Chloride concentration driven corrosion evolution probabilistic cellular automata simulation method

Abstract

The application discloses a kind of chloride ion concentration driven corrosion evolution probability type cellular automaton simulation method, it is related to metal corrosion evolution simulation field, including the following steps: S1: setting cell space size and cell size, construct three-dimensional cell space;S2: definition cell type and in three-dimensional cell space complete the initial distribution of each type cell;S3: set the reaction mechanism of model iteration process, to corrosion probability P a , passivation probability P b , passivation film rupture probability P d Determine the conversion rule between each type of cell;S4: introduce chloride ion concentration matrix and probability enhancement coefficient, attenuation coefficient, by the real-time concentration value of chloride ion to the cell conversion probability in iteration process is modified, realize the aggravation of local corrosion process;S5: compare the corrosion morphology results of test and simulation under the same mass loss rate.This application can effectively reduce the difference of local corrosion on the basis of general corrosion, provide support for steel structure durability evaluation.

Description

Technical Field

[0001] This invention relates to the field of metal corrosion evolution simulation, and specifically to a chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution. Background Technology

[0002] With the rapid advancement of modern industry, the corrosion problem of metallic materials is becoming increasingly prominent, especially in extreme environments such as marine, industrial settings, and urban atmospheres. Metal corrosion not only weakens the material's inherent properties but can also cause structural failure, ultimately leading to significant economic losses and safety hazards. While traditional experimental research can provide direct corrosion data, it is generally time-consuming and costly, and it struggles to fully reproduce the complete corrosion evolution of materials during long-term service. In recent years, cellular automata modeling technology has been gradually incorporated into the research scope of material performance evolution. However, many existing metal corrosion models rely on two-dimensional moving cellular automata for simulation, resulting in high computational costs. Furthermore, the simplified treatment of electrochemical reaction processes fails to fully demonstrate the essential differences between localized and generalized corrosion. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution, which is used to simulate the process of general corrosion and local aggravation in metal corrosion.

[0004] The technical solution adopted by this invention to solve its technical problem is: a chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution, comprising the following steps:

[0005] S1: Based on the volume of the simulated specimen, set the cell space size and cell dimensions to construct a three-dimensional cell space;

[0006] S2: The basic elements involved in the atmospheric corrosion process are simplified into several cell types and the initial distribution of each type of cell is completed in the three-dimensional cell space;

[0007] S3: Define the reaction mechanism for the model iteration process, with the corrosion probability P a passivation probability P b The probability of passivation film rupture, P d Determine the transformation rules between different types of cells;

[0008] S4: Introduce a chloride ion concentration matrix and probability enhancement coefficient and attenuation coefficient. Correct the cell transformation probability in the iteration process by using the real-time concentration value of chloride ions to intensify the local corrosion process.

[0009] S5: Under the same mass loss rate, compare the corrosion morphology characteristics of the accelerated atmospheric corrosion test and the cellular automata simulation.

[0010] Furthermore, the cell type and initial distribution of cell space mentioned in step S2 specifically include:

[0011] (1) The various elements involved in the corrosion process are transformed into cellular units, including a total of 4 types of cells:

[0012] Metal matrix cell M: The metal covered by the metal activated cell A or the metal passivated cell P, which is not in direct contact with the corrosive medium cell C and does not participate in the reaction during the simulation.

[0013] Metal activation cell A: The cell that comes into direct contact with the corrosive medium cell C and directly participates in the corrosion or passivation reaction;

[0014] Metal passivation cell P: represents a metal in a passivated state, which will not undergo corrosion or passivation reaction, but may undergo a process of passivation film dissolution or no chemical reaction. It is located on the outermost layer of the metal and its position is fixed.

[0015] Corrosive medium cell C: Corrosive medium cell C is corrosive and can react with metal activated cell A, causing metal activated cell A to corrode and disappear or generate metal passivation cell P.

