Method for constructing corrosion rate prediction model of metal material

Abstract

The invention relates to the technical field of prediction model construction, in particular to a method for constructing a corrosion rate prediction model of a metal material. The method comprises the following steps: acquiring bridge design data, collecting metal material and electrolyte component data, and constructing a bridge protection primary battery simulation model; electrolyte concentration change information is collected by dynamically analyzing water flow fluid and ion migration, so that the potential difference and the current density of positive and negative electrodes of the primary battery are calculated; evaluating the metal corrosion rate of the protection electrode, and establishing a corresponding prediction model; according to the method, multiple steps including metal activity analysis, ion activity estimation, current density calculation and the like are refined, so that the accuracy and reliability of a prediction result are ensured. According to the invention, the metal corrosion rate prediction model is more accurate through the corrosion rate prediction optimization of the metal material.

Description

Technical Field

[0001] The present invention relates to the technical field of prediction model construction, and in particular to a method for constructing a corrosion rate prediction model for a metal material. Background Art

[0002] A galvanic cell is a device that converts chemical energy into electrical energy. It consists of an anode, a cathode, and an electrolyte. In metal corrosion, the anode of the battery is the main area where corrosion occurs. In a galvanic cell, the anode metal undergoes an oxidation reaction, releasing electrons, while the cathode undergoes a reduction reaction. This process is the root cause of corrosion. Galvanic cell protection electrodes are usually used for cathodic protection, which slows down the corrosion of the anode metal by providing the electrons required for the cathode reaction. The selection and configuration of the protection electrode are crucial for effective corrosion protection. The corrosion rate is closely related to the current density and the potential difference between the cathode and the cathode. Higher current density usually leads to faster corrosion rates, but models usually assume that environmental conditions (such as temperature, humidity, pH value, etc.) remain unchanged, but these factors change dynamically in actual use, resulting in the failure to reasonably predict the change in corrosion rate. Models often focus on the overall corrosion rate and fail to fully consider the impact of local corrosion (such as pitting or crevice corrosion) on the overall corrosion behavior. However, traditional metal material predictions have the problem of inaccurate estimation of the impact of the surrounding environment on metal corrosion and inaccurate estimation of metal corrosion rate. Summary of the invention

[0003] Based on this, it is necessary to provide a method for constructing a corrosion rate prediction model for metal materials to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing a corrosion rate prediction model for metal materials comprises the following steps:

[0005] Step S1: Obtain bridge design data; obtain bridge metal materials and electrolyte composition according to the bridge design data to obtain galvanic cell anode material data and electrolyte composition data; construct a bridge protection galvanic cell simulation model according to the galvanic cell anode material data and electrolyte composition data to obtain a bridge protection galvanic cell simulation model;

[0006] Step S2: Performing a water flow fluid dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow fluid dynamic data around the bridge; performing an ion migration analysis based on the water flow fluid dynamic data around the bridge to obtain water flow ion migration data; collecting electrolyte concentration change data based on the water flow ion migration data and the water flow fluid dynamic data around the bridge to obtain electrolyte concentration change data;

[0007] Step S3: Calculate the cathode and anode potential difference of the bridge protection battery simulation model according to the electrolyte concentration change data to obtain cathode and anode potential difference data of the battery; Calculate the current density according to the cathode and anode potential difference data of the battery to obtain cathode and anode current density data of the battery;

[0008] Step S4: Estimate the metal corrosion rate of the protection electrode of the bridge protection galvanic cell simulation model according to the current density data of the positive and negative electrodes of the galvanic cell and the potential difference data of the positive and negative electrodes of the galvanic cell to obtain the metal corrosion rate data of the protection electrode; construct a metal material corrosion rate prediction model according to the metal corrosion rate data of the protection electrode to obtain the corrosion rate prediction model of the metal material.

[0009] The present invention can ensure that the materials used are consistent with the actual environmental conditions by obtaining bridge design data and determining the anode material and electrolyte composition, thereby improving the accuracy and reliability of the model. Next, the protection galvanic cell simulation model constructed based on these data can provide an in-depth understanding of the reaction characteristics of bridge metal materials in actual water environments. After the water flow fluid dynamic analysis, the water flow dynamic data obtained provides a basis for subsequent ion migration analysis, which helps to accurately understand the distribution and behavior of ions in the water flow, thereby affecting the change in electrolyte concentration. These data are crucial for evaluating the corrosion environment, because the electrolyte concentration directly affects the current density and corrosion rate. The calculation of the cathode and anode potential difference based on the change in electrolyte concentration enables the model to reflect the changes in the actual electrochemical environment and enhances the understanding of the corrosion mechanism. On this basis, the calculation of current density provides the necessary quantitative basis for further corrosion rate estimation and helps identify high corrosion risk areas. This series of steps combines fluid dynamics and electrochemical principles to make the prediction more comprehensive. Finally, the corrosion rate of the protective electrode metal estimated based on the current density and potential difference data can provide an important reference for the design of anti-corrosion measures. In addition, the constructed metal material corrosion rate prediction model, combined with the comprehensive data obtained previously, enables a more scientific prediction capability for the long-term corrosion behavior of metal materials. This prediction model can not only be used for the actual maintenance and management of bridges, but also provide theoretical support and data basis for the corrosion protection of other similar structures, thereby improving the safety and service life of bridges. Overall, this method lays the foundation for the accurate prediction of metal corrosion rates through systematic data acquisition and analysis, and promotes the development of bridge protection technology. Therefore, the present invention is an optimization of the traditional method for constructing a corrosion rate prediction model for metal materials, which solves the problem of inaccurate prediction of the impact of the surrounding environment on metal corrosion and inaccurate prediction of the metal corrosion rate in the traditional method for constructing a corrosion rate prediction model for metal materials, and improves the accuracy of the prediction of the impact of the surrounding environment on metal corrosion and the accuracy of the prediction of the metal corrosion rate.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: obtaining bridge design data;

[0012] Step S12: acquiring bridge metal materials and collecting water electrolyte components according to the bridge design data to obtain bridge metal material data and water electrolyte component data;

[0013] Step S13: selecting protective electrode materials according to the bridge metal material data and the water flow electrolyte composition data to obtain protective electrode material data;

[0014] Step S14: constructing a bridge protection galvanic cell simulation model according to the protection electrode material data, the bridge metal material data and the water flow electrolyte composition data to obtain a bridge protection galvanic cell simulation model.

[0015] The present invention ensures a comprehensive understanding of the overall structure of the bridge and its environment by obtaining bridge design data, which lays the foundation for subsequent corrosion assessment. Next, the bridge metal material and water flow electrolyte composition are collected based on these design data, which can provide accurate material properties and environmental conditions for subsequent analysis, so that the construction of the corrosion model has a practical basis and ensures that the selected material can truly reflect the behavior in the actual environment. This process not only improves the relevance of the model, but also reduces the error caused by improper material selection. In the process of selecting the protective electrode material, according to the data of the bridge metal material and the water flow electrolyte composition, it can ensure that the selected protective electrode has good compatibility and sacrificial effect, thereby maximizing its anti-corrosion effect. Suitable protective electrode materials can not only effectively protect the bridge metal and extend the service life, but also reduce maintenance costs in practical applications. The process of constructing the bridge protection primary battery simulation model integrates all previous data, so that the model can fully reflect the complexity of the actual corrosion environment. This model provides a reliable tool for further electrochemical analysis and corrosion rate prediction, which can help engineers formulate more effective maintenance and protection measures, thereby improving the safety and reliability of bridges in practical applications.

[0016] Preferably, step S13 comprises the following steps:

[0017] Step S131: performing metal activity analysis according to bridge metal material data to obtain bridge metal activity data;

[0018] Step S132: performing electrolyte acidity and alkalinity test and electrolyte concentration calculation according to the water flow electrolyte data to obtain electrolyte acidity and alkalinity data and electrolyte concentration data;

[0019] Step S133: Calculating the electrolyte pH value according to the electrolyte acidity and alkalinity data and the electrolyte concentration data to obtain electrolyte pH value data;

[0020] Step S134: performing bridge metal corrosion resistance analysis on the bridge metal activity data according to the electrolyte pH value data to obtain bridge metal corrosion resistance data;

[0021] Step S135: Select protective electrode materials according to the bridge metal corrosion resistance data and the electrolyte pH value data to obtain protective electrode material data.

