Method for constructing corrosion performance model of metal material and method for determining corrosion performance

Abstract

The present application relates to a method, device, computer device, storage medium, and computer program product for constructing a corrosion performance model of a metal material. The method includes: obtaining an initial corrosion performance model of the metal material, a measured value of the corrosion layer thickness, and combined values of various first constants and second constants; taking the minimum of the sum of squares of the differences between the measured corrosion layer thickness and the calculated corrosion layer thickness values as the objective to obtain initial values of the parameters required for model construction; updating the initial corrosion performance models corresponding to the metal materials; performing iterative processing on the initial values of the parameters required for model construction under the combined values of the various first constants and second constants to obtain target values of the parameters required for model construction under the combined values of the various first constants and second constants, and obtaining a target corrosion performance model of the metal material. Using this method, a corrosion performance model of the metal material can be accurately constructed. In addition, an accurate method, device, computer device, storage medium, and computer program product for determining the corrosion performance of a metal material are also provided.

Description

Technical Field

[0001] The present application relates to the field of nuclear power technology, and in particular, to a method, device, computer device, storage medium, and computer program product for constructing a corrosion performance model of a metal material. In addition, it also relates to a method, device, computer device, storage medium, and computer program product for determining the corrosion performance of a metal material. Background Art

[0002] In a nuclear power plant reactor, corrosion of metal materials during service is ubiquitous. In a sense, corrosion may cause component failures that are more serious than strength damage or fatigue damage of metal materials. Its effects will not only reduce the structural life of the overall component, but also pose potential safety hazards to the operation of the equipment. The related problems caused by corrosion have become one of the important factors affecting nuclear energy safety, and it is particularly important to model the corrosion performance of metal materials.

[0003] In traditional technologies, semi-empirical and semi-theoretical models, such as the Arrhenius equation, are generally used to describe the corrosion performance of metal materials in the service environment. And in the Arrhenius equation, the final corrosion performance is usually calculated through the real-time measurement parameters in the equation. However, the working conditions of metal materials during service usually change and are usually not measurable in real time. Therefore, it is impossible to accurately model the corrosion performance of materials relying on this method. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide an accurate method, device, computer device, computer-readable storage medium, and computer program product for constructing a corrosion performance model of a metal material. In addition, an accurate method, device, computer device, computer-readable storage medium, and computer program product for determining the corrosion performance of a metal material are also provided.

[0005] In a first aspect, the present application provides a method for constructing a corrosion performance model of a metal material. The method includes:

[0006] Obtain an initial model of the corrosion performance of a metal material, a measured value of the corrosion layer thickness, and a combination value of each first constant and second constant; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the corrosion performance of the metal material, and the second constant represents the boundary value of the corrosion layer thickness of the metal material at different stages in the initial model of the corrosion performance of the metal material;

[0007] According to the initial model of the corrosion performance of the metal material and the combination value of each first constant and second constant, obtain a calculated value of each corrosion layer thickness;

[0008] Taking the minimum of the sum of squares of the differences between the measured corrosion layer thickness values and the calculated corrosion layer thickness values as the target, the initial values of the parameters required for model construction under each combination value of the first constants and the second constant are obtained;

[0009] According to the initial values of the parameters required for model construction under each combination value of the first constants and the second constant, the initial corrosion performance models of the corresponding metal materials are updated to obtain the corrosion performance models of the metal materials and the predicted values of the corrosion layer thickness;

[0010] According to the deviations between the predicted corrosion layer thickness values and the measured corrosion layer thickness values, iterative processing is performed on the initial values of the parameters required for model construction under each combination value of the first constants and the second constant to obtain the target values of the parameters required for model construction under each combination value of the first constants and the second constant, and the residual vectors under each combination value of the first constants and the second constant are obtained;

[0011] According to the residual vector corresponding to the minimum modulus among the moduli of the residual vectors, the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant is determined, and the candidate metal material corrosion performance model is updated according to the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain the target metal material corrosion performance model, where the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal combination value of the first constant and the second constant.

[0012] In one embodiment, obtaining each combination value of the first constant and the second constant includes:

[0013] Obtaining the preset range of the first constant and the preset range of the second constant;

[0014] According to the preset range of the first constant and the preset range of the second constant, different first constant values and different second constant values are determined;

[0015] Based on the different first constant values, the different second constant values are traversed respectively to obtain each combination value of the first constant and the second constant.

[0016] In one embodiment, obtaining each calculated corrosion layer thickness value according to the initial corrosion performance model of the metal material and each combination value of the first constant and the second constant includes:

[0017] Obtaining the corrosion time, the heat flux density at the interface between the corrosion layer and the metal material, and the temperature of the outer surface of the corrosion layer;

[0018] According to the heat flux density at the interface between the corrosion layer and the metal material and the temperature of the outer surface of the corrosion layer, the temperature value at the interface between the corrosion layer and the metal material is obtained;

[0019] According to the initial model of the corrosion performance of the metal material, the corrosion time, the temperature value at the interface between the corrosion layer and the metal material, and the combined values of the first constants and the second constants, the calculated values of the thicknesses of the corrosion layers are obtained.

[0020] In one embodiment, the updating of the initial models of the corrosion performance of the corresponding metal materials according to the initial values of the parameters required for model construction under the combined values of the first constants and the second constants, and obtaining the models of the corrosion performance of the metal materials and the predicted values of the thicknesses of the corrosion layers includes:

[0021] Updating the initial models of the corrosion performance of the corresponding metal materials according to the initial values of the parameters required for model construction under the combined values of the first constants and the second constants, to obtain the models of the corrosion performance of the metal materials;

[0022] Performing time integration on the models of the corrosion performance of the metal materials to obtain the predicted values of the thicknesses of the corrosion layers.

[0023] In one embodiment, the iterative processing of the initial values of the parameters required for model construction under the combined values of the first constants and the second constants according to the deviation between the predicted values of the thicknesses of the corrosion layers and the measured values of the thicknesses of the corrosion layers, to obtain the target values of the parameters required for model construction under the combined values of the first constants and the second constants, and obtaining the residual vectors under the combined values of the first constants and the second constants includes:

[0024] Obtaining the iteration termination condition;

[0025] According to the deviation between the predicted values of the thicknesses of the corrosion layers and the measured values of the thicknesses of the corrosion layers, and the iteration termination condition, using the nonlinear least squares method to perform iterative processing on the initial values of the parameters required for model construction under the combined values of the first constants and the second constants, to obtain the target values of the parameters required for model construction under the combined values of the first constants and the second constants, and the residual vectors under the combined values of the first constants and the second constants.

[0026] In one embodiment, the determination of the target values of the parameters required for model construction corresponding to the optimal combined value of the first constants and the second constants according to the residual vector corresponding to the smallest modulus among the moduli of the residual vectors, and updating the candidate model of the corrosion performance of the metal material according to the target values of the parameters required for model construction corresponding to the optimal combined value of the first constants and the second constants, to obtain the target model of the corrosion performance of the metal material includes:

[0027] Obtaining the moduli of the residual vectors according to the residual vectors;

[0028] Selecting the residual vector corresponding to the smallest modulus among the moduli of the residual vectors to obtain the target residual vector;

[0029] Determine the optimal combination value of the first constant and the second constant according to the target residual vector;

[0030] Determine the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the optimal combination value of the first constant and the second constant;

[0031] Determine the candidate metal material corrosion performance model according to the optimal combination value of the first constant and the second constant and each metal material corrosion performance model;

[0032] Update the candidate metal material corrosion performance model according to the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain the target metal material corrosion performance model.

