis). Electrolyte area (length 1-10mm, height 0.5-10mm).

[0039] In one embodiment, the high-temperature composite field model further comprises: an interface evolution field, which describes the position change of the metal interface of the initial pitting morphology based on an arbitrary Lagrangian-Eulerian method. The interface evolution field is used to describe the process that the surface metal interface of the aluminum alloy pitting pit gradually dissolves, retreats and deforms over time as the corrosion reaction proceeds, i.e., the position change, morphology evolution and dynamic update of the corrosion interface. The arbitrary Lagrangian-Eulerian (ALE) method allows the grid to neither be completely bound to the object (Lagrangian) nor be completely fixed in space (Eulerian), but the grid can follow the movement of the corrosion interface, ensuring that the grid will not be severely distorted, and realizing the balanced migration between the corrosion interface deformation and the simulation calculation grid.

[0040] In one embodiment, the high-temperature composite field model further comprises: a corrosion deposition field, which describes the position change of the metal interface of the initial pitting morphology based on a level set method. The "corrosion deposition field" is used to describe how the corrosion products on the electrode surface or in the pitting pit deposit, grow, expand, accumulate on the interface, and how their morphology, position and thickness change over time as the corrosion reaction proceeds. The corrosion deposition process is usually modeled using the level set method: the level set function represents the interface between the corrosion products and the electrolyte; represents the position of the interface of the deposited layer; the interface expands outward over time according to the reaction rate (deposition rate).

[0041] In the simulation process of the pitting model, the above-mentioned five physical fields, including the electrochemical reaction field, the chemical reaction field, the mass transfer field, the interface evolution field and the corrosion deposition field, are coupled to form a high-temperature composite field model, and then the high-temperature composite field model is applied as a boundary condition to the initial pitting morphology for iterative processing. The specific steps are as follows: S401: The physical field is set to select the secondary current distribution, dilute mass transfer, deformation geometry and level set physical field module.

[0042] S402: The model electrochemical reaction specifically includes anode reaction and cathode reaction, involving anode reaction: , cathode reaction: ; the schematic diagram of anode and cathode relationship of aluminum alloy pitting is shown in Figure 4 S403: Model chemical reaction. When aluminum alloy corrosion occurs, the elements in the metal dissolve into the electrolyte solution in the form of cations, and hydrolysis reaction and homogeneous reaction occur with water and anions in the solution. The concentration distribution of ions in the electrolyte solution is affected by the coupling effect of hydrolysis reaction and mass transfer process. For example, the following reactions will be considered in the model constructed under abnormal water conditions containing Cl-:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] S404: Model control equation. For all substances (Na+, Cl-, OH-, H+, Al3+ and the like) in the solution, there is mass conservation, and the concentration change of each substance i can be described using the following equation:

[0049] wherein is the concentration of substance i, is the flux of the substance, is the source term of the substance.

[0050] The charge conservation and electrical neutrality in the solution need to be satisfied at the same time: ​

[0051]

[0052] The homogeneous reaction between the ions entering the electrolyte changes the concentration of the species, and the rate of the reaction is given by

[0053] where and represent the forward and reverse reaction rate constants, respectively, and ξ represents the order of the forward reaction; and represents the order of the reverse reaction.

[0054] In this model, the equilibrium reaction constant, Keq, is used to measure the homogeneous reaction:

[0055] S405: Model species transport. The species flux, Ni, in the model can be expressed by the Nernst-Planck equation. The Nernst-Planck equation is the governing equation for all particles in the solution, including diffusion, electromigration, and convection terms. The Nernst-Planck equation ignores the interaction between ions and is relatively accurate under the condition of dilute solution, and the expression is as follows:

[0056] where is the charge number of species i, is the diffusivity of species i, F is the Faraday constant, is the potential, is the solution flow rate.

[0057] The diffusivity of species i, ui, can be given by the Nernst-Einstein equation:

[0058] Due to the particularity of the size of the pitting, the convection term can be ignored in the model.

[0059] S406: Model boundary conditions. The model boundary conditions are divided into species transport boundary conditions and electrochemical boundary conditions.

[0060] (a) Species transport boundary conditions: At the electrode interface, the electrochemical reaction will lead to the generation or consumption of species. For the species i participating in the electrochemical reaction:

[0061] where is the electrode reaction species, is the number of electron transfer in the electrode reaction,​ is the reaction coefficient, is the current density of the electrode reaction.