[0022] The present invention can identify the reactivity of different metals in a specific environment by analyzing the activity of bridge metal materials, providing an important basis for subsequent corrosion assessment. This analysis enables engineers to formulate more effective anti-corrosion measures based on the characteristics of different metals. Next, acid-base testing and concentration calculation based on water flow electrolyte data help to understand the chemical properties of the electrolyte, thereby laying the foundation for evaluating its corrosion effect on metal materials. The acidity and alkalinity of the electrolyte are directly related to its corrosion potential for metals. On this basis, calculating the pH value data of the electrolyte can further quantify the corrosion environment of the electrolyte, which is crucial for evaluating the corrosion resistance of metals. Combining the pH value of the electrolyte with the activity of the metal for corrosion resistance analysis can accurately determine the corrosion risk of the metal in a specific electrolyte environment. This combined analysis not only helps to identify materials with high corrosion risks, but also guides the selection and application of materials. Finally, the selection of protective electrode materials based on corrosion resistance data and electrolyte pH values ​​can ensure that the selected protective electrode has good protection effect in practical applications. This process ensures the optimal compatibility between the protective electrode and the bridge metal, thereby improving the anti-corrosion effect and structural safety.

[0023] Preferably, step S14 comprises the following steps:

[0024] Step S141: measuring the conductivity of the electrode material according to the protective electrode material data to obtain the conductivity data of the protective electrode material;

[0025] Step S142: performing corrosion resistance test of the protective electrode according to the conductivity data of the protective electrode material to obtain corrosion resistance data of the protective electrode;

[0026] Step S143: performing electrode connection design according to the corrosion resistance data of the protective electrode and the bridge metal material data to obtain electrode connection design data;

[0027] Step S144: performing a galvanic cell path simulation according to the electrode connection design data and the water flow electrolyte composition data to obtain galvanic cell path data;

[0028] Step S145: constructing a bridge protection galvanic cell simulation model according to the electrode connection design data, the galvanic cell path data and the protection electrode corrosion resistance data to obtain a bridge protection galvanic cell simulation model.

[0029] The present invention can quantify the conductivity of the protective electrode material by measuring its conductivity, which provides basic data for subsequent corrosion resistance inspection. Good conductivity is a prerequisite for ensuring the effective operation of the electrode material. This measurement ensures that the selected material can efficiently conduct current in the electrochemical reaction, thereby improving the anti-corrosion effect. The corrosion resistance inspection based on the conductivity data can evaluate the ability of the protective electrode to resist corrosion in the actual environment. This step ensures that the selected electrode material can effectively protect the metal structure in the specific electrolyte environment of the bridge for a long time, reduce corrosion losses, and thus extend the service life of the bridge. The electrode connection design combined with the corrosion resistance data of the protective electrode and the bridge metal material data can optimize the layout and connection mode of the electrode, ensure the uniform distribution of the current, and thus improve the protection effect. Reasonable electrode connection design can effectively reduce contact resistance, enhance the overall performance of the system, and reduce corrosion risks. The bridge protection galvanic cell simulation model constructed by combining the electrode connection design data, the galvanic cell path data, and the corrosion resistance data of the protective electrode can integrate all key parameters and provide an accurate prediction tool for further corrosion behavior analysis. This model provides a scientific basis for the implementation of anti-corrosion measures and can help improve the safety and durability of bridges in practical applications. Overall, these steps provide comprehensive technical support for metal corrosion prediction and protection of bridges through systematic material testing and model construction.

[0030] Preferably, step S2 comprises the following steps:

[0031] Step S21: Performing a water flow dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow dynamic data around the bridge;

[0032] Step S22: Calculate the water flow velocity according to the dynamic data of the water flow around the bridge to obtain the water flow velocity data around the bridge;

[0033] Step S23: analyzing ion migration changes based on the water flow velocity data around the bridge to obtain water flow ion migration change data;

[0034] Step S24: Collect electrolyte concentration change data based on the water flow ion migration change data and the water flow velocity data around the bridge to obtain electrolyte concentration change data.

[0035] The present invention can deeply understand the flow characteristics of water flow around the bridge through the dynamic analysis of water flow fluid based on the bridge protection galvanic cell simulation model, which provides an important basis for evaluating the impact of the environment on corrosion behavior. The flow velocity calculation based on the water flow dynamic data enables a quantitative understanding of the kinetic energy and flow pattern of the water flow. The flow velocity data not only affects the migration rate of ions, but is also closely related to the distribution and concentration changes of electrolytes. Therefore, this analysis provides necessary information for subsequent corrosion assessment. By analyzing the changes in ion migration of flow velocity data, the dynamic behavior of ions in water during the flow process can be revealed. This process is crucial to understanding the impact of electrolytes on metal surfaces, because the migration of ions is directly related to the corrosion rate of metals. Identifying changes in ion migration helps to formulate more effective anti-corrosion measures. The collection of electrolyte concentration changes combined with ion migration change data and water flow velocity data can reflect the dynamic changes in electrolyte concentration in real time. This data is very important for evaluating changes in the corrosive environment, because changes in electrolyte concentration directly affect the activity of electrochemical reactions and the corrosion resistance of metals. These steps provide a scientific basis for corrosion protection of bridges by systematically analyzing the behavior of water flow and electrolytes, enhance protection of bridge safety, and provide data support for subsequent protective measures.

[0036] Preferably, step S23 includes the following steps:

[0037] Step S231: dividing the water flow velocity according to the water flow velocity data around the bridge to obtain the surrounding water flow high velocity data and the surrounding water flow low velocity data;

[0038] Step S232: analyzing the water flow turbulence phenomenon according to the surrounding water flow high velocity data to obtain the surrounding water flow turbulence phenomenon data;

[0039] Step S233: performing ion mixing degree growth calculation according to the surrounding water flow turbulence phenomenon data to obtain ion mixing degree growth data;

[0040] Step S234: performing ion migration rate growth calculation according to the ion mixing degree growth data and the surrounding water flow turbulence phenomenon data to obtain ion migration rate growth data;

[0041] Step S235: performing ion regional aggregation simulation according to the low flow velocity data of the surrounding water flow to obtain water flow ion regional aggregation data;

[0042] Step S236: performing ion concentration gradient analysis based on the water flow ion regional aggregation data and the peripheral water flow low flow rate data to obtain ion concentration gradient data;

[0043] Step S237: performing ion migration rate attenuation calculation according to the ion concentration gradient data and the surrounding water flow low flow rate data to obtain ion migration rate attenuation data;

[0044] Step S238: Perform ion migration change analysis based on the ion migration rate growth data and the ion migration rate attenuation data to obtain water flow ion migration change data.

[0045] The present invention can divide the surrounding water flow into high-velocity and low-velocity areas by dividing the water flow velocity data. This division provides a clear basis for subsequent turbulence analysis and ion migration research. High-velocity areas are often accompanied by stronger turbulence characteristics, which have a significant impact on corrosion behavior, so understanding these velocity distributions can help identify potential high-corrosion risk areas. The turbulence phenomenon analysis of the surrounding water flow high-velocity data helps to reveal the instability and mixing characteristics of the flow. Turbulence can significantly increase the contact frequency between ions and metal surfaces, thereby affecting the corrosion rate. By calculating the growth of ion mixing, the effect of turbulence on ion distribution can be quantified, providing an assessment of the uniformity of ion distribution in the water flow. This is crucial for subsequent ion migration rate analysis, because a higher degree of mixing usually means a more effective electrochemical reaction. The ion migration rate growth calculation based on the ion mixing and turbulence phenomenon data can evaluate the acceleration effect of ion migration under high flow and turbulent conditions. This process is directly related to the corrosion rate, because an increase in the ion migration rate often leads to a faster corrosion process. The simulation of ion regional aggregation of low-velocity data of surrounding water flow can reveal how ions aggregate in specific areas under low flow rates, leading to an increase in local concentration and thus inducing local corrosion. Corrosion hotspots can be identified through analysis of ion concentration gradients, and how ion migration is affected by environmental changes in low-velocity areas can be further explored through calculation of the attenuation of ion migration rates. Combining growth and attenuation data for analysis can comprehensively evaluate the combined impact of water flow conditions on ion migration, providing more refined data support for the prediction of long-term corrosion behavior of bridges.

