Method, device and equipment for predicting and evaluating thermal aging damage of dual-phase steel

Abstract

The invention provides a method, a device and equipment for predicting and evaluating thermal aging damage of dual-phase steel, and particularly relates to a method for predicting and evaluating mechanical properties, internal microstructure damage defect conditions and residual life of dual-phase stainless steel after thermal aging damage by utilizing magnetic comprehensive parameters and thermoelectric force nondestructive testing. The prediction evaluation method comprises the following steps: determining initial Charpy impact energy mechanical performance parameters based on an experiment of the dual-phase steel material, calculating to obtain a Charpy impact energy change curve of the full service life cycle of the material, and then predicting the thermal conductivity of the dual-phase steel material based on magnetic comprehensive parameters and thermal electromotive force physical performance parameters obtained through nondestructive testing. The mechanical property and the microdefect state of the material in the current service state are calculated through a constructed thermal aging damage mechanical model associated with dual-phase steel thermal aging damage characteristic defect parameters, finally the remaining service life of the current material is obtained by comparing a full-service-life-cycle mechanical property change curve of the material, and the precision is larger than or equal to 90%. The current mechanical property deterioration state of the dual-phase steel can be accurately judged, and the safety of a service system of the dual-phase steel is improved.

Description

Technical Field

[0001] The present application relates to the technical field of metal material testing, and more specifically to a method, system and terminal device for evaluating the mechanical properties, internal microstructure damage status and remaining service life of duplex stainless steel after thermal aging damage by using magnetic comprehensive parameters and thermoelectric potential nondestructive testing. Background Art

[0002] The primary circuit main pipeline of a pressurized water reactor nuclear power plant is crucial to the operating life and safety of the nuclear power plant. It is a flow channel with radioactive coolant under high temperature and high pressure, and is part of the system's pressure boundary. Therefore, the primary circuit main pipeline must have properties such as resistance to high temperature and high pressure, corrosion resistance, and resistance to radiation damage. At present, the commonly used material for the main pipeline is duplex stainless steel (DSS) containing ferrite and austenite, which has the dual advantages of ferrite steel and austenite steel. However, during the long-term service of the reactor, duplex stainless steel containing ferrite and austenite is prone to thermal aging embrittlement, which is mainly related to the spinodal decomposition in the ferrite phase.

[0003] In the practical application of reactor DSS, the degradation of mechanical properties has always been a focus of attention in the field of nuclear engineering. The actual service experience of existing nuclear power plants and basic research in the laboratory have proved that due to the changes in the microstructure and composition in the ferrite, thermal aging will significantly lead to an increase in hardness and tensile strength, and a decrease in ductility, Charpy impact energy and fracture toughness over time, resulting in the decomposition and embrittlement of DSS materials. So far, the impact of thermal aging on DSS microstructural damage and mechanical property degradation has been very clear, and the extension of reactor life is currently receiving increasing attention. This life extension involves the reliability of many irreplaceable equipment, such as primary coolant pipes, reactor vessel internals and other key components that are usually made of DSS.

[0004] Based on the above situation, there is an urgent need for a predictive evaluation method that can determine the mechanical properties, internal microstructural damage status and remaining service life of dual-phase steel, a nuclear structural material, after thermal aging damage. Summary of the invention

[0005] The present application provides a method, device and equipment for predicting and evaluating thermal aging damage of duplex steel, which can accurately determine the degradation of mechanical properties of duplex stainless steel in the current service stage of thermal aging, internal microscopic defect damage, remaining service life and other detailed evaluation parameters from a basic theoretical perspective, thereby accurately judging the current service status of duplex stainless steel.

[0006] In a first aspect, the present application provides a method for predicting and evaluating thermal aging damage of dual-phase steel, the method comprising: calculating and generating a mechanical property degradation curve of the dual-phase steel in a thermal aging service stage according to the composition and initial mechanical property parameters of the dual-phase steel material; taking the local enrichment degree of Cr in ferrite as a quantitative parameter of characteristic defects and using the Fe-Cr EAM potential function to calculate a curve of the change of the local enrichment degree of the Cr element in the thermal aging service stage of the dual-phase steel; establishing a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects with Charpy impact energy and calculating the Charpy impact energy mechanical property parameters of the dual-phase steel in the thermal aging service stage; calculating the damage defect state of the dual-phase steel in the thermal aging service stage according to the magnetic comprehensive parameters and the thermoelectric potential parameters; comparing the mechanical property parameters and the damage defect state with the mechanical property degradation curve of the dual-phase steel in the thermal aging service stage to generate the remaining service life of the dual-phase steel material.

[0007] In an optional scheme of the first aspect, when calculating the mechanical property degradation curve of the dual-phase steel during the thermal aging service stage, the method includes: calculating the saturated Charpy impact energy according to the mass percentage of the elements of the dual-phase steel; calculating the shape factor of the room temperature Charpy impact energy change curve and half of the maximum change of the saturated Charpy impact energy according to the initial Charpy impact energy and the saturated Charpy impact energy; calculating the activation energy of the embrittlement process of the dual-phase steel according to the chemical composition of the dual-phase steel material; calculating the thermal aging coefficient according to the Arrhenius formula; using the saturated Charpy impact energy and embrittlement kinetics to represent the change of the room temperature Charpy impact energy with time and temperature, and then generating the mechanical property degradation curve of the dual-phase steel during the thermal aging service stage.

[0008] In an optional solution of the first aspect, when generating the mechanical property degradation curve of the dual-phase steel in the thermal aging service stage, the method further includes: calculating the mechanical property degradation curve of the dual-phase steel in the thermal aging service stage according to the change of the room temperature Charpy impact energy with the thermal aging time:

[0009] ,

[0010] ,in, is the room temperature Charpy impact energy, is the saturated Charpy impact energy, is half of the maximum change in saturated Charpy impact energy. is the shape factor of the room temperature Charpy impact energy variation curve, is the logarithm of time.

[0011] In an optional scheme of the first aspect, when calculating the mechanical property degradation curve of the dual-phase steel in the thermal aging service stage, the method further includes: judging whether the Charpy impact energy drops to a preset threshold value, and if so, defining it as a failure of the dual-phase steel material and generating a saturation node time.

