An electrochemical protection dynamic simulation method and system under multi-factor coupling influence

Abstract

This invention discloses a dynamic simulation method and system for electrochemical protection under the coupled influence of multiple factors, belonging to the field of electrochemical protection numerical simulation technology. The method mainly includes: acquiring electrochemical response data of electrode materials under different combinations of influencing factors; preprocessing the electrochemical response data in a structured manner and saving it to an electrochemical database; constructing an electrode reaction response relationship model based on the electrochemical response data in the electrochemical database, which outputs a corresponding current density according to the input influencing factors and potential; coupling the electrode reaction response relationship model to the electrochemical protection simulation model, using it as a local current density boundary condition for the electrode reaction in the solution, thereby realizing dynamic simulation of electrochemical protection. This invention achieves unified and continuous characterization and stable solution of multiple influencing factors and potential coupled boundary conditions, improving the realism and accuracy of dynamic simulation of electrochemical protection.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of electrochemical protection and numerical simulation, and to a technical solution for constructing electrode reaction boundary conditions based on an electrochemical database and using them for dynamic simulation of electrochemical protection under the coupled influence of multiple factors. Specifically, it relates to a method and system for dynamic simulation of electrochemical protection under the coupled influence of multiple factors. Background Technology

[0002] In the field of electrochemical protection technology, numerical simulation is an important tool for evaluating and optimizing cathodic or anodic protection designs. Existing electrochemical protection simulation methods typically use the polarization behavior of metals under single, static conditions (such as the Tafel equation, Butler-Volmer equation, or potentiodynamic polarization curves) as boundary conditions for solving the problem. However, in actual electrochemical protection systems, the service life of metallic materials often lasts for decades, and the corrosive environmental factors during service (such as temperature, conductivity, dissolved oxygen concentration, pH value, flow rate, etc.) change continuously over time, causing the polarization behavior of the electrodes to be affected by the coupling of multiple factors.

[0003] Traditional boundary condition setting methods struggle to accurately characterize the coupling relationship between current density and multiple influencing factors and potential, and are ill-suited to the dynamic changes of these factors over time in real-world operating conditions. While some existing technologies attempt to incorporate multi-parameter electrochemical data—for example, Chinese patent application CN202510557702.8 discloses a method for simulating the potential distribution of a cathodic protection system under complex corrosion conditions—this method collects polarization data under different conditions, obtains multi-dimensional polarization data through machine learning, and uses this data as the boundary condition for the model electrode to simulate the potential distribution of the cathodic protection system. However, because this approach directly uses the obtained multi-dimensional polarization data as the model electrode boundary condition in the simulation, it neglects the continuous and dynamic updating mechanism of the boundary condition solution when influencing factors change over time. This results in limited boundary condition expressiveness, poor adaptability to dynamic operating conditions, and insufficient stability of nonlinear solutions, leading to discrepancies between the simulation results and actual conditions. Consequently, it fails to meet the needs of electrochemical protection design, simulation, and optimization under real-world environmental conditions. Summary of the Invention

[0004] To address the aforementioned issues of inaccurate characterization of multi-factor coupling relationships and poor adaptability to dynamic operating conditions under boundary conditions, this invention provides a dynamic simulation method and system for electrochemical protection under the influence of multi-factor coupling. This invention primarily achieves unified and continuous characterization and stable solution of multiple influencing factors and potential coupling boundary conditions by integrating an electrochemical database, an electrode reaction response relationship model, and dynamic updating of boundary conditions, thereby improving the realism and accuracy of electrochemical protection dynamic simulation.

[0005] The technical means employed in this invention are as follows: A dynamic simulation method for electrochemical protection under the coupled influence of multiple factors includes the following steps: acquiring electrochemical response data of electrode materials under different combinations of influencing factors, wherein the electrochemical response data includes potential. E With current density i The influencing factors include at least two of temperature, conductivity, dissolved oxygen concentration, pH, and flow rate. The electrochemical response data is pre-processed in a structured manner and then saved to an electrochemical database, which is used to characterize the correspondence between influencing factors, potential, and current density. Based on the electrochemical response data in the electrochemical database, an electrode reaction response relationship model is constructed to output the corresponding current density according to the input influencing factors and potential. The electrode reaction response relationship model is coupled to the electrochemical protection simulation model and used as a local current density boundary condition for the electrode reaction in solving the electrochemical protection simulation model, thereby achieving dynamic simulation of electrochemical protection.

[0006] The above scheme constructs an electrochemical database containing multi-factor coupling information and an electrode reaction response relationship model, and couples them to the simulation model as boundary conditions. This achieves a unified characterization of the coupling effect of multiple factors and potential on current density, and solves the problem of single and static boundary conditions in traditional methods.

