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

Abstract

The application provides a pitting corrosion damage prediction method and related equipment for aluminum alloy under a high-temperature water environment, and the method constructs a multi-physical field coupled pitting corrosion damage simulation model, adopts two-dimensional axisymmetric design, can be expanded to three dimensions in rotation, and integrates the influence of an electrochemical field, a chemical reaction field, a material transport field, and interface evolution and corrosion deposition. The model introduces a Stokes-Einstein equation to correct an ion diffusion coefficient, a Van't Hoff equation based on a constant enthalpy Delta H 0 to correct a balance reaction constant, uses an electrochemical workstation to measure an aluminum alloy polarization curve to obtain accurate model electrochemical boundary conditions, and realizes dynamic prediction of aluminum alloy pitting corrosion expansion under a high-temperature water environment through a progressive process of electrochemical experiment-parameter correction-multi-field coupled simulation. The application can accurately simulate the pitting corrosion damage behavior of aluminum alloy in a high-temperature water environment, provides a scientific basis for corrosion protection, is suitable for pitting corrosion prevention design of high-temperature equipment in the field of nuclear energy and the like, helps to optimize material selection, and has important engineering application value.

Description

Technical Field

[0001] This invention relates to the field of corrosion simulation technology, and more specifically, to a method and related equipment for predicting pitting damage of aluminum alloys in high-temperature water environments. Background Technology

[0002] Aluminum alloys, as lightweight, high-strength, and corrosion-resistant materials, have demonstrated broad application prospects and enormous development potential in numerous fields such as aviation, aerospace, transportation, construction, and electronics since their invention, particularly in key components of nuclear reactors. However, under the operating conditions of a reactor, aluminum alloys are prone to pitting corrosion, posing a severe challenge to the safety and reliability of critical nuclear reactor components.

[0003] Traditional methods for studying pitting corrosion primarily rely on experimental techniques. While these methods provide intuitive corrosion results, they struggle to reveal the microscopic mechanisms underlying the corrosion process. Furthermore, experimental methods are typically time-consuming, costly, and unable to simulate complex operating conditions. Multiphysics simulation has emerged as a promising approach for studying pitting corrosion. Compared to traditional experimental methods, multiphysics simulation can simulate the entire pitting corrosion process in a shorter time, significantly shortening the research cycle. Moreover, multiphysics simulation provides rich visualization results, including potential distribution, concentration distribution, and morphological evolution. These results help to intuitively understand the complex phenomena of pitting corrosion, thereby revealing its underlying mechanisms more deeply and ultimately leading to corresponding preventative and control measures for reducing pitting corrosion in aluminum alloys in nuclear power environments. Therefore, establishing a scientifically sound long-term pitting corrosion model for aluminum alloys is of paramount importance for a deeper understanding of the occurrence mechanism, development process, and prediction of its behavior under different environmental conditions.

[0004] However, a comprehensive study of existing pitting corrosion models reveals that most traditional methods only simulate pitting corrosion in aqueous solutions at room temperature. The acquisition of electrochemical kinetic parameters is limited to theoretical values, and the influence of temperature on electrochemistry, particle diffusion, and chemical reaction constants cannot be considered, thus limiting the application of aluminum alloy pitting corrosion model simulation to high-temperature environments. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and related equipment for predicting pitting damage of aluminum alloys in high-temperature water environment, so as to overcome the above-mentioned disadvantages.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: Firstly, a method for predicting pitting damage in aluminum alloys under high-temperature water conditions, comprising:

[0007] S1. Construct an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional morphology.

[0008] S2. Apply boundary conditions to the initial pitting morphology based on the high-temperature composite field model, and solve it using a transient solver after meshing 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 mass transport field;

[0009] The electrochemical reaction field is generated by measuring polarization curves and fitting parameters under high-temperature liquid conditions; the equilibrium reaction constant in the chemical reaction field is corrected based on a high-temperature water environment; the particle diffusion coefficient in the mass transport field is corrected based on a high-temperature water environment.

[0010] S3. Rotate the simulated pitting morphology to generate a three-dimensional pitting model.

[0011] In one embodiment, the electrochemical reaction field is constructed using the following steps:

[0012] S211. Measure the open circuit potential, AC impedance, and potentiodynamic polarization curve of aluminum alloy in a high-temperature solution environment;

[0013] S212. Calculate the solution resistance based on the AC impedance. Utilizing the solution resistance The potentiodynamic polarization curve is corrected to generate an IR-decreased polarization curve;

[0014] S213. Based on the polarization curve after IR drop, the electrode kinetic parameters are estimated, and the boundary condition values ​​of the electrochemical reaction field are calculated.

[0015] In one embodiment, the solution resistance is calculated based on the AC impedance. Utilizing the solution resistance The potentiodynamic polarization curve is corrected to generate an IR-decreased polarization curve, specifically including:

[0016] S2121. Calculate the solution resistance based on the AC impedance. Obtain the test current Utilizing the solution resistance and the test current Calculate the pressure drop ,include: ;

[0017] S2122, Calculate the potentiodynamic polarization curve and the voltage drop. The difference between them is used to generate the IR-decreased polarization curve.