[0046] Preferably, step S24 includes the following steps:

[0047] Step S241: performing convection effect analysis based on the water flow velocity data around the bridge to obtain surrounding water flow convection effect data;

[0048] Step S242: performing electrolyte local concentration difference attenuation analysis based on surrounding water flow convection effect data to obtain electrolyte local concentration difference attenuation data;

[0049] Step S243: Calculate the growth of the reaction rate of the original battery according to the electrolyte local concentration difference attenuation data and the water flow ion migration change data to obtain the growth data of the reaction rate of the original battery;

[0050] Step S244: collecting electrolyte concentration change data based on the original battery reaction rate growth data and the electrolyte local concentration difference attenuation data to obtain electrolyte concentration change data.

[0051] The present invention can deeply understand the influence of water flow on electrolyte distribution by analyzing the flow velocity data around the bridge, which is crucial for evaluating the corrosion risk in the flow environment. The convection effect can affect the distribution and migration of ions in the water flow, thereby playing an important role in the concentration uniformity of the electrolyte and providing a basis for the subsequent concentration difference analysis. The electrolyte local concentration difference attenuation analysis based on the convection effect data can reveal the changes in electrolyte concentration in different areas under the action of water flow. The attenuation of local concentration differences reflects the diffusion capacity of electrolytes in the flow environment, helps to identify corrosion risk areas, and thus provides guidance for the formulation of anti-corrosion measures. The calculation of the growth rate of the galvanic cell reaction based on the electrolyte local concentration difference attenuation data and the water flow ion migration change data can quantify the effect of electrolyte concentration changes on the galvanic cell reaction rate. This analysis helps to understand the acceleration or slowdown of electrochemical reactions under specific water flow and concentration conditions, and provides important data support for further corrosion behavior prediction. The collection of electrolyte concentration changes combined with the galvanic cell reaction rate growth data and the local concentration difference attenuation data can provide real-time reflection of the dynamic changes of the entire system. This data is of great significance for evaluating changes in the corrosion environment and its impact on bridge metal materials, thereby ensuring that environmental factors are fully considered when designing and implementing anti-corrosion measures.

[0052] Preferably, step S3 comprises the following steps:

[0053] Step S31: analyzing the electrolyte ionization degree according to the electrolyte concentration change data to obtain electrolyte ionization degree data;

[0054] Step S32: estimating ion activity according to the electrolyte ionization degree data and the electrolyte concentration change data to obtain electrolyte ion activity data;

[0055] Step S33: calculating the cathode and anode potential difference of the bridge protection galvanic battery simulation model according to the electrolyte ion activity data and the electrolyte ionization degree data, and obtaining the cathode and anode potential difference data of the galvanic battery;

[0056] Step S34: Calculate the current density based on the potential difference data of the positive and negative electrodes of the original battery and the data of the degree of electrolyte ionization to obtain the current density data of the positive and negative electrodes of the original battery.

[0057] The present invention can deeply understand the degree of dissociation of ions in the electrolyte by analyzing the degree of ionization of the electrolyte concentration change data, thereby providing a basis for evaluating the effectiveness of the electrochemical reaction. This analysis can reveal the changes in the properties of the electrolyte, help optimize the selection and design of the protective electrode material, and improve the anti-corrosion effect. The actual activity of the ions in the solution can be quantified by estimating the ion activity based on the electrolyte ionization degree and concentration change data. Ion activity is an important factor affecting the rate of electrochemical reactions, so this estimate provides key data support for subsequent potential difference and current density calculations. Using the electrolyte ion activity and ionization degree data to calculate the anode and cathode potential difference of the bridge protection galvanic cell simulation model, the potential difference between the electrodes can be accurately evaluated, and this potential difference directly affects the driving force of the reaction and the effectiveness of corrosion protection. The current density calculation based on the cathode and cathode electrode potential difference data and the electrolyte ionization degree can provide a quantitative analysis of the intensity of the electrochemical reaction. Current density is a key indicator for evaluating corrosion rate and protection effect, and this data can provide a guarantee for the long-term durability and safety of the bridge.

[0058] Preferably, step S34 includes the following steps:

[0059] Step S341: Calculate ion density according to electrolyte ionization degree data to obtain ion density data;

[0060] Step S342: Calculate the current flow driving force according to the potential difference data of the positive and negative electrodes of the original battery to obtain the current flow driving force data;

[0061] Step S343: simulating the change of electric field strength of the bridge protection galvanic battery simulation model according to the potential difference data of the positive and negative electrodes of the galvanic battery and the current flow driving force data, and obtaining the change data of the electric field strength of the galvanic battery;

[0062] Step S344: Calculate the current density based on the original battery electric field strength change data, current flow driving force data and ion density data to obtain the current density data of the positive and negative electrodes of the original battery.

[0063] The present invention can accurately evaluate the concentration of ions in the electrolyte by calculating the ion density of the electrolyte ionization degree data, which is crucial for understanding the dynamic behavior of the electrochemical reaction. The acquisition of ion density provides basic data for subsequent current flow and reaction rate analysis, thereby improving the accuracy and reliability of the prediction. The driving mechanism in the battery reaction can be determined by calculating the current flow driving force based on the potential difference data of the positive and negative electrodes of the original battery. This calculation helps to understand the source of current flow and clarify the role of potential difference in driving current flow, thereby providing a scientific basis for optimizing electrode design and protection strategies. The electric field strength change simulation of the bridge protection original battery simulation model using potential difference and current flow driving force data can reveal the change of electric field strength under specific conditions. This simulation not only reflects the dynamic characteristics of the electric field, but also provides an intuitive perspective for understanding the distribution of current in the system and enhances the grasp of electrochemical reactions. The current density calculation based on the electric field strength change data, current flow driving force data and ion density data can quantitatively evaluate the electrochemical reaction intensity of the original battery. Current density is a key indicator reflecting the corrosion rate and protection effect, and accurate current density data provides important support for the long-term safety and durability of the bridge. These steps provide a comprehensive scientific basis for the design and implementation of bridge protection galvanic cells through systematic electrolyte property analysis and electrochemical parameter calculations.

[0064] Preferably, step S4 comprises the following steps:

[0065] Step S41: dividing the current density according to the current density data of the positive and negative electrodes of the original battery to obtain the high-density current data of the positive and negative electrodes of the original battery;

[0066] Step S42: Calculate the dissolution rate growth of the protective electrode according to the high-density data of the current of the positive and negative electrodes of the original battery to obtain the dissolution rate growth data of the protective electrode;

[0067] Step S43: according to the dissolution rate growth data of the protection electrode and the potential difference data of the positive and negative electrodes of the galvanic cell, the metal corrosion rate of the protection electrode is estimated for the bridge protection galvanic cell simulation model to obtain the metal corrosion rate data of the protection electrode;

[0068] Step S44: constructing a prediction model for the metal corrosion rate of the protective electrode of the galvanic cell according to the metal corrosion rate data of the protective electrode to obtain a prediction model for the metal corrosion rate of the protective electrode of the galvanic cell.

[0069] The present invention can divide the current density into high-density and low-density areas by dividing the current density data of the positive and negative electrodes of the primary battery. This division helps to identify the behavioral characteristics of the electrode under different current density conditions, and thus provides a basis for optimizing the protection strategy. High current density areas are usually accompanied by stronger electrochemical reactions, so clarifying these areas can help determine potential corrosion risk points. The calculation of the dissolution rate growth of the protective electrode based on the high-density data of the positive and negative electrodes of the primary battery helps to quantify the dissolution rate of the protective electrode material under a specific current density. This calculation provides important data for understanding the stability of the protective electrode under actual working conditions and lays the foundation for the improvement of the protection design. The metal corrosion rate estimation combined with the dissolution rate growth data of the protective electrode and the potential difference data of the positive and negative electrodes of the primary battery can accurately evaluate the corrosion rate of the protective electrode under specific conditions. This estimation not only helps to understand the dynamic characteristics of the electrochemical reaction, but also the corrosion rate prediction model constructed based on the corrosion rate data of the protective electrode metal can provide an important reference for future bridge maintenance and safety assessment. Through this model, engineers can predict the corrosion behavior of the protective electrode under different working conditions, thereby formulating a more effective maintenance strategy. These steps provide comprehensive scientific support for the design and implementation of bridge protection primary batteries through systematic analysis and prediction.

