CN116825249B

CN116825249B - Method for constructing corrosion performance model of metal material and method for determining corrosion performance

Info

Publication number: CN116825249B
Application number: CN202310757368.1A
Authority: CN
Inventors: 许多挺; 汪韬; 王开元; 金鑫
Original assignee: China General Nuclear Power Corp; China Nuclear Power Technology Research Institute Co Ltd; CGN Power Co Ltd
Current assignee: China General Nuclear Power Corp; China Nuclear Power Technology Research Institute Co Ltd; CGN Power Co Ltd
Priority date: 2023-06-25
Filing date: 2023-06-25
Publication date: 2025-07-29
Anticipated expiration: 2043-06-25
Also published as: CN116825249A

Abstract

The application relates to a method, a device, a computer device, a storage medium and a computer program product for constructing a metal material corrosion performance model. The method comprises the steps of obtaining an initial model of corrosion performance of the metal material, a measured value of thickness of the corrosion layer and a combination value of each first constant and each second constant, obtaining initial values of parameters required by model construction by taking the smallest sum of squares of differences between the measured value of thickness of the corrosion layer and a calculated value of thickness of each corrosion layer as a target, updating the initial model of corrosion performance of the corresponding metal material, and carrying out iterative processing on the initial values of parameters required by model construction under the combination value of each first constant and each second constant to obtain target values of parameters required by model construction under the combination value of each first constant and each second constant, so that the corrosion performance model of the target metal material can be obtained. In addition, an accurate method, apparatus, computer device, storage medium and computer program product for determining corrosion performance of metallic materials are provided.

Description

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

Technical Field

The present application relates to the field of nuclear power technology, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for constructing a corrosion performance model of a metal material. Furthermore, a method, a device, a computer device, a storage medium and a computer program product for determining the corrosion performance of a metallic material are disclosed.

Background

In a nuclear power plant reactor, corrosion generated by metal materials in service is ubiquitous, and in a certain sense, corrosion can generate component failure which is more serious than strength damage or fatigue damage of the metal materials, and the effect of the corrosion can not only reduce the structural life of the whole part, but also buried potential safety hazards for the operation of equipment. The problems associated with corrosion have become one of the important factors affecting nuclear safety, and modeling the corrosion performance caused by metallic materials has become particularly important.

In the conventional technology, semi-empirical and semi-theoretical models, such as an Arrhenius equation, are generally used for describing the corrosion performance of a metal material in a service environment, and in the Arrhenius equation, the final corrosion performance is usually calculated through real-time measurement parameters in the equation, but the working condition of the metal material in service is usually changed, and real-time measurement cannot be usually performed, so that accurate modeling of the corrosion performance of the material by means of the method cannot be realized.

Disclosure of Invention

Based on this, it is necessary to provide an accurate metal material corrosion performance model construction method, apparatus, computer device, computer readable storage medium and computer program product, and further, an accurate metal material corrosion performance determination method, apparatus, computer device, computer readable storage medium and computer program product.

In a first aspect, the application provides a method for constructing a corrosion performance model of a metal material. The method comprises the following steps:

The method comprises the steps of obtaining an initial model of corrosion performance of a metal material, a measured value of thickness of a corrosion layer and a combination value of each first constant and each second constant, wherein the first constant represents an integer constant corresponding to corrosion rates in different stages of the initial model of corrosion performance of the metal material, and the second constant represents a demarcation value of thickness of the corrosion layer of the metal material in different stages of the initial model of corrosion performance of the metal material;

obtaining a calculated value of each corrosion layer thickness according to the initial model of the corrosion performance of the metal material and the combined values of the first constant and the second constant;

Taking the minimum sum of squares of differences between the corrosion layer thickness measured value and the corrosion layer thickness calculated value as a target, and obtaining initial values of parameters required by model construction under the first constant and the second constant combined value;

Constructing a required parameter initial value according to the model under the combination value of the first constant and the second constant, and updating the corresponding initial model of the corrosion performance of each metal material to obtain a corrosion performance model of each metal material and a thickness predicted value of each corrosion layer;

performing iterative processing on initial values of parameters required by model construction under the first constant and the second constant combined values according to deviation of the thickness predicted value of each corrosion layer and the thickness measured value of each corrosion layer, obtaining target values of parameters required by model construction under the first constant and the second constant combined values, and obtaining residual vectors under the first constant and the second constant combined values;

According to the residual vector corresponding to the smallest module in the modes of the residual vectors, determining a model construction required parameter target value corresponding to the optimal first constant and the second constant combination value, and updating a candidate metal material corrosion performance model according to the model construction required parameter target value corresponding to the optimal first constant and the second constant combination value to obtain a target metal material corrosion performance model, wherein the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal first constant and the second constant combination value.

In one embodiment, obtaining each of the first constant and the second constant combined value includes:

Acquiring a first constant preset range and a second constant preset range;

Determining different first constant values and different second constant values according to the first constant preset range and the second constant preset range;

based on the different first constant values, traversing the different second constant values respectively to obtain first constant and second constant combined values.

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

Acquiring corrosion time, heat flux density of an interface between the corrosion layer and the metal material and outer surface temperature of the corrosion layer;

Obtaining the interface temperature value of the corrosion layer and the metal material according to the heat flux density of the interface of the corrosion layer and the metal material and the outer surface temperature of the corrosion layer;

And obtaining the calculated value of each corrosion layer thickness according to the initial model of the corrosion performance of the metal material, the corrosion time, the temperature value of the interface between the corrosion layer and the metal material and the combination value of each first constant and each second constant.

In one embodiment, the constructing the initial value of the required parameter according to the model under the combination of the first constant and the second constant, and updating the initial model of the corrosion performance of each corresponding metal material, to obtain the corrosion performance model of each metal material and the predicted value of the thickness of each corrosion layer, includes:

constructing a required parameter initial value according to the model under the combination value of the first constant and the second constant, and updating the corresponding initial model of the corrosion performance of each metal material to obtain a corrosion performance model of each metal material;

And performing time integration on the corrosion performance models of the metal materials to obtain predicted values of the thickness of each corrosion layer.

In one embodiment, the performing iterative processing on initial values of parameters required for model construction under the first constant and the second constant combined values according to the deviation between the predicted value of the thickness of the etching layer and the measured value of the thickness of the etching layer, obtaining target values of parameters required for model construction under the first constant and the second constant combined values, and obtaining residual vectors under the first constant and the second constant combined values includes:

Acquiring an iteration termination condition;

And carrying out iterative processing on initial values of parameters required by model construction under the first constant and the second constant combined values by adopting a nonlinear least square method according to the deviation of the thickness predicted value of each corrosion layer and the thickness measured value of each corrosion layer and the iteration termination condition to obtain target values of parameters required by model construction under the first constant and the second constant combined values and residual vectors under the first constant and the second constant combined values.