[0018] In one embodiment, the step of estimating electrode kinetic parameters based on the polarization curve after IR drop and calculating the boundary condition assignments for the electrochemical reaction field specifically includes:

[0019] S2131. Construct the electrode dynamics model mechanism function, including:

[0020] ;

[0021] in, For corrosion current density, To passivate current density, For corrosion potential, For the anode Tafel slope, For the cathode Tafel slope, Electrode potential;

[0022] S2132. Calculate a set of optimal electrode kinetic parameters using the least squares method, and assign them as boundary conditions for the electrochemical reaction field, including:

[0023] ;

[0024] ;

[0025] in, The predicted value is from the electrode dynamics model. Represents the set of electrode dynamic parameters. This represents the current density value of the experimental polarization curve.

[0026] In one embodiment, the particle diffusion coefficient in the matter transport field is corrected based on the following steps:

[0027] S221. Obtain the particle diffusion coefficient equation under standard temperature conditions, specifically including: ;in, It is the particle diffusion coefficient at standard temperature. It is Boltzmann's constant. This is the standard temperature, specifically 298.15K. It is the solvent viscosity at standard temperature. It is the radius of the diffusing particle;

[0028] S222, Determine solvent viscosity With target temperature The relationships between them include: ;

[0029] S223. Determine the expression for the particle diffusion coefficient at any temperature, specifically:

[0030] ;

[0031] in, The target temperature at which the reaction occurs.

[0032] In one embodiment, the equilibrium reaction constant in the chemical reaction field is corrected based on the following steps:

[0033] S231. Equation for obtaining the equilibrium reaction constant under standard temperature conditions: ;in, Indicates standard temperature; Represents the gas constant; Indicates the standard Gibbs free energy change;

[0034] S232, Determine With target temperature The relationship between them is as follows: ;in, This represents the standard enthalpy change of the reaction; Represents the standard entropy variable;

[0035] S233. Based on the equation for the equilibrium reaction constant and the standard Gibbs free energy change, determine the target temperature. The equations expressing the equilibrium reaction constant under the given conditions include: ;in, This indicates the standard temperature, specifically 298.15K; Indicates the target temperature at which the reaction occurs.

[0036] In one embodiment, the high-temperature composite field model further includes an interface evolution field, which describes the positional changes of the metal interface of the initial pitting morphology based on an arbitrary Lagrange-Euler method.

[0037] In one embodiment, the high-temperature composite field model further includes a corrosion deposition field, which describes the positional changes of the metal interface of the initial pitting morphology based on the level set method.

[0038] A device for predicting pitting damage in aluminum alloys under high-temperature water conditions includes:

[0039] A construction unit is used to construct an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional morphology.

[0040] The simulation unit is used to apply boundary conditions to the initial pitting morphology based on the high-temperature composite field model, and solve it using a transient solver after meshing 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 mass transport field;

[0041] The electrochemical reaction field is generated by measuring polarization curves and fitting parameters under high-temperature liquid conditions; the equilibrium reaction constant in the chemical reaction field is corrected based on a high-temperature water environment; the particle diffusion coefficient in the mass transport field is corrected based on a high-temperature water environment.

[0042] A rotation unit is used to rotate the simulated pitting morphology to generate a three-dimensional pitting model.

[0043] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0044] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0045] In summary, the present invention has the following beneficial effects: The present invention provides a method for predicting pitting corrosion damage of aluminum alloys in high hydrological environments. Using the method of the present invention, multiple physical fields are first modified based on the high-temperature water environment, and then the multiple physical fields are coupled together and applied to the initial pitting morphology to simulate the initial pitting morphology. This method can accurately obtain the corrosion process of pitting defects in the high-temperature water environment. Furthermore, the present application utilizes the rotation of two-dimensional pitting morphology to generate a three-dimensional pitting model, which can effectively reduce the computational burden of simulation and improve the simulation calculation speed. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method for predicting pitting damage of aluminum alloys in a high-temperature water environment according to the present invention.

[0047] Figure 2 This is a structural diagram of the aluminum alloy pitting damage prediction device under high temperature water environment according to the present invention.

[0048] Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the anode-cathode relationship in an aluminum alloy pitting corrosion embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of the model geometry and mesh division in a specific embodiment of the present invention;

[0051] Figure 6 This is a fitting diagram of the aluminum alloy polarization curve and electrode kinetic model in Embodiment 5 of the present invention;

[0052] Figure 7 This is a schematic diagram of the particle types and equilibrium reactions included in the model in Embodiment 5 of the present invention;

[0053] Figure 8 This is a simulation result of the pH change over time at the bottom of the model erosion pit in Example 5 of the present invention;

[0054] Figure 9 This is a simulation result diagram showing the change in corrosion depth of the pit opening and bottom over time in Example 5 of the present invention;

[0055] Figure 10 In Example 5 of this invention, the corrosion depth of the pit opening and bottom varies with t. 1 / 2 Simulation results of the changes;

[0056] Figure 11 Rotation of the two-dimensional axisymmetric model in Embodiment 5 of the present invention Initial corrosion morphology image after expansion into a 3D model;

[0057] Figure 12 Rotation of the two-dimensional axisymmetric model in Embodiment 5 of the present invention Simulated pitting morphology after expansion into a 3D model;

[0058] Figure 13 This is a flowchart of the method steps of the present invention.