[0070] The invention has the beneficial effect that the invention can ensure that the materials used are consistent with the actual environmental conditions by obtaining the bridge design data and determining the anode material and electrolyte composition, thereby improving the accuracy and reliability of the model. Next, the protection galvanic cell simulation model constructed based on these data can provide an in-depth understanding of the reaction characteristics of the bridge metal material in the actual water environment. After the water flow fluid dynamic analysis, the water flow dynamic data obtained provides a basis for the subsequent ion migration analysis, which helps to accurately understand the distribution and behavior of ions in the water flow, thereby affecting the change of electrolyte concentration. These data are crucial for evaluating the corrosion environment, because the electrolyte concentration directly affects the current density and corrosion rate. The calculation of the cathode and anode potential difference based on the change of electrolyte concentration enables the model to reflect the changes in the actual electrochemical environment and enhances the understanding of the corrosion mechanism. On this basis, the calculation of current density provides the necessary quantitative basis for further corrosion rate estimation and helps identify high corrosion risk areas. This series of steps combines fluid dynamics and electrochemical principles to make the prediction more comprehensive. Finally, the corrosion rate of the protective electrode metal estimated based on the current density and potential difference data can provide an important reference for the design of anti-corrosion measures. In addition, the constructed metal material corrosion rate prediction model, combined with the comprehensive data obtained previously, enables a more scientific prediction capability for the long-term corrosion behavior of metal materials. This prediction model can not only be used for the actual maintenance and management of bridges, but also provide theoretical support and data basis for the corrosion protection of other similar structures, thereby improving the safety and service life of bridges. Overall, this method lays the foundation for the accurate prediction of metal corrosion rates through systematic data acquisition and analysis, and promotes the development of bridge protection technology. Therefore, the present invention is an optimization of the traditional method for constructing a corrosion rate prediction model for metal materials, which solves the problem of inaccurate prediction of the impact of the surrounding environment on metal corrosion and inaccurate prediction of the metal corrosion rate in the traditional method for constructing a corrosion rate prediction model for metal materials, and improves the accuracy of the prediction of the impact of the surrounding environment on metal corrosion and the accuracy of the prediction of the metal corrosion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flow chart of the steps of the method for building a corrosion rate prediction model for metal materials;

[0072] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0073] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0074] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0076] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0077] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0078] To achieve this, please refer to Figures 1 to 3 , a method for constructing a corrosion rate prediction model for a metal material, comprising the following steps:

[0079] Step S1: Obtain bridge design data; obtain bridge metal materials and electrolyte composition according to the bridge design data to obtain galvanic cell anode material data and electrolyte composition data; construct a bridge protection galvanic cell simulation model according to the galvanic cell anode material data and electrolyte composition data to obtain a bridge protection galvanic cell simulation model;

[0080] Step S2: Performing a water flow fluid dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow fluid dynamic data around the bridge; performing an ion migration analysis based on the water flow fluid dynamic data around the bridge to obtain water flow ion migration data; collecting electrolyte concentration change data based on the water flow ion migration data and the water flow fluid dynamic data around the bridge to obtain electrolyte concentration change data;

[0081] Step S3: Calculate the cathode and anode potential difference of the bridge protection battery simulation model according to the electrolyte concentration change data to obtain cathode and anode potential difference data of the battery; Calculate the current density according to the cathode and anode potential difference data of the battery to obtain cathode and anode current density data of the battery;

[0082] Step S4: Estimate the metal corrosion rate of the protection electrode of the bridge protection galvanic cell simulation model according to the current density data of the positive and negative electrodes of the galvanic cell and the potential difference data of the positive and negative electrodes of the galvanic cell to obtain the metal corrosion rate data of the protection electrode; construct a metal material corrosion rate prediction model according to the metal corrosion rate data of the protection electrode to obtain the corrosion rate prediction model of the metal material.

[0083] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for constructing a corrosion rate prediction model for a metal material according to the present invention. In this example, the method for constructing a corrosion rate prediction model for a metal material comprises the following steps:

[0084] Step S1: Obtain bridge design data; obtain bridge metal materials and electrolyte composition according to the bridge design data to obtain galvanic cell anode material data and electrolyte composition data; construct a bridge protection galvanic cell simulation model according to the galvanic cell anode material data and electrolyte composition data to obtain a bridge protection galvanic cell simulation model;

[0085] In the embodiment of the present invention, it is first necessary to consult the relevant bridge design drawings and technical specifications to ensure that the information obtained is accurate and comprehensive. Next, by analyzing the design data, determine the required types and specifications of metal materials, such as the grade of steel and its mechanical property requirements. Subsequently, the anode material is selected, usually stainless steel or galvanized steel, and its composition data is obtained through chemical analysis. At this time, the collection of electrolyte components is mainly focused on the ion composition of the environmental water body, and water quality analysis instruments are used for detection to obtain the specific concentrations of components such as chloride ions and sulfate ions. Finally, the primary battery anode material data and electrolyte composition data are integrated to construct a bridge protection primary battery simulation model.

[0086] Step S2: Performing a water flow fluid dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow fluid dynamic data around the bridge; performing an ion migration analysis based on the water flow fluid dynamic data around the bridge to obtain water flow ion migration data; collecting electrolyte concentration change data based on the water flow ion migration data and the water flow fluid dynamic data around the bridge to obtain electrolyte concentration change data;

[0087] In the embodiment of the present invention, when performing a dynamic analysis of water flow based on the bridge protection primary battery simulation model, computational fluid dynamics (CFD) software is required to simulate the flow state of water flow around the bridge by establishing a fluid domain model and applying boundary conditions. During the analysis process, dynamic data such as water flow velocity, pressure, and flow field distribution are recorded. Afterwards, the ion migration theory is used in combination with the water flow dynamic data to carry out ion migration analysis to obtain the ion diffusion coefficient and migration rate in the water flow. Through these data, the electrolyte concentration change is further calculated. In this process, the electrolyte concentration at different time nodes is continuously monitored to form complete electrolyte concentration change data.

[0088] Step S3: Calculate the cathode and anode potential difference of the bridge protection battery simulation model according to the electrolyte concentration change data to obtain cathode and anode potential difference data of the battery; Calculate the current density according to the cathode and anode potential difference data of the battery to obtain cathode and anode current density data of the battery;

[0089] In the embodiment of the present invention, the Nernst equation is used to calculate the potential difference between the cathode and cathode of the primary cell based on the electrolyte concentration change data. This calculation requires combining the electrolyte concentration change with the standard electrode potential to obtain the potential difference data. Then, using Faraday's law, the obtained potential difference data is converted into current density, and the formula J=k*(E–E0) is used for calculation, where J is the current density, E is the cathode and cathode electrode potential, E0 is the standard electrode potential, and k is the proportional constant. The current density data will provide a basis for the subsequent corrosion rate evaluation, ensuring that in actual operation, the selected parameters strictly correspond to the electrolyte concentration and material properties to improve the accuracy of the calculation.

[0090] Step S4: Estimate the metal corrosion rate of the protection electrode of the bridge protection galvanic cell simulation model according to the current density data of the positive and negative electrodes of the galvanic cell and the potential difference data of the positive and negative electrodes of the galvanic cell to obtain the metal corrosion rate data of the protection electrode; construct a metal material corrosion rate prediction model according to the metal corrosion rate data of the protection electrode to obtain the corrosion rate prediction model of the metal material.

[0091] In an embodiment of the present invention, the simulation model of the bridge protection galvanic cell is further analyzed based on the current density and potential difference data of the positive and negative electrodes of the galvanic cell to estimate the corrosion rate of the protective electrode metal. The corrosion rate of the metal is calculated using the corrosion kinetics formula in combination with the current current density data. At this time, the linear polarization method can be used to analyze the relationship between the corrosion current and the potential to obtain the metal corrosion rate data of the protective electrode. Subsequently, these data are used to construct a metal material corrosion rate prediction model, and statistical methods such as regression analysis are used to fit the relationship between the metal corrosion rate and the influencing factors, and predict future corrosion behavior. Ensure that in this process, all data processing and model establishment fully consider environmental factors and material properties, thereby improving the accuracy and reliability of the prediction.