[0007] As an optional implementation, the method further includes: during the electrochemical protection dynamic simulation process, calling or updating the electrode reaction response relationship model according to changes in influencing factors and / or time.

[0008] The above scheme improves the realism of dynamic simulation by using a dynamic update mechanism to enable the simulation process to adapt to environmental conditions that change over time.

[0009] As an optional implementation, the electrode reaction response relationship model is invoked or updated according to the influencing factors and / or time changes, including: setting at least one of the influencing factors as a time function, a spatial function, an external input function, a monitoring input function or a combination thereof, and dynamically updating the local current density boundary conditions during the simulation to achieve dynamic simulation of electrochemical protection.

[0010] The above scheme achieves real-time dynamic response of boundary conditions by setting influencing factors through functions, thereby enhancing the simulation's adaptability to real working conditions.

[0011] As an optional implementation, the electrochemical response data is subjected to structured preprocessing, including: performing at least one of the following preprocessing operations on the electrochemical response data: resampling, alignment, point filling, denoising, normalization, and interpolation; and saving the preprocessed electrochemical response data as a unified structured data file.

[0012] The above solution improves data consistency and callability by standardizing the data, providing a high-quality data foundation for subsequent model building.

[0013] As an optional implementation, the electrode response relationship model is constructed using at least one of the following: interpolation model, fitting model, regression model, lookup table model, and surrogate model.

[0014] The above solutions offer multiple model construction methods, enhancing the flexibility and applicability of the technical solutions.

[0015] As an optional implementation, when solving the electrochemical protection simulation model, boundary constraints, anomaly detection, validity verification, extrapolation control, fault-tolerant rollback, or protection strategies are set for the input variables and / or output results.

[0016] The above scheme effectively avoids abnormal divergence during the solution process by setting protection strategies, thereby improving the stability and reliability of simulation calculations.

[0017] As an optional implementation, the electrochemical protection simulation model runs on a finite element platform, finite difference platform, boundary element platform, co-simulation platform, or custom numerical solution platform, and the electrode reaction response relationship model is implemented by script programs, compilers, library functions, service interfaces, or a combination thereof.

[0018] The above approach improves the adaptability of the method to different simulation platforms and computing environments.

[0019] Furthermore, this invention also provides a dynamic simulation system for electrochemical protection under the coupled influence of multiple factors, used to implement the method described above, comprising: a data acquisition module, used to acquire electrochemical response data of electrode materials under different combinations of influencing factors, wherein the electrochemical response data includes potential. E With current density i The influencing factors include at least two of temperature, conductivity, dissolved oxygen concentration, pH, and flow rate; a database construction module is used to perform structured preprocessing on the electrochemical response data and save it to an electrochemical database, which is used to characterize the correspondence between influencing factors, potential, and current density; a model construction module is used to construct an electrode reaction response relationship model based on the electrochemical response data in the electrochemical database, which outputs the corresponding current density according to the input influencing factors and potential; a model coupling module is used to couple the electrode reaction response relationship model to the electrochemical protection simulation model, and use it as a local current density boundary condition of the electrode reaction to participate in the solution of the electrochemical protection simulation model, thereby realizing dynamic simulation of electrochemical protection.

[0020] The system described above achieves full-process processing from data acquisition and model building to dynamic simulation through the collaborative work of its various modules, and features a clear structure and complete functions.

[0021] As an optional implementation, the system further includes a model optimization module, which is used to call or update the electrode reaction response relationship model according to changes in influencing factors and / or time during the dynamic simulation of electrochemical protection.

[0022] The above solution achieves dynamic adjustment of the simulation process through optimization modules, further improving the simulation accuracy of the system.

[0023] Furthermore, the present invention also provides a storage medium comprising a stored program, wherein, when the program is executed, it performs a dynamic simulation method for electrochemical protection under the combined influence of multiple factors as described above.

[0024] The aforementioned storage medium provides a carrier for the implementation of the above methods, facilitating the storage, distribution, and application of the technology.

[0025] Compared with the prior art, the present invention has the following advantages: This invention provides a dynamic simulation method and system for electrochemical protection under the influence of multiple factors coupling. By constructing an electrochemical database containing multi-factor coupling information and an electrode reaction response relationship model, and coupling it to the simulation model as a local current density boundary condition, a unified and continuous characterization of the coupling effect of multiple influencing factors and potential on current density is achieved, avoiding the errors caused by dimensionality reduction or segmented switching in traditional methods.

[0026] Meanwhile, by dynamically updating boundary conditions based on changes in influencing factors and time during the simulation process, this invention solves the problem that existing technologies are difficult to adapt to changes in actual dynamic operating conditions, and significantly improves the realism and accuracy of dynamic simulation of electrochemical protection.