[0012] In an optional solution of the first aspect, when calculating the local enrichment degree of Cr, the method includes: using the Fe-Cr EAM potential function to calculate the local enrichment degree of Cr in ferrite: ,in, is the enrichment degree of the kth nearest neighbor of Cr atom, is the concentration of Fe atoms in the neighborhood of Cr atoms, is the global Cr atomic concentration; the Monte Carlo method is used to simulate the relationship between the local Cr enrichment and the thermal aging time; the relationship between the Cr element enrichment and the thermal aging time is fitted: , ,in, is the local enrichment degree of Cr, T is the heat aging service temperature, t is the heat aging time, When the system is in thermal equilibrium (t→∞) The maximum value, H is Growth rate with respect to thermal aging time t.

[0013] In an alternative embodiment of the first aspect, after calculating the local enrichment of Cr in ferrite, if , it means that the Fe concentration in the neighborhood of Cr atoms is consistent with the global Fe concentration, and the dual-phase steel is in a solid solution state; if , it means that most of the Cr atoms in the neighborhood are Cr atoms, and the dual-phase steel is in a phase separation state.

[0014] In an optional scheme of the first aspect, when using the Monte Carlo method to simulate the relationship between the local enrichment degree of Cr and the thermal aging time, the method includes: initializing the Fe-Cr alloy model: FeCr atoms are completely randomly distributed, and the simulation box is set to a preset size; using a single vacancy diffusion mechanism to achieve the position change of FeCr atoms; deciding whether to accept the new configuration according to the metropolis criterion, and determining the physical time according to the KMC time step; and executing in a loop until a preset number of simulation steps is reached.

[0015] In an optional scheme of the first aspect, when establishing a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects and Charpy impact energy, the method includes: substituting a calculation method for a curve of a change in the local enrichment degree of the Cr element during the thermal aging service stage of the dual-phase steel into a calculation method for a curve of a degradation of mechanical properties during the thermal aging service stage of the dual-phase steel, to generate a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects and Charpy impact energy.

[0016] In an optional solution of the first aspect, when building a dual-phase steel thermal aging damage mechanical model, the method includes: fitting the relationship between room temperature Charpy impact energy and Cr element enrichment degree:

[0017] , generate Charpy impact energy-Cr enrichment degree, Cr element enrichment degree-aging time; according to the saturation node time, generate the curve change relationship between Charpy impact energy and Cr element enrichment degree of duplex steel in the thermal aging service stage.

[0018] In an optional solution of the first aspect, when calculating the damage defect state of dual-phase steel in the thermal aging service stage, the method includes: fitting the relationship between the experimentally measured thermoelectric potential and the thermal aging time;

[0019] , ,in, is the thermoelectric potential change, is the saturation thermoelectric potential change, is the speed of the thermoelectric potential change with respect to the thermal aging time, F is the initial growth rate of the thermoelectric potential, C is the general constant of the formula for fitting the thermoelectric potential and time relationship curve, which is used to fit the correlation between the formula and the curve. is the natural logarithm; according to the relationship between Cr enrichment and thermal aging time, the relationship between thermoelectric potential and Cr enrichment is fitted; the PCA data statistical processing method is used to fit several magnetic parameters to generate magnetic comprehensive parameters; the relationship between the magnetic comprehensive parameters and Cr enrichment is fitted:

[0020] , , where Y is the magnetic comprehensive parameter; based on the relationship between the thermoelectric potential and the magnetic comprehensive parameter and the Cr enrichment degree, the damage defect state of the dual-phase steel in the thermal aging service stage is generated.

[0021] In an optional scheme of the first aspect, when fitting the relationship between the thermoelectric potential and the Cr enrichment degree, the method includes:

[0022] Calculate the relationship between thermal aging time and thermoelectric potential change:

[0023] ,in, ;

[0024] Substitute the relationship between Cr enrichment degree and time: ;

[0025] Relationship between the thermoelectric potential and Cr enrichment degree: .

[0026] In an alternative embodiment of the first aspect, when generating the remaining service life of the dual phase steel material, the method comprises:

[0027] Calculate the relationship between Charpy impact energy and thermoelectric potential:

[0028] .

[0029] In an optional solution of the first aspect, when generating the remaining service life of the dual-phase steel material, the method includes: calculating the relationship between the Charpy impact energy and the magnetic comprehensive parameter according to the relationship between the magnetic comprehensive parameter and the Cr enrichment degree:

[0030] .

[0031] In a second aspect, the present application also provides a dual-phase steel thermal aging damage prediction and evaluation device for executing the above-mentioned prediction and evaluation method, including: a mechanical curve calculation module, which is used to calculate and generate a mechanical property degradation curve of the dual-phase steel in the thermal aging service stage according to the composition and initial mechanical property parameters of the dual-phase steel material; a characteristic defect evolution module, which is used to use the local enrichment degree of Cr in ferrite as a quantitative parameter of characteristic defects and use the Fe-Cr EAM potential function to calculate the change curve of the local enrichment degree of Cr element in the thermal aging service stage of the dual-phase steel; a mechanical property calculation module, which is used to establish a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects and Charpy impact energy and calculate the Charpy impact energy mechanical property parameters of the dual-phase steel in the thermal aging service stage; a characteristic defect calculation module, which calculates the damage defect state of the dual-phase steel in the thermal aging service stage according to the magnetic comprehensive parameters and the thermoelectric potential parameters; a damage prediction and evaluation module, which is used to compare the mechanical property parameters and the damage defect state with the mechanical property degradation curve of the dual-phase steel in the thermal aging service stage to generate the remaining service life of the dual-phase steel material.

[0032] In an optional scheme of the second aspect, the mechanical curve calculation module is also used to calculate the saturated Charpy impact energy according to the mass percentage of the elements of the dual-phase steel; calculate the shape factor of the room temperature Charpy impact energy change curve and half of the maximum change of the saturated Charpy impact energy according to the initial Charpy impact energy and the saturated Charpy impact energy; calculate the activation energy of the embrittlement process of the dual-phase steel according to the chemical composition of the dual-phase steel material; calculate the thermal aging coefficient according to the Arrhenius formula; use the saturated Charpy impact energy and embrittlement kinetics to represent the change of the room temperature Charpy impact energy with time and temperature, and then generate the mechanical properties degradation curve of the dual-phase steel in the thermal aging service stage.