[0059] In the diagram: 1. Building element; 2. Simulation element; 3. Rotation element. Detailed Implementation

[0060] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0061] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0062] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented 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 this application.

[0063] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0064] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

[0066] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0067] Example 1

[0068] To address the aforementioned problems, this invention provides a method for predicting pitting corrosion damage in aluminum alloys under high-temperature water conditions. Taking aluminum alloy as an example, the environment is 50℃-150℃, with normal water chemistry conditions (ultrapure water) / abnormal water chemistry conditions (containing Cl-, Fe3+). Simulation calculations are performed using COMSOL multiphysics simulation software to study the corrosion relationship between a single pit and the surrounding aluminum alloy, and the oxidation reaction of aluminum occurring within the pit. ), while an oxygen reduction reaction occurs on the surrounding aluminum alloy surface ( The diagram illustrates the relationship between the anode and cathode in aluminum alloy pitting corrosion, which drives the pitting process. Figure 4 As shown.

[0069] This invention focuses on predicting pitting corrosion damage in aluminum alloys under high-temperature water conditions. First, it's necessary to identify the factors influencing the pitting corrosion process in high-temperature water environments. Then, these factors are modified based on the high-temperature water environment. Second, a high-temperature composite field model comprehensively considers the coupling relationships of multiple factors, including electrochemical reactions, chemical reactions, mass transport, interface evolution, and corrosion precipitation. It introduces anodic dissolution, cathodic oxygen reduction, aluminum ion hydrolysis, and their composite reaction with chloride ions. The ion transport process is described based on the Nernst-Planck equation, and the influence of corrosion product deposition on the electrode surface and its feedback effect are dynamically simulated using ALE and level set methods, thus comprehensively reflecting the pitting corrosion damage mechanism of aluminum alloys under complex working conditions. Finally, the pitting corrosion model is simplified by using a two-dimensional axisymmetric design to construct the model. This simplifies computational complexity while retaining core features, and the two-dimensional model can be rotated to generate a three-dimensional model. Ultimately, a pitting corrosion model prediction result combining multi-field coupling and located in a high-temperature water environment is obtained.

[0070] Based on the above, the steps for predicting a defect in an aluminum alloy battery can be summarized as follows:

[0071] S1. Constructing an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional form; wherein, the formation of the initial pitting defect is a local corrosion initiation point caused by local micro-inhomogeneity, environmental factors or physical damage in the early service or manufacturing process of the metallic material; the initial pitting 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 on the computing device, enabling the device to maintain high-precision calculation for a long time;

[0072] S2. Boundary conditions are applied to the initial pitting morphology based on the high-temperature composite field model, and after meshing, a transient solver is used to solve the model, generating a simulated pitting morphology. The simulated pitting morphology is specifically a two-dimensional form. The high-temperature composite field model includes at least an electrochemical reaction field, a chemical reaction field, and a mass transport field. Specifically, the fields most significantly 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 transport field. Therefore, these three fields need to be corrected for the high-temperature water environment to ensure the model accurately reflects the differences in the mechanism of pitting behavior changing with temperature. In this embodiment, the above three field models are first corrected for high temperature: the electrochemical reaction field is generated by measuring polarization curves and fitting parameters under high-temperature liquid conditions; 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 mass transport field is corrected based on the high-temperature water environment.

[0073] S3. Rotate the simulated pitting morphology to generate a three-dimensional pitting model.

[0074] The steps for correcting the electrochemical reaction field include:

[0075] Experimental preparation: Cut aluminum alloy, grind to 1000#-2000# sandpaper, ultrasonically clean with alcohol for 5-20 minutes, exposing an area of ​​1cm. 2 Ultrapure water / containing Cl - / Contains Fe 3+ The solution was placed in a high-temperature environment, and the pH value was adjusted to 4-7 using NaOH / HCl. The three-electrode system included: (1) working electrode: aluminum alloy; (2) reference electrode: Ag / AgCl (saturated KCl); (3) auxiliary electrode: platinum sheet. The reactor was in a high-temperature environment with precise temperature control. Electrochemical workstation, model: Gamry Reference 600+.

[0076] S211. Measure the open circuit potential, AC impedance, and potentiodynamic polarization curve of aluminum alloy in a high-temperature solution environment; wherein, the open circuit potential is 30min-60min, the AC impedance starts at a frequency of 10000Hz-20000Hz and ends at a frequency of 100Hz-1000Hz, the potentiodynamic polarization starts at a potential 200mV-600mV below the open circuit potential and ends at a potential 200mV-600mV above the open circuit potential, and the scan speed is 0.166mV / s-1 mV / s.