[0062] For electrochemically inactive species i:

[0063] At the top boundary, the concentration of the substance is equal to the bulk concentration:

[0064] For other boundaries without ion flow in and out, it is a flux-free boundary condition:

[0065] (b) Electrochemical boundary conditions: At the anode interface, the relationship between the conductivity (σ) of the electrolyte solution and the electrochemical reaction current density (ia) on the anode interface satisfies the following formula:

[0066] Where ia represents the electrochemical reaction current density on the anode interface; i0,a is the exchange current density of the anode; icorr is the corrosion current density; ba is the Tafel slope of the anode; φs is the solid phase potential; φl is the liquid phase potential; Eeq,a is the equilibrium potential of the anode; E = φs - φl is the electrode potential; Ecorr is the corrosion potential. In the embodiments of the present application, no additional potential is applied to the metal, i.e. the solid phase potential φs is zero, and the electrode potential E is the opposite of the liquid phase potential φl. The conductivity σ of the electrolyte solution is calculated according to the following formula Conductivity which can be calculated according to the following formula.

[0067]

[0068] At the cathode interface, the relationship between the conductivity (σ) of the electrolyte solution and the electrochemical reaction current density (ic) on the cathode interface satisfies the following formula:

[0069] Where ic represents the electrochemical reaction current density on the cathode interface; i0,c is the exchange current density of the cathode; bc is the Tafel slope of the cathode.

[0070] At the insulating boundary, the normal potential gradient of the electrolyte solution is zero, satisfying the following formula:

[0071] S407: Model interface evolution. In this model, arbitrary Lagrangian-Eulerian (ALE) method is introduced to analyze the interface evolution of the metal dissolution and the deposit layer growth. The mesh displacement can be solved by the following equation:

[0072]

[0073] The corrosion boundary movement can be expressed as:

[0074] where Vdep is the normal deformation velocity caused by the metal dissolution and the deposit layer growth. The first term on the right side of the equation represents the moving rate of the gap wall caused by the metal dissolution (calculated according to Faraday's law), and the second term is the moving rate of the gap wall caused by the deposit of the corrosion product.

[0075] where:

[0076] where, is the porosity of the deposit layer, is the generation rate of

[0077] When the concentration of Al3+ and OH- exceeds the solubility limit, the deposit begins to form:

[0078] The generation rate of is calculated by the following equation:

[0079] where is the rate constant of the precipitation reaction, is the solubility product constant of is the supersaturation of

[0080] is a step function, expressed as:

[0081] No displacement is assumed at the solution boundary:

[0082] S408: Corrosion precipitation effect. In the pitting process, ​​​The formation of deposits is a time-dependent process, and the porosity of the deposits varies with time and space. The parameter ε is used to describe the precipitation porosity, which can be calculated by the following formula, indicating that the precipitation of corrosion products is related to the decrease of porosity:

[0083] The corrosion process is accompanied by the formation of precipitated products. The formation of precipitated products will cause new phases to appear in the solution, which will significantly affect the mass transfer of the material, so the diffusion coefficient needs to be modified to take this effect into account. The Bruggeman relationship with a coefficient of 1.5 is commonly used to describe the influence of porosity and tortuosity on porous layers, so the diffusion coefficient of the material in the porous membrane can be estimated by the following equation: At the same time, the effective conductivity of the electrolyte through the porous membrane also needs to be corrected:

[0084] At the same time, the effective conductivity of the electrolyte through the porous membrane also needs to be corrected:

[0085] The material transport properties of the electrolyte zone and the precipitated zone are described using the level set method, respectively, to dynamically simulate the influence of corrosion product deposition on the electrode surface and its feedback on the corrosion process, as well as the formation of the corrosion product deposition layer.

[0086] S409: Meshing. The two-dimensional model uses a free triangular mesh, with the pitting pit surface being densified (minimum size 1 μm) and the electrolyte area being gradually meshed (maximum size 100 μm), with a total number of cells ≈50,000. The three-dimensional extension is a two-dimensional model rotated around the z-axis, generating a tetrahedral mesh (number of cells ≈200,000), with emphasis on densifying the pit opening and pit wall (minimum size 2 μm).

[0087] S4010: Transient study. Transient time 0-5000h, adaptive step size, Newton iteration ≤15 times, convergence residual < 1e-6. Laplacian smoothing is triggered every 100 time steps (or when the grid distortion >25%), ensuring that the minimum angle of the pit wall elements is >15°.