[0027] Furthermore, by setting protection strategies for inputs and outputs, this invention effectively improves the stability and convergence efficiency of the nonlinear solution process, and has good platform adaptability and engineering applicability. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1This is a flowchart of the dynamic simulation method for electrochemical protection under the multi-factor coupling influence of an embodiment of the present invention.

[0030] Figure 2 These are typical polarization curves obtained from tests in embodiments of the present invention, wherein (a) is a typical anodic polarization curve and (b) is a typical cathodic polarization curve.

[0031] Figure 3 These are schematic diagrams of the storage tank, anode, and experimental setup according to embodiments of the present invention.

[0032] Figure 4 This is a graph showing the changes in influencing factors over time in an embodiment of the present invention.

[0033] Figure 5 The results are obtained from the simulation of the final moment in the embodiment of the present invention, where (a) is the electrolyte current density distribution cloud map and (b) is the surface potential distribution cloud map.

[0034] Figure 6 The figures show the simulation and experimental results of the surface potential distribution along the radius over 25 days according to an embodiment of the present invention, where (a) is the simulation result and (b) is the experimental result.

[0035] Figure 7 These are detailed comparison charts of the potential distribution simulation results and experimental results of the embodiments of the present invention, wherein (a) is the overall comparison chart for 25 days; (b) is the comparison chart for day 5; (c) is the comparison chart for day 10; (e) is the comparison chart for day 15; (f) is the comparison chart for day 20; and (g) is the comparison chart for day 25.

[0036] Figure 8 These are a series of graphs showing the changes in electrolyte current density distribution over 20 days according to an embodiment of the present invention, wherein (a) is the electrolyte current density distribution on day 5; (b) is the electrolyte current density distribution on day 10; (c) is the electrolyte current density distribution on day 15; and (d) is the electrolyte current density distribution on day 20.

[0037] Figure 9 These are graphs showing the changes in surface potential distribution over 20 days according to an embodiment of the present invention, wherein (a) is the surface potential distribution on day 5; (b) is the surface potential distribution on day 10; (c) is the surface potential distribution on day 15; and (d) is the surface potential distribution on day 20. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0039] Example 1: like Figure 1 As shown, this embodiment provides a dynamic simulation method for electrochemical protection under the coupled influence of multiple factors. This method constructs an electrochemical database covering multi-dimensional influencing factors, and based on this database, builds an electrode reaction response relationship model capable of responding to complex operating condition changes. Finally, this model is coupled into the simulation solver, achieving accurate simulation of the dynamic behavior of the electrochemical protection system.

[0040] Specifically, the method includes the following steps: Step S100: Obtain electrochemical response data of the electrode material under different combinations of influencing factors, wherein the electrochemical response data includes potential. E With current density i The influencing factors include at least two of the following: temperature, conductivity, dissolved oxygen concentration, pH, and flow rate.

[0041] In practical electrochemical protection engineering scenarios, the polarization behavior of electrode materials is often not influenced by a single factor in isolation, but rather is the result of the synergistic effect of multiple environmental factors. For example, in a marine environment, changes in temperature alter the reaction rate constant, while changes in dissolved oxygen concentration directly affect the cathode depolarization process; both together determine the current density output. Therefore, the different combinations of influencing factors described in this step specifically refer to discretizing and sampling at least two influencing factors (e.g., using a full factorial experimental design) to obtain a family of polarization curves that can reflect the coupling effect of multiple factors. It should be understood that although this embodiment lists specific physical quantities such as temperature and conductivity, in other embodiments, the influencing factors can also be extended to other environmental parameters that significantly alter the electrode polarization state, such as pressure and salinity, as long as these parameters can be numerically quantified and participate in model construction.

[0042] Step S200: After performing structured preprocessing on the electrochemical response data, the data is saved to an electrochemical database, which is used to characterize the relationship between influencing factors, potential and current density.

[0043] Since the raw data acquired in step S100 may originate from different testing equipment, literature records, or simulation results, its data format, sampling interval, and potential range often differ. Direct use of this data can lead to difficulties in model training or decreased accuracy. Therefore, this step emphasizes structured preprocessing, that is, transforming unstructured or semi-structured raw polarization data into a dataset with a unified format and a unified potential grid. This electrochemical database essentially constructs a multidimensional mapping space, with the input dimensions being various influencing factors and potentials, and the output dimension being current density. Unlike traditional databases that only store a single polarization curve, the database described in this invention explicitly stores the coupling correlation between multiple factors and the electrochemical response, providing a data foundation for subsequent models to capture complex nonlinear relationships.

[0044] For step S200, the electrochemical response data is subjected to structured preprocessing, including: performing at least one of the following preprocessing operations on the electrochemical response data: resampling, alignment, point filling, denoising, normalization, and interpolation; and saving the preprocessed electrochemical response data as a unified structured data file.