[0033] In an optional solution of the second aspect, the mechanical curve calculation module is also used to determine whether the Charpy impact energy drops to a preset threshold value. If so, it is defined as failure of the dual-phase steel material and a saturation node time is generated.

[0034] In an optional scheme of the second aspect, the characteristic defect evolution module is also used to calculate the local enrichment degree of Cr in ferrite using the Fe-Cr EAM potential function; simulate the relationship between the local enrichment degree of Cr and the thermal aging time using the Monte Carlo method; and fit the relationship between the enrichment degree of Cr element and the thermal aging time.

[0035] In an optional scheme of the second aspect, the characteristic defect evolution module is also used to initialize the Fe-Cr alloy model: FeCr atoms are completely randomly distributed, and the simulation box is set to a preset size; the position of FeCr atoms is changed by using a single vacancy diffusion mechanism; whether to accept the new configuration is determined according to the metropolis criterion, and the physical time is determined according to the KMC time step; and the cycle is executed until the preset number of simulation steps is reached.

[0036] In an optional scheme of the second aspect, the mechanical properties calculation module is also used to substitute the calculation method of the local enrichment degree change curve of the dual-phase steel during the thermal aging service stage into the calculation method of the mechanical properties degradation curve during the thermal aging service stage of the dual-phase steel, so as to generate a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects and Charpy impact energy.

[0037] In an optional scheme of the second aspect, the mechanical properties calculation module is also used to fit the relationship between the room temperature Charpy impact energy and the Cr element enrichment degree, and generate a curve change relationship between the Charpy impact energy and the Cr element enrichment degree of the dual-phase steel in the thermal aging service stage according to the saturation node time.

[0038] In an optional scheme of the second aspect, the characteristic defect calculation module is also used to fit the relationship between the experimentally measured thermoelectric potential and the thermal aging time; according to the relationship between the Cr enrichment degree and the thermal aging time, the relationship between the thermoelectric potential and the Cr enrichment degree is fitted; a PCA data statistical processing method is used to fit a number of magnetic parameters to generate magnetic comprehensive parameters; the relationship between the magnetic comprehensive parameters and the Cr enrichment degree is fitted; according to the relationship between the thermoelectric potential and the magnetic comprehensive parameters and the Cr enrichment degree respectively, the damage defect state of the dual-phase steel in the thermal aging service stage is generated.

[0039] In an optional solution of the second aspect, the characteristic defect calculation module is also used to calculate the relationship between the thermal aging time and the thermoelectric potential change, substitute the relationship between the Cr enrichment degree and the time, and generate the relationship between the thermoelectric potential and the Cr enrichment degree.

[0040] In an optional solution of the second aspect, the damage prediction evaluation module is further used to calculate the relationship between Charpy impact energy and thermoelectric potential.

[0041] In an optional solution of the second aspect, the damage prediction evaluation module is further used to calculate the relationship between the Charpy impact energy and the magnetic comprehensive parameter based on the relationship between the magnetic comprehensive parameter and the Cr enrichment degree.

[0042] In the third aspect, the present application provides a dual-phase steel thermal aging damage prediction and evaluation device including the above-mentioned prediction and evaluation device, and also includes: a magnetic parameter detector for collecting magnetic property data of dual-phase steel materials and integrating the collected magnetic property data into magnetic comprehensive parameters; a thermoelectric potential detector for collecting thermoelectric potential data of the magnetic force of dual-phase steel.

[0043] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate one or more embodiments of the present application and, together with the description, serve to explain the principles of the present application and to enable one of ordinary skill in the relevant art to make and use the present application.

[0045] Figure 1 The present invention is a flowchart of an exemplary dual-phase steel thermal aging damage prediction and evaluation method according to some embodiments of the present application.

[0046] Figure 2 It is a schematic diagram of a relationship curve between an exemplary fitting material saturated Charpy impact energy and material parameters according to some embodiments of the present application.

[0047] Figure 3 It is a schematic diagram of the EDS surface scan Cr element segregation of an exemplary #1 and #2 dual-phase steel samples aged at 400° C. for different times according to some embodiments of the present application.

[0048] Figure 4 It is a flowchart of an exemplary Monte Carlo method simulation method according to some embodiments of the present application.

[0049] Figure 5 This is a schematic diagram of an exemplary relationship between the local enrichment degree of Cr and the thermal aging time according to some embodiments of the present application.

[0050] Figure 6 is an exemplary S according to some embodiments of the present application Cr Schematic diagram of the linear relationship between the reciprocal and the reciprocal of thermal aging time t.

[0051] Figure 7 It is a schematic diagram of an exemplary curve of Charpy impact energy changing with SRO according to some embodiments of the present application.

[0052] Figure 8 It is a curve diagram of an exemplary thermoelectric potential fitting result according to some embodiments of the present application.

[0053] Fig. 9 Schematic diagram of the relationship between an exemplary thermoelectric potential and SRO according to some embodiments of the present application.

[0054] Fig.10 This is a connection diagram of an exemplary magnetic parameter detector according to some embodiments of the present application.

[0055] Fig.11 It is a schematic diagram showing the relationship between the comprehensive magnetic parameters and the aging time of an exemplary #1 and #2 dual-phase steel sample according to some embodiments of the present application.

[0056] Fig.12 This is a schematic diagram of the relationship between an exemplary thermoelectric potential and Charpy impact energy according to some embodiments of the present application.

[0057] Fig.13 This is a connection diagram of an exemplary electronic device according to some embodiments of the present application.

[0058] Description of the Figures in the Specification:

[0059] 30. Magnetic parameter detector, 300. Multi-sensor assembly, 301. Magnetic field excitation signal generation, 302. Magnetic field induction signal acquisition, 303. Eddy current impedance signal acquisition, 304. Computer, 401. Memory, 402. Processor, 403. Communication interface. DETAILED DESCRIPTION

[0060] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present application will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application.

[0061] In the present application, two types of Z3CN20.09M DSS dual-phase steels #1 and #2 are taken as examples to demonstrate the dual-phase steel thermal aging damage prediction and evaluation method of the present application.