[0016] (2) Establish the initial three-dimensional cell space distribution for metal corrosion simulation:

[0017] Assuming that the three-dimensional cellular space is composed of I×J×K unit cells, the contact relationship between cells is determined by the von Neumann neighborhood. The selected cell may only react with its six adjacent cells in the top, bottom, front, back, left, and right directions.

[0018] Considering the actual corrosion reaction, the constructed three-dimensional cellular space is divided into an upper corrosion environment region, an activation reaction region, and a lower stable metal region:

[0019] Corrosion environment region: In the initial cellular space state, all I×J×1 layer cells are occupied by corrosion medium cells C; in subsequent iterations, the corrosion environment region is composed of corrosion medium cells C that are adjacent to metal activation cells A and metal passivation cells P.

[0020] Activation reaction region: In the initial cellular space state, the I×J×2 layers are all metal activation cells A. In subsequent iterations, the metal passivation cells P and metal activation cells A are located below the corrosion medium cells C and are adjacent to the corrosion medium cells C.

[0021] Stable metal region: The metal matrix cell M below the I×J×(K-3) layer in the initial cellular space state; the metal matrix cell M located below the metal passivation cell P and the metal activation cell A in subsequent iterations, and not adjacent to the corrosion medium cell C. The cells in the stable metal region do not react in each calculation step.

[0022] Furthermore, the reaction mechanism described in step S3 is as follows:

[0023] At the start of each iteration, the system counts all cells that have a von Neumann-type adjacency rule with the corrosive medium cell C, including metal-activated cells A and metal-passivated cells P, and performs corresponding cell reaction probability determinations. The reactions include the following events:

[0024] (1) Event D1: Metal activated cell A will corrode with probability P a The transformation into corrosive medium cell C indicates metal ablation, meaning that the metal anodic ionization process occurs at this point. At this time, the metal matrix cell M adjacent to the metal activated cell A is transformed into metal activated cell A.

[0025] (2) Event D2: Metal-activated cell A will passivate with probability P b It is transformed into a metal passivation cell P, that is, the anode and cathode are very close at this point or there are hydroxide ions close to this point. After the metal is ionized, it immediately combines with hydroxide ions in the solution to generate corrosion products that adhere to the metal surface.

[0026] (3) Event D3: Metal-activated cell A will activate with probability P c =1-P a -P b The site remains unchanged, at which point no reaction occurs or it only participates in the electrochemical reaction as a cathode.

[0027] (4) Event D4: The metal passivation cell P will break with the probability P of the passivation film breaking. d The metal is transformed into a corrosive medium cell C. It is assumed that the ionization of the corrosion products at this site is due to the adsorption of chloride ions. At this time, the metal matrix cell M adjacent to the metal passivation cell P is transformed into a metal activation cell A.

[0028] (5) Event D5: The metal passivated cell P will have a probability P e =1-P d The site remains unchanged, at which point no reaction occurs or it only participates in the electrochemical reaction as a cathode.

[0029] Events D1, D2, and D3 are mutually exclusive; events D4 and D5 are mutually exclusive.

[0030] Furthermore, the localized corrosion enhancement method described in step S4, by associating chloride ion adsorption with the process of metal passivation cell P breaking down into corrosion medium cell C, introduces a chloride ion matrix to dynamically control the concentration distribution. Through the chloride ion concentration matrix superposition mechanism, it achieves an increase in metal dissolution probability, a decrease in passivation probability, and an increase in the passivation film dissolution probability, thereby promoting the simulation of localized corrosion enhancement. The specific steps are as follows:

[0031] (1) Construct the chloride ion concentration matrix C: The chloride ion concentration matrix has the same size as the plane of the cell space and is a two-dimensional matrix of I×J. The matrix elements correspond to the concentration values ​​of adsorbed chloride ions at the horizontal plane position of the cell space.

[0032] ;

[0033] in This represents the chloride ion concentration at position (i,j) in the initial chloride ion concentration matrix. Initially, all matrix elements are zero.