[0077] S212. Calculate the solution resistance based on the AC impedance. Utilizing the solution resistance The potentiodynamic polarization curve is corrected to generate an IR-decreased polarization curve, specifically including:

[0078] S2121. Calculate 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. Besides the potential change caused by the electrode reaction itself, voltage loss occurs due to the resistance of the solution as the current flows, leading to inaccurate data. In other words, the measured data includes the IR drop caused by the electrode reaction potential plus the solution resistance. Therefore, the latter needs to be subtracted to accurately reflect the electrode's true polarization behavior. In electrochemical impedance spectroscopy (EIS), the solution resistance... The impedance spectrum is obtained by fitting the impedance values ​​in the high-frequency range. In electrochemical testing, a small sinusoidal AC voltage perturbation (frequency range from mHz to MHz) is applied to the electrode system, and the response current is recorded to obtain the system's impedance spectrum. At high frequencies, the interface capacitance is approximately short-circuited, and reaction polarization and diffusion resistance are not yet apparent; 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 range) of the impedance spectrum and the horizontal axis represents the solution resistance. .

[0079] Obtain the solution resistance Then, obtain the test current. Utilizing the solution resistance and the test current Calculate the pressure drop ,include: ;

[0080] S2122, Calculate the potentiodynamic polarization curve and the voltage drop. The difference between them is used to generate the IR-decreased polarization curve;

[0081] S213. Based on the polarization curve after IR drop, estimate the electrode kinetic parameters and calculate the boundary condition assignments for the electrochemical reaction field, specifically including:

[0082] S2131: Constructing the mechanism function of the electrode dynamics model, including:

[0083] ;

[0084] in, For corrosion current density, To passivate current density, For corrosion potential, For the anode Tafel slope, For the cathode Tafel slope, This refers to the electrode potential; among the parameters mentioned above, , , , , All five parameters are key parameters describing the electrochemical boundary conditions of the model. These key parameters are denoted as the set of electrode kinetic parameters. .

[0085] S2133. Calculate a set of optimal electrode kinetic parameters using the least squares method, and assign them as boundary conditions for the electrochemical reaction field, including:

[0086] ;

[0087] ;

[0088] in, The electrode dynamics model is based on the electrode potential of... The corresponding predicted value at that time Represents the set of electrode dynamic parameters. This represents the current density value of the experimental polarization curve.

[0089] Least squares is a model fitting method that finds optimal parameters by minimizing the sum of squared errors between predicted and actual experimental values. Because electrode kinetics models are nonlinear functions, direct differentiation is difficult, and there may be multiple minima. Therefore, algorithms can be used to solve for the parameters and substitute them into the model's electrochemical boundary. These algorithms include, but are not limited to, genetic algorithms, particle swarm optimization, gray wolf algorithms, and Levenberg-Marquardt algorithms.

[0090] In one embodiment, the particle diffusion coefficient in the matter transport field is corrected based on the following steps:

[0091] S221. Obtain the particle diffusion coefficient equation under standard temperature conditions, specifically including: ;in, It is the particle diffusion coefficient at standard temperature. It is Boltzmann's constant. This is the standard temperature, specifically 298.15K. It is the solvent viscosity at standard temperature. It is the radius of the diffusing particle;

[0092] S222, Determine solvent viscosity With target temperature The relationships between them include: ;

[0093] S223. Determine the expression for the particle diffusion coefficient at any temperature, specifically:

[0094] ;

[0095] in, The target temperature at which the reaction occurs.

[0096] In one embodiment, the equilibrium reaction constant is corrected based on the following steps:

[0097] The equilibrium reaction constant in the chemical reaction field is corrected based on the following steps, specifically including:

[0098] S231. Equation for obtaining the equilibrium reaction constant under standard temperature conditions: ;in, Indicates standard temperature; Represents the gas constant; Indicates the standard Gibbs free energy change;

[0099] S232, Determine With target temperature The relationship between them is as follows: ;in, This represents the standard enthalpy change of the reaction; Represents the standard entropy variable;

[0100] S233. Based on the equation for the equilibrium reaction constant and the standard Gibbs free energy change, determine the target temperature. The equations expressing the equilibrium reaction constant under the given conditions include: ;in, This indicates the standard temperature, specifically 298.15K; Indicates the target temperature at which the reaction occurs.

[0101] In one embodiment, such as Figure 5 As shown, the initial pitting geometry is designed with a radius of r0 = 10-1000 μm (simulating early pitting), a depth of h0 = 10-1000 μm, and axial symmetry (z-axis is the axis of symmetry). The electrolyte region has a length of 1-10 mm and a height of 0.5-10 mm.

[0102] In one embodiment, the high-temperature composite field model further includes an interface evolution field, which, based on the arbitrary Lagrange-Euler method, describes the positional changes of the metal interface of the initial pitting morphology. The interface evolution field describes the process by which the metal interface on the surface of the aluminum alloy pitting gradually dissolves, retreats, and deforms over time as the corrosion reaction proceeds; that is, the positional changes, morphological evolution, and dynamic updates of the corrosion interface. The arbitrary Lagrange-Euler method (ALE) allows the mesh to be neither completely bound to the object (Lagrange) nor completely fixed in space (Euler), but rather the mesh can move with the corrosion interface, ensuring that the mesh is not severely distorted and achieving a balanced migration between the deformation of the corrosion interface and the simulation calculation mesh.