[0062] refer to Figure 1 As shown, Figure 1 A flow chart of the evaluation method of the present application is shown, and the method comprises:

[0063] S1: The mechanical property degradation curve of dual-phase steel during the thermal aging service stage is calculated based on the composition and initial mechanical property parameters of the dual-phase steel material.

[0064] Specifically, in this application, reference is made to Table 1, which shows the element composition mass percentage (wt.%) of two types of Z3CN20.09M DSS dual-phase steels #1 and #2:

[0065] Table 1. Element composition mass percentage (wt.%) of Z3N20.09M dual-phase steel

[0066]

[0067] Among them, #1 and #2 represent Z3N20.09M DSS duplex steel from two different origins.

[0068] Calculation of saturated Charpy impact energy based on the element mass percentage of #1 Z3N20.09M DSS dual phase steel :

[0069] First, calculate the material coefficient Φ of the dual-phase steel:

[0070] , ;

[0071] Among them, Cr is chromium, Si is silicon, Mn is manganese, C is carbon, and N is nitrogen. is the ferrite content, and the specific calculation formula is:

[0072] ,

[0073] ,

[0074] .

[0075] Among them, the above and Φ calculation formula, which fits the relationship curve between the saturated Charpy impact energy of the material and the material parameters, refer to Figure 2 For example; the above , , The constants in the calculation formula are all derived from the Hull equivalent factor.

[0076] Then, according to the initial Charpy impact energy and saturated Charpy impact energy Calculate α (shape factor of the room temperature Charpy impact energy curve) and β (half the maximum change in saturated Charpy impact energy) where:

[0077] , ; Among them, the initial Charpy impact energy Can be used The typical value of the standard Charpy V-notch sample, the initial Charpy impact energy of Z3N20.09M type dual-phase steel actually measured in this application is: #1: ;

[0078] #2: .

[0079] Then, the activation energy Q of the embrittlement process of the dual-phase steel is described according to the chemical composition of the Z3N20.09M DSS dual-phase steel. The activation energy Q is the relationship between the activation energy of hot embrittlement and the chemical composition proposed in the existing NUREG / CR-4513 method. The specific calculation formula is:

[0080] ,in, is the logarithm of time.

[0081] Then, the thermal aging coefficient P is calculated according to the Arrhenius formula. The specific calculation method is:

[0082] ,in, is the temperature, and its addition to 273 represents the thermodynamic temperature, whose unit is Kelvin, represented by k.

[0083] The formula of heat aging coefficient P is obtained by extrapolating Arrhenius We get: R is a constant, usually 19.143.

[0084] Then, according to the existing NUREG / CR-4513, Rev.1 document record: the saturated Charpy impact energy and embrittlement kinetics are used to represent the change of room temperature Charpy impact energy with time and temperature. The specific room temperature Charpy impact energy The decrease over time is expressed as:

[0085] ,

[0086] Thermal aging at 400°C: ; Among them, all materials reach the saturated Charpy impact energy after long-term aging (that is, the minimum value reached by the material mainly due to spinodal decomposition ), the actual value of the saturated Charpy impact energy of a specific material has nothing to do with the aging temperature (250-450°C), but depends on the chemical composition of the steel, specifically refer to NUREG / CR-4513, Rev.1 file; therefore, for the Z3N20.09M DSS dual-phase steel of this application, the variation curve of the room temperature Charpy impact energy with the thermal aging time t (i.e., the mechanical property degradation curve of the Z3N20.09M DSS dual-phase steel in the thermal aging service stage) can be obtained:

[0087] #1: ,

[0088] #2: ; Among them, the logarithm of time Take 3.46~3.47, Take 3.39~3.48.

[0089] Furthermore, it is defined that when the room temperature Charpy impact energy drops to a value close to the saturated Charpy impact energy When the material fails, the saturation node time is obtained. .

[0090] In practical applications, the characteristic defects of thermal aging damage in dual-phase steel are mainly segregation of Fe-Cr elements. For specific experimental results, please refer to Figure 3 As shown, Figure 3 The EDS surface scan Cr element segregation of #1 and #2 dual-phase steel samples thermally aged at 400°C for different times is shown.

[0091] S2: The local enrichment degree of Cr in ferrite is taken as a quantitative parameter of characteristic defects and the Fe-Cr EAM potential function is used to calculate the variation curve of the local enrichment degree of Cr element in the dual-phase steel during the thermal aging service stage.

[0092] Specifically, this application adopts the Fe-Cr EAM potential function, focusing on the local enrichment of Cr in ferrite, and defines its parameters as , the specific calculation formula is as follows:

[0093] ,in, is the enrichment degree of the kth nearest neighbor of Cr atom, is the concentration of Fe atoms in the neighborhood of Cr atoms, is the global Cr atomic concentration; if , it means that the Fe concentration in the neighborhood of Cr atoms is consistent with the global Fe concentration, and the dual-phase steel is in a solid solution state; if , it means that most of the Cr atoms in the neighborhood are Cr atoms, and the dual-phase steel is in a phase separation state.

[0094] Then, the Monte Carlo method was used to simulate the relationship between the local enrichment degree of Cr and the thermal aging time. Figure 4 As shown, Figure 4 The flowchart of the Monte Carlo method used in this application is shown, and its brief process is as follows:

[0095] S20: Initialize the Fe-Cr alloy model: FeCr atoms are completely randomly distributed, and the simulation box size is set to: 5x5x5-40x40x40.

[0096] S21: The position change of FeCr atoms is achieved by using the single vacancy diffusion mechanism.

[0097] Among them, single vacancy diffusion is a common diffusion mechanism, in which the vacancy (missing atomic position) moves in the simulation box, causing the position of nearby atoms to change. For FeCr alloy, assuming there is a vacancy, this vacancy can exchange positions with neighboring Fe or Cr atoms, resulting in a change in the overall atomic position; the specific steps are:

[0098] Randomly select an Fe or Cr atom adjacent to the vacancy as a candidate atom;

[0099] Calculate the energy change after the atom and the vacancy exchange positions.

[0100] S22: Decide whether to accept the new configuration based on the metropolis criterion and determine the physical time based on the KMC time step.