In one embodiment, the determining, according to the residual vector corresponding to the smallest module among the modes of each residual vector, the model corresponding to the optimal first constant and the second constant combination value to construct the required parameter target value, and constructing, according to the model corresponding to the optimal first constant and the second constant combination value, the model corresponding to the optimal first constant and the second constant combination value to construct the required parameter target value to update the candidate metal material corrosion performance model, and obtaining the target metal material corrosion performance model includes:

Obtaining a model of each residual vector according to each residual vector;

Selecting a residual vector corresponding to the smallest module in the modules of the residual vectors to obtain a target residual vector;

determining optimal first constant and second constant combined values according to the target residual vector;

determining a model construction required parameter target value corresponding to the optimal first constant and the second constant combination value according to the optimal first constant and the second constant combination value;

Determining a candidate metal material corrosion performance model according to the optimal first constant and second constant combined value and the corrosion performance models of the metal materials;

and constructing a required parameter target value according to a model corresponding to the optimal first constant and the second constant combination value, and updating the candidate metal material corrosion performance model to obtain a target metal material corrosion performance model.

In a second aspect, the application also provides a device for constructing the corrosion performance model of the metal material. The device comprises:

The system comprises a basic data acquisition module, a corrosion performance initial model of a metal material, a corrosion layer thickness measurement value and a combination value of first constants and second constants, wherein the first constants represent integer constants corresponding to corrosion rates in different stages of the corrosion performance initial model of the metal material, and the second constants represent demarcation values of corrosion layer thicknesses of the metal material in different stages of the corrosion performance initial model of the metal material;

the corrosion layer thickness calculation module is used for obtaining the calculated value of the thickness of each corrosion layer according to the initial model of the corrosion performance of the metal material and the combined value of each first constant and each second constant;

The parameter initial value acquisition module is used for obtaining a parameter initial value required by model construction under each first constant and second constant combined value by taking the minimum sum of squares of differences between the corrosion layer thickness measured value and each corrosion layer thickness calculated value as a target;

The model initial updating module is used for constructing required parameter initial values according to the models under the first constant and the second constant combination values, updating corresponding initial models of the corrosion performance of the metal materials, and obtaining corrosion performance models of the metal materials and thickness predicted values of the corrosion layers;

The parameter target value acquisition module is used for carrying out iterative processing on initial values of parameters required by model construction under the first constant and the second constant combination value according to the deviation of the thickness predicted value of each corrosion layer and the thickness measured value of each corrosion layer, obtaining target values of parameters required by model construction under the first constant and the second constant combination value, and obtaining residual vectors under the first constant and the second constant combination value;

The model secondary updating module is used for determining a model construction required parameter target value corresponding to an optimal first constant and a second constant combination value according to a residual vector corresponding to a minimum module in modes of the residual vectors, and updating a candidate metal material corrosion performance model according to the model construction required parameter target value corresponding to the optimal first constant and the second constant combination value to obtain a target metal material corrosion performance model, wherein the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal first constant and the second constant combination value.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

The method, the device, the computer equipment, the storage medium and the computer program product for constructing the metal material corrosion performance model take the square sum of the difference between the corrosion layer thickness measured value and the calculated value of each corrosion layer thickness as a target, obtain the initial value of the model construction required parameter under the combination value of the first constant and the second constant, update the corresponding initial model of each metal material corrosion performance according to the combination value of the first constant and the second constant and the initial value of the model construction required parameter, and then carry out iterative processing on the initial value of the model construction required parameter to update the initial value of the model construction required parameter to obtain the target value of the model construction required parameter, and then determine the optimal initial value of the model construction required parameter corresponding to the combination value of the first constant and the second constant, thereby accurately obtaining the final target metal material corrosion performance model.

In a sixth aspect, the present application also provides a method for determining corrosion performance of a metal material, the method comprising:

Acquiring the outer surface temperature of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material and the corrosion time;

Determining the corrosion performance of the metal material by adopting a target metal material corrosion performance model according to the temperature of the outer surface of the corrosion layer, the heat flow density of the interface between the corrosion layer and the metal material and the corrosion time;

The corrosion performance model of the target metal material is built by adopting the method for building the corrosion performance model of the metal material.

In a seventh aspect, the present application also provides a metallic material corrosion performance determining apparatus, the apparatus comprising:

The model input data acquisition module is used for acquiring the outer surface temperature of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material and the corrosion time;

The corrosion performance determining module is used for determining the corrosion performance of the metal material by adopting a target metal material corrosion performance model according to the outer surface temperature of the corrosion layer, the heat flow density of the interface between the corrosion layer and the metal material and the corrosion time;

In an eighth aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

In a ninth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

In a tenth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

The method, the device, the computer equipment, the storage medium and the computer program product for determining the corrosion performance of the metal material acquire the temperature of the outer surface of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material and the corrosion time, and determine the corrosion performance of the metal material by adopting a target metal material corrosion performance model according to the temperature of the outer surface of the corrosion layer, the heat flux density of 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 adopting the method for constructing the metal material corrosion performance model. In the whole process, the corrosion performance of the metal material can be accurately determined by adopting the corrosion performance model of the target metal material by acquiring the input variable data of the model.

Drawings

FIG. 1 is a diagram of an application environment for a method for modeling corrosion performance of a metal material and a method for determining corrosion performance of a metal material in one embodiment;

FIG. 2 is a flow chart of a method for modeling corrosion performance of a metallic material in one embodiment;

FIG. 3 is a flow chart of a method for modeling corrosion performance of a metallic material according to another embodiment;

FIG. 4 is a flow chart of a method for determining corrosion performance of a metallic material in one embodiment;

FIG. 5 is a graph showing the comparison of model predictions and measured values for 0um random disturbance measurement data in one embodiment;

FIG. 6 is a graph showing the comparison of model predictions and measured values for 5um random disturbance measurement data in one embodiment;

FIG. 7 is a graph showing the comparison of model predictions and measurements for 10um random disturbance measurement data in one embodiment;

FIG. 8 is a block diagram of an apparatus for modeling corrosion performance of a metallic material in one embodiment;

FIG. 9 is a block diagram showing a structure of a corrosion performance determining apparatus for a metallic material in one embodiment;

fig. 10 is an internal structural view of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The method for determining the corrosion performance of the metal material provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The whole metal material corrosion performance determining method comprises a metal material corrosion performance model construction stage and a metal material corrosion performance model application stage, and the data processing process of the two stages in practical application is described below.