[0033] In a second aspect, the present application also provides a device for constructing a metal material corrosion performance model. The device includes:

[0034] A basic data acquisition module, configured to acquire an initial model of the metal material corrosion performance, a measured value of the corrosion layer thickness, and each combination value of the first constant and the second constant; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the metal material corrosion performance, and the second constant represents the demarcation value of the corrosion layer thickness of the metal material at different stages in the initial model of the metal material corrosion performance;

[0035] A corrosion layer thickness calculation module, configured to obtain each corrosion layer thickness calculation value according to the initial model of the metal material corrosion performance and each combination value of the first constant and the second constant;

[0036] A parameter initial value acquisition module, configured to take the minimum sum of squares of the differences between the measured value of the corrosion layer thickness and each corrosion layer thickness calculation value as the target to obtain the initial values of the parameters required for model construction under each combination value of the first constant and the second constant;

[0037] A model initial update module, configured to update the corresponding initial models of the metal material corrosion performance according to the initial values of the parameters required for model construction under each combination value of the first constant and the second constant to obtain each metal material corrosion performance model and each corrosion layer thickness prediction value;

[0038] A parameter target value acquisition module, configured to perform iterative processing on the initial values of the parameters required for model construction under each combination value of the first constant and the second constant according to the deviation between each corrosion layer thickness prediction value and the measured value of the corrosion layer thickness to obtain the target values of the parameters required for model construction under each combination value of the first constant and the second constant, and obtain the residual vector under each combination value of the first constant and the second constant;

[0039] The model secondary update module is used to determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the residual vector corresponding to the minimum modulus among the moduli of the residual vectors, and update the candidate metal material corrosion performance model according to the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant, so as to obtain the target metal material corrosion performance model, where the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal combination value of the first constant and the second constant.

[0040] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0041] Obtain the initial model of the metal material corrosion performance, the measured value of the corrosion layer thickness, and each combination value of the first constant and the second constant; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the metal material corrosion performance, and the second constant represents the boundary value of the corrosion layer thickness of the metal material at different stages in the initial model of the metal material corrosion performance;

[0042] Obtain the calculated values of the corrosion layer thickness according to the initial model of the metal material corrosion performance and each combination value of the first constant and the second constant;

[0043] Taking the minimum sum of squares of the differences between the measured value of the corrosion layer thickness and the calculated values of the corrosion layer thickness as the target, obtain the initial values of the parameters required for model construction under each combination value of the first constant and the second constant;

[0044] Update the corresponding initial models of the metal material corrosion performance according to the initial values of the parameters required for model construction under each combination value of the first constant and the second constant, so as to obtain the corrosion performance models of the metal materials and the predicted values of the corrosion layer thickness;

[0045] Perform iterative processing on the initial values of the parameters required for model construction under each combination value of the first constant and the second constant according to the deviation between the predicted values of the corrosion layer thickness and the measured value of the corrosion layer thickness, so as to obtain the target values of the parameters required for model construction under each combination value of the first constant and the second constant, and obtain the residual vectors under each combination value of the first constant and the second constant;

[0046] Determine the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the residual vector corresponding to the minimum modulus among the moduli of the residual vectors, and update the candidate metal material corrosion performance model according to the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain the target metal material corrosion performance model, where the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal combination value of the first constant and the second constant.

[0047] Fourthly, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0048] Obtain an initial model of the corrosion performance of a metal material, a measured value of the corrosion layer thickness, and each combination value of the first constant and the second constant; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the corrosion performance of the metal material, and the second constant represents the boundary value of the corrosion layer thickness of the metal material at different stages in the initial model of the corrosion performance of the metal material;

[0049] Obtain the calculated values of the corrosion layer thickness according to the initial model of the corrosion performance of the metal material and each combination value of the first constant and the second constant;

[0050] Aim at minimizing the sum of the squares of the differences between the measured value of the corrosion layer thickness and the calculated values of the corrosion layer thickness to obtain the initial values of the parameters required for model construction under each combination value of the first constant and the second constant;

[0051] Update the corresponding initial models of the corrosion performance of the metal materials according to the initial values of the parameters required for model construction under each combination value of the first constant and the second constant to obtain the corrosion performance models of the metal materials and the predicted values of the corrosion layer thickness;

[0052] Perform iterative processing on the initial values of the parameters required for model construction under each combination value of the first constant and the second constant according to the deviation between the predicted values of the corrosion layer thickness and the measured value of the corrosion layer thickness to obtain the target values of the parameters required for model construction under each combination value of the first constant and the second constant, and obtain the residual vectors under each combination value of the first constant and the second constant;

[0053] Determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the residual vector corresponding to the minimum modulus among the moduli of the residual vectors, and update the candidate metal material corrosion performance model according to the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant, so as to obtain the target metal material corrosion performance model, where the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal combination value of the first constant and the second constant.

[0054] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0055] Obtain an initial model of the corrosion performance of a metal material, a measured value of the corrosion layer thickness, and various combination values of a first constant and a second constant; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the corrosion performance of the metal material, and the second constant represents the boundary value of the corrosion layer thickness of the metal material at different stages in the initial model of the corrosion performance of the metal material;

[0056] Obtain calculated values of the corrosion layer thickness according to the initial model of the corrosion performance of the metal material and the various combination values of the first constant and the second constant;

[0057] Taking the minimum sum of the squares of the differences between the measured value of the corrosion layer thickness and the calculated values of the corrosion layer thickness as the target, obtain the initial values of the parameters required for model construction under the various combination values of the first constant and the second constant;

[0058] Update the corresponding initial models of the corrosion performance of the metal materials according to the initial values of the parameters required for model construction under the various combination values of the first constant and the second constant, so as to obtain models of the corrosion performance of the metal materials and predicted values of the corrosion layer thickness;

[0059] Perform iterative processing on the initial values of the parameters required for model construction under the various combination values of the first constant and the second constant according to the deviation between the predicted values of the corrosion layer thickness and the measured value of the corrosion layer thickness, so as to obtain the target values of the parameters required for model construction under the various combination values of the first constant and the second constant, and obtain residual vectors under the various combination values of the first constant and the second constant;

[0060] Determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the residual vector corresponding to the minimum modulus among the moduli of the residual vectors, and update the candidate metal material corrosion performance model according to the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant, so as to obtain the target metal material corrosion performance model, where the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal combination value of the first constant and the second constant.

[0061] The above metal material corrosion performance model construction method, device, computer equipment, storage medium and computer program product aim to minimize the sum of squares of the differences between the measured corrosion layer thickness values and the calculated corrosion layer thickness values, obtain the initial values of the parameters required for model construction under the combination value of the first constant and the second constant, and then update the initial corrosion performance models of the corresponding metal materials according to the combination value of the first constant and the second constant and the initial values of the parameters required for model construction. Furthermore, through the deviation between the predicted corrosion layer thickness value obtained from the updated initial corrosion performance models of the metal materials and the measured corrosion layer thickness value, iterative processing is performed on the initial values of the parameters required for model construction to update the initial values of the parameters required for model construction, obtain the target values of the parameters required for model construction, and then determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant, so as to accurately obtain the final target metal material corrosion performance model.

[0062] In a sixth aspect, the present application further provides a method for determining the corrosion performance of a metal material. The method includes:

[0063] Obtaining the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time;

[0064] Determining the corrosion performance of the metal material according to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time by using the target metal material corrosion performance model;

[0065] Wherein, the target metal material corrosion performance model is established by using the above metal material corrosion performance model construction method.

[0066] In a seventh aspect, the present application further provides a device for determining the corrosion performance of a metal material. The device includes:

[0067] A model input data acquisition module for obtaining the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time;

[0068] A corrosion performance determination module for determining the corrosion performance of the metal material according to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time by using the target metal material corrosion performance model;

[0069] Wherein, the target metal material corrosion performance model is established by using the above metal material corrosion performance model construction method.

[0070] In an eighth aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0071] Obtain the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time;

[0072] According to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, determine the corrosion performance of the metal material by using the corrosion performance model of the target metal material;

[0073] Wherein, the corrosion performance model of the target metal material is established by using the corrosion performance model construction method of the metal material as described above.

[0074] In a ninth aspect, the present application also provides a computer-readable storage medium. On the computer-readable storage medium, a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0075] Obtain the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time;

[0076] According to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, determine the corrosion performance of the metal material by using the corrosion performance model of the target metal material;

[0077] Wherein, the corrosion performance model of the target metal material is established by using the corrosion performance model construction method of the metal material as described above.