[0045] Specifically, since raw polarization curve data from experimental tests or literature extraction often suffers from uneven sampling intervals, inconsistent potential ranges, or the presence of random noise, directly constructing a database can lead to computational distortion or decreased interpolation accuracy at the boundaries of the model. Therefore, this embodiment adopts the following processing flow: First, a unified standard potential sequence is set (e.g., from -1.5V to 0.5V, with a step size of 0.001V). Through resampling and alignment operations, all polarization curves from different sources are mapped onto this standard sequence. Second, for potential intervals with missing data, linear interpolation or spline interpolation is used to fill in the missing points to ensure data continuity. Simultaneously, methods such as moving average or wavelet analysis are used to denoise the raw data, eliminating random interference during the testing process. Finally, to eliminate the influence of different influencing factors on model training, input variables such as temperature and conductivity can be normalized. After the above preprocessing, the data is saved as a unified structured data file such as a CSV file, Excel spreadsheet, or database record. This standardized data structure not only improves data reading efficiency, but more importantly, it ensures the consistency of input and output during the training and invocation of the electrode response relationship model, thus avoiding simulation crashes caused by data format mismatch from the source.

[0046] Step S300: Construct an electrode reaction response relationship model based on the electrochemical response data in the electrochemical database to output the corresponding current density according to the input influencing factors and potential.

[0047] The core of this step lies in transforming discrete database records into a continuous, computable mathematical model. The electrode reaction-response relationship model can be viewed as a complex function mapper. Specifically, when a specific set of environmental parameters (such as temperature T=30℃, pH=8) and potential are input, the model can be transformed into a continuous, computable mathematical model. E At that time, the model can quickly and accurately output the corresponding current density. i This eliminates the need to repeatedly query massive data tables during the simulation process.

[0048] For the construction of the electrode reaction response relationship model in step S300, the electrode reaction response relationship model is constructed using at least one of the following: interpolation model, fitting model, regression model, lookup table model, and surrogate model. Specifically, the selection of the model needs to comprehensively consider data density, computational efficiency, and nonlinearity. For example, when the data points in the electrochemical database are densely and regularly distributed, an interpolation model (such as multidimensional linear interpolation or spline interpolation) can be used. This method is fast and can accurately pass through all data points, meeting the simulation scenarios with high real-time requirements. When the data contains some noise or requires a clear mathematical expression, a fitting model or regression model (such as polynomial fitting or nonlinear regression) can be used. A smooth functional relationship can be obtained through algorithms such as least squares, thereby filtering out random errors in the data. In scenarios facing high-dimensional influencing factors and limited computational resources, a surrogate model (such as the Kriging model, response surface model, or neural network model) is preferred. The surrogate model can significantly reduce computational overhead while ensuring a certain level of accuracy, and is especially suitable for iterative solution processes that require repeated calls to boundary conditions. It should be understood that the above models can be used individually or in combination. For example, data can be encrypted first using an interpolation model, and then a surrogate model can be trained to improve generalization ability. In a preferred embodiment, the electrode reaction response relationship model adopts a four-dimensional regular grid interpolation model based on an electrochemical database, with temperature as the input. T Electrical conductivity sigma Dissolved oxygen concentration DO and potential E The output is current density. i ,Right now i=F(T,sigma,DO,E) Specifically, the discrete data in the electrochemical database are reconstructed into a four-dimensional regular grid tensor according to four dimensions: temperature, conductivity, dissolved oxygen concentration, and potential. I(T a sigma b ,DO c ,E d ) For any query point ( T, sigma, DO, E) The corresponding current density value is calculated using four-dimensional linear interpolation. Preferably, it can be expressed as: in, w mnpq The interpolation weights are determined based on the relative distances between the query point and its adjacent grid points.

[0049] More preferably, the partial derivative of the electrode response model with respect to potential can also be calculated. This information is then used as auxiliary derivative information in the solver for nonlinear solutions. One implementation method is to use a central difference form for approximate calculation: in, h This represents the sampling step size or its proportional value in the potential direction.

[0050] The aforementioned model and its derivative auxiliary information can continuously characterize multiple influencing factors and potential coupling boundary conditions while maintaining the physical meaning of the measured data. It also helps to reduce solution oscillations under nonlinear boundary conditions and improve convergence stability and computational efficiency.