And a stage of constructing a corrosion performance model of the metal material. First, the terminal 102 sends a metallic material corrosion performance model construction request to the server 104, where the metallic material corrosion performance model construction request carries the metallic material corrosion performance initial model, the corrosion layer thickness measurement value, and the value required for combining the respective first constants and second constants. The server 104 receives a metallic material corrosion performance model construction request, acquires a metallic material corrosion performance initial model, a corrosion layer thickness measured value and a model construction required parameter initial value under each first constant and second constant combination value carried in the metallic material corrosion performance model construction request, acquires an integer constant corresponding to each first constant and second constant combination value according to the model construction required value under each first constant and second constant combination value, characterizes an integer constant corresponding to corrosion rate in different stages of the metallic material corrosion performance initial model, characterizes a boundary value of a metallic material corrosion layer thickness in different stages of the metallic material corrosion performance initial model, acquires a layer thickness calculated value according to the metallic material corrosion performance initial model and a combination value of each first constant and second constant, acquires a model construction required parameter initial value under each first constant and second constant combination value by taking the square sum of differences between the corrosion layer thickness measured value and each corrosion layer thickness calculated value as a target value, acquires an optimal parameter vector under each model construction required parameter initial value under each first constant and second constant combination value, acquires an optimal parameter value under each metallic material corrosion performance initial model corresponding to each metallic material corrosion performance initial model and corrosion performance and each layer thickness predicted value by updating, acquires an optimal parameter vector under each model construction required parameter initial value under each first constant and second constant combination value under each layer thickness predicted value and a model construction required parameter value under each first constant and second constant combination value as a model construction required parameter value under each first constant and a second constant layer thickness predicted value under each layer thickness predicted value, and a model under each layer thickness predicted value and a model required constant layer thickness value under each model and a model required constant layer is calculated under a model is obtained by a minimum value under a constant is calculated, and constructing a required parameter target value according to a model corresponding to the optimal first constant and the second constant combination value, and updating a candidate metal material corrosion performance model to obtain a target metal material corrosion performance model, wherein the candidate metal material corrosion performance model is a metal material corrosion performance model corresponding to the optimal first constant and the second constant combination value.

And a metal material corrosion performance model application stage. After the metal material corrosion performance model is built and when the terminal 102 needs to apply the target metal material corrosion performance model of the server 104, a metal material corrosion performance determining request is sent to the server 104, the metal material corrosion performance determining request carries the corrosion layer outer surface temperature, the heat flux density of the corrosion layer and the metal material interface, and the corrosion time, and when the server 104 receives the metal material corrosion performance determining request, the metal material corrosion performance determining request extracts the corrosion layer outer surface temperature, the heat flux density of the corrosion layer and the metal material interface, and the corrosion time, and determines the metal material corrosion performance by adopting the target metal material corrosion performance model according to the corrosion layer outer surface temperature, the heat flux density of the corrosion layer and the metal material interface, and the corrosion time.

The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In one embodiment, as shown in fig. 2, a method for constructing a corrosion performance model of a metal material is provided, and the method is applied to the server 104 in fig. 1 for illustration, and includes the following steps:

s100, obtaining an initial model of corrosion performance of the metal material, a measured value of thickness of the corrosion layer and a combination value of each first constant and each second constant, wherein the first constant represents an integer constant corresponding to corrosion rates in different stages of the initial model of corrosion performance of the metal material, and the second constant represents a demarcation value of thickness of the corrosion layer of the metal material in different stages of the initial model of corrosion performance of the metal material.

The initial model of the corrosion performance of the metal material refers to an initial model which is not subjected to numerical substitution, namely a two-stage Arrhenius equation initial model, and can describe the corrosion behavior of a great number of metal materials in a service environment. The mathematical model can be:

S(t=0)=0

Wherein S is the thickness of the corrosion layer, N is a first constant, lambda is a second constant, T is the corrosion time, C ₁ is a third constant, C ₂ is a fourth constant, Q ₁ is a first activation energy, Q ₂ is a second activation energy, and T is the temperature of the interface between the corrosion layer and the metal material. C ₁、C₂、Q₁, Q ₂ are parameters required for subsequent model construction. And as the thickness of the corrosion layer of the metal material is not smooth at the turning position of the metal material, lambda is taken as the demarcation value of the thickness of the corrosion layer at different turning stages. S is less than or equal to lambda and is the corrosion performance model of the metal material in the pre-turning stage, and S is more than lambda and is the corrosion performance model of the metal material in the post-turning stage. When the etching time t is 0, the etching layer thickness S is also 0. Corrosion layer thickness measurement As an actual measurement of the thickness of the etch layer in the ith time step, i=1, 2, 3 once.

Specifically, the initial model of corrosion performance of the metal material can be obtained by taking S as a target output value, wherein the variable amount in the initial model is the corrosion time T and the temperature T of the interface between the corrosion layer and the metal material, and the remaining parameters N, λ, C ₁、C₂、Q₁ and Q ₂ are all fixed amounts, so that the application determines the optimal values of the six fixed amount parameters. Since the derivative of the initial model of the corrosion performance of the metal material at the turning position of the thickness of the corrosion layer is not smooth, and N is a positive integer, it is very difficult to directly determine six parameters from the technical point of view, and the established algorithm is difficult to ensure convergence and stability, so that the problem needs to be simplified, and various combination values of [ N, lambda ] can be determined first.

Further, the terminal sends a metallic material corrosion performance model construction request to the server, the server receives the metallic material corrosion performance model construction request, acquires a metallic material corrosion performance initial model, a corrosion layer thickness measurement value and values required by combination of the first constants and the second constants carried in the metallic material corrosion performance model construction request, and analyzes and processes the values required by combination of the first constants and the second constants to obtain combined values [ N, lambda ] of the first constants and the second constants.

S200, obtaining calculated values of thickness of each corrosion layer according to the initial model of corrosion performance of the metal material and the combination value of each first constant and each second constant.

Specifically, after obtaining each first constant and each second constant combined value, substituting each first constant and each second constant combined value into a metal material corrosion performance initial model, wherein the metal material corrosion performance initial model is simplified to be solved for only four parameters of C ₁、C₂、Q₁ and Q ₂.