[0078] In a tenth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0079] Obtain the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time;

[0080] According to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, determine the corrosion performance of the metal material by using the corrosion performance model of the target metal material;

[0081] Wherein, the corrosion performance model of the target metal material is established by using the corrosion performance model construction method of the metal material as described above.

[0082] The above-mentioned method, device, computer equipment, storage medium, and computer program product for determining the corrosion performance of metal materials obtain the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time; and determine the corrosion performance of the metal material by using the target metal material corrosion performance model according to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, wherein the target metal material corrosion performance model is established by using the above-mentioned metal material corrosion performance model construction method. Throughout the process, by obtaining the input variable data of the model, the corrosion performance of the metal material can be accurately determined by using the target metal material corrosion performance model. Description of the Drawings

[0083] Figure 1 It is an application environment diagram of the metal material corrosion performance model construction method and the metal material corrosion performance determination method in an embodiment;

[0084] Figure 2 It is a flowchart of the metal material corrosion performance model construction method in an embodiment;

[0085] Figure 3 It is a flowchart of the metal material corrosion performance model construction method in another embodiment;

[0086] Figure 4 It is a flowchart of the metal material corrosion performance determination method in an embodiment;

[0087] Figure 5 It is the comparison result of the model prediction value and the measured value when measuring the data of 0um random perturbation in a specific application example;

[0088] Figure 6 It is the comparison result of the model prediction value and the measured value when measuring the data of 5um random perturbation in a specific application example;

[0089] Figure 7 It is the comparison result of the model prediction value and the measured value when measuring the data of 10um random perturbation in a specific application example;

[0090] Figure 8 It is a structural block diagram of the metal material corrosion performance model construction device in an embodiment;

[0091] Figure 9 It is a structural block diagram of the metal material corrosion performance determination device in an embodiment;

[0092] Figure 10 It is an internal structure diagram of a computer device in an embodiment. Detailed Embodiments

[0093] To make the objectives, technical solutions and advantages of this application more clear and understandable, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for explaining this application and are not used to limit this application.

[0094] The method for determining the corrosion performance of metal materials provided by the embodiments of this application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed in the cloud or other network servers. The entire method for determining the corrosion performance of metal materials includes a metal material corrosion performance model construction stage and a metal material corrosion performance model application stage. The data processing processes in actual applications for the two stages will be described below.

[0095] Stage of constructing the corrosion performance model of metal materials. First, the terminal 102 sends a request for constructing the corrosion performance model of metal materials to the server 104. The request for constructing the corrosion performance model of metal materials carries the initial model of the corrosion performance of metal materials, the measured value of the corrosion layer thickness, and the required values for each combination of the first constants and the second constants. The server 104 receives the request for constructing the corrosion performance model of metal materials, and obtains the initial model of the corrosion performance of metal materials, the measured value of the corrosion layer thickness, and the required values for each combination of the first constants and the second constants carried in the request for constructing the corrosion performance model of metal materials; obtains the combination values of the first constants and the second constants according to the required values for each combination of the first constants and the second constants; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the corrosion performance of metal materials, and the second constant represents the demarcation value of the corrosion layer thickness of the metal material at different stages in the initial model of the corrosion performance of metal materials; obtains the calculated values of the corrosion layer thickness according to the initial model of the corrosion performance of metal materials and the combination values of the first constants and the second constants; takes the minimum sum of the squares of the differences between the measured value of the corrosion layer thickness and the calculated values of the corrosion layer thickness as the goal, and obtains the initial values of the parameters required for model construction under each combination value of the first constants and the second constants; updates the corresponding initial models of the corrosion performance of each metal material according to the initial values of the parameters required for model construction under each combination value of the first constants and the second constants, and obtains the corrosion performance models of each metal material and the predicted values of the corrosion layer thickness; performs iterative processing on the initial values of the parameters required for model construction under each combination value of the first constants and the second constants according to the deviation between the predicted values of the corrosion layer thickness and the measured value of the corrosion layer thickness, obtains the target values of the parameters required for model construction under each combination value of the first constants and the second constants, and obtains the residual vectors under each combination value of the first constants and the second constants; determines the target value of the parameters required for model construction corresponding to the optimal combination value of the first constants and the second constants according to the residual vector corresponding to the minimum modulus among the moduli of each residual vector, and updates the candidate corrosion performance model of the metal material according to the target value of the parameters required for model construction corresponding to the optimal combination value of the first constants and the second constants, and obtains the target corrosion performance model of the metal material, where the candidate corrosion performance model of the metal material is the corrosion performance model of the metal material corresponding to the optimal combination value of the first constants and the second constants.

[0096] Application stage of the metal material corrosion performance model. After the metal material corrosion performance model is constructed and the terminal 102 needs to apply the target metal material corrosion performance model of the application server 104, a metal material corrosion performance determination request is sent to the server 104. The metal material corrosion performance determination request carries the outer surface temperature of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time. When the server 104 receives the metal material corrosion performance determination request, it extracts the outer surface temperature of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time in the metal material corrosion performance determination request, and determines the metal material corrosion performance by using the target metal material corrosion performance model according to the outer surface temperature of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time.

[0097] Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0098] In one embodiment, as Figure 2 shown, a method for constructing a metal material corrosion performance model is provided. Taking the method applied to Figure 1 the server 104 therein as an example for illustration, the method includes the following steps:

[0099] S100, obtain the initial model of the metal material corrosion performance, the measured value of the corrosion layer thickness, and the combined values of the first constants and the second constants; the first constant represents an integer constant corresponding to the corrosion rate in different stages in the initial model of the metal material corrosion performance, and the second constant represents the demarcation value of the corrosion layer thickness of the metal material in different stages of the initial model of the metal material corrosion performance.

[0100] Among them, the initial model of the metal material corrosion performance in this application refers to the initial model without numerical substitution, that is, the two-stage Arrhenius equation initial model, which can describe the corrosion behavior of most metal materials in the service environment. Its mathematical model can be:

[0101]

[0102]

[0103] S(t = 0) = 0

[0104] Wherein, S is the thickness of the corrosion layer, N is the first constant, λ is the second constant, t is the corrosion time, C1 is the third constant, C2 is the fourth constant, Q1 is the first activation energy, Q2 is the second activation energy, and T is the temperature at the interface between the corrosion layer and the metal material. C1, C2, Q1, and Q2 are parameters required for subsequent model construction. And since the thickness of the corrosion layer of the metal material is not smooth at the turning point of the metal material, λ is taken as the demarcation value of the corrosion layer thickness at different turning stages. S ≤ λ is the corrosion performance model of the metal material in the pre-turning stage, and S > λ is the corrosion performance model of the metal material in the post-turning stage. In addition, when the corrosion time t is 0, the corrosion layer thickness S is also 0. The measured value of the corrosion layer thickness is the actual measured value of the corrosion layer thickness within the i-th time step, where i = 1, 2, 3, …… n.

[0105] Specifically, from the above initial model of the corrosion performance of the metal material, it can be obtained that taking S as the target output value, the variables in the initial model are the corrosion time t and the temperature T at the interface between the corrosion layer and the metal material, and the remaining parameters N, λ, C1, C2, Q1, and Q2 are all fixed values. Therefore, this application determines the optimal values of these six fixed parameter values. Since the derivative of the initial model of the corrosion performance of the metal material is not smooth at the turning point of the corrosion layer thickness, and N is a positive integer, it is very difficult to directly determine the six parameters from a technical perspective, and it is difficult to ensure the convergence and stability of the established algorithm. Therefore, the problem needs to be simplified. At this time, various combined values of [N, λ] can be determined first.