[0103] In one embodiment, the high-temperature composite field model further includes a corrosion deposition field, which, based on the level set method, describes the positional changes of the metal interface at the initial pitting morphology. The "corrosion deposition field" describes how corrosion products on the electrode surface or within the pitting pits deposit, grow, expand, and accumulate at the interface as the corrosion reaction proceeds, and the changes in their morphology, location, and thickness over time. The corrosion deposition process is typically modeled using the level set method: level set function... This indicates the interface between corrosion products and the electrolyte; This indicates the location of the sedimentary interface; this interface expands outward over time according to the reaction rate (deposition rate).

[0104] In the simulation of pitting corrosion, the five physical fields mentioned above—electrochemical reaction field, chemical reaction field, mass transport field, interface evolution field, and corrosion deposition field—need to be coupled to form a high-temperature composite field model. This high-temperature composite field model is then applied as a boundary condition to the initial pitting corrosion morphology, and the initial pitting corrosion morphology is iteratively processed. The specific steps are as follows:

[0105] S401: The physics field settings are set to secondary current distribution, rare matter transport, deformable geometry and horizontal collection physics field modules.

[0106] S402: The model electrochemical reaction specifically includes anodic and cathodic reactions, involving the anodic reaction: Cathode reaction: A schematic diagram of the anode-cathode relationship in pitting corrosion of aluminum alloy is shown below. Figure 4 As shown;

[0107] S403: Model chemical reaction. When aluminum alloys corrode, elements in the metal dissolve into the electrolyte solution as cations, undergoing hydrolysis and homogeneous reactions with water and anions in the solution. The concentration distribution of ions in the electrolyte solution is affected by the coupling effect of hydrolysis and mass transfer processes. For example, the following reaction will be considered in the model constructed for abnormal water conditions containing Cl-:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] S404: Model governing equations. For all substances in the solution ( , , , , (etc.) There is a law of conservation of matter; every substance... The concentration change can be described by the following equation:

[0115]

[0116] in For matter concentration, The flux of matter. This refers to the source of matter.

[0117] The solution must simultaneously satisfy charge conservation and electroneutrality:

[0118]

[0119]

[0120] Homogeneous reactions occur between ions entering the electrolyte, thereby changing the concentration of the substance. The reaction rate is expressed as... express:

[0121]

[0122] In the formula and These represent the forward and reverse reaction rate constants, respectively. Indicates the forward reaction order; It represents the reverse reaction order.

[0123] The equilibrium reaction constant Keq is used in this model to measure homogeneous reactions:

[0124]

[0125] S405: Model Mass Transport. The mass 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 neglects the interactions between ions and is relatively accurate under dilute solution conditions. The expression is as follows:

[0126]

[0127] in For matter The number of charges, For matter The mobility, where F is the Faraday constant. Potential, The solution flow rate is denoted as .

[0128] For matter flow rate This can be given by the Nernst-Einstein equations:

[0129]

[0130] Due to the unique size of pitting corrosion, the convection term can be ignored in the model.

[0131] S406: Model boundary conditions. Model boundary conditions are divided into mass transport boundary conditions and electrochemical boundary conditions.

[0132] (a) Mass transport boundary conditions: At the electrode interface, electrochemical reactions will lead to the production or consumption of matter. For species involved in the electrochemical reaction... :

[0133]

[0134] in The types of electrode reactions, The number of electrons transferred in the electrode reaction. It is the reaction coefficient. It is the current density of the electrode reaction.

[0135] For species i that do not participate in electrochemical reactions:

[0136]

[0137] At the top boundary, the substance concentration equals the volume concentration:

[0138]

[0139] For other boundary conditions where there is no ion flow inflow or outflow, it is a flux-free boundary condition:

[0140]

[0141] (b) Electrochemical boundary conditions:

[0142] At the anode interface, the conductivity of the electrolyte solution... Electrochemical reaction current density at the anode interface The relationship between them satisfies the following formula:

[0143]

[0144] in, This represents the current density of the electrochemical reaction at the anode interface; The exchange current density at the anode; This represents the corrosion current density. The Tafel slope of the anode; This is the solid-state potential; This is the liquid phase potential; This is the equilibrium potential of the anode; Electrode potential; This refers to the corrosion potential. In embodiments of the present invention, no additional potential, i.e., solid-state potential, is applied to the metal. Zero, electrode potential Liquid phase potential The opposite of the value. The conductivity of an electrolyte solution. Calculate according to the following formula

[0145] electrical conductivity The following formula can be used for calculation.

[0146]

[0147] At the cathode interface, the conductivity of the electrolyte solution... Electrochemical reaction current density at the cathode interface The relationship between them satisfies the following formula:

[0148]

[0149] in, This represents the current density of the electrochemical reaction at the cathode interface; The exchange current density of the cathode; is the Tafel slope of the cathode.

[0150] At the insulating boundary, the normal potential gradient of the electrolyte solution is zero, satisfying the following formula:

[0151]

[0152] S407: Model Interface Evolution. In this model, the Arbitrary Lagrange-Eulerian (ALE) method is introduced to analyze the interface evolution between metal dissolution and the deposited layer. The mesh displacement can be obtained by solving the following formula:

[0153]

[0154]

[0155] The movement of the corrosion boundary can be represented as:

[0156]

[0157] In the formula, The normal deformation rate is caused by metal dissolution and deposition layer growth. The first term on the right side of the equation represents the rate of movement of the crevice wall caused by metal dissolution (calculated according to Faraday's law), and the second term is the rate of movement of the crevice wall caused by corrosion product deposition.