[0092] Preferably, step S1 comprises the following steps:

[0093] Step S11: obtaining bridge design data;

[0094] Step S12: acquiring bridge metal materials and collecting water electrolyte components according to the bridge design data to obtain bridge metal material data and water electrolyte component data;

[0095] Step S13: selecting protective electrode materials according to the bridge metal material data and the water flow electrolyte composition data to obtain protective electrode material data;

[0096] Step S14: constructing a bridge protection galvanic cell simulation model according to the protection electrode material data, the bridge metal material data and the water flow electrolyte composition data to obtain a bridge protection galvanic cell simulation model.

[0097] In the embodiment of the present invention, firstly, the structural information of the bridge is collected from the relevant design documents and technical specifications, including the type, span, load requirements and material specifications of the bridge. In this stage, the design drawings are read using CAD software to ensure the accuracy of each design parameter. The important parameters recorded include the material strength, tensile strength and corrosion resistance of the bridge. According to the bridge design data, the metal materials used need to be obtained in detail. This includes selecting suitable metal materials, such as high-strength steel or aluminum alloy, and conducting material tests to confirm their physical and chemical properties. Use metal analysis instruments to measure the composition of the material to ensure that it meets the design requirements. At the same time, the electrolyte composition of the water body around the bridge is collected, and the concentration of chloride ions, sulfate ions and other important ions in the water sample is analyzed using a water quality detection instrument, and the data is recorded to obtain the electrolyte composition of the water flow. After completing the collection of the bridge metal material data and the water flow electrolyte composition data, the selection of the protective electrode material is required. The basis for the selection is the data obtained previously. For example, for an environment with a high chloride ion concentration, stainless steel or galvanized materials are usually preferred as protective electrodes to improve corrosion resistance. To ensure that the selected materials are suitable for the specific environment, electrochemical tests, such as polarization curve tests, are carried out to evaluate the corrosion current density of different materials under the current electrolyte conditions. These test results will form the data of the protective electrode material to ensure that the material has good electrochemical performance and durability, clarify the geometric structure of the model, and use numerical calculation tools to establish a three-dimensional model based on the actual size and shape of the bridge. Next, set the material properties, including the conductivity and corrosion current density of each part. Subsequently, the electrolyte concentration distribution and potential conditions of the water flow are incorporated into the model by applying boundary conditions. Through the electrochemical reaction kinetics, the current distribution and corrosion behavior under different environmental conditions are simulated, and finally a simulation model of the bridge protection galvanic cell is obtained.

[0098] Preferably, step S13 comprises the following steps:

[0099] Step S131: performing metal activity analysis according to bridge metal material data to obtain bridge metal activity data;

[0100] Step S132: performing electrolyte acidity and alkalinity test and electrolyte concentration calculation according to the water flow electrolyte data to obtain electrolyte acidity and alkalinity data and electrolyte concentration data;

[0101] Step S133: Calculating the electrolyte pH value according to the electrolyte acidity and alkalinity data and the electrolyte concentration data to obtain electrolyte pH value data;

[0102] Step S134: performing bridge metal corrosion resistance analysis on the bridge metal activity data according to the electrolyte pH value data to obtain bridge metal corrosion resistance data;

[0103] Step S135: Select protective electrode materials according to the bridge metal corrosion resistance data and the electrolyte pH value data to obtain protective electrode material data.

[0104] In an embodiment of the present invention, firstly, the acquired bridge metal material data is collected, including the chemical composition of the metal and its physical properties. The activity series (for example, the Nernst equation) is used to sort each metal material to determine its electrochemical behavior under specific environmental conditions. Through laboratory testing, the standard electrode comparison method is used to measure the potential changes of different metals in a simulated environment and record the electrode potential data. These data are used to analyze the activity of the metal, identify the metal that reacts in the corrosive environment, and generate bridge metal activity data, perform an acid-base test on the electrolyte, use a pH meter or test paper to measure the pH value of the water sample, and record the acid-base data. On this basis, the electrolyte concentration is calculated. Using the known electrolyte composition data, the concentration formula is applied to calculate the actual concentration of the electrolyte, and the concentration of the main ions, such as chloride ions, sulfate ions and other ions that affect corrosion, are analyzed. Ensure that all data are repeatedly verified to improve the accuracy of the data, and form electrolyte acid-base data and concentration data, and calculate the pH value of the electrolyte based on the electrolyte acid-base data and concentration data obtained in the previous step. Combined with the ion concentration and acid-base characteristics, the Henderson-Hasselbalch equation or related chemical calculation formula is used to solve the specific pH value. In this calculation process, the balance of the main acidic and alkaline ions in the water needs to be considered to ensure that the contribution of each ion to the pH is accurately evaluated. By combining experimental data with theoretical calculations, the pH value data of the electrolyte is finally obtained. On the basis of the electrolyte pH value data, the corrosion resistance of the bridge metal is analyzed. The metal activity data is combined with the pH value data, and the corrosion rate of the metal under a specific pH environment is measured by corrosion rate experiments, immersion tests or electrochemical impedance tests. According to the test results, the statistical analysis method is used to evaluate the effect of different pH values ​​on the corrosion resistance of the metal, and the corrosion resistance data of the bridge metal is formed. Based on the corrosion resistance data of the bridge metal and the pH value data of the electrolyte, the protective electrode material is selected. In the analysis process, the corrosion resistance of the metal and the environmental conditions are considered, and the electrode materials with excellent corrosion resistance, such as titanium alloy or galvanized steel, are selected in combination with the activity analysis results. Through electrochemical testing, the current density and corrosion resistance of different protective electrode materials in actual environments are evaluated to ensure that the selected materials can effectively reduce the metal corrosion rate. Finally, the protective electrode material data is formed.

[0105] Preferably, step S14 comprises the following steps:

[0106] Step S141: measuring the conductivity of the electrode material according to the protective electrode material data to obtain the conductivity data of the protective electrode material;

[0107] Step S142: performing corrosion resistance test of the protective electrode according to the conductivity data of the protective electrode material to obtain corrosion resistance data of the protective electrode;

[0108] Step S143: performing electrode connection design according to the corrosion resistance data of the protective electrode and the bridge metal material data to obtain electrode connection design data;

[0109] Step S144: performing a galvanic cell path simulation according to the electrode connection design data and the water flow electrolyte composition data to obtain galvanic cell path data;

[0110] Step S145: constructing a bridge protection galvanic cell simulation model according to the electrode connection design data, the galvanic cell path data and the protection electrode corrosion resistance data to obtain a bridge protection galvanic cell simulation model.

[0111] In the embodiment of the present invention, when measuring the conductivity of the protective electrode material, a standard test sample must be prepared first to ensure that the size and shape of the sample are consistent. The conductivity is measured by the four-probe method, and four probes are evenly arranged on the surface of the material, a constant current is applied and the corresponding voltage value is measured. Use a corresponding measuring instrument, such as a multimeter or a dedicated conductivity meter, to record the voltage data and calculate the conductivity according to the measurement formula. Ensure that multiple tests are performed at different temperatures to eliminate the influence of temperature on the conductivity, and finally obtain accurate conductivity data of the protective electrode material. After obtaining the conductivity data of the protective electrode material, corrosion resistance inspection is required. The electrochemical impedance spectroscopy (EIS) technology is used to perform a long-term immersion experiment on the protective electrode material. The protective electrode material is placed in an environment containing a measured electrolyte, and the impedance data is recorded regularly using an electrochemical workstation. The corrosion behavior of the material is analyzed by the Nyquist diagram and the Bode diagram, and the polarization resistance is calculated to evaluate its corrosion resistance. This process generates the corrosion resistance data of the protective electrode, and the electrode connection design is performed based on the corrosion resistance data of the protective electrode and the bridge metal material data. During the design process, the layout and connection method of the electrodes are first analyzed to ensure that the connection can effectively disperse the current and reduce the risk of local corrosion. Computer-aided design (CAD) software is used to draw the electrode connection diagram, clarify the material and welding process of the connection point, and consider the contact resistance between the electrode and the metal material to optimize the electrode connection design data. Based on the electrode connection design data, the galvanic cell path is simulated. Using numerical calculation software, a three-dimensional model of the battery path is established, and the electrode connection design data and water flow electrolyte composition data are input. By setting the boundary conditions of the battery path, the current flow and the ion migration process of the electrolyte are simulated. Important parameters such as current density and potential distribution are recorded to generate galvanic cell path data. According to the electrode connection design data, galvanic cell path data and protection electrode corrosion resistance data, the bridge protection galvanic cell simulation model is constructed. In this process, dedicated simulation software is used to import all the data obtained in the early stage to establish a complete electrochemical model. The electrochemical parameters of each electrode and metal material are set to simulate the current distribution and corrosion process.