[0101] Among them, the Metropolis criterion is used to decide whether to accept the exchange operation of atoms and vacancies, that is, whether to accept the new configuration; the specific steps are:

[0102] If the exchange operation results in a decrease in energy, the operation is accepted;

[0103] If the exchange operation results in an increase in energy, the operation is accepted with a set probability, for example:

[0104] Set the probability to , For energy, is the Boltzmann constant, is the heat aging service temperature;

[0105] Generate a random number r between 0 and 1. If r is less than , then accept the new configuration (i.e., the atom and the vacancy exchange positions); if r is greater than or equal to , the new configuration is rejected and the original atomic positions are retained.

[0106] In a Kinetic Monte Carlo (KMC) simulation, determining the time step is a key step to relate each jump or move to the actual physical time; the specific steps are:

[0107] For each exchange process between an atom and a vacancy, calculate the corresponding hopping rate;

[0108] Calculate the total jump rate for each possible event;

[0109] Randomly generate a time step;

[0110] The most likely event is selected based on the jump rate, and the positions of vacancies and atoms are updated;

[0111] Increase physical time by one step.

[0112] S23: Repeat S21-S22 until the simulation step number requirement set by the operator is reached.

[0113] Therefore, the simulation results are referenced Figure 5 As shown, Figure 5The relationship between the local enrichment of Cr and the thermal aging time is shown; the green area represents the aggregation of Cr atoms, and the red area represents the aggregation of Fe atoms.

[0114] Then the relationship between Cr element enrichment and thermal aging time is fitted:

[0115] , ;in, is the local enrichment degree of Cr, T is the heat aging service temperature, t is the heat aging time, When the system is in thermal equilibrium (t→∞) The maximum value, H is The growth rate of thermal aging time t; where, Without considering the temperature T and alloy composition The impact, therefore It is a function of the heat aging time t, which is a double reciprocal linear function. The logarithmic processing formula on both sides makes it a linear representation. The fitting formula and the simulation value reference Figure 6 As shown, Figure 6 Shows The linear relationship between the reciprocal and the reciprocal of the heat aging time t.

[0116] S3: Substitute the calculation method of the curve of the change of the local enrichment degree of the Cr element in the thermal aging service stage of the dual-phase steel into the calculation method of the mechanical property degradation curve in the thermal aging service stage of the dual-phase steel, generate a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects and Charpy impact energy, and then calculate the Charpy impact energy mechanical property parameters of the dual-phase steel in the thermal aging service stage.

[0117] The specific modeling process is to fit the room temperature Charpy impact energy and Cr enrichment Relationship:

[0118] according to

[0119] Available ;

[0120] The output result is: Room temperature Charpy impact energy -Cr element enrichment ; Cr element enrichment -Aging time t. Then according to the saturation time node , the room temperature Charpy impact energy of the thermal aging service stage is obtained and Cr enrichment The curve change relationship.

[0121] From the above process, the relationship between the room temperature Charpy impact energy and SRO parameters of the two duplex steels can be obtained:

[0122] #1: ,

[0123] #2: ; Among them, SRO is the local enrichment degree of the alloy components, which is mainly expressed as the enrichment degree of Cr in this application.

[0124] For specific fitting conditions, please refer to Figure 7 As shown in the figure, the Charpy impact energy curve with SRO is obtained. The curve fitting result is in good agreement with the experimental results, with an accuracy of ≥90%. The difference in the curve width range is caused by the range of the time logarithm. According to the thermodynamics and formula limits, the Charpy impact energy under long-term thermal aging experimental conditions There is a lower limit, There is an upper limit.

[0125] S4: Calculate the damage and defect state of dual-phase steel during thermal aging service stage based on the comprehensive magnetic parameters and thermoelectric potential parameters.

[0126] Specifically, the thermoelectric potential parameters are obtained through a thermoelectric potential parameter detector. The thermoelectric potential parameter detector in this application can be any existing thermoelectric potential detector as long as it can detect the thermoelectric potential parameters of the material. It can measure the Seebeck coefficient of metal materials, is sensitive to changes in material composition and mechanical properties, and can be used for: 1) detection and research of metal material hardness, phase change, radiation embrittlement, thermal aging embrittlement, etc.; 2) metal material sorting and quality identification.

[0127] After obtaining the thermoelectric potential parameters, the relationship between the thermoelectric potential (Seebeck coefficient V) measured in the experiment and the aging time t is fitted:

[0128] , ,in, is the thermoelectric potential change, is the change in saturated thermoelectric potential (t→∞), is the speed of thermoelectric potential change with respect to thermal aging time (τ can be regarded as half-saturation time), F is the initial growth rate of thermoelectric potential, where It is the general constant of the formula when fitting the thermoelectric potential and time relationship curve, which is used to fit the correlation between the formula and the curve; therefore, the thermoelectric potential fitting results of #1 and #2 are referenced Figure 8 As shown, it corresponds well to the experimental data results. In this application, is a large negative number. To simplify the formula, F≈1. ;

[0129] The relationship between thermal aging time and thermoelectric potential change is:

[0130] ,in, , for After deformation, take the logarithm of both sides;

[0131] Substitute the relationship between Cr enrichment and time: ;

[0132] Relationship between the thermoelectric potential and Cr enrichment degree: ;in, is the natural logarithm.

[0133] Specific fitting reference Fig. 9 As shown, Fig. 9 A graph of thermoelectric potential versus SRO is shown.

[0134] Specifically, the magnetic comprehensive parameters are obtained by a magnetic parameter detector, which refers to Fig.10 As shown, Fig.10 A connection diagram of the magnetic parameter detector of the present application is shown.

[0135] The magnetic parameter detector mainly includes three modules: detection probe (multi-sensor assembly 300), box instrument equipment and computer 304. The detection probe is a multi-sensor assembly 300, which integrates four electromagnetic methods (Barkhausen noise signal, multi-frequency eddy current parameter signal, incremental permeability signal, harmonic analysis signal) detection sensors, and is composed of sensors, detection coils, magnetic yokes, etc. The acquisition circuit, circuit integration system, A / D converter, etc. are placed in the box, mainly completing the acquisition and processing of analog signals. Computer 304 is to complete related machine learning detection implementation and other software operations: including but not limited to control measurement operations, setting related parameters, data processing and evaluation, and result visualization. The software system is based on modular design and is implemented in pycharm using python language. At the same time, the detection probe (multi-sensor component) is connected to the box through a shielded coaxial cable; the power supply of the box comes from the ordinary 220V AC power of the wall; the box sends the detection data to the computer 304 through Ethernet, and the computer 304 also controls the circuit operation in the box through the Ethernet data line; the computer 304 processes all the detection data and associates the detection data with the physical characteristics of the detection object in the form of numbers and charts.