S300, taking the minimum sum of squares of differences between the corrosion layer thickness measured value and the corrosion layer thickness calculated value as a target, and obtaining initial values of parameters required by model construction under the combined values of the first constant and the second constant.

Specifically, the calculated values of the thickness of each etching layer are compared with the measured values of the thickness of each etching layer obtained in advance, and the sum of squares of the differences between the measured values of the thickness of each etching layer and the calculated values of the thickness of each etching layer is targeted to be minimum, so that initial values of parameters C ₁、C₂、Q₁ and Q ₂ required for model construction under the combined values of the first constant and the second constant for achieving the aim are obtained.

S400, constructing a required parameter initial value according to the model under the combination value of each first constant and each second constant, and updating the corresponding initial model of the corrosion performance of each metal material to obtain the corrosion performance model of each metal material and the predicted value of each corrosion layer thickness.

Specifically, the initial values of parameters required by model construction under the combination values of the first constants and the second constants are substituted into corresponding initial models of corrosion performance of the metal materials, so that the initial models of the corrosion performance of the metal materials are updated to obtain the models of the corrosion performance of the metal materials, at the moment, the values of C ₁、C₂、Q₁ and Q ₂ in the models of the corrosion performance of the metal materials are all inaccurate initial values, and the corrosion layer thickness predicted values determined by the inaccurate initial values are obtained by forward solving the models of the corrosion performance of the metal materials.

S500, carrying out iterative processing on initial values of parameters required by model construction under each first constant and each second constant combination value according to deviation of each corrosion layer thickness predicted value and each corrosion layer thickness measured value, obtaining target values of parameters required by model construction under each first constant and each second constant combination value, and obtaining residual vectors under each first constant and each second constant combination value.

Wherein, the residual error refers to the difference between the actual measured value and the calculated value in the mathematical statistics.

Specifically, the initial values of C ₁、C₂、Q₁ and Q ₂ in the corrosion performance model of each metal material are subjected to iterative processing through the deviation of the predicted value of the thickness of each corrosion layer and the measured value of the thickness of each corrosion layer, so that the optimal parameter target value for model construction is obtained, and the residual vector of the corrosion performance model of the metal material under the combination value of each first constant and each second constant is obtained.

S600, determining a model construction required parameter target value corresponding to the optimal first constant and the second constant combination value according to the residual vector corresponding to the smallest module in the modes of the residual vectors, and updating a candidate metal material corrosion performance model according to the model construction required parameter target value corresponding to the optimal first constant and the second constant combination value to obtain a target metal material corrosion performance model, wherein the candidate metal material corrosion performance model is the metal material corrosion performance model corresponding to the optimal first constant and the second constant combination value.

Specifically, firstly, determining a model corresponding to an optimal first constant and a second constant combination value through a residual vector to construct a required parameter target value, and constructing a candidate model substituted by a required parameter initial value by adopting the model corresponding to the optimal first constant and the second constant combination value before updating to obtain a target metal material corrosion performance model. The method for determining the model corresponding to the optimal first constant and the second constant combined value to construct the required parameter target value through the residual vector may be to calculate a modulus of each residual vector, obtain residual vectors corresponding to the smallest modulus in all the models, and determine the model corresponding to the optimal first constant and the second constant combined value to construct the required parameter target value.

In the method for constructing the metal material corrosion performance model, the square sum of the difference between the corrosion layer thickness measured value and the corrosion layer thickness calculated value is taken as a target, and a model construction required parameter initial value under a first constant and a second constant combined value is obtained, so that the corresponding metal material corrosion performance initial model is updated according to the first constant and the second constant combined value and the model construction required parameter initial value, and the model construction required parameter initial value is updated through the corrosion layer thickness predicted value obtained from each updated metal material corrosion performance initial model and the deviation of the corrosion layer thickness measured value, and iterative processing is carried out on the model construction required parameter initial value to update the model construction required parameter initial value, so as to obtain a model construction required parameter target value, and then the optimal model construction required parameter target value corresponding to the first constant and the second constant combined value is determined, so that the final target metal material corrosion performance model is accurately obtained.

In one embodiment, obtaining each of the first constant and the second constant combined value comprises:

the method comprises the steps of obtaining a first constant preset range and a second constant preset range, determining different first constant values and different second constant values according to the first constant preset range and the second constant preset range, and traversing the different second constant values based on the different first constant values to obtain each first constant and second constant combined value.

Specifically, the first constant and the second constant transmitted by the terminal are combined to a required value, i.e. a first constant preset range and a second constant preset range. The first constant preset range and the second constant preset range are set according to historical experience, for example, the first constant preset range is generally [1,3], and the first constant is an integer constant, so the first constant is generally 1,2 and 3. The second constant preset range is generally 2-6, a value is obtained at intervals of 0.2 in the second constant preset range according to the first constant preset range and the second constant preset range, different first constant values N and different second constant values lambda are determined, and different second constant values are traversed respectively based on the different first constant values 1,2 and 3 to obtain combined values of the first constant and the second constant. For example, the first constant and the second constant may be combined values of [1,2], [1,2.2], [2,5.4], etc. When k ₁ different first constant values and k ₂ different second constant values are provided, k ₁*k₂ first constant and k ₁*k₂ second constant combined values are obtained. The optimal first constant and second constant combined value is then determined by a hyper-parameter adjustment method.

In this embodiment, by obtaining the combined values of the first constant and the second constant, the initial model of the corrosion performance of the metal material can be simplified from six parameters to be determined to four parameters, the corrosion performance model of the target metal material can be obtained more efficiently, and the combined values of the first constant and the second constant are combined values within a reasonable range set by historical experience, so that accuracy is further provided.

In one embodiment, obtaining each corrosion layer thickness calculation value according to the initial model of the corrosion performance of the metal material and each of the first constant and the second constant combination value comprises:

The method comprises the steps of obtaining corrosion time, heat flux density of an interface between a corrosion layer and a metal material and temperature of the outer surface of the corrosion layer, obtaining a temperature value of the interface between the corrosion layer and the metal material according to the heat flux density of the interface between the corrosion layer and the metal material and the temperature of the outer surface of the corrosion layer, and obtaining a calculated value of the thickness of each corrosion layer according to an initial model of corrosion performance of the metal material, the corrosion time, the temperature value of the interface between the corrosion layer and the metal material and each first constant and each second constant.