[0112] Preferably, step S2 comprises the following steps:

[0113] Step S21: Performing a water flow dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow dynamic data around the bridge;

[0114] Step S22: Calculate the water flow velocity according to the dynamic data of the water flow around the bridge to obtain the water flow velocity data around the bridge;

[0115] Step S23: analyzing ion migration changes based on the water flow velocity data around the bridge to obtain water flow ion migration change data;

[0116] Step S24: Collect electrolyte concentration change data based on the water flow ion migration change data and the water flow velocity data around the bridge to obtain electrolyte concentration change data.

[0117] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0118] Step S21: Performing a water flow dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow dynamic data around the bridge;

[0119] In the embodiment of the present invention, when performing a water flow fluid dynamic analysis, a fluid domain model around the bridge is first constructed to clarify the geometric shape and size of the model. Computational fluid dynamics (CFD) tools are applied to input environmental conditions and boundary conditions, such as water temperature, flow rate, and bridge shape, to perform fluid dynamic simulation. By numerically solving the fluid equation, simulating the flow state of water around the bridge, recording data such as fluid velocity, pressure distribution, and flow field changes, and generating water flow fluid dynamic data around the bridge.

[0120] Step S22: Calculate the water flow velocity according to the dynamic data of the water flow around the bridge to obtain the water flow velocity data around the bridge;

[0121] In the embodiment of the present invention, the water flow velocity is calculated based on the dynamic data of the water flow fluid around the bridge. The CFD simulation results are post-processed using data analysis software to extract the flow velocity information of a specific area. In the fluid model, multiple sampling points are selected, the local flow velocity of each point is calculated and the average value is taken to obtain the overall flow velocity data. The flow velocity is combined with parameters such as pressure and density using relevant formulas to verify the calculation results of the flow velocity and ensure the accuracy and representativeness of the data.

[0122] Step S23: analyzing ion migration changes based on the water flow velocity data around the bridge to obtain water flow ion migration change data;

[0123] In the embodiment of the present invention, the ion migration change analysis is carried out based on the water flow velocity data around the bridge. First, the ion migration model is constructed by combining the diffusion coefficient and flow velocity of the electrolyte using the fluid dynamics theory. On this basis, the migration process of different ions in the water flow is simulated through numerical calculation, with special attention paid to the behavior of chloride ions and sulfate ions. The concentration changes of ions in different time periods are recorded, and the migration effect of ions caused by the change of water flow velocity during the flow process is analyzed.

[0124] Step S24: Collect electrolyte concentration change data based on the water flow ion migration change data and the water flow velocity data around the bridge to obtain electrolyte concentration change data.

[0125] In the embodiment of the present invention, based on the previously obtained ion migration change data, combined with the flow rate data, the electrolyte concentration changes in different regions and time points are analyzed by calculation formulas. The chemical equilibrium equation is used to consider the reaction rate and diffusion effects of ions to simulate the dynamic change process of electrolytes in water flow. By setting multiple monitoring points, the changes in electrolyte concentration under different environmental conditions are systematically recorded to form comprehensive electrolyte concentration change data.

[0126] Preferably, step S23 includes the following steps:

[0127] Step S231: dividing the water flow velocity according to the water flow velocity data around the bridge to obtain the surrounding water flow high velocity data and the surrounding water flow low velocity data;

[0128] Step S232: analyzing the water flow turbulence phenomenon according to the surrounding water flow high velocity data to obtain the surrounding water flow turbulence phenomenon data;

[0129] Step S233: performing ion mixing degree growth calculation according to the surrounding water flow turbulence phenomenon data to obtain ion mixing degree growth data;

[0130] Step S234: performing ion migration rate growth calculation according to the ion mixing degree growth data and the surrounding water flow turbulence phenomenon data to obtain ion migration rate growth data;

[0131] Step S235: performing ion regional aggregation simulation according to the low flow velocity data of the surrounding water flow to obtain water flow ion regional aggregation data;

[0132] Step S236: performing ion concentration gradient analysis based on the water flow ion regional aggregation data and the peripheral water flow low flow rate data to obtain ion concentration gradient data;

[0133] Step S237: performing ion migration rate attenuation calculation according to the ion concentration gradient data and the surrounding water flow low flow rate data to obtain ion migration rate attenuation data;

[0134] Step S238: Perform ion migration change analysis based on the ion migration rate growth data and the ion migration rate attenuation data to obtain water flow ion migration change data.

[0135] In an embodiment of the present invention, a flow velocity cutoff value is first set, for example, the flow velocity is divided into two categories: high flow velocity and low flow velocity. Through a data analysis tool, the acquired water flow velocity data is statistically analyzed, and the flow velocity data above a specific threshold is identified and recorded as the surrounding water flow high flow velocity data. At the same time, the flow velocity data below the threshold is recorded as the surrounding water flow low flow velocity data. The distribution characteristics of the flow velocity data are displayed in the form of a histogram or a distribution diagram, so as to clearly understand the water flow conditions in different flow velocity intervals, select an analysis model of turbulence, such as a Reynolds number model, and determine the flow state by calculating the flow velocity, fluid density and viscosity. Using fluid mechanics theory, the turbulence intensity and structure are evaluated, and the vortex and instability of the flow are recorded. Using a turbulence model, such as a large eddy simulation (LES) or an eddy model, the turbulence characteristics of the high flow velocity area are deeply analyzed. According to the turbulence intensity and velocity distribution, the turbulence phenomenon data of the surrounding water flow is obtained, and the uniformity of the distribution of ions in the water flow is evaluated by combining the turbulence analysis results, using a mixed diffusion model. By setting the mixing degree parameter, the influence of different turbulence intensities on the ion mixing degree is analyzed using a numerical calculation method. By simulating the ion distribution in different time periods and recording the changes in ion concentration, the data on the growth of ion mixing degree are finally obtained. The relationship between migration rate and mixing degree is established, and the diffusion equation is used to evaluate the promoting effect of turbulence on ion migration rate. Combined with the changes in mixing degree, the growth trend of ion migration rate under different turbulent conditions is calculated by time series analysis. Finally, the data on the growth of ion migration rate is obtained, and the simulation environment is set to ensure that the flow conditions are consistent with low flow rates. Particle tracking method or numerical computational fluid dynamics method is used to simulate the movement trajectory of ions in a low flow rate environment. Through the simulation process, the aggregation phenomenon of ions in specific areas is recorded, the influence of flow rate changes on ion distribution is evaluated, and the regional aggregation data of water flow ions is obtained. Using the obtained ion aggregation data, the ion concentration in different areas is calculated, and a concentration distribution model is established. The gradient calculation formula is applied to analyze the spatial changes in ion concentration and determine the ion concentration gradient. The accuracy and representativeness of the data are ensured by the multi-point sampling method, and the ion concentration gradient data is finally generated. Using Fick's law and the concentration gradient information, the attenuation trend of ion migration under low flow conditions is analyzed. By calculating the relationship between ion migration rate and concentration gradient, the changes in migration rate in different time periods are recorded. Finally, the ion migration rate attenuation data is obtained, and the ion migration change analysis is performed based on the ion migration rate growth data and the ion migration rate attenuation data. In this process, a comprehensive model of ion migration is established in combination with the growth and attenuation data obtained previously, and the migration characteristics of ions under different water flow conditions are analyzed. By comparing the migration rates in different regions and times and recording their change patterns, the water flow ion migration change data is obtained.