[0158] in:

[0159]

[0160] In the formula, It is the porosity of the sedimentary layer. yes The generation rate.

[0161] When the concentrations of Al3+ and OH- exceed the solubility limit, Sediments begin to form:

[0162]

[0163] Precipitation reaction The generation rate is calculated using the following formula:

[0164]

[0165] in for The rate constant of the precipitation reaction, yes The solubility product constant. yes Supersaturation is defined as:

[0166]

[0167] It is a step function, expressed as:

[0168]

[0169] Assume there is no displacement at the solution boundary:

[0170]

[0171] S408: Effect of corrosion and precipitation. During pitting corrosion... Sediment formation is a time-dependent process, and the porosity of sediments varies with time and space. The parameter ε describes the sediment porosity, which can be calculated using the following formula, indicating that the precipitation of corrosion products is related to the decrease in porosity:

[0172]

[0173] The corrosion process is accompanied by the formation of precipitates. The formation of precipitates leads to the appearance of new phases in the solution, significantly hindering mass transfer. Therefore, the diffusion coefficient needs to be modified to account for this effect. The Brugmann relation with a coefficient of 1.5 is commonly used to describe porous layers under the influence of porosity and tortuosity. Therefore, the diffusion coefficient of matter in porous membranes... It can be estimated using the following equation:

[0174] At the same time, the effective conductivity of the electrolyte through the porous membrane also needs to be corrected:

[0175]

[0176] At the same time, the effective conductivity of the electrolyte through the porous membrane also needs to be corrected:

[0177]

[0178] The level set method is used to describe the mass transport properties of the electrolyte region and the precipitation region, respectively. The effects of corrosion product deposition on the electrode surface and its feedback on the corrosion process are dynamically simulated, as well as the formation of the corrosion product deposition layer.

[0179] S409: Meshing. The 2D model uses a free triangular mesh, with finer meshing on the pitting surface (minimum size 1μm) and a gradient meshing in the electrolyte region (maximum size 100μm), for a total of approximately 50,000 elements. The 3D model is expanded by rotating the 2D model around the z-axis to generate a tetrahedral mesh (approximately 200,000 elements), with a focus on finer meshing the pit openings and walls (minimum size 2μm).

[0180] S4010: Transient study. Transient time 0-5000h, adaptive step size, Newton iterations ≤15 times, convergence residual < Laplacian smoothing is triggered every 100 time steps (or when mesh distortion > 25%) to ensure that the minimum angle of the pit wall elements is > 15°.

[0181] S4011: Visualized data. Pitting pit morphology evolution (2D / 3D plots), pit bottom pH change over time, pitting growth rate, corrosion current density change curve, and product deposition thickness.

[0182] This implementation method achieves accurate prediction of pitting corrosion in aluminum alloys at high temperatures (50-150℃) through a progressive process of electrochemical experiments, parameter correction, and multi-field coupled simulation.

[0183] Example 2

[0184] Please see Figure 2 A device for predicting pitting damage of aluminum alloys in a high-temperature water environment, the device comprising:

[0185] Construction unit 1 is used to construct an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional morphology;

[0186] Simulation unit 2 is used to apply boundary conditions to the initial pitting morphology based on the high-temperature composite field model, and solve it using a transient solver after meshing 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 mass transport field;

[0187] The electrochemical reaction field is generated by measuring polarization curves and fitting parameters under high-temperature liquid conditions; the equilibrium reaction constant in the chemical reaction field is corrected based on a high-temperature water environment; the particle diffusion coefficient in the mass transport field is corrected based on a high-temperature water environment.

[0188] Rotation unit 3 is used to rotate the simulated pitting morphology to generate a three-dimensional pitting model.

[0189] Specific limitations regarding the aluminum alloy pitting damage prediction device under high-temperature water conditions can be found in the limitations of the aluminum alloy pitting damage prediction method under high-temperature water conditions mentioned above, and will not be repeated here. Each module in the aforementioned aluminum alloy pitting damage prediction device under high-temperature water conditions can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independently of the processor, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0190] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the present application. The specific aluminum alloy pitting damage prediction device under high temperature water environment may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0191] Example 3

[0192] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting pitting damage of aluminum alloys in a high-temperature water environment as described in Example 1.

[0193] Example 4

[0194] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. When executed by the processor, the computer program implements a method for predicting pitting corrosion damage in aluminum alloys under high-temperature water conditions.

[0195] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0196] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: including:

[0197] S1. Construct an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional morphology.

[0198] S2. Apply boundary conditions to the initial pitting morphology based on the high-temperature composite field model, and solve it using a transient solver after meshing 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 mass transport field;

[0199] The electrochemical reaction field is generated by measuring polarization curves and fitting parameters under high-temperature liquid conditions; the equilibrium reaction constant in the chemical reaction field is corrected based on a high-temperature water environment; the particle diffusion coefficient in the mass transport field is corrected based on a high-temperature water environment.

[0200] S3. Rotate the simulated pitting morphology to generate a three-dimensional pitting model.