[0051] In solving the electrochemical protection simulation model, the electrode reaction response relationship model is usually highly nonlinear, and direct coupling can easily lead to numerical solution divergence or non-convergence. Therefore, when solving the electrochemical protection simulation model, boundary constraints, anomaly detection, validity verification, extrapolation control, fault-tolerant rollback, or protection strategies are set for the input variables and / or output results. As a specific implementation, the boundary constraints include: setting the influencing factors of the input to upper and lower limits within the range covered by the electrochemical database; when the input value exceeds the range, it is restricted to the corresponding boundary value; setting a controlled extrapolation interval for the input potential, preferably a buffer zone that extends to both sides of the potential database range by a preset proportion, and performing controlled calculations within this buffer zone. The anomaly detection includes: validating the output current density; when a non-numerical value, infinity, or abnormal jump occurs, the current calculation result is determined to be invalid. The fault-tolerant rollback includes: when an invalid result is detected, the input potential is rolled back to the strict boundary range of the database and the corresponding current density is recalculated; if necessary, the solution time step can be further reduced, the nonlinear iterative relaxation parameters adjusted, or the current calculation step re-executed to prevent non-physical values ​​from entering the solver and causing calculation divergence. Through the above boundary constraints, controlled extrapolation, anomaly detection, and fault-tolerant rollback mechanisms, the stability and robustness of the solution when multiple coupled boundary conditions are involved can be improved.

[0052] Furthermore, the electrochemical protection simulation model runs on a finite element platform, finite difference platform, boundary element platform, co-simulation platform, or custom numerical solution platform. The electrode reaction response relationship model is implemented by script programs, compilers, library functions, service interfaces, or a combination thereof. In a preferred embodiment, the electrochemical protection simulation model is built on the COMSOL Multiphysics finite element simulation platform, which has powerful multiphysics coupling solution capabilities and can handle complex geometric models and boundary conditions. The electrode reaction response relationship model is implemented through MATLAB script programs. Utilizing MATLAB's rich matrix operations and toolbox functions, multidimensional interpolation calculations can be efficiently performed. The model coupling module is implemented through the COMSOL LiveLink for MATLAB interface. Specifically, during simulation, the COMSOL solver calls MATLAB functions through the interface at each iteration step, passing the potential and environmental parameters of the current node to MATLAB. After MATLAB completes the calculation, it returns the current density to COMSOL as the boundary condition. This interactive approach leverages the advantages of mature commercial software solvers and flexibly extends the custom boundary condition model through the programming interface, greatly improving the engineering feasibility of the technical solution. Of course, in other embodiments, a Python script can be used in conjunction with the FEniCS open-source finite element library, or the model can be compiled into a C++ dynamic link library for the simulation platform to call directly, in order to further improve the calculation speed.

[0053] Step S400: The electrode reaction response relationship model is coupled to the electrochemical protection simulation model and used as the local current density boundary condition of the electrode reaction in the solution of the electrochemical protection simulation model, thereby realizing the dynamic simulation of electrochemical protection.

[0054] In traditional electrochemical simulations, boundary conditions are typically set as fixed Tafel slopes or single polarization curve functions, failing to reflect environmental changes. This embodiment, however, embeds the aforementioned electrode reaction-response relationship model into the simulation solver through interface technology. During each iteration, the simulation solver transmits the real-time potentials of each node on the electrode surface and the current environmental parameters to the electrode reaction-response relationship model. The model then calculates and returns the corresponding current density value, which serves as the boundary condition for that node in the next round of solving the physical field equations. This coupling mechanism transforms the boundary conditions from static preset values ​​into dynamically changing boundaries that adapt to potential and environmental factors, thereby enabling dynamic simulation of the multi-factor coupling effects under real-world conditions.

[0055] More preferably, the method further includes calling or updating the electrode reaction response relationship model according to changes in influencing factors and / or time during the electrochemical protection dynamic simulation process.

[0056] This is the core step in achieving dynamic simulation in this invention. In traditional static simulations, once the boundary conditions are set, they remain unchanged, failing to reflect the diurnal or seasonal changes in parameters such as temperature and humidity over time in the real environment. This embodiment overcomes this limitation by introducing a dynamic update mechanism. Specifically, the electrode reaction response relationship model is invoked or updated based on influencing factors and / or time changes, including: setting at least one of the influencing factors as a time function, spatial function, external input function, monitoring input function, or a combination thereof, and dynamically updating the local current density boundary conditions during the simulation process to achieve dynamic simulation of electrochemical protection.

[0057] In practice, this dynamic updating can be achieved in several ways. For example, if the influencing factors are set as a time function, then within each time step of the simulation solver, the system will automatically read the temperature value T(t) and dissolved oxygen concentration DO(t) corresponding to the current time t, and update these dynamically changing parameters along with the potential distribution on the electrode surface at that time. E (x, y, z, t) are input together into the electrode response model. After calculation, the model outputs the current density at each node at that moment. i (x,y,z,t) allows for real-time updating of boundary conditions. If the influencing factors are set as spatial functions, the impact of environmental parameter differences at different depths of the tank or different locations on the electrochemical protection effect can be simulated. Furthermore, environmental data collected in real-time by on-site sensors can be input into the simulation model via external input functions or monitoring input functions, achieving real-time simulation prediction at the digital twin level.