[0034] (2) Setting rules for the superposition and growth of chloride ion concentration: During the iteration process, when the metal passivation cell P at any position (i,j) in the cell space horizontal plane breaks down and transforms into the corrosion medium cell C, the concentration at position (i,j) in the corresponding chloride ion matrix increases. However, chloride ion concentration does not increase indefinitely; there is a chloride ion concentration threshold. When the concentration at (i,j) in the chloride ion concentration matrix reaches At that time, the chloride ion concentration value returns to zero:

[0035] ;

[0036] in , Let represent the concentration at position (i,j) of the chloride ion matrix at times t and t+1. This represents the increase in chloride ion concentration. This represents the maximum upper limit of chloride ion concentration.

[0037] (3) Setting the influence mechanism of chloride ion concentration: setting the metal corrosion enhancement coefficient Metal passivation attenuation coefficient Passivation film rupture enhancement coefficient In any iteration step, when performing cell type conversion, it is necessary to consider the correction of chloride ion concentration on the cell conversion probability. The corrected cell conversion probability is as follows:

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] in , , All are constant values. , All are greater than 1. Less than 1 and greater than 0.

[0044] Furthermore, step S5 specifically includes: randomly extracting 30 two-dimensional height curves along the longitudinal direction of the specimen cross section after simulation and experiment; manually statistically analyzing the corrosion pit data of all curves, mainly including the diameter and depth of the corrosion pits; and performing logarithmic distribution fitting on the obtained simulation and experiment corrosion pit depth and diameter-to-depth ratio data to obtain the result distribution of the two and comparing them.

[0045] This invention provides a chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution. Cells are updated using probabilistic transformation rules, and a chloride ion concentration matrix is ​​introduced to enhance the model and accurately simulate the evolution of localized corrosion. This method offers the following advantages: First, through the probabilistic cellular transformation mechanism, the corrosion environment does not need to distinguish between corrosion cells and water cells. Only the corrosion film environment needs to be characterized by single-layer corrosion medium cells. Metal matrix cells within the steel that are not directly in contact with the corrosion medium do not require iterative calculations. Each iteration only needs to calculate a small number of cells in the reaction region, significantly reducing computational load and greatly improving computational efficiency compared to mobile cellular automaton corrosion models. Second, by linking chloride ion adsorption and metal passivation film rupture processes, a chloride ion matrix is ​​introduced to dynamically control the concentration distribution, effectively achieving the function of enhancing localized corrosion. This invention can effectively recreate localized corrosion on the basis of general corrosion, providing support for the evaluation of steel structure corrosion performance. Attached Figure Description

[0046] Figure 1 This is a flowchart of a probabilistic cellular automaton simulation method for corrosion evolution driven by chloride ion concentration.

[0047] Figure 2 It is a schematic diagram of cell types, cell space, and cell transformation rules;

[0048] Figure 3 This is a flowchart of the probability correction process for the chloride ion matrix during the iteration process;

[0049] Figure 4 This is a schematic diagram of the chloride ion matrix and a diagram of the chloride ion superposition mechanism;

[0050] Figure 5 It is a corrosion simulation image of corrosion morphology;

[0051] Figure 6 This is a schematic diagram of two-dimensional curve pit extraction.

[0052] Figure 7 This is a comparison diagram of corrosion morphology characteristics. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0054] Reference Figure 1 This embodiment provides a chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution, including the following steps:

[0055] S1: Using a 2cm×2cm×2mm specimen as a sample, a three-dimensional spatial grid with a cell space of 100×100×100 is set, where the length:width:height of each grid cell unit is 10:10:1 and the length of each unit is 20μm.

[0056] S2: The basic elements involved in the atmospheric corrosion process are simplified into several cell types and the initial distribution of each type of cell is completed in the three-dimensional cell space;

[0057] S3: Define the reaction mechanism for the model iteration process, with the corrosion probability P a passivation probability P b The probability of passivation film rupture, P d Determine the transformation rules between different types of cells;

[0058] S4: Introduce a chloride ion concentration matrix and probability enhancement coefficient and attenuation coefficient. Correct the cell transformation probability in the iteration process by using the real-time concentration value of chloride ions to intensify the local corrosion process.