[0106] Further, the terminal sends a request for constructing the corrosion performance model of the metal material to the server. The server receives the request for constructing the corrosion performance model of the metal material, obtains the initial model of the corrosion performance of the metal material, the measured value of the corrosion layer thickness, and the required values of various combinations of the first constant and the second constant carried in the request for constructing the corrosion performance model of the metal material, and analyzes and processes the required values of various combinations of the first constant and the second constant to obtain various combined values of the first constant and the second constant [N, λ].

[0107] S200, according to the initial model of the corrosion performance of the metal material and various combined values of the first constant and the second constant, obtain the calculated values of the corrosion layer thickness.

[0108] Specifically, after obtaining various combined values of the first constant and the second constant, substitute the various combined values of the first constant and the second constant into the initial model of the corrosion performance of the metal material. At this time, the initial model of the corrosion performance of the metal material is simplified to only need to solve for the four parameters C1, C2, Q1, and Q2. The solution method adopted in this application is to first obtain the calculated values of the corrosion layer thickness of the initial model of the corrosion performance of the metal material after substituting different combined values of the first constant and the second constant.

[0109] S300, with the goal of minimizing the sum of squares of the differences between the measured corrosion layer thickness values and the calculated values of each corrosion layer thickness, obtain the initial values of the parameters required for model construction under each combination value of the first constants and the second constants.

[0110] Specifically, compare the calculated values of each corrosion layer thickness with the measured corrosion layer thickness values obtained in advance, and with the goal of minimizing the sum of squares of the differences between the measured corrosion layer thickness values and the calculated values of each corrosion layer thickness, obtain the initial values of the parameters C1, C2, Q1, and Q2 required for model construction under each combination value of the first constants and the second constants to achieve this goal.

[0111] S400, based on the initial values of the parameters required for model construction under each combination value of the first constants and the second constants, update the initial models of the corrosion performance of each metal material to obtain the corrosion performance models of each metal material and the predicted values of each corrosion layer thickness.

[0112] Specifically, substitute the initial values of the parameters required for model construction under each combination value of the first constants and the second constants into the corresponding initial models of the corrosion performance of the metal materials to update the initial models of the corrosion performance of each metal material, and obtain the corrosion performance models of each metal material. At this time, the values of C1, C2, Q1, and Q2 in each corrosion performance model of the metal materials are all inaccurate initial values, and by performing a forward solution on each corrosion performance model of the metal materials, obtain the predicted values of each corrosion layer thickness determined by the inaccurate initial values.

[0113] S500, based on the deviation between the predicted values of each corrosion layer thickness and the measured corrosion layer thickness, perform iterative processing on the initial values of the parameters required for model construction under each combination value of the first constants and the second constants to obtain the target values of the parameters required for model construction under each combination value of the first constants and the second constants, and obtain the residual vector under each combination value of the first constants and the second constants.

[0114] Among them, in mathematical statistics, the residual refers to the difference between the actual measured value and the calculated value.

[0115] Specifically, through the deviation between the predicted values of each corrosion layer thickness and the measured corrosion layer thickness, perform iterative processing on the initial values of C1, C2, Q1, and Q2 in each corrosion performance model of the metal materials to obtain the optimal target values of the parameters required for model construction, and obtain the residual vector of the corrosion performance model of the metal materials under each combination value of the first constants and the second constants.

[0116] S600. Determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the residual vector with the smallest modulus among the moduli of each residual vector, and update the candidate metal material corrosion performance model according to the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain the target metal material corrosion performance model, where the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal combination value of the first constant and the second constant.

[0117] Specifically, first, it is necessary to determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant through the residual vector, and update the candidate model previously substituted with the initial values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain the target metal material corrosion performance model. Among them, the method of determining the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant through the residual vector can be to calculate the modulus of each residual vector, obtain the residual vector corresponding to the smallest modulus among all moduli, and determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant.

[0118] In the above method for constructing the metal material corrosion performance model, with the goal of minimizing the sum of the squares of the differences between the measured corrosion layer thickness and the calculated corrosion layer thicknesses, the initial values of the parameters required for model construction under the combination value of the first constant and the second constant are obtained. Then, according to the combination value of the first constant and the second constant, and the initial values of the parameters required for model construction, the initial corrosion performance models of each metal material are updated. Further, through the deviation between the predicted corrosion layer thickness obtained from the updated initial corrosion performance models of each metal material and the measured corrosion layer thickness, the initial values of the parameters required for model construction are iteratively processed to update the initial values of the parameters required for model construction, obtain the target values of the parameters required for model construction, and then determine the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant, so as to accurately obtain the final target metal material corrosion performance model.

[0119] In one embodiment, obtaining the combination values of each first constant and the second constant includes:

[0120] Obtain the preset range of the first constant and the preset range of the second constant; according to the preset range of the first constant and the preset range of the second constant, determine different first constant values and different second constant values; based on different first constant values, respectively traverse different second constant values to obtain the combination values of each first constant and the second constant.

[0121] Specifically, the required values for the combinations of each first constant and second constant sent by the terminal are the preset ranges of the first constant and the preset range of the second constant. The preset range of the first constant and the preset range of the second constant are both set based on historical experience. For example, the preset range of the first constant is generally [1, 3], and the first constant is an integer constant, so the first constant generally takes 1, 2, 3. The preset range of the second constant generally takes 2 to 6; according to the preset range of the first constant and the preset range of the second constant, a value is taken every 0.2 within the preset range of the second constant to determine different first constant values N and different second constant values λ; then based on different first constant values 1, 2, 3, different second constant values are traversed respectively to obtain the combination values of each first constant and second constant. For example, the combination values of the first constant and the second constant can be combinations such as [1, 2], [1, 2.2], [2, 5.4], etc. When there are k1 different first constant values and k2 different second constant values, the number of combination values of each first constant and second constant obtained is k1 * k2. Subsequently, the optimal combination value of the first constant and the second constant will be determined by the hyperparameter adjustment method.

[0122] In this embodiment, by obtaining the combination values of each first constant and second constant, the initial model of the corrosion performance of the metal material can be simplified from needing to determine six parameters to determining four parameters, and the target metal material corrosion performance model can be obtained more efficiently. Moreover, the combination values of each first constant and second constant are all within the reasonable range set by historical experience, so it also has accuracy.

[0123] In one embodiment, according to the initial model of the corrosion performance of the metal material and the combination values of each first constant and second constant, obtaining the calculated values of each corrosion layer thickness includes:

[0124] Obtain the corrosion time, the heat flux density at the interface between the corrosion layer and the metal material, and the temperature of the outer surface of the corrosion layer; according to the heat flux density at the interface between the corrosion layer and the metal material and the temperature of the outer surface of the corrosion layer, obtain the temperature value at the interface between the corrosion layer and the metal material; according to the initial model of the corrosion performance of the metal material, the corrosion time, the temperature value at the interface between the corrosion layer and the metal material, and the combination values of each first constant and second constant, obtain the calculated values of each corrosion layer thickness.

[0125] Specifically, substituting the combined values of each first constant and the second constant into the initial model of the corrosion performance of the metal material, at this time, the initial model of the corrosion performance of the metal material only needs to solve for four parameters, namely C1, C2, Q1, and Q2. This application will use a decoupling method to separately solve the initial values of [C1, Q1] and [C2, Q2] in the pre-transition stage and the post-transition stage. For general metal material corrosion problems, the thickness of the corrosion layer after the transition accounts for a relatively large proportion in the entire corrosion layer thickness. Therefore, it can be assumed that there is only the post-transition stage, solve for the initial values of [C2, Q2], and then solve for the initial values of [C1, Q1]. And to ensure that the four parameters can be accurately solved, first, the other parameters will be obtained: the corrosion time t i，j and the temperature T at the interface between the corrosion layer and the metal material. Among them, the corrosion time is the jth time of the ith corrosion layer thickness measurement value. Since T cannot be directly measured, the outer surface temperature Tf of the corrosion layer needs to be used i,j and the heat flux density H at the current time i,j to calculate. Therefore, it is necessary to obtain the heat flux density H at the interface between the corrosion layer and the metal material i,j and the outer surface temperature Tf of the corrosion layer i,j to obtain the temperature T at the interface between the corrosion layer and the metal material. The specific expression is as follows:

[0126]

[0127] In the formula, i, j = 1, 2, 3,..., m, Tf i,j is the jth outer surface temperature history of the ith corrosion layer thickness measurement value, H i,j refers to the jth heat flux density history of the ith corrosion layer thickness measurement value, K is the thermal conductivity of the corrosion layer, is the corrosion layer thickness predicted by the model. The meaning of j is that since a single corrosion layer thickness measurement value corresponds to a series of time, outer surface temperature history, and heat flux density history, so one i will correspond to a series of j. When i = 1 and j = 1, Tf i,j is the first outer surface temperature history of the first corrosion layer thickness measurement value; when i = 1 and j = 2, Tf i,j is the second outer surface temperature history of the first corrosion layer thickness measurement value at the first time step; and so on.