[0058] Combination Figure 4 As shown in the specific application scenario of this embodiment, the three influencing factors—temperature, conductivity, and dissolved oxygen concentration—are all set as functions of time. During the simulation, as the simulation time progresses from day 0 to day 25, the temperature generally shows a trend of increasing and then decreasing in stages, the conductivity changes relatively little, and the dissolved oxygen concentration first decreases and then rebounds. The simulation solver recalculates the current density boundary conditions under the current environmental parameters at each time step, thereby capturing the dynamic change of the sacrificial anode output current with environmental factors. This mechanism makes the simulation results no longer a static snapshot under a single operating condition, but a dynamic image reflecting the evolution of the electrochemical protection system throughout its entire service life, significantly improving the realism of the simulation results and their engineering guidance value.

[0059] To verify the effectiveness and accuracy of the electrochemical protection dynamic simulation method under the coupled influence of multiple factors provided by this invention, this embodiment takes a sacrificial anode protection system in the sedimentation water area of ​​a storage tank as a specific application scenario, combined with... Figures 2 to 9The specific implementation process and simulation results of the method are explained in detail.

[0060] First, experimental design and data acquisition were conducted. For the sedimentation water environment at the bottom of the storage tank, three key influencing factors—temperature, conductivity, and dissolved oxygen concentration—were selected, and a three-factor, five-level full-factor experiment was designed. As shown in Table 1, the temperature levels were set to 30℃, 35℃, 40℃, 45℃, and 50℃; the conductivity levels were set to 40mS / cm, 50mS / cm, 60mS / cm, 70mS / cm, and 80mS / cm; and the dissolved oxygen concentration levels were set to 0.2mg / L, 0.6mg / L, 1.2mg / L, 2.0mg / L, and 3.0mg / L. Based on the tank bottom plate material of Q235 carbon steel and the Al-Zn-In series AC-2 type aluminum alloy sacrificial anode, anodic and cathodic polarization curves were tested under the above 125 combined operating conditions, obtaining a total of 250 polarization curve data. Figure 2 Typical anodic and cathodic polarization curves are shown. It should be understood that although the above three factors were selected in this embodiment, in practical applications, other influencing factors such as pH and flow rate can be added according to specific environmental characteristics to construct a higher-dimensional electrochemical database.

[0061] Table 1. Level values ​​of each factor in the full factorial experiment.

[0062] Secondly, an electrochemical database was constructed. The raw polarization curve data obtained from the experiments underwent structured preprocessing. Specific operations included: setting a uniform potential sequence grid; resampling and aligning the raw data to ensure all curves had current density values ​​at the same potential nodes; using spline interpolation to fill in missing potential intervals; and removing abnormal noise points from the testing process. Finally, the processed data was saved as structured data files in CSV format, forming the cathodic electrochemical database and the anodic electrochemical database, respectively. This database explicitly characterizes the multidimensional mapping relationship between temperature, conductivity, dissolved oxygen concentration, potential, and current density.

[0063] Subsequently, an electrode reaction-response relationship model was established and coupled. A MATLAB script was used to read the aforementioned CSV database file and construct a multidimensional linear interpolation model based on a regular grid as the electrode reaction-response relationship model. This model can quickly interpolate and calculate the corresponding current density based on the input temperature, conductivity, dissolved oxygen concentration, and potential value. Simultaneously, a numerical difference method was used to calculate the approximate derivative information of the model with respect to potential to assist in subsequent nonlinear solutions. An electrochemical protection simulation model of the storage tank was established in the COMSOL Multiphysics finite element simulation platform, as follows... Figure 3As shown, the geometric parameters of the tank and anode were set according to the experimental setup. Using the COMSOL LiveLink for MATLAB interface, the constructed electrode reaction response model and its derivative information were coupled into the simulation model, defined as the local current density boundary condition on the electrode surface. In the solution settings, boundary constraints were set for the input variables to prevent the interpolation model from extrapolating outside the database range; and anomaly detection and fault-tolerant rollback strategies were enabled to ensure the stability of the solution process. Furthermore, dynamic simulation parameters were set. Based on actual monitoring data, temperature, conductivity, and dissolved oxygen concentration were set as functions that change with time. Figure 4 As shown, within the 25-day simulation period, the temperature generally showed a trend of increasing in stages followed by a decrease, while the conductivity changed relatively little, and the dissolved oxygen concentration first decreased and then rebounded. During the simulation, the solver automatically read the environmental parameters at each time step and updated the boundary conditions in real time by calling the electrode reaction response relationship model, thus realizing dynamic simulation under the coupled influence of multiple factors.