[0088] S4011: Visualization data. Pitting pit morphology evolution (2D / 3D plot), pit bottom pH vs. time plot, pitting growth rate plot, corrosion current density variation curve plot, product deposition thickness plot.

[0089] The present embodiment realizes the accurate prediction of aluminum alloy pitting corrosion at high temperature (50-150°C) through the progressive process of electrochemical experiment-parameter modification-multi-field coupled simulation.

[0090] Example Two Referring to Figure 2 , the aluminum alloy pitting corrosion damage prediction device under high-temperature water environment comprises: A construction unit 1 is configured to construct an initial pitting corrosion morphology based on a pitting corrosion parameter; the initial pitting corrosion morphology is specifically a two-dimensional morphology; A simulation unit 2 is configured to apply a boundary condition to the initial pitting corrosion morphology based on a high-temperature composite field model, and then divide a grid and solve by using a transient solver to generate a simulated pitting corrosion morphology; the simulated pitting corrosion morphology is specifically a two-dimensional morphology; wherein the high-temperature composite field model at least includes an electrochemical reaction field, a chemical reaction field and a mass transfer field; The electrochemical reaction field is generated by measuring a polarization curve under high-temperature liquid conditions and fitting parameters; the equilibrium reaction constant in the chemical reaction field is modified based on the high-temperature water environment; the particle diffusion coefficient in the mass transfer field is modified based on the high-temperature water environment; A rotation unit 3 is configured to rotate the simulated pitting corrosion morphology to generate a three-dimensional pitting corrosion model.

[0091] The specific limitations of the aluminum alloy pitting corrosion damage prediction device under high-temperature water environment can be referred to the limitations of the aluminum alloy pitting corrosion damage prediction method under high-temperature water environment in the above, which will not be repeated here. Each module in the above aluminum alloy pitting corrosion damage prediction device under high-temperature water environment can be realized by software, hardware and combination thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to the above modules by the processor.

[0092] Those skilled in the art can understand that Figure 2 The structure shown in the above is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the scheme of the present application. The specific aluminum alloy pitting corrosion damage prediction device under high-temperature water environment can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0093] Embodiment three A computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the aluminum alloy pitting corrosion damage prediction method under high-temperature water environment as described in embodiment 1.

[0094] Embodiment four In one embodiment, a computer device is provided, which can be a server, and its internal structure diagram can be as shown in Figure 3As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The computer program is executed by the processor to implement the aluminum alloy pitting corrosion damage prediction method under high-temperature water environment.

[0095] Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0096] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the following steps: S1, constructing an initial pitting corrosion morphology based on pitting corrosion parameters; the initial pitting corrosion morphology is specifically a two-dimensional morphology; S2, applying boundary conditions to the initial pitting corrosion morphology based on a high-temperature composite field model, and after dividing the grid, solving with a transient solver to generate a simulated pitting corrosion morphology; the simulated pitting corrosion morphology is specifically a two-dimensional morphology; wherein the high-temperature composite field model at least includes: an electrochemical reaction field, a chemical reaction field and a mass transport field; The electrochemical reaction field is generated by measuring the polarization curve under high-temperature liquid conditions and fitting parameters; the equilibrium reaction constant in the chemical reaction field is modified based on the high-temperature water environment; the particle diffusion coefficient in the mass transport field is modified based on the high-temperature water environment; S3, rotating the simulated pitting corrosion morphology to generate a three-dimensional pitting corrosion model.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in each embodiment provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM) and the like.

[0098] Embodiment five Further, the present application also provides an actual pitting damage simulation example.

[0099] (1) Use an electrochemical workstation to sequentially measure open circuit potential, alternating current impedance, and dynamic potential polarization curve. The open circuit potential is measured for 30 minutes, the alternating current impedance starts at 10000 Hz and ends at 100 Hz, the dynamic potential polarization starts at 400 mV below the open circuit potential and ends at 400 mV above the open circuit potential, and the scanning speed is 0.166 mV / s. The solution resistance Ru=18 is obtained by testing, and the fitting of the aluminum alloy IR drop after polarization curve and electrode kinetics model solved by Levenberg-Marquardt algorithm is shown in Figure 6 The fitting effect is excellent, and R 2 =0.9984.

[0100] The electrode kinetics parameters required by the electrochemical boundary in the simulation model are as follows: , , , , .