[0136] Preferably, step S3 comprises the following steps:

[0137] Step S31: analyzing the electrolyte ionization degree according to the electrolyte concentration change data to obtain electrolyte ionization degree data;

[0138] Step S32: estimating ion activity according to the electrolyte ionization degree data and the electrolyte concentration change data to obtain electrolyte ion activity data;

[0139] Step S33: calculating the cathode and anode potential difference of the bridge protection galvanic battery simulation model according to the electrolyte ion activity data and the electrolyte ionization degree data, and obtaining the cathode and anode potential difference data of the galvanic battery;

[0140] Step S34: Calculate the current density based on the potential difference data of the positive and negative electrodes of the original battery and the data of the degree of electrolyte ionization to obtain the current density data of the positive and negative electrodes of the original battery.

[0141] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0142] Step S31: analyzing the electrolyte ionization degree according to the electrolyte concentration change data to obtain electrolyte ionization degree data;

[0143] In the embodiment of the present invention, the degree of ionization of the electrolyte is determined by measuring the concentration of the electrolyte and its corresponding pH value and using the ionization equilibrium equation. This process involves using a chemical analysis instrument, such as an ion chromatograph, to detect the concentration of each ion in the solution and calculate the degree of ionization of the electrolyte through the Stokkey stoichiometric relationship. Finally, the ionization data of the electrolyte at different concentrations are recorded.

[0144] Step S32: estimating ion activity according to the electrolyte ionization degree data and the electrolyte concentration change data to obtain electrolyte ion activity data;

[0145] In the embodiment of the present invention, the activity of each ion is calculated using a formula based on the Debye-Helmholtz theory and the obtained degree of ionization. Taking into account the interaction between different ions in the solution and the effect of concentration on the activity, the electrolyte ion activity data is obtained by combining experimental data with theoretical calculations.

[0146] Step S33: calculating the cathode and anode potential difference of the bridge protection galvanic battery simulation model according to the electrolyte ion activity data and the electrolyte ionization degree data, and obtaining the cathode and anode potential difference data of the galvanic battery;

[0147] In the embodiment of the present invention, the potential of the cathode and anode is calculated by using the Nye-Ahrens equation in combination with the ion activity and concentration. The contribution of different ions to the potential is analyzed by inputting the degree of ionization of the electrolyte. The calculated cathode and anode potential difference data are recorded and sorted to effectively support the subsequent current density calculation.

[0148] Step S34: Calculate the current density based on the potential difference data of the positive and negative electrodes of the original battery and the data of the degree of electrolyte ionization to obtain the current density data of the positive and negative electrodes of the original battery.

[0149] In the embodiment of the present invention, first, the current density is calculated by using the formula by combining the potential difference and the conductivity of the electrolyte through Faraday's law. In this process, the influence of the geometric shape and area of ​​the cathode and anode on the current distribution is considered to ensure the accuracy of the calculation. Through data analysis software, the current density is associated with the concentration and ionization degree of the electrolyte to generate the current density data of the cathode and anode of the primary cell.

[0150] Preferably, step S1 comprises the following steps:

[0151] Step S341: Calculate ion density according to electrolyte ionization degree data to obtain ion density data;

[0152] Step S342: Calculate the current flow driving force according to the potential difference data of the positive and negative electrodes of the original battery to obtain the current flow driving force data;

[0153] Step S343: simulating the change of electric field strength of the bridge protection galvanic battery simulation model according to the potential difference data of the positive and negative electrodes of the galvanic battery and the current flow driving force data, and obtaining the change data of the electric field strength of the galvanic battery;

[0154] Step S344: Calculate the current density based on the original battery electric field strength change data, current flow driving force data and ion density data to obtain the current density data of the positive and negative electrodes of the original battery.

[0155] In the embodiment of the present invention, the molecular weight and ionization constant of the electrolyte must first be clarified. Using the degree of ionization and concentration information, the overall concentration of the electrolyte is distributed to each ion using a formula to calculate the density of each ion. In specific operations, the mass fraction method can be used to combine the total mass of the electrolyte with the molecular weight of each ion to obtain the ion density. Laboratory equipment such as a hydrometer or an electronic balance is used to ensure the accuracy of the measurement of the ion density. Finally, the ion density data is recorded, and Ohm's law is used to combine the potential difference with the conductivity to calculate the current driving force. In the formula, the current driving force is equal to the product of the potential difference and the electrolyte conductivity. At this time, it is necessary to ensure that the conductivity data used comes from an accurate experimental measurement, and a conductivity meter is usually used for measurement. By combining conductivity with potential difference, the current flow driving force data is obtained, and the Maxwell equations and related electromagnetic theories are applied to input the potential difference and current driving force of the positive and negative electrodes to simulate the distribution and changes of the electric field. Using numerical calculation software such as COMSOL Multiphysics or Ansys Electromagnetics, a model is established and discretized to calculate the intensity changes of the electric field under different conditions. Apply the following formula: Current density is equal to the product of electric field strength and electrolyte conductivity. Input electric field strength and conductivity to calculate current density. Combined with previously obtained ion density data, evaluate how current density changes at different ion concentrations. Use data analysis tools to organize and visualize the results to ensure the accuracy and reliability of current density data.

[0156] Preferably, step S4 comprises the following steps:

[0157] Step S41: dividing the current density according to the current density data of the positive and negative electrodes of the original battery to obtain the high-density current data of the positive and negative electrodes of the original battery;

[0158] Step S42: Calculate the dissolution rate growth of the protective electrode according to the high-density data of the current of the positive and negative electrodes of the original battery to obtain the dissolution rate growth data of the protective electrode;

[0159] Step S43: according to the dissolution rate growth data of the protection electrode and the potential difference data of the positive and negative electrodes of the galvanic cell, the metal corrosion rate of the protection electrode is estimated for the bridge protection galvanic cell simulation model to obtain the metal corrosion rate data of the protection electrode;

[0160] Step S44: constructing a prediction model for the metal corrosion rate of the protective electrode of the galvanic cell according to the metal corrosion rate data of the protective electrode to obtain a prediction model for the metal corrosion rate of the protective electrode of the galvanic cell.

[0161] In the embodiment of the present invention, the current density is divided according to the current density data of the positive and negative electrodes of the original battery. First, a reasonable threshold value needs to be determined so that the current density data can be divided into two categories: high density and low density. In this process, the data analysis method is used to count the current density and draw a distribution map to identify the interval of high current density. By comparing the mean and standard deviation of the current density data, the critical value of high density is set, and the current density data exceeding this value is screened out and recorded as the high density data of the positive and negative electrodes of the original battery. The dissolution rate growth of the protective electrode is calculated according to the high density data of the positive and negative electrodes of the original battery. In this process, the electrochemical principle is used to apply the Nernst equation and Faraday's law to evaluate the effect of high current density on the dissolution rate of the protective electrode. The current density is associated with the electrochemical reaction rate of the protective electrode material, and the dissolution rate growth of the electrode under high current density is calculated. In this calculation process, the electrochemical workstation can be used to measure the experimental data to ensure the accuracy of the calculation. Finally, the dissolution rate growth data of the protective electrode is obtained. First, a relationship model between current and metal corrosion rate is established, and the electrochemical corrosion rate formula is applied to calculate in combination with the known potential difference. By comparing the dissolution rate data under different potential difference conditions, the impact on the metal corrosion rate is evaluated to ensure that the impact of each variable has been taken into account. Finally, the metal corrosion rate data of the protective electrode is recorded, and a multivariate regression model related to the metal corrosion rate is established using statistical methods and machine learning algorithms, with input of influencing factors such as current density, potential difference, and ion concentration. Software tools such as the SciPy library in MATLAB or Python are used for model training and verification, and the generalization ability of the model is ensured through cross-validation. The resulting galvanic cell protective electrode metal corrosion rate prediction model can effectively predict the corrosion rate under different environmental conditions, providing a scientific basis for the long-term durability evaluation of bridges.