[0136] Therefore, the basic electromagnetic hardware structure of the instrument includes five modules, namely, multi-sensor assembly 300, magnetic field excitation signal generation 301, magnetic field induction signal acquisition 302, eddy current impedance signal acquisition 303 and computer 304 (to realize control, processing and display). The basic functions of the instrument are as follows: the magnetic field excitation signal generation 301 module generates a magnetic field excitation signal to drive the multi-sensor assembly to work, the computer 304 obtains the magnetic field induction signal of the sample under test through the magnetic field induction signal acquisition 302 module and relies on the multi-sensor assembly, and collects the impedance information of the eddy current excitation induction coil through the eddy current impedance signal acquisition 303 module, forms a control closed loop based on the excitation signal and the acquisition signal, and obtains various test parameters of the sample under test. The computer 304 performs intelligent signal data processing on various parameters, analyzes the correlation between the test data and the physical properties of the sample under test, and all test action records and test data can be repeated, traced, exported, and formed into charts.

[0137] The Barkhausen noise signal, multi-frequency eddy current parameter signal, incremental permeability signal, and harmonic analysis signal of the sample under test collected by the magnetic parameter detector through the sensor are all processed through data preprocessing, characteristic parameter extraction, and characteristic parameter data processing, and then sent to the multi-parameter data fusion algorithm (such as neural network regression algorithm, machine learning classification algorithm, etc.) to be mapped into the physical parameters of the sample under test.

[0138] A total of 62 characteristic magnetic parameters were obtained through the magnetic parameter detector, and the extracted magnetic characteristic parameters are summarized in Table 2:

[0139] Table 2. Summary of extracted characteristic magnetic parameters

[0140]

[0141] After the above characteristic magnetic parameters are collected, the PCA principal component analysis method is used to linearly combine the 62 characteristic magnetic parameters into 6 main variables, and then these 6 main variables are transformed into the magnetic comprehensive parameter Y (the magnetic comprehensive parameter Y is equivalent to the thermal aging time t). The fitting results are shown in Fig.11 As shown, Fig.11The relationship between the magnetic comprehensive parameters and aging time of two types of Z3CN20.09M DSS dual-phase steel #1 and #2 is shown. For example, the magnetic comprehensive parameters of two types of Z3CN20.09M DSS dual-phase steel #1 and #2 are: #1: Y=4250+8.6173*PC1+4.6244*PC2-15.7019*PC3-28.5064*PC4-53.1757*PC5, #2: Y=4250-8.8242*PC1+22.2514*PC2-2.4750*PC3-16.6443*PC4+6.2109*PC5, where PCn is just a variable substitution sign, the coefficients of the two materials are not the same, and PCn is a linear combination of the magnetic comprehensive parameters; the average value needs to be subtracted before the magnetic comprehensive parameters are substituted. The specific parameters are:

[0142] 1. M max' =M max -average(M max ), refer to the following table:

[0143]

[0144] 2. PC1=M max '*-0.0036+M ave '*-0.0007+M br '*-0.0001+M hc '*0.0023+…*…, and so on, refer to the following table:

[0145]

[0146] Error estimation: There are 10 sets of data at each time. The standard deviations of the 62 parameters are calculated and then substituted into the table below:

[0147]

[0148] Therefore, taking #1 dual-phase steel as an example, the fitting equation of the main component is:

[0149] Y=4250+8.6173087277922633*PC1+4.6244195880789114*PC2+(-15.701875791 4582583*PC3)+(-28.5063832494472287*PC4)+(-53.1757140490943740*PC5).

[0150] The main component composition is shown in the following table:

[0151]

[0152] The correlation coefficient between the principal component and the dependent variable and its square:

[0153] The correlation coefficient between PC1 and dependent variable Y: 0.4352.

[0154] The square of the correlation coefficient (R 2 ):0.1894.

[0155] The correlation coefficient between PC2 and dependent variable Y is 0.1459.

[0156] The square of the correlation coefficient (R 2 ): 0.0213.

[0157] The correlation coefficient between PC3 and dependent variable Y: -0.3341.

[0158] The square of the correlation coefficient (R 2 ): 0.1116.

[0159] The correlation coefficient between PC4 and dependent variable Y: -0.4782.

[0160] The square of the correlation coefficient (R 2 ):0.2287.

[0161] The correlation coefficient between PC5 and dependent variable Y: -0.6701.

[0162] The square of the correlation coefficient (R 2 ):0.4490.

[0163] Therefore, the relationship between magnetic comprehensive parameter Y and Cr enrichment degree was established. Relationship:

[0164] ,

[0165] .

[0166] S5: The mechanical property parameters and damage defect state are compared with the mechanical property degradation curve of the dual-phase steel during the thermal aging service stage to generate the remaining service life of the dual-phase steel material.

[0167] Specifically, the mechanical property parameters and damage defect states are compared with the mechanical property degradation curve of dual-phase steel during the thermal aging service stage, and the relationship between Charpy impact energy and thermoelectric potential is generated respectively:

[0168] ; Fitting results reference Fig.12 As shown, Fig.12 The relationship between Charpy impact energy and thermoelectric potential is shown;

[0169] The relationship between the generated Charpy impact energy and the magnetic comprehensive parameter Y is:

[0170] .

[0171] In summary, the dual-phase steel thermal aging damage prediction and evaluation method of the present application can accurately determine the mechanical property degradation status, internal microscopic defect damage status, remaining service life status and other detailed evaluation parameters of the dual-phase steel in the current service stage of thermal aging from a basic theoretical perspective, thereby accurately judging the current service status of the dual-phase steel to improve the safety of its service system.