[0128] In addition, for K, the thermal conductivity of the corrosion layer is a function of temperature. Therefore, to determine the thermal conductivity of the corrosion layer, the average temperature of the inner and outer surfaces of the corrosion layer needs to be known first. The outer surface temperature is Tf i，j, to calculate the inner surface temperature, an initial value needs to be assumed first. This initial value is used to calculate the thermal conductivity of the corrosion layer, and the thermal conductivity of this corrosion layer in turn affects the inner surface temperature of the corrosion layer. Therefore, this process requires repeated iteration until there is no obvious change in the inner surface temperature of the corrosion layer, and then it is considered that the accurate inner surface temperature value of the corrosion layer is obtained. For Although the current target model has not been established yet and the model parameters are unknown, from a practical physical perspective, the thickness of the corrosion layer is very thin and its influence on temperature is very limited. Therefore, we can first obtain a set of inaccurate model parameters, and these parameters can be used to roughly obtain the predicted value of the corrosion layer thickness. The accuracy of T calculated by this set of inaccurate model parameters is sufficient.

[0129] When solving the initial values of [C2, Q2], integrate the post-transition stage of the initial model of the corrosion performance of the metal material and then take the logarithm to obtain And define the calculated value of the corrosion layer thickness as The calculated value of the corrosion layer thickness at the last time step Corrosion time t i , and the temperature T at the interface between the corrosion layer and the metal material i Substitute them to obtain the calculated value of the corrosion layer thickness at this time:

[0130]

[0131] After obtaining the calculated value of the corrosion layer thickness, with the goal of minimizing the sum of the squares of the differences between the measured value of the corrosion layer thickness and each calculated value of the corrosion layer thickness, the initial values of the parameters required for model construction under each combination of the first constant and the second constant are obtained. First, obtain the function of the sum of the squares of the differences between the measured value of the corrosion layer thickness and each calculated value of the corrosion layer thickness:

[0132]

[0133] Perform linear least squares solution on the sum of the squares of the differences to minimize the sum of the squares of the differences between the measured value of the corrosion layer thickness and each calculated value of the corrosion layer thickness. That is, perform linear regression solution in matrix form to obtain:

[0134]

[0135] It can be obtained that the initial values of the parameters required for model construction [C2, Q2] are the solutions of the following equations: Thus, [C2, Q2] is solved. After the solution is completed, the models of the two stages before and after the transition are simultaneously combined. Using the linear least squares method, first, based on the initial model of the corrosion performance of the metal material before and after the transition, and the combined values of each first constant and second constant, the calculated values of each corrosion layer thickness are obtained. Then, with the goal of minimizing the sum of the squares of the differences between the measured corrosion layer thickness and the calculated corrosion layer thickness, the initial values of [C1, Q1] under the combined values of each first constant and second constant are obtained. Through the above steps, the initial values of the parameters [C2, Q2] and [C1, Q1] required for model construction under the combined values of each first constant and second constant can be obtained.

[0136] In this embodiment, by obtaining the calculated values of the thickness of each corrosion layer of the initial model of the corrosion performance of the metal material, it is possible to efficiently obtain the initial values of the parameters required for model construction under the combination values of the first constant and the second constant with the goal of minimizing the sum of the squares of the differences between the measured corrosion layer thickness and the calculated corrosion layer thickness.

[0137] In one embodiment, the initial values of the required parameters are constructed based on the model under the combined values of the first constant and the second constant, and the initial corrosion performance models of the corresponding metal materials are updated to obtain the corrosion performance models of the metal materials and the predicted values of the thickness of the corrosion layers, including:

[0138] According to the model construction under the combination value of each first constant and the second constant, the required parameter initial values are updated, and the corresponding initial corrosion performance model of each metal material is obtained; the corrosion performance model of each metal material is time-integrated to obtain the predicted value of the thickness of each corrosion layer.

[0139] Specifically, at this time, the parameter values in the initial model of the corrosion performance of each metal material are all known. The initial parameter values required for the model construction under the combination of the first constant and the second constant are substituted into the corresponding initial model of the corrosion performance of the metal material to update and obtain the corrosion performance model of each metal material. The corrosion performance model of each metal material is time-integrated, and the numerical forward solution method is used to solve the predicted value of each corrosion layer thickness at each time measurement point of the model. Among them, when the corrosion time is 0, the predicted value of the corrosion layer thickness is 0, and the predicted value of the corrosion layer thickness at the previous time step is used as To calculate T1, that is:

[0140]

[0141] In the formula, is the predicted value of corrosion layer thickness at time step i, T oi is the outer surface temperature of the corrosion layer at the i-th time step during the forward solution.

[0142] In this embodiment, by performing time integration on the corrosion performance models of various metal materials, it is possible to efficiently obtain the predicted values of the thicknesses of various corrosion layers.

[0143] In one embodiment, according to the deviation between the predicted value of the thickness of each corrosion layer and the measured value of the thickness of the corrosion layer, iterative processing is performed on the initial values of the parameters required for model construction under each combination value of the first constant and the second constant, to obtain the target values of the parameters required for model construction under each combination value of the first constant and the second constant, and to obtain the residual vector under each combination value of the first constant and the second constant, including:

[0144] Obtain the iteration termination condition; according to the deviation between the predicted value of the thickness of each corrosion layer and the measured value of the thickness of the corrosion layer, and the iteration termination condition, use the nonlinear least squares method to perform iterative processing on the initial values of the parameters required for model construction under each combination value of the first constant and the second constant, to obtain the target values of the parameters required for model construction under each combination value of the first constant and the second constant, and the residual vector under each combination value of the first constant and the second constant.

[0145] Specifically, obtain the iteration termination condition. The iteration termination conditions in this application include the initial termination condition, the standard termination condition, and the model parameter termination condition. When one of the termination conditions is satisfied, the iteration stops. Define the residual vector r(C1, Q1, C2, Q2), and the Jacobi matrix J(C1, Q1, C2, Q2) of the residual vector, where:

[0146]

[0147] In J(C1, Q1, C2, Q2), the calculation of the partial derivative uses the two-point forward numerical differentiation formula, as follows:

[0148]

[0149]

[0150]

[0151]

[0152] Among them, h1, h2, h3, h4 are the perturbation amounts for partial derivative calculation, and are adjustable input parameters.

[0153] Furthermore, according to the deviation between the predicted value of the thickness of each corrosion layer and the measured value of the thickness of the corrosion layer, use the nonlinear least squares method to perform iterative processing on the initial values of the parameters required for model construction under each combination value of the first constant and the second constant. The processing steps are: A. The initial value of the parameter required for model construction is redefined as [C 1,0 、Q 1,0 、C 2,0 、Q 2,0, at this time, the iteration number i = 0; B. Determine whether the iteration termination condition is satisfied. If so, the iteration ends; C. Solve the equation to obtain the correction amount d i , where J is the above-mentioned Jacobi matrix; D. Obtain the next iteration value [C 1，i , Q 1，i , C 2，i , Q 2，i + αd i , and jump to step B, where α is the step size coefficient and the initial value is 1.

[0154] During the solution process, sometimes the correction amount will be too large. This is because being close to singularity will lead to an incorrect correction amount d i solved. For the stability of the solution, after step C is solved, the step size coefficient α of the correction direction d i needs to satisfy the Armijo criterion: ||r i+1 || ≤ ||r i + αJ i d i ||; if not satisfied, then halve α until it is satisfied.

[0155] Furthermore, 1. Initial termination condition: refers to the conditional judgment carried out before the iteration number is 0. If the initial termination condition is satisfied, the initial value of the model construction is already accurate, and it is used as the target value of the parameters required for model construction without iteration. Among them, τ1 and subsequent τ2, τ3 are set as adjustable input parameters.

[0156] 2. Standard termination condition: In the formula, S is the vector composed of the measured values of the corrosion layer thickness. It refers to the relative change in the predicted thickness of the corrosion layer between two iteration steps. If satisfied, it indicates that the change in the measured value of the corrosion layer thickness between two iteration steps is small, and it is considered that no further iteration is required at this time.

[0157] 3. Model parameter termination condition If satisfied, it is considered that the model parameters have converged and the iteration can be ended. Model parameter termination condition:

[0158] After the iteration ends, the target values of the parameters required for model construction under each combination value of the first constant and the second constant, and the residual vector under each combination value of the first constant and the second constant will be obtained.

[0159] In this embodiment, by setting the iteration termination condition, the target values of the parameters required for model construction under each combination value of the first constant and the second constant can be determined more precisely.

[0160] In one embodiment, as Figure 3As shown, S600 includes:

[0161] S610, obtaining the modulus of each residual vector according to each residual vector.

[0162] Specifically, the modulus of a residual vector refers to the length of the residual vector. If [N, λ] has k1×K2 sets of values, then each set will obtain a corresponding residual vector, and at this time, the modulus of the corresponding residual vector is obtained.

[0163] S620, selecting the residual vector corresponding to the minimum modulus among the moduli of the residual vectors to obtain the target residual vector.

[0164] S630, determining the optimal combination value of the first constant and the second constant according to the target residual vector.

[0165] Specifically, since the target residual vector is obtained in the model under each combination value of the first constant and the second constant, the optimal combination value of the first constant and the second constant corresponding to the target residual vector can be determined according to the target residual vector.

[0166] S640, determining the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the optimal combination value of the first constant and the second constant.

[0167] Specifically, according to the optimal combination value of the first constant and the second constant, the target value of the parameters required for model construction obtained by iteration under this optimal combination value of the first constant and the second constant is determined.

[0168] S650, determining the candidate metal material corrosion performance model according to the optimal combination value of the first constant and the second constant and each metal material corrosion performance model.

[0169] Specifically, the metal material corrosion performance model corresponding to the optimal combination value of the first constant and the second constant is found in each metal material corrosion performance model as the candidate metal material corrosion performance model.

[0170] S660, updating the candidate metal material corrosion performance model according to the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain the target metal material corrosion performance model.

[0171] Specifically, since the candidate metal material corrosion performance model at this time is already the model after substituting the optimal combination value of the first constant and the second constant, the candidate metal material corrosion performance model is updated according to the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant, and the target metal material corrosion performance model can be obtained.

[0172] In this embodiment, by comparing the norms of the residual vectors, the target values of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant can be accurately determined, thereby obtaining the target metal material corrosion performance model.

[0173] In one embodiment, as Figure 4 shown, a method for determining the corrosion performance of a metal material is also provided. Taking the application of this method to Figure 1 the server 104 as an example, the method includes the following steps:

[0174] S700, obtain the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time.

[0175] S800, according to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, use the target metal material corrosion performance model to determine the corrosion performance of the metal material; wherein, the target metal material corrosion performance model is established by using the metal material corrosion performance model construction method as described above.

[0176] Specifically, when the target metal material corrosion performance model needs to be applied after being constructed, since the above six fixed quantity parameters have all reached the optimal values, the parameters that change in the model at this time are the temperature T at the interface between the corrosion layer and the metal material and the corrosion time t. And the temperature T at the interface between the corrosion layer and the metal material is obtained through the temperature of the outer surface of the corrosion layer and the heat flux density at the interface between the corrosion layer and the metal material. Therefore, the terminal can send a metal material corrosion performance determination request to the server. The metal material corrosion performance determination request carries the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time. The server obtains the metal material corrosion performance determination request sent by the terminal, extracts the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time t carried in the request, obtains the temperature T at the interface between the corrosion layer and the metal material according to the temperature of the outer surface of the corrosion layer and the heat flux density at the interface between the corrosion layer and the metal material, and based on the temperature T at the interface between the corrosion layer and the metal material and the corrosion time t, uses the target metal material corrosion performance model to determine the corrosion performance of the metal material, that is, obtains the predicted value of the corrosion layer thickness.

[0177] The above method for determining the corrosion performance of a metal material obtains the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time; according to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, uses the target metal material corrosion performance model to determine the corrosion performance of the metal material; wherein, the target metal material corrosion performance model is established by using the metal material corrosion performance model construction method as described above. During the entire determination process, by obtaining the input variable data of the model, the corrosion performance of the metal material can be accurately determined by using the target metal material corrosion performance model.

[0178] In one embodiment, a certain alloy material is used to test the corrosion performance model of the target metal material constructed in this application from three dimensions: stability, correctness, and convergence speed. Stability means that the algorithm is not sensitive to errors during the calculation process, and the error has little impact on the accuracy of the calculation result; correctness means that for any legal input, the correct result should be given after a finite number of executions; convergence speed refers to the speed at which the algorithm iteratively converges. And in this test embodiment, random perturbations are considered to simulate measurement errors. For example Figures 5 - 7 are the comparison results of the predicted values and measured values of the model when the random perturbation measurement data are 0um, 5um, and 10um respectively. And the test results of the alloy corrosion performance model are as shown in Table 1 below:

[0179] Table 1 Test Results of the Alloy Corrosion Performance Model

[0180] Parameter Undisturbed test 5-μm random perturbation test 10-μm random perturbation test N 3 2 3 λ (μm) 2.6 4.8 3.8 <![CDATA[C1(m 2 / s or m 3 / s)]]> <![CDATA[9.9731×10 -14 > <![CDATA[9.8414×10 -7 > <![CDATA[1.0004×10 -13 > <![CDATA[Q1(K)]]> 14724.21 16730.91 14357.73 <![CDATA[C2 (m / s)]]> <![CDATA[2.1402×10 -6 > <![CDATA[1.1129×10 -6 > <![CDATA[3.1299×10 -6 > <![CDATA[Q2(K)]]> 9735.83 9304.97 9987.22 P-M mean (μm) 0.0862 -0.1688 -0.5036 P-M standard deviation (μm) 0.5972 2.9481 5.8615

[0181] Among them, P - predicted value, M - measured value. The closer the mean of P - M is to 0, the higher the accuracy of the model established by the method; the smaller the standard deviation of P - M, the higher the precision of the model established by the method.

[0182] From the above test results, it can be seen that: a. The predicted values of the corrosion model are in good agreement with the measured values. For the unperturbed case, the difference between the predicted value and the measured value of the corrosion model is very small, and its mean is only 0.0862μm; for the perturbed case, the standard deviation of the deviation between the predicted value and the measured value of the corrosion model is less than 0.6 times the perturbation amount. Therefore, this modeling method has good stability and correctness. b. The numerical method has a fast convergence speed. The maximum number of iterations for solving by the nonlinear least squares method does not exceed 10 times, and the overall time consumption is less.

[0183] In one embodiment, the preparation of experimental data can be carried out first, such as the measured value of the thickness of the i-th corrosion layer, the historical temperature Tf of the outer surface of the j-th corrosion layer of the measured value of the thickness of the i-th corrosion layer i,j , the historical heat flux density H of the j-th corrosion layer of the measured value of the thickness of the i-th corrosion layer i,j , and the corrosion time t of the j-th corrosion layer of the measured value of the thickness of the i-th corrosion layer i,j , and the interface temperature value T of the corrosion layer and the metal material can be obtained through Tf i,j , and H i,j . i,j。Then, perform a traversal loop on the first constant and the second constant of the model's basic parameters to obtain the combined values of each first constant and second constant [N, λ], simplifying the solution of the six parameters to the solution of four parameters. With the goal of minimizing the sum of the squares of the differences between the measured corrosion layer thickness values and the calculated corrosion layer thickness values for each combination, use the linear least squares method to obtain the initial values of the parameters required for model construction under each combination of the first constant and the second constant. Then, based on the initial values of the parameters required for model construction under each combination of the first constant and the second constant, update the initial corrosion performance models of the corresponding metal materials to obtain the corrosion performance models of the metal materials, and perform a forward solution on the corrosion performance models of the metal materials to obtain the predicted corrosion layer thickness values; based on the deviation between the predicted corrosion layer thickness values and the measured corrosion layer thickness values, use the nonlinear least squares method to perform iterative processing on the initial values of the parameters required for model construction under each combination of the first constant and the second constant, and determine whether convergence occurs. If convergence occurs, obtain the target values of the parameters required for model construction under each combination of the first constant and the second constant, and determine whether it is the last parameter required for model construction. If so, determine the target metal material corrosion performance model.

[0184] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear description in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0185] Based on the same inventive concept, the embodiments of the present application also provide a metal material corrosion performance model construction and a metal material corrosion performance determination device for respectively implementing the above-mentioned metal material corrosion performance model construction and metal material corrosion performance determination methods. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above methods. Therefore, the specific limitations in one or more embodiments of the metal material corrosion performance model construction and metal material corrosion performance determination device provided below can refer to the limitations on the metal material corrosion performance model construction and metal material corrosion performance determination methods in the above text, and will not be repeated here.

[0186] In one embodiment, as Figure 8As shown in the figure, a device for constructing a corrosion performance model of a metal material is provided, including: a basic data acquisition module 100, a corrosion layer thickness calculation module 200, a parameter initial value acquisition module 300, a model primary update module 400, a parameter target value acquisition module 500, and a model secondary update module 600, where:

[0187] The basic data acquisition module 100 is used to acquire the initial model of the corrosion performance of the metal material, the measured value of the corrosion layer thickness, and the combined values of each first constant and second constant; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the corrosion performance of the metal material, and the second constant represents the boundary value of the corrosion layer thickness of the metal material at different stages in the initial model of the corrosion performance of the metal material.

[0188] The corrosion layer thickness calculation module 200 is used to obtain the calculated values of each corrosion layer thickness according to the initial model of the corrosion performance of the metal material and the combined values of each first constant and second constant.

[0189] The parameter initial value acquisition module 300 is used to take the minimum sum of squares of the differences between the measured value of the corrosion layer thickness and the calculated values of each corrosion layer thickness as the target, and obtain the initial values of the parameters required for model construction under the combined values of each first constant and second constant.

[0190] The model primary update module 400 is used to update the initial models of the corrosion performance of the corresponding metal materials according to the initial values of the parameters required for model construction under the combined values of each first constant and second constant, and obtain the corrosion performance models of the corresponding metal materials and the predicted values of each corrosion layer thickness.

[0191] The parameter target value acquisition module 500 is used to perform iterative processing on the initial values of the parameters required for model construction under the combined values of each first constant and second constant according to the deviation between the predicted value of each corrosion layer thickness and the measured value of the corrosion layer thickness, obtain the target values of the parameters required for model construction under the combined values of each first constant and second constant, and obtain the residual vector under the combined values of each first constant and second constant.

[0192] The model secondary update module 600 is used to determine the target value of the parameters required for model construction corresponding to the optimal combined value of the first constant and the second constant according to the residual vector corresponding to the minimum modulus among the moduli of each residual vector, and update the candidate corrosion performance model of the metal material according to the target value of the parameters required for model construction corresponding to the optimal combined value of the first constant and the second constant, and obtain the target corrosion performance model of the metal material, where the candidate corrosion performance model of the metal material is the corrosion performance model of the metal material corresponding to the optimal combined value of the first constant and the second constant.

[0193] In one embodiment, the basic data acquisition module 100 is further configured to obtain a first constant preset range and a second constant preset range; determine different first constant values and different second constant values according to the first constant preset range and the second constant preset range; and traverse different second constant values based on different first constant values to obtain combinations of each first constant and second constant.

[0194] In one embodiment, the corrosion layer thickness calculation module 200 is further configured to obtain the corrosion time, the heat flux density at the interface between the corrosion layer and the metal material, and the outer surface temperature of the corrosion layer; obtain the interface temperature value between the corrosion layer and the metal material according to the heat flux density at the interface between the corrosion layer and the metal material and the outer surface temperature of the corrosion layer; and obtain each corrosion layer thickness calculation value according to the initial model of the metal material corrosion performance, the corrosion time, the interface temperature value between the corrosion layer and the metal material, and combinations of each first constant and second constant.

[0195] In one embodiment, the model first update module 400 is further configured to update the initial models of the corrosion performance of each metal material according to the initial values of the parameters required for model construction under combinations of each first constant and second constant to obtain models of the corrosion performance of each metal material; and perform time integration on the models of the corrosion performance of each metal material to obtain predicted values of the thickness of each corrosion layer.

[0196] In one embodiment, the parameter target value acquisition module 500 is further configured to obtain an iteration termination condition; perform iterative processing on the initial values of the parameters required for model construction under combinations of each first constant and second constant by using the nonlinear least squares method according to the deviation between the predicted value of the corrosion layer thickness and the measured value of the corrosion layer thickness and the iteration termination condition, to obtain the target values of the parameters required for model construction under combinations of each first constant and second constant and the residual vector under combinations of each first constant and second constant.

[0197] In one embodiment, the model second update module 600 is further configured to obtain the norm of each residual vector according to each residual vector; select the residual vector corresponding to the minimum norm among the norms of the residual vectors to obtain a target residual vector; determine an optimal combination value of the first constant and the second constant according to the target residual vector; determine the target value of the parameter required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the optimal combination value of the first constant and the second constant; determine a candidate metal material corrosion performance model according to the optimal combination value of the first constant and the second constant and the models of the corrosion performance of each metal material; and update the candidate metal material corrosion performance model according to the target value of the parameter required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain a target metal material corrosion performance model.

[0198] In one embodiment, as Figure 9As shown, a device for determining the corrosion performance of a metal material is provided, including: a model input data acquisition module 700 and a corrosion performance determination module 800, where:

[0199] The model input data acquisition module 700 is configured to acquire the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time.

[0200] The corrosion performance determination module 800 is configured to determine the corrosion performance of the metal material by using a target metal material corrosion performance model based on the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time; wherein, the target metal material corrosion performance model is established by using the above-mentioned metal material corrosion performance model construction method.

[0201] Each module in the above-mentioned metal material corrosion performance model construction and the metal material corrosion performance determination device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned each module.

[0202] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 10 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the initial model of the metal material corrosion performance and the measured value of the corrosion layer thickness. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for constructing a metal material corrosion performance model and determining the metal material corrosion performance.