Specifically, each first constant and each second constant are substituted into an initial model of corrosion performance of the metal material, and at this time, the initial model of corrosion performance of the metal material only needs to solve four parameters of C ₁、C₂、Q₁ and Q ₂. For general metal material corrosion problems, the thickness of the etched layer after turning occupies a larger proportion in the whole thickness of the etched layer, so that only the post-turning stage can be assumed to be adopted to solve the initial value of [ C ₂,Q₂ ] and then solve the initial value of [ C ₁,Q₁ ]. In order to ensure that the four parameters can be accurately solved, the other parameters, namely the corrosion time T _i,j and the temperature T of the interface between the corrosion layer and the metal material, are firstly obtained. The etching time is the jth time of the thickness measurement value of the ith etching layer, and since T cannot be directly measured, the etching time is calculated by using the etching layer external surface temperature Tf _i,j and the heat flow density H _i,j at the current time, and therefore the temperature T of the etching layer and the metal material interface needs to be obtained by obtaining the heat flow density H _i,j of the etching layer and the metal material interface and the etching layer external surface temperature Tf _i,j, and the specific expression is as follows:

where i, j=1, 2,3,..m, tf _i,j is the j-th corrosion layer outer surface temperature history of the i-th corrosion layer thickness measurement, H _i,j is the j-th heat flux density history of the i-th corrosion layer thickness measurement, K is the thermal conductivity of the corrosion layer, The etch layer thickness is predicted for the model. The meaning of j is that since a single etch layer thickness measurement corresponds to a series of times, outer surface temperature histories, and heat flux density histories, one i will correspond to a series of j, tf _i,j is the 1 st outer surface temperature history of the 1 st etch layer thickness measurement when i=1, j=1, tf _i,j is the 2 nd outer surface temperature history of the 1 st time step etch layer thickness measurement when i=1, j=2, and so on.

In addition, for K, the thermal conductivity of the etch layer is a function of temperature, so to determine the thermal conductivity of the etch layer, the average temperature of the inner and outer surfaces of the etch layer is first known. The outer surface temperature is Tf _i,j, the inner surface temperature is calculated by first assuming an initial value, and the initial value is used to calculate the thermal conductivity of the corrosion layer, which in turn affects the inner surface temperature of the corrosion layer, so that the process needs to be iterated repeatedly until the inner surface temperature of the corrosion layer has no obvious change, and the accurate inner surface temperature value of the corrosion layer is not considered to be obtained. For the followingAlthough the current target model is not established, the model parameters are unknown, but from the practical physical point of view, the thickness of the corrosion layer is very thin and its effect on temperature is very limited, so we can first obtain a set of insufficiently accurate model parameters, with which we can roughly learn the predicted value of the corrosion layer thickness, the accuracy of the calculated T for this set of less accurate model parameters being sufficient.

When solving the initial value of [ C ₂,Q₂ ], integrating the post-turning stage of the initial model of the corrosion performance of the metal material and taking the logarithm to obtainAnd define the calculated thickness of the etching layer asCalculated thickness of corrosion layer at last time stepSubstituting the corrosion time T _i and the temperature T _i of the interface between the corrosion layer and the metal material to obtain the calculated thickness value of the corrosion layer at the moment:

After obtaining the calculated value of the corrosion layer thickness, taking the minimum square sum of the differences between the measured value of the corrosion layer thickness and the calculated value of each corrosion layer thickness as a target, obtaining initial values of parameters required by model construction under the combined values of each first constant and each second constant comprises the following steps of firstly obtaining a function of the square sum of the differences between the measured value of the corrosion layer thickness and the calculated value of each corrosion layer thickness:

And solving the square sum of the differences by a linear least square method so as to minimize the square sum of the differences between the corrosion layer thickness measured value and each corrosion layer thickness calculated value. The method comprises the following steps of performing linear regression solution in a matrix form to obtain:

The initial value [ C ₂,Q₂ ] of the parameters required by the model construction can be obtained as a solution of the following equation: And solving [ C ₂,Q₂ ], after the solving is completed, simultaneously combining the models of the two stages before turning and after turning, adopting a linear least square method, firstly obtaining calculated values of thickness of each corrosion layer according to an initial model of corrosion performance of the metal material of the two stages before turning and after turning, and combined values of each first constant and each second constant, and then obtaining initial values of [ C ₁,Q₁ ] under combined values of each first constant and each second constant by taking the square sum of differences between the measured values of thickness of each corrosion layer and the calculated values of thickness of each corrosion layer as a target. Through the steps, initial values of parameters required by model construction under the combined values of the first constant and the second constant can be obtained [ C ₂,Q₂]、[C₁,Q₁ ].

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 initial values of parameters required for model construction under the combined values of the first constant and the second constant, with the subsequent objective of minimizing the sum of squares of differences between the measured values of the thickness of the corrosion layer and the calculated values of the thickness of each corrosion layer.

In one embodiment, constructing initial values of required parameters according to models under the first constant and the second constant combination value, updating initial models corresponding to corrosion performance of each metal material, and obtaining corrosion performance models of each metal material and predicted values of thickness of each corrosion layer comprises:

and (3) constructing a required parameter initial value according to the model under the combination value of the first constant and the second constant, updating the corresponding initial model of the corrosion performance of each metal material to obtain a corrosion performance model of each metal material, and performing time integration on the corrosion performance model of each metal material to obtain a predicted value of each corrosion layer thickness.

Specifically, at this time, the parameter values in the initial model of the corrosion performance of each metal material are known, the initial values of the parameters required by the model construction under the combined values of the first constants and the second constants are substituted into the corresponding initial model of the corrosion performance of the metal material to update, and the corrosion performance model of each metal material is obtained. And (3) carrying out time integration on the corrosion performance models of the metal materials, and solving the predicted values of the thickness of each corrosion layer of the models at each time measurement point by adopting a numerical forward solving method. Wherein when the etching time is 0, the predicted value of the etching layer thickness is 0, and the predicted value of the etching layer thickness of the last time step is used asTo calculate T ₁, namely:

In the formula, And T _oi is the predicted value of the thickness of the ith time step corrosion layer, and T _oi is the temperature of the outer surface of the ith time step corrosion layer when forward solving.

In the embodiment, the time integration is performed on the corrosion performance model of each metal material, so that the predicted value of the thickness of each corrosion layer can be obtained efficiently.