[0059] S5: Using specimens subjected to neutral salt spray corrosion for 30 days as the standard, by adjusting the parameters of the cellular automata, and ensuring that the mass loss rate of both is consistent, the corrosion morphology characteristics of the actual specimens and those simulated by the cellular automata are compared. The characteristic values ​​include the distribution of pit depth and the average distribution of pit depth-to-diameter ratio.

[0060] In this embodiment, the cell type and initial cell space distribution mentioned in step S2 are as follows: Figure 2 As shown, it specifically includes:

[0061] (1) The various elements involved in the corrosion process are transformed into cellular units, including a total of 4 types of cells:

[0062] Metal matrix cell M: The metal covered by the metal activated cell A or the metal passivated cell P, which is not in direct contact with the corrosive medium cell C and does not participate in the reaction during the simulation.

[0063] Metal activation cell A: The cell that comes into direct contact with the corrosive medium cell C and directly participates in the corrosion or passivation reaction;

[0064] Metal passivation cell P: represents a metal in a passivated state, which will not undergo corrosion or passivation reaction, but may undergo a process of passivation film dissolution or no chemical reaction. It is located on the outermost layer of the metal and its position is fixed.

[0065] Corrosive medium cell C: Corrosive medium cell C is corrosive and can react with metal activated cell A, causing metal activated cell A to corrode and disappear or generate metal passivation cell P.

[0066] (2) Establish the initial three-dimensional cell space distribution for metal corrosion simulation:

[0067] Assuming that the three-dimensional cellular space is composed of I×J×K unit cells, the contact relationship between cells is determined by the von Neumann neighborhood. The selected cell may only react with its six adjacent cells in the top, bottom, front, back, left, and right directions.

[0068] Considering the actual corrosion reaction, the constructed three-dimensional cellular space is divided into an upper corrosion environment region, an activation reaction region, and a lower stable metal region, such as... Figure 2 As shown:

[0069] Corrosion environment region: In the initial cellular space state, all I×J×1 layer cells are occupied by corrosion medium cells C; in subsequent iterations, the corrosion environment region is composed of corrosion medium cells C that are adjacent to metal activation cells A and metal passivation cells P;

[0070] Activation reaction region: In the initial cellular space state, the I×J×2 layers are all metal activation cells A. In subsequent iterations, the metal passivation cells P and metal activation cells A are located below the corrosion medium cells C and are adjacent to the corrosion medium cells C.

[0071] Stable metal region: The metal matrix cell M below the I×J×(K-3) layer in the initial cellular space state; the metal matrix cell M located below the metal passivation cell P and the metal activation cell A in subsequent iterations, and not adjacent to the corrosion medium cell C. The cells in the stable metal region do not react in each calculation step.

[0072] In this embodiment, the reaction mechanism described in step S3 is as follows:

[0073] At the start of each iteration, the system counts all cells that have a von Neumann-type adjacency rule with the corrosive medium cell C, including metal-activated cells A and metal-passivated cells P, and performs corresponding cell reaction probability determinations. The reactions include the following events:

[0074] (1) Event D1: Metal activated cell A will corrode with probability P a The transformation into corrosive medium cell C indicates metal ablation, meaning that the metal anodic ionization process occurs at this point. At this time, the metal matrix cell M adjacent to the metal activated cell A is transformed into metal activated cell A.

[0075] (2) Event D2: Metal-activated cell A will passivate with probability P b It is transformed into a metal passivation cell P, that is, the anode and cathode are very close at this point or there are hydroxide ions close to this point. After the metal is ionized, it immediately combines with hydroxide ions in the solution to generate corrosion products that adhere to the metal surface.