[0172] In some embodiments, the present application also provides a dual-phase steel thermal aging damage prediction and evaluation device for executing the above-mentioned prediction and evaluation method, including: a mechanical curve calculation module, which is used to calculate and generate a mechanical property degradation curve of the dual-phase steel in the thermal aging service stage according to the composition and initial mechanical property parameters of the dual-phase steel material; a characteristic defect evolution module, which is used to use the local enrichment degree of Cr in ferrite as a characteristic defect quantitative parameter and use the Fe-Cr EAM potential function to calculate the change curve of the local enrichment degree of Cr element in the thermal aging service stage of the dual-phase steel; a mechanical property calculation module, which is used to establish a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects and Charpy impact energy and calculate the Charpy impact energy mechanical property parameters of the dual-phase steel in the thermal aging service stage; a characteristic defect calculation module, which calculates the damage defect state of the dual-phase steel in the thermal aging service stage according to the magnetic comprehensive parameters and the thermoelectric potential parameters; a damage prediction and evaluation module, which is used to compare the mechanical property parameters and the damage defect state with the mechanical property degradation curve of the dual-phase steel in the thermal aging service stage to generate the remaining service life of the dual-phase steel material.

[0173] In some embodiments, reference Fig.13 As shown, Fig.13 The electronic device is a block diagram of an electronic device for implementing an embodiment of the present application. The electronic device includes: a memory 401 and a processor 402. The memory 401 stores a computer program that can be run on the processor 402. When the processor 402 executes the computer program, the method in the above embodiment is implemented. The number of the memory 401 and the processor 402 can be one or more.

[0174] The electronic device also includes: a communication interface 403, which is used to communicate with external devices and perform data exchange transmission.

[0175] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the memory 401, the processor 402 and the communication interface 403 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.11 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0176] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0177] The embodiment of the present application provides a computer-readable storage medium storing a computer program, which implements the method provided in the embodiment of the present application when the program is executed by the processor 402.

[0178] The embodiment of the present application also provides a chip, which includes a processor 402, which is used to call and execute instructions stored in the memory 401 from the memory 401, so that a communication device equipped with the chip executes the method provided by the embodiment of the present application.

[0179] The embodiment of the present application also provides a chip, including: an input interface, an output interface, a processor 402 and a memory 401. The input interface, the output interface, the processor 402 and the memory 401 are connected through an internal connection path. The processor 402 is used to execute the code in the memory 401. When the code is executed, the processor 402 is used to execute the method provided in the embodiment of the application.

[0180] It should be understood that the processor 402 may be a central processing unit (CPU) 402, or other general-purpose processors 402, digital signal processors (DSP) 402, application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 402 may be a microprocessor 402 or any conventional processor 402, etc. It is worth noting that the processor 402 may be a processor 402 supporting the Advanced RISCMachines (ARM) architecture.

[0181] Further, the above-mentioned memory 401 may include a read-only memory 401 and a random access memory 401, and may also include a non-volatile random access memory 401. The memory 401 may be a volatile memory 401 or a non-volatile memory 401, or may include both a volatile and a non-volatile memory 401. Among them, the non-volatile memory 401 may include a read-only memory 401 (Read-Only Memory, ROM), a programmable read-only memory 401 (Programmable ROM, PROM), an erasable programmable read-only memory 401 (Erasable PROM, EPROM), an electrically erasable programmable read-only memory 401 (Electrically EPROM, EEPROM) or a flash memory. The volatile memory 401 may include a random access memory 401 (Random Access Memory, RAM), which is used as an external cache. By way of exemplary but not limiting description, many forms of RAM are available. For example, static random access memory 401 (Static RAM, SRAM), dynamic random access memory 401 (Dynamic Random Access Memory, DRAM), synchronous dynamic random access memory 401 (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory 401 (Double Data Rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory 401 (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory 401 (Synchlink DRAM, SLDRAM) and direct memory bus random access memory 401 (Direct Rambus RAM, DR RAM).

[0182] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0183] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting and evaluating thermal aging damage of dual-phase steel, characterized in that: The method comprises: The mechanical property degradation curve of the dual-phase steel during the thermal aging service stage is calculated and generated according to the composition and initial mechanical property parameters of the dual-phase steel material; The local enrichment of Cr in ferrite is taken as a quantitative parameter of characteristic defects and the Fe-Cr EAM potential function is used to calculate the variation curve of the local enrichment of Cr element in dual-phase steel during the thermal aging service stage. A mechanical model of thermal aging damage of dual-phase steel correlating microscopic characteristic defects with Charpy impact energy is established, and the mechanical performance parameters of Charpy impact energy of dual-phase steel during thermal aging service are calculated. The damage and defect state of dual-phase steel in thermal aging service stage is calculated based on comprehensive magnetic parameters and thermoelectric potential parameters; The mechanical property parameters and damage defect states are compared with the mechanical property degradation curve of the dual-phase steel during the thermal aging service stage to generate the remaining service life of the dual-phase steel material.

2. The evaluation method according to claim 1, characterized in that: When calculating the mechanical property degradation curve of the dual-phase steel during the thermal aging service stage, the method includes: The saturated Charpy impact energy of duplex steel is calculated based on the element mass percentage; The shape factor of the room temperature Charpy impact energy change curve and half of the maximum change of the saturated Charpy impact energy are calculated based on the initial Charpy impact energy and the saturated Charpy impact energy; Calculate the activation energy of the embrittlement process of duplex steel based on the chemical composition of the duplex steel material; Calculate the thermal aging coefficient according to the Arrhenius formula; The saturated Charpy impact energy and embrittlement kinetics are used to represent the variation of room temperature Charpy impact energy with time and temperature, and then the mechanical property degradation curve of dual-phase steel in the thermal aging service stage is generated.

3. The evaluation method according to claim 2, characterized in that: When generating a mechanical property degradation curve of dual-phase steel in a thermal aging service stage, the method further comprises: According to the change of room temperature Charpy impact energy with thermal aging time, the mechanical property degradation curve of dual-phase steel in the thermal aging service stage is calculated: ， ,in, is the room temperature Charpy impact energy, is the saturated Charpy impact energy, is half of the maximum change in saturated Charpy impact energy. is the shape factor of the room temperature Charpy impact energy variation curve, is the logarithm of time.