[0201] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this 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. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0202] Example 5

[0203] Furthermore, this application also provides a practical simulation example of pitting damage.

[0204] (1) Using an electrochemical workstation, the open-circuit potential, AC impedance, and potentiodynamic polarization curves were measured sequentially. The open-circuit potential was measured over 30 minutes; the AC impedance was measured with an initial frequency of 10000 Hz and an ending frequency of 100 Hz; the potentiodynamic polarization was measured with an initial potential 400 mV below the open-circuit potential and an ending potential 400 mV above the open-circuit potential, at a scan rate of 0.166 mV / s. The measured solution resistance Ru = 18. The fitting results of the polarization curves of aluminum alloy after IR drop and the electrode kinetic model obtained using the Levenberg-Marquardt algorithm are shown in [reference needed]. Figure 6 Excellent fitting effect, R 2 =0.9984.

[0205] The electrode kinetic parameters required for the electrochemical boundary in the simulation model are as follows:

[0206] , , , , .

[0207] The expression for the current density at the anode interface is:

[0208]

[0209] Expression for cathode interface current density:

[0210]

[0211] (2) Parameter correction under high temperature environment

[0212] The types of particles and equilibrium reactions involved in the simulation of this model are shown in [link to simulation]. Figure 7 .

[0213] Correcting the ion diffusion coefficient using the Stokes-Einstein equation:

[0214]

[0215] Based on constant enthalpy The Van't Hoff equation corrects the equilibrium reaction constant:

[0216]

[0217] See Table 1 and Table 2 for specific data.

[0218]

[0219] Table 1. Diffusion coefficients of different species

[0220]

[0221] Table 2. Expression for Equilibrium Reaction Constant

[0222] (3) Multiphysics coupling

[0223] Secondary current distribution, rare matter transport, deformable geometry, and horizontal ensemble physical field modules are added to the geometric model. The expressions for the anode interface current density and the cathode interface current density are substituted into the electrochemical boundary conditions inside and outside the pitting pit in the model, respectively. The rare matter transport field is then set... Eleven particles and their corresponding initial concentrations and diffusion coefficients, six equilibrium reactions and their corresponding equilibrium reaction constants were used. An open boundary was set at the electrolyte region boundary, with the boundary concentration equal to the initial concentration. Deformed geometry was applied at the anode boundary of the pitting corrosion pits to realize the pitting corrosion evolution. The Arbitrary Lagrange-Eulerian (ALE) method and the level set method were introduced to dynamically simulate the influence 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 was used, with finer meshes on the pitting pit surface and a gradient mesh in the electrolyte region. The transient time was 90 hours, with an adaptive step size, Newton iterations ≤ 15 times, and convergence residuals < Laplacian smoothing is triggered when mesh distortion exceeds 25%.

[0224] (4) Simulation results

[0225] Figure 8 This is a simulation result of the pH change at the bottom of the pit over time in this embodiment. The pH at the bottom of the pit decreases as the corrosion time increases. The pH drops rapidly in the first 10 days, and then decreases slowly, stabilizing at around 3.8.

[0226] Figure 9 , Figure 10 This figure shows the simulation results of the corrosion depth at the pit opening and bottom as a function of time in this embodiment. The corrosion depth increases with corrosion time, and the corrosion at the pit opening is more severe than that at the bottom. After time conversion, it can be concluded that the corrosion depth at the pit opening and bottom is proportional to t. 1 / 2 This is consistent with the experimental conclusions of most researchers regarding the pitting corrosion depth of aluminum alloys.

[0227] Figure 11 , Figure 12 Rotation of the two-dimensional axisymmetric model in this embodiment The corrosion morphology changes after being expanded into a 3D model. The initial morphology of pitting corrosion and the corrosion morphology after 90 days are shown respectively.

[0228] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting pitting corrosion damage of aluminum alloys in high-temperature water environments, applied to water environments of 50°C to 150°C, characterized by, include: S1. Construct the initial pitting morphology based on pitting parameters; The initial pitting morphology is specifically a two-dimensional morphology; S2. Apply boundary conditions to the initial pitting morphology based on the high-temperature composite field model, and solve it using a transient solver after meshing to generate a simulated pitting morphology; the simulated pitting morphology is specifically a two-dimensional morphology; wherein, the high-temperature composite field model includes: an electrochemical reaction field, a chemical reaction field, and a mass transport field; The electrochemical reaction field is constructed using 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. Calculate the solution resistance based on the AC impedance. Utilizing the solution resistance The potentiodynamic polarization curve is corrected to generate an IR-decreased polarization curve; S213. Based on the polarization curve after IR drop, the electrode dynamics parameters are estimated, and the boundary condition values ​​of the electrochemical reaction field are calculated. The particle diffusion coefficient in the matter transport field is corrected based on the following steps: S221. Obtain the particle diffusion coefficient equation under standard temperature conditions, specifically including: ;in, It is the particle diffusion coefficient at standard temperature. It is Boltzmann's constant. This is the standard temperature, specifically 298.15K. It is the solvent viscosity at standard temperature. It is the radius of the diffusing particle; S222, Determine solvent viscosity With target temperature The relationships between them include: ; S223. Determine the expression for the particle diffusion coefficient at any temperature, specifically: ； The equilibrium reaction constant in the chemical reaction field is corrected based on the following steps: S231. Equation for obtaining the equilibrium reaction constant under standard temperature conditions: ;in, Indicates standard temperature; Represents the gas constant; Indicates the standard Gibbs free energy change; This represents the equilibrium reaction constant at standard temperature; S232, Determine With target temperature The relationship between them is as follows: ;in, This represents the standard enthalpy change of the reaction; Represents the standard entropy variable; S233. Based on the equation for the equilibrium reaction constant under standard temperature conditions and the standard Gibbs free energy change, determine the equilibrium reaction constant at the target temperature. The equations expressing the equilibrium reaction constant under the given conditions include: ;in, This indicates the standard temperature, specifically 298.15K; S3. Rotate the simulated pitting morphology to generate a three-dimensional pitting model.