[0203] Those skilled in the art can understand, Figure 10The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0204] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0205] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0206] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0207] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0208] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0209] The above-described embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for constructing a corrosion performance model of a metal material, characterized in that, The method includes: Obtaining an initial model of the corrosion performance of a metallic material, a measured value of the corrosion layer thickness, and combination values of various first constants and second constants; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the corrosion performance of the metallic material, and the second constant represents the demarcation value of the corrosion layer thickness of the metallic material at different stages in the initial model of the corrosion performance of the metallic material; Obtaining calculated values of the corrosion layer thickness according to the initial model of the corrosion performance of the metallic material and the combination values of the first constants and the second constants; Taking the minimum of the sum of the squares of the differences between the measured value of the corrosion layer thickness and the calculated values of the corrosion layer thickness as the objective, obtaining initial values of the parameters required for model construction under the combination values of the first constants and the second constants; Updating the initial models of the corrosion performance of the corresponding metallic materials according to the initial values of the parameters required for model construction under the combination values of the first constants and the second constants, obtaining models of the corrosion performance of the metallic materials and predicted values of the corrosion layer thickness; Performing iterative processing on the initial values of the parameters required for model construction under the combination values of the first constants and the second constants according to the deviations between the predicted values of the corrosion layer thickness and the measured value of the corrosion layer thickness, obtaining target values of the parameters required for model construction under the combination values of the first constants and the second constants, and obtaining residual vectors under the combination values of the first constants and the second constants; Obtaining the norms of the residual vectors according to the residual vectors; selecting the residual vector corresponding to the minimum norm among the norms of the residual vectors to obtain a target residual vector; determining an optimal combination value of the first constant and the second constant according to the target residual vector; determining the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant according to the optimal combination value; determining a candidate model of the corrosion performance of the metallic material according to the optimal combination value of the first constant and the second constant and the models of the corrosion performance of the metallic materials; updating the candidate model of the corrosion performance of the metallic material according to the target value of the parameters required for model construction corresponding to the optimal combination value of the first constant and the second constant to obtain a target model of the corrosion performance of the metallic material.

2. The method according to claim 1, wherein The obtaining of the combination values of the first constants and the second constants includes: Obtaining a preset range of the first constant and a preset range of the second constant; Determining different first constant values and different second constant values according to the preset range of the first constant and the preset range of the second constant; Based on the different first constant values, respectively traversing the different second constant values to obtain combination values of the first constants and the second constants.

3. The method according to claim 1, wherein The obtaining of the calculated values of the corrosion layer thickness according to the initial model of the corrosion performance of the metallic material and the combination values of the first constants and the second constants includes: Obtaining the corrosion time, the heat flux density at the interface between the corrosion layer and the metallic material, and the temperature of the outer surface of the corrosion layer; Obtaining the temperature value at the interface between the corrosion layer and the metallic material according to the heat flux density at the interface between the corrosion layer and the metallic material and the temperature of the outer surface of the corrosion layer; Based on the initial model of the corrosion performance of the metal material, the corrosion time, the temperature value at the interface between the corrosion layer and the metal material, and the combination values of the first constants and the second constants, the calculated values of the thickness of each corrosion layer are obtained.

4. The method according to claim 1, characterized in that Updating the initial models of the corrosion performance of the corresponding metal materials according to the initial values of the parameters required for model construction under the combination values of the first constants and the second constants, to obtain the corrosion performance models of the metal materials and the predicted values of the thickness of each corrosion layer, including: Updating the initial models of the corrosion performance of the corresponding metal materials according to the initial values of the parameters required for model construction under the combination values of the first constants and the second constants, to obtain the corrosion performance models of the metal materials; Performing time integration on the corrosion performance models of the metal materials to obtain the predicted values of the thickness of each corrosion layer.

5. The method according to claim 1, characterized in that Based on the deviation between the predicted values of the thickness of each corrosion layer and the measured values of the thickness of the corrosion layer, performing iterative processing on the initial values of the parameters required for model construction under the combination values of the first constants and the second constants, to obtain the target values of the parameters required for model construction under the combination values of the first constants and the second constants, and obtaining the residual vectors under the combination values of the first constants and the second constants, including: Obtaining the iteration termination condition; Based on the deviation between the predicted values of the thickness of each corrosion layer and the measured values of the thickness of the corrosion layer, and the iteration termination condition, using the nonlinear least squares method to perform iterative processing on the initial values of the parameters required for model construction under the combination values of the first constants and the second constants, to obtain the target values of the parameters required for model construction under the combination values of the first constants and the second constants, and the residual vectors under the combination values of the first constants and the second constants.

6. A method for determining the corrosion performance of a metal material, characterized in that The method includes: Obtaining the temperature on the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time; Based on the temperature on the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, using the corrosion performance model of the target metal material to determine the corrosion performance of the metal material; [[ID=]]10 7. The method according to claim 6, characterized in that, wherein, the corrosion performance model of the target metal material is established by using the method described in any one of claims 1-5. Based on the temperature on the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time, using the corrosion performance model of the target metal material to determine the corrosion performance of the metal material, including: Detecting the temperature at the interface between the corrosion layer and the metal material according to the temperature on the outer surface of the corrosion layer and the heat flux density at the interface between the corrosion layer and the metal material; 8. An apparatus for constructing a corrosion performance model of a metal material, characterized in that Based on the temperature at the interface between the corrosion layer and the metal material and the corrosion time, using the corrosion performance model of the target metal material to determine the corrosion performance of the metal material, wherein the corrosion performance of the metal material is the predicted value of the thickness of the corrosion layer. The device includes: A basic data acquisition module, configured to acquire the initial model of the corrosion performance of the metal material, the measured value of the thickness of the corrosion layer, and the combination values of the first constants and the second constants; the first constant represents an integer constant corresponding to the corrosion rate at different stages in the initial model of the corrosion performance of the metal material, and the second constant represents the demarcation value of the thickness of the corrosion layer of the metal material at different stages in the initial model of the corrosion performance of the metal material. The corrosion layer thickness calculation module is used to obtain the calculated values of the thickness of each corrosion layer according to the initial model of the corrosion performance of the metal material and the combined values of the first constants and the second constants; The parameter initial value acquisition module is used to obtain the initial values of the parameters required for model construction under the combined values of the first constants and the second constants with the goal of minimizing the sum of the squares of the differences between the measured values of the corrosion layer thickness and the calculated values of the thickness of each corrosion layer; The model initial update module is used to update the initial models of the corrosion performance of the corresponding metal materials according to the initial values of the parameters required for model construction under the combined values of the first constants and the second constants, and obtain the corrosion performance models of the metal materials and the predicted values of the thickness of each corrosion layer; The parameter target value acquisition module is used to perform iterative processing on the initial values of the parameters required for model construction under the combined values of the first constants and the second constants according to the deviations between the predicted values of the thickness of each corrosion layer and the measured values of the corrosion layer thickness, obtain the target values of the parameters required for model construction under the combined values of the first constants and the second constants, and obtain the residual vectors under the combined values of the first constants and the second constants; The model secondary update module is used to obtain the norms of the residual vectors according to the residual vectors; select the residual vector corresponding to the minimum norm among the norms of the residual vectors to obtain the target residual vector; determine the optimal combined value of the first constant and the second constant according to the target residual vector; determine the target value of the parameter required for model construction corresponding to the optimal combined value of the first constant and the second constant according to the optimal combined value of the first constant and the second constant; determine the candidate corrosion performance model of the metal material according to the optimal combined value of the first constant and the second constant and the corrosion performance models of the metal materials; update the candidate corrosion performance model of the metal material according to the target value of the parameter required for model construction corresponding to the optimal combined value of the first constant and the second constant to obtain the target corrosion performance model of the metal material.

9. The device according to claim 8, characterized in that The corrosion layer thickness calculation module is further used to obtain the corrosion time, the heat flux density at the interface between the corrosion layer and the metal material, and the temperature of the outer surface of the corrosion layer; obtain the temperature value at the interface between the corrosion layer and the metal material according to the heat flux density at the interface between the corrosion layer and the metal material and the temperature of the outer surface of the corrosion layer; Obtain the calculated values of the thickness of each corrosion layer according to the initial model of the corrosion performance of the metal material, the corrosion time, the temperature value at the interface between the corrosion layer and the metal material, and the combined values of the first constants and the second constants.

10. A device for determining the corrosion performance of a metal material, characterized in that, The device includes: The model input data acquisition module is used to acquire the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time; The corrosion performance determination module is used to determine the corrosion performance of the metal material by using the target corrosion performance model of the metal material according to the temperature of the outer surface of the corrosion layer, the heat flux density at the interface between the corrosion layer and the metal material, and the corrosion time; Wherein, the target corrosion performance model of the metal material is established by using the method according to any one of claims 1-5.

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