In one embodiment, performing iterative processing on initial values of parameters required for model construction under each of the first constant and the second constant combination value according to deviation of the predicted value of thickness of each of the corrosion layers and the measured value of thickness of the corrosion layers, obtaining target values of parameters required for model construction under each of the first constant and the second constant combination value, and obtaining residual vectors under each of the first constant and the second constant combination value includes:

And carrying out iteration processing on initial values of parameters required by model construction under each first constant and each second constant combined value by adopting a nonlinear least square method according to the deviation of the thickness predicted value of each corrosion layer and the thickness measured value of the corrosion layer and the iteration termination condition to obtain target values of parameters required by model construction under each first constant and each second constant combined value and residual vectors under each first constant and each second constant combined value.

Specifically, an iteration termination condition is acquired. The iteration termination conditions in the present application include initial termination conditions, standard termination conditions, and model parameter termination conditions. When one of the termination conditions is met, the iteration stops. Defining a residual vector r (C ₁,Q₁,C₂,Q₂), and a Jacobi matrix J (C ₁,Q₁,C₂,Q₂) of residual vectors, wherein:

In J (C ₁,Q₁,C₂,Q₂), partial differentiation is calculated using a two-point forward numerical differential formula as follows:

wherein h ₁、h₂、h₃、h₄ is the disturbance quantity calculated by partial differential and is an adjustable input parameter.

Further, according to the deviation of the thickness predicted value of each corrosion layer and the thickness measured value of the corrosion layer, adopting a nonlinear least square method to carry out iterative processing on initial values of parameters required by model construction under each first constant and each second constant combined value, wherein the processing steps are that A, the initial values of the parameters required by model construction are redefined as [ C _1,0、Q_1,0、C_2,0、Q_2,0 ], at the moment, the iteration times i= 0;B are judged whether iteration termination conditions are met or not, and then the iteration is ended, and C, solving an equationObtaining a correction amount D _i, wherein J is the Jacobi matrix, D, obtaining a next iteration value [ C _1,i,Q_1,i,C_2,i,Q_2,i]+αd_i ], and jumping to the step B, wherein alpha is a step size coefficient, and the initial value is 1.

During the solving process, the correction amount is sometimes excessive due toThe approach to singular may result in the solved correction amount d _i being incorrect. For stability of the solution, after the solution of step C is completed, the step size coefficient alpha of the correction direction d _i needs to meet Armijo criterion: |r _i+1||≤||r_i+αJ_id_i |, and if not, the step size coefficient alpha is halved until the step size coefficient alpha is met.

Further, 1, initial termination condition: the method refers to condition judgment before the iteration number is 0, if the initial termination condition is met, the initial value of the model construction is accurate, the initial value is used as a parameter target value required by the model construction, and iteration is not needed. Wherein τ ₁ and subsequent τ ₂、τ₃ are set as adjustable input parameters.

2. Standard termination conditions: Where S is a vector of etch layer thickness measurements. The relative change of the predicted thickness of the corrosion layer refers to two iteration steps, and if the relative change is satisfied, the measured value of the thickness of the corrosion layer between the two iteration steps is characterized to be smaller, and the corrosion layer is considered to be unnecessary to iterate again at the moment.

3. If the model parameter termination condition is satisfied, the model parameter is considered to be converged, and the iteration can be ended. Model parameter termination conditions:

After the iteration is finished, a target value of a parameter required by model construction under each first constant and each second constant combination value and a residual vector under each first constant and each second constant combination value are obtained.

In this embodiment, by setting the iteration termination condition, the target value of the parameter required for model construction under each of the first constant and the second constant combined value can be determined more accurately.

In one embodiment, as shown in fig. 3, S600 includes:

s610, obtaining the modulus of each residual vector according to each residual vector.

Specifically, the modulus of the residual vector refers to the length of the residual vector. If [ N, lambda ] has K1X K2 groups, each group will acquire the corresponding residual vector, and the modulus of the corresponding residual vector is acquired.

S620, selecting a residual vector corresponding to the smallest modulo among the modes of the residual vectors, and obtaining a target residual vector.

S630, determining an optimal first constant and a second constant combined value according to the target residual vector.

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

And S640, determining a model construction required parameter target value corresponding to the optimal first constant and the second constant combination value according to the optimal first constant and the second constant combination value.

Specifically, according to the optimal first constant and the second constant combined value, determining a model construction required parameter target value obtained by iteration under the optimal first constant and the optimal second constant combined value.

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

Specifically, a metal material corrosion performance model corresponding to the optimal first constant and the second constant combination value is found out from all metal material corrosion performance models to serve as a candidate metal material corrosion performance model.

S660, constructing a required parameter target value according to a model corresponding to the optimal first constant and the second constant combination value, and updating the candidate metal material corrosion performance model to obtain a target metal material corrosion performance model.

Specifically, since the candidate metal material corrosion performance model at this time is already a model obtained by substituting the optimal first constant and the second constant combination value, a required parameter target value is constructed according to the model corresponding to the optimal first constant and the second constant combination value, and the candidate metal material corrosion performance model is updated, so that the target metal material corrosion performance model can be obtained.

In this embodiment, by comparing the modes of the residual vectors, it is able to accurately determine the model corresponding to the optimal first constant and the second constant combination value to construct the required parameter target value, thereby obtaining the corrosion performance model of the target metal material.

In one embodiment, as shown in fig. 4, there is further provided a method for determining corrosion performance of a metal material, which is described by taking the method applied to the server 104 in fig. 1 as an example, and includes the following steps:

S700, acquiring the outer surface temperature of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material and the corrosion time.

S800, determining the corrosion performance of the metal material by adopting a target metal material corrosion performance model according to the outer surface temperature of the corrosion layer, the heat flow density of the interface between the corrosion layer and the metal material and the corrosion time, wherein the target metal material corrosion performance model is built by adopting the method for building the metal material corrosion performance model.

Specifically, when the target metal material corrosion performance model is required to be applied after being constructed, the six fixed quantity parameters are all optimal, so that parameters changed in the model are the temperature T and the corrosion time T of the interface between the corrosion layer and the metal material, the temperature T of 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 of the interface between the corrosion layer and the metal material, therefore, the terminal can send a metal material corrosion performance determining request to the server, the metal material corrosion performance determining request carries the temperature of the outer surface of the corrosion layer and the heat flux density and the corrosion time of the interface between the corrosion layer and the metal material, the server obtains the temperature T and the corrosion time T of the interface between the corrosion layer and the metal material, and the target metal material corrosion performance model is adopted to determine the metal material corrosion performance, namely, the predicted thickness value of the corrosion layer is obtained.