[0076] (3) Event D3: Metal-activated cell A will activate with probability P c =1-P a -P b The site remains unchanged, at which point no reaction occurs or it only participates in the electrochemical reaction as a cathode.

[0077] (4) Event D4: The metal passivation cell P will break with the probability P of the passivation film breaking. d The metal is transformed into a corrosive medium cell C. It is assumed that the ionization of the corrosion products at this site is due to the adsorption of chloride ions. At this time, the metal matrix cell M adjacent to the metal passivation cell P is transformed into a metal activation cell A.

[0078] (5) Event D5: The metal passivated cell P will have a probability P e =1-P d The site remains unchanged, at which point no reaction occurs or it only participates in the electrochemical reaction as a cathode.

[0079] Events D1, D2, and D3 are mutually exclusive; events D4 and D5 are mutually exclusive.

[0080] In this embodiment, the localized corrosion enhancement method described in step S4 introduces a chloride ion matrix by relating chloride ion adsorption to the process of metal passivation cell P breaking down into corrosion medium cell C. This matrix dynamically controls the concentration distribution, and through the chloride ion concentration matrix superposition mechanism, it achieves increased metal dissolution probability, decreased passivation probability, and increased passivation film dissolution probability, thereby promoting localized corrosion enhancement simulation. The specific steps are as follows:

[0081] (1) Construct the chloride ion concentration matrix C: The chloride ion concentration matrix has the same size as the plane of the cell space and is a two-dimensional matrix of I×J. The matrix elements correspond to the concentration values ​​of adsorbed chloride ions at the horizontal plane position of the cell space.

[0082] ;

[0083] in This represents the chloride ion concentration at position (i,j) in the initial chloride ion concentration matrix. Initially, all matrix elements are zero.

[0084] (2) Setting rules for the superposition and growth of chloride ion concentration: During the iteration process, when the metal passivation cell P at any position (i,j) in the cell space horizontal plane breaks down and transforms into the corrosion medium cell C, the concentration at position (i,j) in the corresponding chloride ion matrix increases. However, chloride ion concentration does not increase indefinitely; there is a chloride ion concentration threshold. When the concentration at (i,j) in the chloride ion concentration matrix reaches At that time, the chloride ion concentration value returns to zero:

[0085] ;

[0086] in , Let represent the concentration at position (i,j) of the chloride ion matrix at times t and t+1. This represents the increase in chloride ion concentration. This represents the maximum upper limit of chloride ion concentration.

[0087] (3) Setting the influence mechanism of chloride ion concentration: setting the metal corrosion enhancement coefficient Metal passivation attenuation coefficient Passivation film rupture enhancement coefficient In any iteration step, when performing cell type conversion, it is necessary to consider the correction of chloride ion concentration on the cell conversion probability. The corrected cell conversion probability is as follows:

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] in , , All are constant values. , All are greater than 1. Less than 1 and greater than 0.

[0094] Figure 3 This is a flowchart illustrating the probability correction of the chloride ion matrix during the iteration process.

[0095] by Figure 2 Taking the cell space shown as an example, based on the actual mass loss rate of the specimen, the cell transformation probability parameter is set as [P]. a ,P b ,P c [0.3, 0.8, 0.01], Chloride ion concentration threshold =5、 =10、 =0.8、 =6. Increment of chloride ion concentration =1, Figure 4 This describes the chloride ion concentration matrix and its corresponding positions within the activated reaction region cells during a specific iteration stage, along with the chloride ion concentration superposition mechanism. When the cellular automaton iterates 230 times and reaches the corresponding mass loss rate of the actual specimen, the iteration stops and the corresponding corrosion morphology is output as shown below. Figure 5 As shown, the positive z-axis corresponds to the corrosion depth on the specimen surface, and the z-value height of the specimen surface is 0 when there is no corrosion.