2. The method for predicting pitting damage of aluminum alloys in a high-temperature water environment according to claim 1, characterized in that, The solution resistance is calculated based on the AC impedance. Utilizing the solution resistance The potentiodynamic polarization curve is corrected to generate an IR-decreased polarization curve, specifically including: S2121. Calculate the solution resistance based on the AC impedance. Obtain the test current Utilizing the solution resistance and the test current Calculate the pressure drop ,include: ; S2122, Calculate the potentiodynamic polarization curve and the voltage drop. The difference between them is used to generate the IR-decreased polarization curve.

3. The method for predicting pitting damage of aluminum alloys in a high-temperature water environment according to claim 2, characterized in that, The step of estimating electrode kinetic parameters based on the polarization curve after IR drop and calculating the boundary condition assignments for the electrochemical reaction field specifically includes: S2131. Construct the electrode dynamics model mechanism function, including: ； in, For corrosion current density, To passivate current density, For corrosion potential, For the anode Tafel slope, For the cathode Tafel slope, Electrode potential; S2132. Calculate a set of optimal electrode kinetic parameters using the least squares method, and assign them as boundary conditions for the electrochemical reaction field, including: ； ； in, The predicted value is from the electrode dynamics model. Represents the set of electrode dynamic parameters. This represents the current density value of the experimental polarization curve.

4. The method for predicting pitting damage of aluminum alloys in a high-temperature water environment according to claim 1, characterized in that, The high-temperature composite field model also includes an interface evolution field, which describes the positional changes of the metal interface of the initial pitting morphology based on the arbitrary Lagrange-Euler method.

5. The method for predicting pitting damage of aluminum alloys in a high-temperature water environment according to claim 1, characterized in that, The high-temperature composite field model also includes a corrosion deposition field, which describes the positional changes of the metal interface of the initial pitting morphology based on the level set method.

6. A device for predicting pitting damage of aluminum alloys in high-temperature water environments, applicable to water environments of 50℃-150℃, characterized in that... The aluminum alloy pitting damage prediction device under high-temperature water environment includes: A construction unit is used to construct an initial pitting morphology based on pitting parameters; the initial pitting morphology is specifically a two-dimensional morphology. The simulation unit is used to apply boundary conditions to the initial pitting morphology based on the high-temperature composite field model, and solve it using a transient solver after meshing to generate a simulated pitting morphology; the simulated pitting morphology is specifically a two-dimensional morphology; wherein, the high-temperature composite field model includes: an electrochemical reaction field, a chemical reaction field, and a mass transport field; The electrochemical reaction field is constructed using the following steps, specifically including: Measure the open circuit potential, AC impedance, and potentiodynamic polarization curve of aluminum alloy in a high-temperature solution environment; Calculate the solution resistance based on the AC impedance. Utilizing the solution resistance The potentiodynamic polarization curve is corrected to generate an IR-decreased polarization curve; Based on the polarization curve after IR drop, the electrode kinetic parameters are estimated, and the boundary condition assignment of the electrochemical reaction field is calculated. The particle diffusion coefficient in the matter transport field is corrected based on the following steps: The equation for the particle diffusion coefficient under standard temperature conditions is obtained, specifically including: ;in, It is the particle diffusion coefficient at standard temperature. It is Boltzmann's constant. This is the standard temperature, specifically 298.15K. It is the solvent viscosity at standard temperature. It is the radius of the diffusing particle; Determine solvent viscosity With target temperature The relationships between them include: ; The expression for the particle diffusion coefficient at any temperature is determined as follows: ； The equilibrium reaction constant in the chemical reaction field is corrected based on the following steps: The equation for obtaining the equilibrium reaction constant under standard temperature conditions: ;in, Indicates standard temperature; Represents the gas constant; Indicates the standard Gibbs free energy change; Sure With target temperature The relationship between them is as follows: ;in, This represents the standard enthalpy change of the reaction; Represents the standard entropy variable; Based on the equation for the equilibrium reaction constant and the standard Gibbs free energy change, the target temperature is determined. The equations expressing the equilibrium reaction constant under the given conditions include: ;in, This indicates the standard temperature, specifically 298.15K; A rotation unit is used to rotate the simulated pitting morphology to generate a three-dimensional pitting model.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method for predicting pitting damage of aluminum alloys in a high-temperature water environment as described in any one of claims 1-5.

Images