[0101] Anode interface current density expression:

[0102] The cathode interface current density expression is:

[0103] (2) Parameter correction in high temperature environment The particle types and equilibrium reactions involved in the simulation of this model are shown in Figure 7 .

[0104] The ion diffusion coefficient is corrected by the Stokes-Einstein equation:

[0105] Based on constant enthalpy The Van't Hoff equation corrected equilibrium reaction constant:

[0106] Specific data are shown in Tables 1 and 2.

[0107]

[0108] Table 1 Diffusion coefficients of different species

[0109] Table 2 Equilibrium reaction constant expression (3) Multi-physics coupling Add secondary current distribution, dilute species transfer, deformation geometry and level set physics modules to the geometric model, substitute the anode interface current density expression and cathode interface current density expression into the electrochemical boundary conditions inside and outside the pitting pit in the model respectively, and set the dilute species transfer field. Eleven particles and their corresponding initial concentrations and diffusion coefficients, six equilibrium reactions and their corresponding equilibrium reaction constants are included. An open boundary is set at the boundary of the electrolyte region, and the boundary concentration is equal to the initial concentration. A deformed geometry is set at the anode boundary of the pit to realize the evolution of pitting corrosion. The arbitrary Lagrangian-Eulerian (ALE) method and the level set method are introduced to dynamically simulate the effect of corrosion product deposition on the electrode surface and its feedback effect on the corrosion process, as well as the formation of the corrosion product deposition layer. A free triangular mesh is used, the pit surface is encrypted, and the electrolyte region is gradient meshed. The transient time is 90h, the adaptive step size, the Newton iteration ≤ 15 times, and the convergence residual < Laplace smoothing is triggered when the mesh distortion is greater than 25%.

[0110] (4) Simulation results Figure 8For the simulation results of the model pit bottom pH change with time in this embodiment, the pit bottom pH decreases with the increase of corrosion time, the pH decreases rapidly in the first 10 days, then the pH decreases slowly, and the pH stabilizes at about 3.8.

[0111] Figure 9 , Figure 10 For the simulation results of the model pit mouth and pit bottom corrosion depth change with time in this embodiment. The pit corrosion depth increases with the increase of corrosion time, the corrosion of the pit mouth is more serious than that of the pit bottom, and the time can be converted to obtain that the corrosion depth of the pit mouth and the pit bottom is proportional to t 1 / 2 , which is consistent with the experimental conclusion of the corrosion depth of aluminum alloy pitting corrosion observed by most researchers.

[0112] Figure 11 , Figure 12 For the simulation results of the model pit mouth and pit bottom corrosion depth change with time in this embodiment. The pit corrosion depth increases with the increase of corrosion time, the corrosion of the pit mouth is more serious than that of the pit bottom, and the time can be converted to obtain that the corrosion depth of the pit mouth and the pit bottom is proportional to t After expanding to a three-dimensional model, the corrosion morphology change graph is shown. The initial pitting morphology and the corrosion morphology after 90 days are shown respectively.

[0113] The above only describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solutions falling within the concept of the present application shall be considered as falling within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A method for predicting pitting damage of aluminum alloy in high temperature water environment, characterized by: include: S1. Constructing an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional morphology; S2. Applying boundary conditions to the initial pitting morphology based on a high-temperature composite field model, dividing the mesh and solving it with a transient solver to generate a simulated pitting morphology; the simulated pitting morphology is specifically a two-dimensional morphology; wherein the high-temperature composite field model includes at least: an electrochemical reaction field, a chemical reaction field, and a material transport field; The electrochemical reaction field is generated by measuring polarization curves under high-temperature liquid conditions and fitting parameters; the equilibrium reaction constant in the chemical reaction field is corrected based on the high-temperature water environment; and the particle diffusion coefficient in the material transport field is corrected based on the high-temperature water environment. S3. Rotate the simulated pitting morphology to generate a three-dimensional pitting model.

2. The method for predicting pitting damage of aluminum alloy in a high-temperature water environment according to claim 1, characterized in that: The electrochemical reaction field is constructed by the following steps, specifically including: S211. Measure the open circuit potential, AC impedance, and potentiodynamic polarization curve of aluminum alloy in a high-temperature solution environment; S212, calculating the solution resistance based on the AC impedance , using the solution resistance Correcting the potentiodynamic polarization curve to generate a post-IR drop polarization curve; S213 , estimating electrode kinetic parameters based on the post-IR drop polarization curve, and calculating and obtaining boundary condition assignments of the electrochemical reaction field.