[0162] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a corrosion rate prediction model for metal materials, characterized in that: The following steps are involved: Step S1: Obtain bridge design data; obtain bridge metal materials and electrolyte composition according to the bridge design data to obtain galvanic cell anode material data and electrolyte composition data; construct a bridge protection galvanic cell simulation model according to the galvanic cell anode material data and electrolyte composition data to obtain a bridge protection galvanic cell simulation model; Step S2: Performing a water flow dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow dynamic data around the bridge; Perform ion migration analysis based on the dynamic data of water flow around the bridge to obtain water flow ion migration data; The electrolyte concentration change data is collected based on the water flow ion migration data and the water flow fluid dynamic data around the bridge to obtain the electrolyte concentration change data; Step S3: Calculate the cathode and anode potential difference of the bridge protection battery simulation model according to the electrolyte concentration change data to obtain cathode and anode potential difference data of the battery; Calculate the current density according to the cathode and anode potential difference data of the battery to obtain cathode and anode current density data of the battery; Step S4: Estimate the metal corrosion rate of the protection electrode of the bridge protection galvanic cell simulation model according to the current density data of the positive and negative electrodes of the galvanic cell and the potential difference data of the positive and negative electrodes of the galvanic cell to obtain the metal corrosion rate data of the protection electrode; construct a metal material corrosion rate prediction model according to the metal corrosion rate data of the protection electrode to obtain the corrosion rate prediction model of the metal material.

2. The method for constructing a corrosion rate prediction model for metal materials according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining bridge design data; Step S12: acquiring bridge metal materials and collecting water electrolyte components according to the bridge design data to obtain bridge metal material data and water electrolyte component data; Step S13: selecting protective electrode materials according to the bridge metal material data and the water flow electrolyte composition data to obtain protective electrode material data; Step S14: constructing a bridge protection galvanic cell simulation model according to the protection electrode material data, the bridge metal material data and the water flow electrolyte composition data to obtain a bridge protection galvanic cell simulation model.

3. The method for constructing a corrosion rate prediction model for metal materials according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: performing metal activity analysis according to bridge metal material data to obtain bridge metal activity data; Step S132: performing electrolyte acidity and alkalinity test and electrolyte concentration calculation according to the water flow electrolyte data to obtain electrolyte acidity and alkalinity data and electrolyte concentration data; Step S133: Calculating the electrolyte pH value according to the electrolyte acidity and alkalinity data and the electrolyte concentration data to obtain electrolyte pH value data; Step S134: performing bridge metal corrosion resistance analysis on the bridge metal activity data according to the electrolyte pH value data to obtain bridge metal corrosion resistance data; Step S135: Select protective electrode materials according to the bridge metal corrosion resistance data and the electrolyte pH value data to obtain protective electrode material data.

4. The method for constructing a corrosion rate prediction model for metal materials according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: measuring the conductivity of the electrode material according to the protective electrode material data to obtain the conductivity data of the protective electrode material; Step S142: performing corrosion resistance test of the protective electrode according to the conductivity data of the protective electrode material to obtain corrosion resistance data of the protective electrode; Step S143: performing electrode connection design according to the corrosion resistance data of the protective electrode and the bridge metal material data to obtain electrode connection design data; Step S144: performing a galvanic cell path simulation according to the electrode connection design data and the water flow electrolyte composition data to obtain galvanic cell path data; Step S145: constructing a bridge protection galvanic cell simulation model according to the electrode connection design data, the galvanic cell path data and the protection electrode corrosion resistance data to obtain a bridge protection galvanic cell simulation model.

5. The method for constructing a corrosion rate prediction model for metal materials according to claim 3, characterized in that: Step S2 includes the following steps: Step S21: Performing a water flow dynamic analysis based on the bridge protection galvanic cell simulation model to obtain water flow dynamic data around the bridge; Step S22: Calculate the water flow velocity according to the dynamic data of the water flow around the bridge to obtain the water flow velocity data around the bridge; Step S23: analyzing ion migration changes based on the water flow velocity data around the bridge to obtain water flow ion migration change data; Step S24: Collect electrolyte concentration change data based on the water flow ion migration change data and the water flow velocity data around the bridge to obtain electrolyte concentration change data.

6. The method for constructing a corrosion rate prediction model for metal materials according to claim 5, characterized in that: Step S23 includes the following steps: Step S231: dividing the water flow velocity according to the water flow velocity data around the bridge to obtain the surrounding water flow high velocity data and the surrounding water flow low velocity data; Step S232: analyzing the water flow turbulence phenomenon according to the surrounding water flow high velocity data to obtain the surrounding water flow turbulence phenomenon data; Step S233: performing ion mixing degree growth calculation according to the surrounding water flow turbulence phenomenon data to obtain ion mixing degree growth data; Step S234: performing ion migration rate growth calculation according to the ion mixing degree growth data and the surrounding water flow turbulence phenomenon data to obtain ion migration rate growth data; Step S235: performing ion regional aggregation simulation according to the low flow velocity data of the surrounding water flow to obtain water flow ion regional aggregation data; Step S236: performing ion concentration gradient analysis based on the water flow ion regional aggregation data and the peripheral water flow low flow rate data to obtain ion concentration gradient data; Step S237: performing ion migration rate attenuation calculation according to the ion concentration gradient data and the surrounding water flow low flow rate data to obtain ion migration rate attenuation data; Step S238: Perform ion migration change analysis based on the ion migration rate growth data and the ion migration rate attenuation data to obtain water flow ion migration change data.

7. The method for constructing a corrosion rate prediction model for metal materials according to claim 5, characterized in that: Step S24 includes the following steps: Step S241: performing convection effect analysis based on the water flow velocity data around the bridge to obtain surrounding water flow convection effect data; Step S242: performing electrolyte local concentration difference attenuation analysis based on surrounding water flow convection effect data to obtain electrolyte local concentration difference attenuation data; Step S243: Calculate the growth of the reaction rate of the original battery according to the electrolyte local concentration difference attenuation data and the water flow ion migration change data to obtain the growth data of the reaction rate of the original battery; Step S244: collecting electrolyte concentration change data based on the original battery reaction rate growth data and the electrolyte local concentration difference attenuation data to obtain electrolyte concentration change data.

8. The method for constructing a corrosion rate prediction model for metal materials according to claim 6, characterized in that: Step S5 includes the following steps: Step S31: analyzing the electrolyte ionization degree according to the electrolyte concentration change data to obtain electrolyte ionization degree data; Step S32: estimating ion activity according to the electrolyte ionization degree data and the electrolyte concentration change data to obtain electrolyte ion activity data; Step S33: calculating the cathode and anode potential difference of the bridge protection galvanic battery simulation model according to the electrolyte ion activity data and the electrolyte ionization degree data, and obtaining the cathode and anode potential difference data of the galvanic battery; Step S34: Calculate the current density based on the potential difference data of the positive and negative electrodes of the original battery and the data of the degree of electrolyte ionization to obtain the current density data of the positive and negative electrodes of the original battery.

9. The method for constructing a corrosion rate prediction model for metal materials according to claim 7, characterized in that: Step S34 includes the following steps: Step S341: Calculate ion density according to electrolyte ionization degree data to obtain ion density data; Step S342: Calculate the current flow driving force according to the potential difference data of the positive and negative electrodes of the original battery to obtain the current flow driving force data; Step S343: simulating the change of electric field strength of the bridge protection galvanic battery simulation model according to the potential difference data of the positive and negative electrodes of the galvanic battery and the current flow driving force data, and obtaining the change data of the electric field strength of the galvanic battery; Step S344: Calculate the current density based on the original battery electric field strength change data, current flow driving force data and ion density data to obtain the current density data of the positive and negative electrodes of the original battery.

10. The method for constructing a corrosion rate prediction model for metal materials according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: dividing the current density according to the current density data of the positive and negative electrodes of the original battery to obtain the high-density current data of the positive and negative electrodes of the original battery; Step S42: Calculate the dissolution rate growth of the protective electrode according to the high-density data of the current of the positive and negative electrodes of the original battery to obtain the dissolution rate growth data of the protective electrode; Step S43: according to the dissolution rate growth data of the protection electrode and the potential difference data of the positive and negative electrodes of the galvanic cell, the metal corrosion rate of the protection electrode is estimated for the bridge protection galvanic cell simulation model to obtain the metal corrosion rate data of the protection electrode; Step S44: constructing a prediction model for the metal corrosion rate of the protective electrode of the galvanic cell according to the metal corrosion rate data of the protective electrode to obtain a prediction model for the metal corrosion rate of the protective electrode of the galvanic cell.

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