[0096] In this embodiment, step S5 specifically includes: randomly extracting 30 two-dimensional height curves along the longitudinal direction of the specimen cross-section after simulation and experiment; manually statistically analyzing the corrosion pit data of all curves, mainly including the diameter and depth of the corrosion pits, such as... Figure 6 As shown. Logarithmic distribution fitting was applied to the obtained simulation and experimental data on pit depth and diameter-to-depth ratio to obtain the resulting distributions, as shown below. Figure 7 As shown, the model can effectively simulate localized corrosion while ensuring overall corrosion, i.e., the mass loss rate. The simulation results show that the pit depth distribution and the mean diameter-to-depth ratio are similar to the experimental results.

[0097] Although the preferred embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other modifications under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these modifications are within the scope of protection of the present invention.

Claims

1. A probabilistic cellular automaton simulation method for corrosion evolution driven by chloride ion concentration, characterized in that, Includes the following steps: S1: Based on the volume of the simulated specimen, set the cell space size and cell dimensions to construct a three-dimensional cell space; S2: The basic elements involved in the atmospheric corrosion process are simplified into several cell types and the initial distribution of each type of cell is completed in the three-dimensional cell space; S3: Define the reaction mechanism for the model iteration process, with the corrosion probability P a passivation probability P b The probability of passivation film rupture, P d Determine the transformation rules between different types of cells; S4: Introduce a chloride ion concentration matrix and probability enhancement coefficient and attenuation coefficient. Correct the cell transformation probability in the iteration process by using the real-time concentration value of chloride ions to enhance the local corrosion process. S5: Under the same mass loss rate, compare the corrosion morphology characteristics of the accelerated atmospheric corrosion test and the cellular automata simulation. The localized corrosion enhancement method described in step S4, by associating chloride ion adsorption with the process of metal passivation cell P breaking down into corrosion medium cell C, introduces a chloride ion matrix to dynamically control the concentration distribution. Through the chloride ion concentration matrix superposition mechanism, it achieves an increase in metal dissolution probability, a decrease in passivation probability, and an increase in the passivation film dissolution probability, thereby promoting the simulation of localized corrosion enhancement. The specific steps are as follows: (1) Construct the chloride ion concentration matrix C: The chloride ion concentration matrix has the same size as the plane of the cell space and is a two-dimensional matrix of I×J. The matrix elements correspond to the concentration values ​​of adsorbed chloride ions at the horizontal plane position of the cell space. ； in This represents the chloride ion concentration at position (i,j) in the initial chloride ion concentration matrix. Initially, all matrix elements are zero. (2) Setting rules for the superposition and growth of chloride ion concentration: During the iteration process, when the metal passivation cell P at any position (i,j) in the cell space horizontal plane breaks down and transforms into the corrosion medium cell C, the concentration at position (i,j) in the corresponding chloride ion matrix increases. However, chloride ion concentration does not increase indefinitely; there is a chloride ion concentration threshold. When the concentration at (i,j) in the chloride ion concentration matrix reaches At that time, the chloride ion concentration value returns to zero: ； in , Let represent the concentration at position (i,j) of the chloride ion matrix at times t and t+1. This represents the increase in chloride ion concentration. This represents the maximum upper limit of chloride ion concentration. (3) Setting the influence mechanism of chloride ion concentration: setting the metal corrosion enhancement coefficient Metal passivation attenuation coefficient Passivation film rupture enhancement coefficient In any iteration step, when performing cell type conversion, it is necessary to consider the correction of chloride ion concentration on the cell conversion probability. The corrected cell conversion probability is as follows: ； ； ； ； ； in , , All are constant values. , All are greater than 1. Less than 1 and greater than 0.