3. The method for predicting pitting damage of aluminum alloy in a high-temperature water environment according to claim 2, wherein: The solution resistance is calculated based on the AC impedance , using the solution resistance The potentiodynamic polarization curve is corrected to generate a post-IR drop polarization curve, specifically comprising: S2121, calculating the solution resistance based on the AC impedance , get the test current , using the solution resistance And the test current , calculate the pressure drop ,include: ; S2122, calculating the potentiodynamic polarization curve and the voltage drop The difference between them is used to generate the IR drop polarization curve.

4. The method for predicting pitting damage of aluminum alloy in a high-temperature water environment according to claim 3, characterized in that: The electrode kinetic parameter estimation based on the post-IR drop polarization curve and the calculation of the boundary condition assignment of the electrochemical reaction field specifically include: S2131. Constructing the electrode kinetic model mechanism function, including: ； in, is the corrosion current density, is the passivation current density, is the corrosion potential, is the anodic Tafel slope, is the cathode Tafel slope, is the electrode potential; S2132. Calculating a set of optimal electrode kinetic parameters using a least squares method as boundary condition assignments for the electrochemical reaction field, including: ； ； in, is the predicted value of the electrode kinetic model, represents the set of electrode kinetic parameters, represents the current density value of the experimental polarization curve.

5. The method for predicting pitting damage of aluminum alloy in a high-temperature water environment according to claim 1, characterized in that: The particle diffusion coefficient is corrected based on the following steps, specifically including: S221, obtaining the particle diffusion coefficient equation, specifically including: ; in, is the particle diffusion coefficient, is the Boltzmann constant, is the target temperature for the reaction, is the solvent viscosity, is the radius of the diffusing particle; S222. Determine solvent viscosity and temperature The relationships between ； S223. Determine the expression for the particle diffusion coefficient at any temperature, specifically: ； in, is the standard temperature, is the particle diffusion coefficient at standard temperature, is the solvent viscosity at standard temperature.

6. The method for predicting pitting damage of aluminum alloy in a high-temperature water environment according to claim 1, characterized in that: The equilibrium reaction constant is corrected based on the following steps, specifically including: S231, obtaining the particle diffusion coefficient equation: ; in, Indicates absolute temperature; represents the gas constant; represents the standard Gibbs free energy change; S232, confirm and absolute temperature The relationship between them is specifically: ; in, represents the standard reaction enthalpy variable; represents the standard entropy variable; S233, based on the particle diffusion coefficient equation and the standard Gibbs free energy change, determine the The expression of the particle diffusion coefficient equation under the conditions includes: ; in, Indicates standard temperature, specifically 298.15K; Indicates the target temperature at which the reaction occurs.

7. The method for predicting pitting damage of aluminum alloy in a high-temperature water environment according to claim 1, characterized in that: The high-temperature composite field model further includes an interface evolution field, which describes the position change of the metal interface of the initial pitting morphology based on the arbitrary Lagrange-Euler method.

8. The method for predicting pitting damage of aluminum alloy in a high-temperature water environment according to claim 1, wherein: The high-temperature composite field model further includes a corrosion deposition field, which describes the position change of the metal interface of the initial pitting morphology based on the level set method.

9. A device for predicting pitting damage of aluminum alloy in high-temperature water environment, characterized in that: The device for predicting pitting damage of aluminum alloy in a high-temperature water environment comprises: A construction unit is used to construct an initial pitting corrosion morphology based on pitting corrosion parameters; the initial pitting corrosion morphology is specifically a two-dimensional morphology; A simulation unit is configured to apply boundary conditions to the initial pitting morphology based on a high-temperature composite field model, divide the mesh, and solve the problem using a transient solver to generate a simulated pitting morphology; the simulated pitting morphology is specifically a two-dimensional morphology; wherein the high-temperature composite field model includes at least: an electrochemical reaction field, a chemical reaction field, and a material transport field; The electrochemical reaction field is generated by measuring polarization curves under high-temperature liquid conditions and fitting parameters; the equilibrium reaction constant in the chemical reaction field is corrected based on the high-temperature water environment; and the particle diffusion coefficient in the material transport field is corrected based on the high-temperature water environment. The rotation unit is used to rotate the simulated pitting morphology to generate a three-dimensional pitting model.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method for predicting pitting corrosion damage of aluminum alloy in a high-temperature water environment as described in any one of claims 1 to 7 is implemented.

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