The method for determining the corrosion performance of the metal material comprises the steps of obtaining the temperature of the outer surface of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material and the corrosion time, and determining the corrosion performance of the metal material by adopting a target metal material corrosion performance model according to the temperature of the outer surface of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material and the corrosion time, wherein the target metal material corrosion performance model is built by adopting the method for building the metal material corrosion performance model. In the whole determining process, the corrosion performance of the metal material can be accurately determined by adopting the corrosion performance model of the target metal material by acquiring the input variable data of the model.

In one embodiment, the corrosion performance model of the target metal material constructed by the application is tested from three dimensions of stability, correctness and convergence speed by adopting a certain alloy material. The stability means that the algorithm is insensitive to errors in the calculation process, the errors have little influence on the accuracy of the calculation result, the correctness means that the correct result is given after any legal input is executed in a limited step, and the convergence speed is the speed of iterative convergence of the algorithm. And in this test embodiment, random perturbations are considered to simulate measurement errors. The model predictions and measurements are compared for random perturbation measurements of 0um, 5um, and 10um, respectively, as shown in FIGS. 5-7. And the test results of the resulting alloy corrosion performance model are shown in table 1 below:

TABLE 1 test results of alloy Corrosion Performance models

Parameters (parameters)	Undisturbed testing	5Um random disturbance test	10Um random disturbance test
				N	3	2	3
λ(μm)	2.6	4.8	3.8
				C1 (m ²/s or m ³/s)	9.9731×10^-14	9.8414×10^-7	1.0004×10^-13
Q₁(K)	14724.21	16730.91	14357.73
				C₂(m/s)	2.1402×10^-6	1.1129×10^-6	3.1299×10^-6
Q₂(K)	9735.83	9304.97	9987.22
				P-M mean (mum)	0.0862	-0.1688	-0.5036
P-M standard deviation (μm)	0.5972	2.9481	5.8615

Wherein P is the predicted value, M is the measured value. The closer the average value of P-M is to 0, the higher the accuracy of the model established by the method is, and the smaller the standard deviation of P-M is, the higher the accuracy of the model established by the method is.

The test results prove that the predicted value and the measured value of the corrosion model accord well. For the undisturbed condition, the difference between the predicted value and the measured value of the corrosion model is very small, the average value is 0.0862 mu m, and for the disturbed condition, the standard deviation of the deviation between the predicted value and the measured value of the corrosion model is smaller than 0.6 times of the disturbed quantity, so that the modeling method has better stability and accuracy. b. The numerical method has high convergence rate. The iteration number of the nonlinear least square method solving is not more than 10 at most, and the whole time consumption is less.

In one embodiment, preparation of experimental data, such as an i-th etching layer thickness measurement, a j-th etching layer outer surface temperature history Tf _i,j of the i-th etching layer thickness measurement, a j-th heat flow density history H _i,j of the i-th etching layer thickness measurement, and a j-th etching time T _i,j of the i-th etching layer thickness measurement, may be performed first, and an etching layer and metal material interface temperature value T _i,j may be obtained through Tf _i,j and H _i,j. And then traversing the first constant and the second constant of the basic parameters of the model to obtain combined values [ N, lambda ] of the first constant and the second constant, simplifying the solution of six parameters into the solution of four parameters, taking the square sum of the difference between the measured value of the corrosion layer thickness and the calculated value of the thickness of each corrosion layer as the target, and obtaining initial values of parameters required by the model construction under the combined values of the first constant and the second constant by adopting a linear least square method. And according to the deviation of the thickness predicted value of each corrosion layer and the thickness measured value of the corrosion layer, adopting a nonlinear least square method to carry out iterative processing on the initial value of the parameters required by the model construction under each first constant and each second constant combination value, judging whether convergence is carried out, if convergence is carried out, obtaining the target value of the parameters required by the model construction under each first constant and each second constant combination value, judging whether the model construction is the last model construction, if so, determining the corrosion performance model of the target metal material.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a metal material corrosion performance model construction and a metal material corrosion performance determination device for realizing the metal material corrosion performance model construction and the metal material corrosion performance determination method respectively. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitations of the embodiment of the apparatus for constructing one or more metal material corrosion performance models and determining metal material corrosion performance provided below can be referred to above for the limitations of the method for constructing metal material corrosion performance models and determining metal material corrosion performance, which are not repeated here.

In one embodiment, as shown in FIG. 8, there is provided a metallic material corrosion performance model construction apparatus, comprising 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, wherein:

The basic data acquisition module 100 is configured to acquire an initial model of corrosion performance of the metal material, a measured thickness of the corrosion layer, and a combination of first constants and second constants, where the first constants represent integer constants corresponding to corrosion rates in different stages of the initial model of corrosion performance of the metal material, and the second constants represent boundary values of corrosion layer thicknesses of the metal material in different stages of the initial model of corrosion performance of the metal material.

The corrosion layer thickness calculation module 200 is configured to obtain calculated values of the corrosion layer thickness 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 obtaining module 300 is configured to obtain initial values of parameters required for model construction under the combined values of the first constant and the second constant, with the goal of minimizing the sum of squares of differences between the measured value of the thickness of the etching layer and the calculated value of the thickness of each etching layer.

The model initial updating module 400 is configured to construct initial values of required parameters according to the models under the first constant and the second constant combined values, update the initial models of corrosion performance of the corresponding metal materials, and obtain corrosion performance models of the metal materials and predicted values of thickness of the corrosion layers.

The parameter target value obtaining module 500 is configured to perform iterative processing on initial values of parameters required for model construction under each of the first constant and the second constant combination value according to deviations of the thickness predicted value and the thickness measured value of the corrosion layer, obtain target values of parameters required for model construction under each of the first constant and the second constant combination value, and obtain residual vectors under each of the first constant and the second constant combination value.

The model secondary updating module 600 is configured to determine a model construction required parameter target value corresponding to the optimal first constant and the second constant combination value according to a residual vector corresponding to a smallest module among the models of the residual vectors, and update a candidate metal material corrosion performance model according to the model construction required parameter target value corresponding to the optimal first constant and the second constant combination value, so as to obtain a target metal material corrosion performance model, where the candidate metal material corrosion performance model is a metal material corrosion performance model corresponding to the optimal first constant and the second constant combination value.

In one embodiment, the basic data acquisition module 100 is further configured to acquire 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 the different second constant values based on the different first constant values to acquire each first constant and each second constant combined value.