4. The evaluation method according to claim 3, characterized in that: When calculating the mechanical property degradation curve of dual-phase steel in the thermal aging service stage, the method further comprises: It is determined whether the Charpy impact energy drops to the preset threshold. If so, it is defined as failure of the duplex steel material and the saturation node time is generated.

5. The evaluation method according to claim 1, characterized in that: When calculating the local enrichment degree of Cr, the method comprises: The Fe-Cr EAM potential function is used to calculate the local enrichment of Cr in ferrite: ,in, is the enrichment degree of the kth nearest neighbor of Cr atom, is the concentration of Fe atoms in the neighborhood of Cr atoms, is the global Cr atomic concentration; The Monte Carlo method was used to simulate the relationship between the local enrichment of Cr and the thermal aging time. Fitting the relationship between Cr element enrichment and thermal aging time: , ,in, is the local enrichment degree of Cr, T is the heat aging service temperature, t is the heat aging time, When the system is in thermal equilibrium (t→∞) The maximum value, H is Growth rate with respect to thermal aging time t.

6. The evaluation method according to claim 5, characterized in that: After calculating the local enrichment of Cr in ferrite, if , it means that the Fe concentration in the neighborhood of Cr atoms is consistent with the global Fe concentration, and the dual-phase steel is in a solid solution state; if , it means that most of the Cr atoms in the neighborhood are Cr atoms, and the dual-phase steel is in a phase separation state.

7. The evaluation method according to claim 5, characterized in that: When the Monte Carlo method is used to simulate the relationship between the local enrichment degree of Cr and the thermal aging time, the method includes: Initialize the Fe-Cr alloy model: FeCr atoms are completely randomly distributed and the simulation box is set to a preset size; The position change of FeCr atoms is achieved by using the single vacancy diffusion mechanism; Decide whether to accept the new configuration based on the metropolis criterion, and determine the physical time based on the KMC time step; The cycle is executed until the preset number of simulation steps is reached.

8. The evaluation method according to claim 1 or 5, characterized in that: When establishing a dual-phase steel thermal aging damage mechanical model correlating microscopic characteristic defects and Charpy impact energy, the method includes: The calculation method of the change curve of the local enrichment degree of Cr element in the thermal aging service stage of dual-phase steel is substituted into the calculation method of the mechanical property degradation curve in the thermal aging service stage of dual-phase steel to generate a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects and Charpy impact energy.

9. The evaluation method according to claim 8, characterized in that: When modeling the thermal aging damage mechanical model of dual-phase steel, the method includes: Fitting the relationship between room temperature Charpy impact energy and Cr element enrichment: , generate Charpy impact energy-Cr enrichment degree, Cr element enrichment degree-aging time; According to the saturation node time, the curve relationship between the Charpy impact energy and the Cr element enrichment degree of the dual-phase steel during the thermal aging service stage is generated.

10. The evaluation method according to claim 1, characterized in that: When calculating the damage defect state of dual-phase steel during the thermal aging service stage, the method includes: Fitting the relationship between the experimentally measured thermoelectric potential and thermal aging time; , ,in, is the thermoelectric potential change, is the saturation thermoelectric potential change, is the speed of the thermoelectric potential change with respect to the thermal aging time, F is the initial growth rate of the thermoelectric potential, and C is the general constant of the formula when fitting the thermoelectric potential and time relationship curve, which is used to fit the correlation between the formula and the curve; According to the relationship between Cr enrichment and thermal aging time, the relationship between thermoelectric potential and Cr enrichment is fitted; The PCA data statistical processing method is used to fit several magnetic parameters and generate magnetic comprehensive parameters; The relationship between the fitting magnetic comprehensive parameters and Cr enrichment degree: , , where Y is the comprehensive magnetic parameter; According to the relationship between thermoelectric potential and comprehensive magnetic parameters and Cr enrichment degree, the damage defect state of dual-phase steel in the thermal aging service stage is generated.

11. The evaluation method according to claim 10, characterized in that: When fitting the relationship between thermoelectric potential and Cr enrichment, the method includes: Calculate the relationship between thermal aging time and thermoelectric potential change: ,in, ; Substitute the relationship between Cr enrichment degree and time: ; Relationship between the thermoelectric potential and Cr enrichment degree: .

12. The evaluation method according to claim 10, characterized in that: In generating a remaining service life of a dual phase steel material, the method comprises: Calculate the relationship between Charpy impact energy and thermoelectric potential: 。 13. The evaluation method according to claim 10, characterized in that: In generating a remaining service life of a dual phase steel material, the method comprises: Establish the relationship between the magnetic comprehensive parameter Y and the Cr enrichment degree: ， ； Calculate the relationship between Charpy impact energy and magnetic comprehensive parameters: 。 14. A dual-phase steel thermal aging damage prediction and evaluation device for executing the prediction and evaluation method according to any one of claims 1 to 13, characterized in that: include: The mechanical curve calculation module is used to calculate and generate the mechanical property degradation curve of the dual-phase steel during the thermal aging service stage according to the composition and initial mechanical property parameters of the dual-phase steel material; Characteristic defect evolution module, which is used to take the local enrichment of Cr in ferrite as a quantitative parameter of characteristic defects and use the Fe-Cr EAM potential function to calculate the change curve of the local enrichment of Cr element in the dual-phase steel during the thermal aging service stage; Mechanical properties calculation module, used to establish a dual-phase steel thermal aging damage mechanical model that associates microscopic characteristic defects with Charpy impact energy and calculate the Charpy impact energy mechanical properties parameters of the dual-phase steel during the thermal aging service stage; Characteristic defect calculation module, which calculates the damage defect state of dual-phase steel during the thermal aging service stage based on the comprehensive magnetic parameters and thermoelectric potential parameters; The damage prediction evaluation module is used to compare the mechanical property parameters and damage defect state with the mechanical property degradation curve of the dual-phase steel in the thermal aging service stage to generate the remaining service life of the dual-phase steel material.

15. A dual-phase steel thermal aging damage prediction and evaluation device having the prediction and evaluation device according to claim 14, characterized in that: Also includes: Magnetic parameter detector, used to collect magnetic property data of dual-phase steel materials and integrate the collected magnetic property data into magnetic comprehensive parameters; Thermoelectric potential detector is used to collect thermoelectric potential data of dual-phase steel magnetic force.

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