[0064] Finally, the simulation results are analyzed and verified. Figure 5 The simulation results show the electrolyte current density distribution cloud map and surface potential distribution cloud map at the final moment, which intuitively reflects the current field and potential field distribution of the protection system. Figure 6 Simulation and experimental results of the surface potential distribution along the radius over 25 days are presented. Figure 7 The simulation results of the potential distribution were compared with the experimental results in detail. Ultimately, the simulation results showed a high degree of consistency with the experimental results, with an average agreement rate of 97.82% for the potential results and 91.33% for the anode output current results. This high-precision agreement strongly demonstrates that the method of this invention can accurately capture the influence of multi-factor coupling on electrode reaction behavior and, through a dynamic update mechanism, realistically reflect the impact of environmental parameters changing over time on the electrochemical protection system. Figure 8 and Figure 9 The dynamic evolution of electrolyte current density and surface potential distribution over 20 days was further demonstrated, clearly showing the dynamic law that the protective current density gradually increases and the surface potential gradually shifts positively with increasing temperature and consumption of anode material, which is consistent with theoretical expectations. This embodiment, through quantitative comparison of experimental data and simulation results, fully verifies the authenticity, accuracy, and engineering applicability of the technical solution of this invention.

[0065] Example 2: This embodiment provides a dynamic simulation system for electrochemical protection under the coupled influence of multiple factors. This system is used to implement the method described in Embodiment 1. Through a modular architecture design, the system organically integrates data acquisition, model construction, and simulation solution, realizing an automated processing flow from multi-source heterogeneous data to dynamic simulation results.

[0066] Specifically, the system includes a data acquisition module, a database construction module, a model construction module, and a model coupling module. The data acquisition module is used to acquire electrochemical response data of the electrode material under different combinations of influencing factors, and the electrochemical response data includes potential. E With current density i The influencing factors include at least two of temperature, conductivity, dissolved oxygen concentration, pH, and flow rate. At the hardware level, the data acquisition module can function as a communication interface connected to the electrochemical workstation, or as a program script extracting data from a Laboratory Information Management System (LIMS). Specifically, the data acquisition module can connect to the electrochemical workstation via GPIB, USB, or Ethernet interfaces to read polarization curve data in real time; alternatively, it can access a remote database via a network interface to download historical polarization data recorded in literature. This module is responsible for uniformly converting raw data from different sources and in different formats into a system-recognizable format, providing standardized input for subsequent processing.

[0067] The database construction module is used to perform structured preprocessing on the electrochemical response data and save it to an electrochemical database, which is used to characterize the correspondence between influencing factors, potential, and current density. This module can be deployed on a local server's storage medium or on a cloud database server. It integrates data cleaning and reconstruction algorithms, automatically performing the aforementioned preprocessing operations such as resampling, alignment, and denoising. The database construction module and the data acquisition module are connected via a data bus. After the data acquisition module completes the input of a batch of data, the database construction module automatically triggers the preprocessing process and stores the processed structured data in the form of a multidimensional array or relational data table, establishing a high-dimensional mapping space of "influencing factors - potential - current density".

[0068] The model building module is used to construct an electrode reaction response model based on electrochemical response data in the electrochemical database, which outputs a corresponding current density according to input influencing factors and potential. This module is the core computing unit of the system, typically consisting of a high-performance processor (CPU or GPU) and modeling software running on it. The model building module reads the structured data generated by the database building module through an internal data interface and constructs a mathematical model according to preset algorithm strategies (such as interpolation algorithms, fitting algorithms, etc.). This module encapsulates the generated model parameters or model files into callable function interfaces, such as compiled dynamic link library (.dll) files or Web API service interfaces, for subsequent modules to call.

[0069] The model coupling module couples the electrode reaction response model to the electrochemical protection simulation model, and uses it as a local current density boundary condition for the electrode reaction in the electrochemical protection simulation model solution, thereby achieving dynamic simulation of electrochemical protection. This module acts as a bridge between the mathematical model and the physical simulation. In terms of software architecture, the model coupling module typically includes an adapter layer interface, enabling data interaction with commercial simulation software such as COMSOL and ANSYS, or custom solvers. Specifically, during the simulation solution process, the model coupling module listens for requests from the simulation solver in real time. When the solver calculates the electrode surface boundary conditions, the model coupling module automatically captures the time, potential, and environmental parameter information of the current node and passes them as input variables to the electrode reaction response model generated by the model building module. After the model calculation returns the current density value, the model coupling module feeds this value back to the simulation solver, using it as the boundary condition for that node in the equation system solution. This bidirectional interactive data flow mechanism ensures that the boundary conditions can dynamically change with the simulation process.

[0070] As an optional implementation, the system also includes a model optimization module, which is used to call or update the electrode reaction response relationship model based on changes in influencing factors and / or time during the dynamic simulation of electrochemical protection. This module acts as the system's dynamic scheduling center, and it has a pre-built time function library and an external interface monitoring program. When the simulation scenario involves dynamic operating conditions, the model optimization module calculates the values ​​of each influencing factor at the current moment based on the preset time function or real-time input from external sensors, and triggers the model coupling module to update the boundary condition parameters. For example, when the ambient temperature changes over time, the model optimization module calculates the current temperature value T(t) and transmits it to the model coupling module, thereby achieving real-time updating of the simulation boundary conditions. By setting this module, the system can be extended from static simulation to dynamic simulation, significantly improving the system's adaptability to real-world complex operating conditions.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic simulation method for electrochemical protection under the coupled influence of multiple factors, characterized in that, Includes the following steps: Acquire electrochemical response data of electrode materials under different combinations of influencing factors, wherein the electrochemical response data includes potential. E With current density i The influencing factors include at least two of the following: temperature, conductivity, dissolved oxygen concentration, pH, and flow rate. The electrochemical response data is preprocessed in a structured manner and then saved to an electrochemical database, which is used to characterize the relationship between influencing factors, potential and current density. An electrode reaction response relationship model is constructed based on the electrochemical response data in the electrochemical database to output the corresponding current density according to the input influencing factors and potential. The electrode reaction response relationship model is coupled into the electrochemical protection simulation model and used as a local current density boundary condition of the electrode reaction in the solution of the electrochemical protection simulation model, thereby realizing the dynamic simulation of electrochemical protection.

2. The dynamic simulation method for electrochemical protection under the coupled influence of multiple factors as described in claim 1, characterized in that, The method further includes: during the dynamic simulation of electrochemical protection, calling or updating the electrode reaction response relationship model according to changes in influencing factors and / or time.

3. The method for dynamic simulation of electrochemical protection under the coupled influence of multiple factors as described in claim 2, characterized in that, The electrode response relationship model is invoked or updated based on influencing factors and / or changes over time, including: At least one of the influencing factors is set as a time function, a space function, an external input function, a monitoring input function, or a combination thereof, and the local current density boundary conditions are dynamically updated during the simulation to achieve dynamic simulation of electrochemical protection.

4. The dynamic simulation method for electrochemical protection under the coupled influence of multiple factors as described in claim 1, characterized in that, The electrochemical response data undergoes structured preprocessing, including: The electrochemical response data is subjected to at least one of the following preprocessing operations: resampling, alignment, point filling, denoising, normalization, and interpolation. The preprocessed electrochemical response data is saved as a uniform structured data file.

5. The dynamic simulation method for electrochemical protection under the coupled influence of multiple factors according to claim 1, characterized in that, The electrode response relationship model is constructed using at least one of the following: interpolation model, fitting model, regression model, lookup table model, and surrogate model.

6. The dynamic simulation method for electrochemical protection under the coupled influence of multiple factors according to claim 1, characterized in that, When solving the electrochemical protection simulation model, boundary constraints, anomaly detection, validity verification, extrapolation control, fault tolerance rollback, or protection strategies are set for the input variables and / or output results.

7. The dynamic simulation method for electrochemical protection under the coupled influence of multiple factors according to claim 1, characterized in that, The electrochemical protection simulation model runs on a finite element platform, finite difference platform, boundary element platform, co-simulation platform, or custom numerical solution platform, and the electrode reaction response relationship model is implemented by script programs, compilers, library functions, service interfaces, or a combination thereof.

8. A dynamic simulation system for electrochemical protection under the coupled influence of multiple factors, used to implement the method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire electrochemical response data of electrode materials under different combinations of influencing factors, including potential. E With current density i The influencing factors include at least two of the following: temperature, conductivity, dissolved oxygen concentration, pH, and flow rate. The database construction module is used to perform structured preprocessing on the electrochemical response data and save it to the electrochemical database, which is used to characterize the correspondence between influencing factors, potential and current density. The model building module is used to build an electrode reaction response relationship model based on the electrochemical response data in the electrochemical database, which outputs the corresponding current density according to the input influencing factors and potential. The model coupling module is used to couple the electrode reaction response relationship model to the electrochemical protection simulation model, and participate in the solution of the electrochemical protection simulation model as the local current density boundary condition of the electrode reaction, thereby realizing the dynamic simulation of electrochemical protection.

9. The electrochemical protection dynamic simulation system under the coupled influence of multiple factors according to claim 8, characterized in that, The system also includes a model optimization module, which is used to call or update the electrode reaction response relationship model according to the changes in influencing factors and / or time during the dynamic simulation of electrochemical protection.

10. A storage medium, characterized in that, The storage medium includes a stored program, wherein when the program is executed, it performs the electrochemical protection dynamic simulation method under the multi-factor coupling influence as described in any one of claims 1 to 7.

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