2. The chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution according to claim 1, characterized in that, The cell type and initial distribution of cell space mentioned in step S2 specifically include: (1) The various elements involved in the corrosion process are transformed into cellular units, including a total of 4 types of cells: Metal matrix cell M: The metal covered by the metal activated cell A or the metal passivated cell P, which is not in direct contact with the corrosive medium cell C and does not participate in the reaction during the simulation. Metal activation cell A: A cell that comes into direct contact with the corrosive medium cell C and directly participates in the corrosion or passivation reaction; Metal passivation cell P: represents a metal in a passivated state, which will not undergo corrosion or passivation reaction, but may undergo a process of passivation film dissolution or no chemical reaction. It is located on the outermost layer of the metal and its position is fixed. Corrosive medium cell C: Corrosive medium cell C is corrosive and can react with metal activated cell A, causing metal activated cell A to corrode and disappear or to generate metal passivation cell P. (2) Establish the initial three-dimensional cell space distribution for metal corrosion simulation: Assuming that the three-dimensional cellular space is composed of I×J×K unit cells, the contact relationship between cells is determined by the von Neumann neighborhood. The selected cell may only react with its six adjacent cells in the top, bottom, front, back, left, and right directions. Considering the actual corrosion reaction, the constructed three-dimensional cellular space is divided into an upper corrosion environment region, an activation reaction region, and a lower stable metal region: Corrosion environment region: In the initial cellular space state, all I×J×1 layer cells are occupied by corrosion medium cells C; in subsequent iterations, the corrosion environment region is composed of corrosion medium cells C that are adjacent to metal activation cells A and metal passivation cells P. Activation reaction region: In the initial cellular space state, the I×J×2 layers are all metal activation cells A. In subsequent iterations, the metal passivation cells P and metal activation cells A are located below the corrosion medium cells C and are adjacent to the corrosion medium cells C. Stable metal region: The metal matrix cell M below the I×J×(K-3) layer in the initial cellular space state; the metal matrix cell M located below the metal passivation cell P and the metal activation cell A in subsequent iterations, and not adjacent to the corrosion medium cell C. The cells in the stable metal region do not react in each calculation step.

3. The chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution according to claim 2, characterized in that, The reaction mechanism described in step S3 is as follows: At the start of each iteration, the system counts all cells that have a von Neumann-type adjacency rule with the corrosive medium cell C, including metal-activated cells A and metal-passivated cells P, and performs corresponding cell reaction probability determinations. The reactions include the following events: (1) Event D1: Metal activated cell A will corrode with probability P a The transformation into corrosive medium cell C indicates metal ablation, meaning that the metal anodic ionization process occurs at this point. At this time, the metal matrix cell M adjacent to the metal activated cell A is transformed into metal activated cell A. (2) Event D2: Metal-activated cell A will passivate with probability P b It is transformed into a metal passivation cell P, that is, the anode and cathode are very close at this point or there are hydroxide ions close to this point. After the metal is ionized, it immediately combines with hydroxide ions in the solution to generate corrosion products that adhere to the metal surface. (3) Event D3: Metal-activated cell A will activate with probability P c =1-P a -P b The site remains unchanged, at which point no reaction occurs or it only participates in the electrochemical reaction as a cathode. (4) Event D4: The metal passivation cell P will break with the probability P of the passivation film breaking. d The metal is transformed into a corrosive medium cell C. It is assumed that the ionization of the corrosion products at this site is due to the adsorption of chloride ions. At this time, the metal matrix cell M adjacent to the metal passivation cell P is transformed into a metal activation cell A. (5) Event D5: The metal passivated cell P will have a probability P e =1-P d The site remains unchanged, at which point no reaction occurs or it only participates in the electrochemical reaction as a cathode. Events D1, D2, and D3 are mutually exclusive; events D4 and D5 are mutually exclusive.

4. The chloride ion concentration-driven probabilistic cellular automaton simulation method for corrosion evolution according to claim 3, characterized in that, Step S5 specifically includes: randomly extracting 30 two-dimensional height curves along the longitudinal direction of the specimen cross section after simulation and experiment; manually statistically analyzing the corrosion pit data of all curves, mainly including the diameter and depth of the corrosion pits; and performing logarithmic distribution fitting on the obtained simulation and experiment corrosion pit depth and diameter-to-depth ratio data to obtain the result distribution of the two and comparing them.

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