In one embodiment, the corrosion layer thickness calculation module 200 is further configured to obtain a corrosion time, a heat flux density of an interface between the corrosion layer and the metal material, and an outer surface temperature of the corrosion layer, obtain an interface temperature value between the corrosion layer and the metal material according to the heat flux density of the interface between the corrosion layer and the metal material, and obtain a calculated value of each corrosion layer thickness according to the initial model of corrosion performance of the metal material, the corrosion time, the interface temperature value between the corrosion layer and the metal material, and each first constant and each second constant combination value.

In one embodiment, the model initial updating module 400 is further configured to construct a required parameter initial value according to the model under the combined values of the first constant and the second constant, update the corresponding initial model of corrosion performance of each metal material to obtain a corrosion performance model of each metal material, and time integrate the corrosion performance models of each metal material to obtain a predicted value of each corrosion layer thickness.

In one embodiment, the parameter target value obtaining module 500 is further configured to obtain an iteration termination condition, and perform an iteration process on initial values of parameters required for model construction under each of the first constants and the second constants by using a nonlinear least square method according to the deviation between the predicted value of the thickness of each of the corrosion layers and the measured value of the thickness of the corrosion layer and the iteration termination condition, so as to obtain target values of parameters required for model construction under each of the first constants and the second constants, and residual vectors under each of the first constants and the second constants.

In one embodiment, the model secondary updating module 600 is further configured to obtain a modulus of each residual vector according to each residual vector, select a residual vector corresponding to a minimum modulus of the residual vector to obtain a target residual vector, determine an optimal first constant and a second constant combination value according to the target residual vector, determine a model corresponding to the optimal first constant and the second constant combination value according to the optimal first constant and the second constant combination value to construct a required parameter target value, determine a candidate metal material corrosion performance model according to the optimal first constant and the second constant combination value and each metal material corrosion performance model, and construct the required parameter target value according to the model corresponding to the optimal first constant and the second constant combination value to update the candidate metal material corrosion performance model to obtain the target metal material corrosion performance model.

In one embodiment, as shown in FIG. 9, there is provided a metallic material corrosion performance determining apparatus, comprising a model input data acquisition module 700 and a corrosion performance determining module 800, wherein:

the model input data acquisition module 700 is used for acquiring the outer surface temperature of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material and the corrosion time.

The corrosion performance determining module 800 is configured to determine the corrosion performance of the metal material by using a target metal material corrosion performance model according to the temperature of the outer surface of the corrosion layer, the heat flux density of the interface between the corrosion layer and the metal material, and the corrosion time, where the target metal material corrosion performance model is built by using the method for building the metal material corrosion performance model.

The above-described metallic material corrosion performance model construction, and the respective modules in the metallic material corrosion performance determination apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. 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. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing data such as an initial model of the corrosion performance of the metal material, a thickness measurement value of the corrosion layer and the like. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a metallic material corrosion performance model construction, metallic material corrosion performance determination method.

It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. A method for constructing a corrosion performance model of a metal material, the method comprising:

The method comprises the steps of obtaining modes of residual vectors according to the residual vectors, selecting a residual vector corresponding to the smallest mode in the modes of the residual vectors to obtain a target residual vector, determining an optimal first constant and a second constant combination value according to the target residual vector, determining a model corresponding to the optimal first constant and the second constant combination value according to the optimal first constant and the second constant combination value to construct a required parameter target value, determining a candidate metal material corrosion performance model according to the optimal first constant and the second constant combination value and the corrosion performance models of the metal materials, and constructing a required parameter target value according to the model corresponding to the optimal first constant and the second constant combination value to update the candidate metal material corrosion performance model to obtain the target metal material corrosion performance model.

2. The method of claim 1, wherein the obtaining each of the first constant and the second constant combined value comprises:

Acquiring a first constant preset range and a second constant preset range;

3. The method of claim 1, wherein obtaining each calculated corrosion layer thickness value based on the initial model of corrosion performance of the metallic material and the first and second constant combined values comprises:

4. The method of claim 1, wherein the constructing initial values of the required parameters according to the models under the combined values of the first constants and the second constants, updating the initial models of the corrosion performance of the corresponding metal materials, and obtaining the corrosion performance models of the metal materials and the predicted values of the thickness of the corrosion layers comprise:

5. The method according to claim 1, wherein the iteratively processing initial values of parameters required for model construction at the combined values of the first constants and the second constants according to deviations of the predicted values of the thickness of the erosion layer from the measured values of the thickness of the erosion layer to obtain target values of parameters required for model construction at the combined values of the first constants and the second constants, and obtaining residual vectors at the combined values of the first constants and the second constants comprises:

Acquiring an iteration termination condition;

6. A method for determining corrosion performance of a metallic material, the method comprising:

Wherein the corrosion performance model of the target metal material is established by the method as claimed in any one of claims 1 to 5.

7. The method of claim 6, wherein determining the corrosion performance of the metallic material using a target metallic material corrosion performance model based on the temperature of the outer surface of the corrosion layer, the heat flux density of the corrosion layer at the metallic material interface, and the corrosion time, comprises:

detecting the temperature of 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 of the interface between the corrosion layer and the metal material;

And determining the corrosion performance of the metal material by adopting a target metal material corrosion performance model according to the temperature of the interface between the corrosion layer and the metal material and the corrosion time, wherein the corrosion performance of the metal material is a corrosion layer thickness predicted value.

8. A metallic material corrosion performance model construction apparatus, characterized by comprising:

The model secondary updating module is used for obtaining modes of the residual vectors according to the residual vectors, selecting a residual vector corresponding to the smallest mode in the modes of the residual vectors to obtain a target residual vector, determining an optimal first constant and a second constant combination value according to the target residual vector, determining a model corresponding to the optimal first constant and the second constant combination value according to the optimal first constant and the second constant combination value to construct a required parameter target value, determining a candidate metal material corrosion performance model according to the optimal first constant and the second constant combination value and the corrosion performance models of the metal materials, constructing a required parameter target value according to the model corresponding to the optimal first constant and the second constant combination value, and updating the candidate metal material corrosion performance model to obtain the target metal material corrosion performance model.

9. The apparatus of claim 8, wherein the etch layer thickness calculation module is further configured to obtain an etch time, a heat flux density at an interface between the etch layer and the metal material, and an outer surface temperature of the etch layer, obtain an etch layer and metal material interface temperature value based on the heat flux density at the interface between the etch layer and the metal material, and the outer surface temperature of the etch layer, and obtain each etch layer thickness calculation value based on the initial model of the metal material etch performance, the etch time, the etch layer and metal material interface temperature value, and each first constant and each second constant combination value.

10. A metallic material corrosion performance determining apparatus, the apparatus comprising: