Method, device and equipment for predicting and evaluating hot aging damage of duplex steel

Abstract

The present application provides a method, device and equipment for predicting and evaluating the thermal aging damage of duplex steel. It is a method for predicting and evaluating the mechanical properties, internal microstructure damage defects and remaining life after the thermal aging damage of duplex stainless steel by using magnetic comprehensive parameters and thermoelectric potential for non-destructive testing. The prediction and evaluation method includes experimentally determining the initial Charpy impact work mechanical property parameters of duplex steel materials, calculating the change curve of Charpy impact work during the entire service life cycle of the materials, and then based on the magnetic comprehensive parameters and thermoelectric potential physical property parameters obtained by non-destructive testing, through the constructed thermal aging damage mechanical model that correlates the characteristic defect parameters of duplex steel thermal aging damage, calculating the mechanical properties and microscopic defect states of the materials under the current service state. Finally, by comparing the mechanical property change curve of the materials during the entire service life cycle, the remaining service life of the current materials is obtained, with an accuracy of ≥90%. It can accurately judge the current mechanical property deterioration state of duplex steel and improve the safety of its service system.

Description

Technical Field

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

[0002] The primary loop main pipe of a pressurized water reactor nuclear power plant is crucial for the operating life and safety of the nuclear power plant. It is a flow channel for radioactive coolant under high temperature and high pressure and is part of the system pressure-bearing boundary. Therefore, the primary loop main pipe should possess properties such as high temperature and high pressure resistance, corrosion resistance, and radiation damage resistance. Currently, the commonly used material for the main pipe 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, the 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 actual application of DSS in reactors, the deterioration of mechanical properties has always been the focus of attention in the field of nuclear engineering. The actual service experience of existing nuclear power plants and basic research in laboratories have proved that due to changes in the microstructure and composition in ferrite, with the passage of aging time, thermal aging will significantly lead to an increase in hardness and tensile strength, and a decrease in ductility, Charpy impact energy and fracture toughness, thus resulting in the decomposition and embrittlement of DSS materials. So far, the influence of thermal aging on the microstructure damage and mechanical property degradation of DSS has been very clear. Currently, the extension of the reactor life has received increasing attention, and this life extension involves the reliability of many irreplaceable equipment, such as primary coolant pipes, internal components of the reactor vessel and other key components 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 microstructure damage condition and remaining service life of duplex 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 duplex steel thermal aging damage, which can accurately determine multiple detailed evaluation parameters such as the deterioration condition of the mechanical properties, internal microscopic defect damage condition, and remaining service life of duplex stainless steel at the current service stage from the perspective of basic theory, so as to accurately judge the current service state of duplex stainless steel.

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

[0007] In an alternative embodiment of the first aspect, when calculating the mechanical property degradation curve for the thermal aging service stage of the duplex steel, the method includes: calculating the saturated Charpy impact energy based on the mass percentage of elements in the duplex steel; calculating the shape factor of the room temperature Charpy impact energy change curve and half of the maximum change in the saturated Charpy impact energy based on the initial Charpy impact energy and the saturated Charpy impact energy; calculating the activation energy of the embrittlement process of the duplex steel based on the chemical composition of the duplex 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 room temperature Charpy impact energy with time and temperature, and then generating the mechanical property degradation curve for the thermal aging service stage of the duplex steel.

[0008] In an alternative embodiment of the first aspect, when generating the mechanical property degradation curve for the thermal aging service stage of the duplex steel, the method further includes: calculating the mechanical property degradation curve for the thermal aging service stage of the duplex steel according to the change of room temperature Charpy impact energy with thermal aging time:

[0009] ,

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

[0011] In an alternative embodiment of the first aspect, when calculating the mechanical property degradation curve for the thermal aging service stage of the duplex steel, the method further includes: determining whether the Charpy impact energy has dropped to a preset threshold. If so, it is defined that the duplex steel material has failed, and the saturated node time is generated.

[0012] In an alternative embodiment of the first aspect, when calculating the local enrichment degree of Cr, the method includes: calculating the local enrichment degree of Cr in ferrite by using the Fe-Cr EAM potential function: , where is the enrichment degree of the k-th nearest neighbor of Cr atoms, is the concentration of Fe atoms in the neighborhood of Cr atoms, is the global concentration of Cr atoms; simulating the relationship between the local enrichment degree of Cr and the thermal aging time by using the Monte Carlo method; fitting the relationship between the enrichment degree of Cr element and the thermal aging time: , , where is the local enrichment degree of Cr, T is the thermal aging service temperature, t is the thermal aging time, is the maximum value of the system at thermal equilibrium when t → ∞, and H is the growth rate with respect to the thermal aging time t.

[0013] In an alternative embodiment of the first aspect, after calculating the local enrichment degree 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 duplex steel is in a solid solution state; if , it means that most of the atoms in the neighborhood of Cr atoms are Cr atoms, and the duplex steel is in a phase separation state.

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

[0015] In an alternative embodiment of the first aspect, when establishing the thermal aging damage mechanics model of duplex steel that correlates microstructural characteristic defects and Charpy impact energy, the method includes: substituting the calculation method of the change curve of the local enrichment degree of Cr element during the thermal aging service stage of duplex steel into the calculation method of the mechanical property degradation curve during the thermal aging service stage of duplex steel to generate the thermal aging damage mechanics model of duplex steel that correlates microstructural characteristic defects and Charpy impact energy.

[0016] In an alternative embodiment of the first aspect, when modeling the thermal aging damage mechanics model of duplex steel, the method includes: fitting the relationship between the Charpy impact energy at room temperature and the enrichment degree of Cr element:

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

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

[0019] , , where, is the change in thermoelectric potential, is the change in saturated thermoelectric potential, is the rate of change of the thermoelectric potential 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 when fitting the relationship curve between the thermoelectric potential and time, and is used to fit the correlation degree between the formula and the curve, is the natural logarithm; according to the relationship between the Cr enrichment degree and the thermal aging time, fit the relationship between the thermoelectric potential and the Cr enrichment degree; use the PCA data statistical processing method to fit several magnetic parameters to generate a magnetic comprehensive parameter; fit the relationship between the magnetic comprehensive parameter and the Cr enrichment degree:

[0020] , , where Y is the magnetic comprehensive parameter; according to the relationships between the thermoelectric potential and the magnetic comprehensive parameter and the Cr enrichment degree respectively, generate the damage defect state in the thermal aging service stage of the duplex steel.

[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 the thermal aging time and the change in thermoelectric potential:

[0023] , where, ;

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

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

[0026] In an optional scheme of the first aspect, when generating the remaining service life of the duplex steel material, the method includes:

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

[0028] .

[0029] In an alternative of the first aspect, when generating the remaining service life of the duplex steel material, the method includes:

[0030] According to the relationship between the magnetic comprehensive parameters and the degree of Cr enrichment, calculate the relationship between the Charpy impact energy and the magnetic comprehensive parameters:

[0031] 。

[0032] In a second aspect, the present application also provides a duplex steel thermal aging damage prediction and evaluation device for performing the above-mentioned prediction evaluation method, including: a mechanical curve calculation module, configured to calculate and generate a mechanical property degradation curve of the duplex steel during the thermal aging service stage according to the composition and initial mechanical property parameters of the duplex steel material; a characteristic defect evolution module, configured to use the degree of local Cr enrichment in the ferrite as a characteristic defect quantitative parameter and calculate the change curve of the degree of local Cr enrichment in the duplex steel during the thermal aging service stage by using the Fe-Cr EAM potential function; a mechanical property calculation module, configured to establish a duplex steel thermal aging damage mechanical model that correlates microscopic characteristic defects and Charpy impact energy and calculate the Charpy impact energy mechanical property parameters of the duplex steel during the thermal aging service stage; a characteristic defect calculation module, configured to calculate the damage defect state of the duplex steel during the thermal aging service stage according to the magnetic comprehensive parameters and thermoelectric potential parameters; a damage prediction and evaluation module, configured to compare the mechanical property parameters and the damage defect state with the mechanical property degradation curve of the duplex steel during the thermal aging service stage to generate the remaining service life of the duplex steel material.

[0033] In an alternative of the second aspect, the mechanical curve calculation module is further configured to calculate the saturated Charpy impact energy according to the element mass percentages of the duplex steel; calculate the shape factor of the room temperature Charpy impact energy change curve and half of the maximum change in the saturated Charpy impact energy according to the initial Charpy impact energy and the saturated Charpy impact energy; calculate the activation energy of the duplex steel embrittlement process according to the chemical composition of the duplex 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 further generate the mechanical property degradation curve of the duplex steel during the thermal aging service stage.

[0034] In an alternative of the second aspect, the mechanical curve calculation module is further configured to determine whether the Charpy impact energy drops to a preset threshold, and if so, define it as the failure of the duplex steel material and generate the saturated node time.

[0035] In an alternative of the second aspect, the characteristic defect evolution module is further configured to calculate the degree of local Cr enrichment in the ferrite by using the Fe-Cr EAM potential function; simulate the relationship between the degree of local Cr enrichment and the thermal aging time by using the Monte Carlo method; fit the relationship between the degree of Cr element enrichment and the thermal aging time.

[0036] In an alternative solution of the second aspect, the characteristic defect evolution module is further configured to initialize the Fe-Cr alloy model: the Fe and Cr atoms are completely randomly distributed, the simulation box is set to a preset size; the positions of the Fe and Cr atoms are changed by using the single vacancy diffusion mechanism; according to the metropolis criterion, it is determined whether to accept the new configuration, and the physical time is determined according to the KMC time step; the loop is executed until the preset number of simulation steps is reached.

[0037] In an alternative solution of the second aspect, the mechanical property calculation module is further configured to substitute the calculation method of the change curve of the local enrichment degree of Cr element during the thermal aging service stage of the dual-phase steel into the calculation method of the mechanical property degradation curve during the thermal aging service stage of the dual-phase steel, and generate a dual-phase steel thermal aging damage mechanics model that correlates microscopic characteristic defects and Charpy impact energy.

[0038] In an alternative solution of the second aspect, the mechanical property calculation module is further configured to fit the relationship between the Charpy impact energy at room temperature and the enrichment degree of Cr element, and generate the curve change relationship between the Charpy impact energy and the enrichment degree of Cr element during the thermal aging service stage of the dual-phase steel according to the saturation node time.

[0039] In an alternative solution of the second aspect, the characteristic defect calculation module is further configured 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, fit the relationship between the thermoelectric potential and the Cr enrichment degree; use the PCA data statistical processing method to fit several magnetic parameters to generate a magnetic comprehensive parameter; fit the relationship between the magnetic comprehensive parameter and the Cr enrichment degree; according to the relationships between the thermoelectric potential and the magnetic comprehensive parameter and the Cr enrichment degree respectively, generate the damage defect state during the thermal aging service stage of the dual-phase steel.

[0040] In an alternative solution of the second aspect, the characteristic defect calculation module is further configured to calculate the relationship between the thermal aging time and the change of the thermoelectric potential, and substitute it into the relationship between the Cr enrichment degree and the time to generate the relationship between the thermoelectric potential and the Cr enrichment degree.

[0041] In an alternative solution of the second aspect, the damage prediction and evaluation module is further configured to calculate the relationship between the Charpy impact energy and the thermoelectric potential.

[0042] In an alternative solution of the second aspect, the damage prediction and evaluation module is further configured to calculate 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.

[0043] In a third aspect, the present application provides a dual-phase steel thermal aging damage prediction and evaluation device including the above prediction and evaluation device, further including: a magnetic parameter detector, configured to collect magnetic property data of the dual-phase steel material and integrate the collected magnetic property data into a magnetic comprehensive parameter; a thermoelectric potential detector, configured to collect thermoelectric potential data of the magnetic force of the dual-phase steel.

[0044] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings incorporated herein and forming a part of the specification illustrate one or more embodiments of this application, and together with the description are used to explain the principles of this application and to enable those of ordinary skill in the relevant art to make and use this application.

[0046] Figure 1 is a flowchart of an exemplary method for predicting the thermal aging damage of dual-phase steel according to some embodiments of this application.

[0047] Figure 2 is a schematic diagram showing the relationship curve of fitting the saturated Charpy impact energy of the material and the material parameters according to some embodiments of this application.

[0048] Figure 3 is a schematic diagram showing the EDS surface scan Cr element segregation of No. 1 and No. 2 dual-phase steel samples aged at 400 °C for different times according to some embodiments of this application.

[0049] Figure 4 is a flowchart of an exemplary Monte Carlo method simulation method according to some embodiments of this application.

[0050] Figure 5 is a schematic diagram showing the variation relationship of the local enrichment degree of Cr with the thermal aging time according to some embodiments of this application.

[0051] Figure 6 is an exemplary Cr schematic diagram of the linear relationship between the reciprocal of S and the reciprocal of the thermal aging time t.

[0052] Figure 7 is a schematic diagram of the variation curve of the Charpy impact energy with SRO according to some embodiments of this application.

[0053] Figure 8 is a schematic diagram of the curve of the fitting result of the thermoelectric potential according to some embodiments of this application.

[0054] Figure 9 is a schematic diagram of the relationship between the thermoelectric potential and SRO according to some embodiments of this application.

[0055] Figure 10 is a schematic diagram of the connection of an exemplary magnetic parameter detector according to some embodiments of this application.

[0056] Figure 11 Schematic diagram showing the relationship between the magnetic comprehensive parameters and the aging time of an exemplary duplex steel sample No. 1 and No. 2 according to some embodiments of the present application.

[0057] Figure 12 Schematic diagram showing the relationship between the thermoelectric potential and the Charpy impact energy according to some embodiments of the present application.

[0058] Figure 13 Schematic diagram showing the connection of an exemplary electronic device according to some embodiments of the present application.

[0059] Description of the reference numerals in the drawings:

[0060] 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 implementation manners

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application.

[0062] In the present application, the duplex steel thermal aging damage prediction and evaluation method of the present application is demonstrated by taking two types of Z3CN20.09M DSS duplex steels, No. 1 and No. 2, as examples.

[0063] Reference Figure 1 As shown Figure 1 The flowchart of the evaluation method of the present application is shown, and the method includes:

[0064] S1: Calculate and generate the mechanical property degradation curve of the duplex steel during the thermal aging service stage according to the composition and initial mechanical property parameters of the duplex steel material.

[0065] Specifically, in the present application, as shown in Table 1, Table 1 shows the elemental composition mass percentages (wt.%) of two types of Z3CN20.09M DSS duplex steels, No. 1 and No. 2:

[0066] Table 1. Elemental composition mass percentages (wt.%) of Z3N20.09M type duplex steel

[0067]

[0068] Among them, No. 1 and No. 2 respectively represent the Z3N20.09M duplex steel from two production areas.

[0069] Calculate the saturated Charpy impact energy according to the elemental mass percentage of the Z3N20.09M duplex steel of No. 1 :

[0070] First, calculate the material coefficient Φ of the duplex steel:

[0071] , ;

[0072] Among them, Cr is chromium element, Si is silicon element, Mn is manganese element, C is carbon element, N is nitrogen element, is the ferrite content, and the specific calculation formula is:

[0073] ,

[0074] ,

[0075] .

[0076] Among them, regarding the above and the calculation formula of Φ, which fits the relationship curve between the saturated Charpy impact energy of the material and the material parameters, refer to Figure 2 as an example; the above , , The constants in the calculation formula all come from the Hull equivalent factor.

[0077] Then, calculate α (the shape factor of the room temperature Charpy impact energy change curve) and β (half of the maximum change in the saturated Charpy impact energy) according to the initial Charpy impact energy and the saturated Charpy impact energy where:

[0078] , ; among them, the initial Charpy impact energy can use the standard Charpy V-notch sample with the typical value of . In this application, the initial Charpy impact energy of the Z3N20.09M type duplex steel actually measured is: No. 1: ;

[0079] No. 2: .

[0080] Then, describe the activation energy Q of the embrittlement process of duplex steel according to the chemical composition of Z3N20.09M DSS duplex steel. For the activation energy Q, it is the relationship between the activation energy of thermal embrittlement and the chemical composition proposed in the existing NUREG / CR-4513 method. The specific calculation formula is:

[0081] , where is the logarithm of time.

[0082] Then, calculate the thermal aging coefficient P according to the Arrhenius formula. The specific calculation method is:

[0083] , where is the temperature, and adding 273 to it represents the thermodynamic temperature, with the unit of Kelvin, denoted by k.

[0084] Among them, the formula of the thermal aging coefficient P is obtained by extrapolating the Arrhenius , where R is a constant, usually taken as 19.143.

[0085] Then, according to the existing NUREG / CR-4513, Rev.1 document record: use the saturated Charpy impact energy and embrittlement kinetics to represent the change of room-temperature Charpy impact energy with time and temperature. Specifically, the room-temperature Charpy impact energy decrease with time is expressed as:

[0086] ,

[0087] Thermal aging at 400 °C: ; among them, all materials reach the saturated Charpy impact energy after long-term aging (i.e., 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 is independent of the aging temperature (250 - 450 °C), but depends on the chemical composition of the steel. Specifically, refer to the NUREG / CR-4513, Rev.1 document; thus, for the Z3N20.09M DSS duplex steel of this application, the change curve of the room-temperature Charpy impact energy with the thermal aging time t can be obtained (i.e., the mechanical property degradation curve of the Z3N20.09M DSS duplex steel during the thermal aging service stage):

[0088] No. 1: ,

[0089] No. 2: ; among them, the logarithm of time takes 3.46 - 3.47, takes 3.39 - 3.48.

[0090] Furthermore, it is defined that when the room-temperature Charpy impact energy drops to close to the saturated Charpy impact energy When the material fails, the saturation node time is obtained therefrom. .

[0091] In practical applications, the characteristic defects of the thermal aging damage of duplex steel are mainly the 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 No. 1 and No. 2 duplex steel samples thermally aged at 400 °C for different times is shown.

[0092] S2: Take the degree of local enrichment of Cr in ferrite as the characteristic defect quantification parameter and use the Fe-Cr EAM potential function to calculate the change curve of the degree of local enrichment of Cr element during the thermal aging service stage of duplex steel.

[0093] Specifically, this application uses the Fe-Cr EAM potential function, focuses on the degree of local enrichment of Cr in ferrite, and defines its parameter as , and its specific calculation formula is as follows:

[0094] , where is the enrichment degree of the k-th nearest neighbor of Cr atoms, is the concentration of Fe atoms in the neighborhood of Cr atoms, is the global Cr atom concentration; if , it means that the Fe concentration in the neighborhood of Cr atoms is the same as the global Fe concentration, and the duplex steel is in a solid solution state; if , it means that most of the atoms in the neighborhood of Cr atoms are Cr atoms, and the duplex steel is in a phase separation state.

[0095] Then, use the Monte Carlo method to simulate the relationship between the local enrichment degree of Cr and the thermal aging time. Please refer to 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:

[0096] S20: Initialize the Fe-Cr alloy model: The Fe and Cr atoms are completely randomly distributed, and the size of the simulation box is set to: 5x5x5 - 40x40x40.

[0097] S21: Use the single vacancy diffusion mechanism to change the positions of Fe and Cr atoms.

[0098] Among them, single vacancy diffusion is a common diffusion mechanism, in which vacancies (missing atomic positions) move in the simulation box, causing changes in the positions of nearby atoms. For the Fe-Cr alloy, assuming there is a vacancy in it, this vacancy can exchange positions with neighboring Fe or Cr atoms, resulting in changes in the overall atomic positions; the specific steps are as follows:

[0099] Randomly select an Fe or Cr atom adjacent to the vacancy as the candidate atom;

[0100] Calculate the energy change after the atom and the vacancy exchange positions.

[0101] S22: Determine whether to accept the new configuration according to the Metropolis criterion, and determine the physical time according to the KMC time step.

[0102] Among them, the Metropolis criterion is used to determine whether to accept the exchange operation of the atom and the vacancy, that is, whether to accept the new configuration; the specific steps are as follows:

[0103] If the exchange operation results in an energy decrease, accept the operation;

[0104] If the exchange operation results in an energy increase, accept the operation with a set probability, for example:

[0105] The set probability is , is the energy, is the Boltzmann constant, is the thermal aging service temperature;

[0106] Generate a random number r between 0 and 1. If r is less than , then accept the new configuration (that is, the atom and the vacancy exchange positions); if r is greater than or equal to , then reject the new configuration and retain the original atomic position.

[0107] In the kinetic Monte Carlo (KMC) simulation, the determination of the time step is a key step for associating each jump or movement with the actual physical time; the specific steps are as follows:

[0108] For each exchange process of the atom and the vacancy, calculate the corresponding jump rate;

[0109] Calculate the total jump rate of each possible event;

[0110] Randomly generate a time step;

[0111] Select the most likely event according to the jump rate and update the positions of the vacancy and the atom;

[0112] Increase the physical time by one step.

[0113] S23: Repeat S21 - S22 until the simulation step requirement set by the operator is reached.

[0114] Thus, calculate the simulation results as shown in Figure 5 shown, Figure 5The variation relationship of the local enrichment degree of Cr with the thermal aging time is shown; among them, the green area represents the aggregation of Cr atoms, and the red area represents the aggregation of Fe atoms.

[0115] Furthermore, the relationship between the enrichment degree of Cr element and the thermal aging time is fitted:

[0116] , ; among them, is the local enrichment degree of Cr, T is the thermal aging service temperature, t is the thermal aging time, is the maximum value of the system at thermal equilibrium when t → ∞ , H is the growth rate with respect to the thermal aging time t; among them, is without considering the influence of temperature T and alloy composition , so is a function of the thermal aging time t, which is a double-reciprocal linear function. Take the logarithm of both sides of the formula to make it linearly represented. This fitting formula and the simulation value are referred to Figure 6 as shown, Figure 6 shows the linear relationship between the reciprocal and the reciprocal of the thermal aging time t.

[0117] S3: Substitute the calculation method of the change curve of the local enrichment degree of Cr element in the thermal aging service stage of duplex steel into the calculation method of the mechanical property deterioration curve in the thermal aging service stage of duplex steel to generate a duplex steel thermal aging damage mechanics model that correlates microscopic characteristic defects and Charpy impact energy, and then calculate the Charpy impact energy mechanical property parameters in the thermal aging service stage of duplex steel.

[0118] Among them, the specific modeling process is to fit the relationship between the Charpy impact energy at room temperature and the enrichment degree of Cr element :

[0119] According to

[0120] it can be obtained ;

[0121] The output result is: Charpy impact energy - enrichment degree of Cr element ; enrichment degree of Cr element - aging time t. Furthermore, according to the saturation time node , the curve change relationship between the Charpy impact energy at room temperature and the enrichment degree of Cr element in the thermal aging service stage is obtained.

[0122] From the above process, the relational expressions of the Charpy impact energy at room temperature and the SRO parameter for two types of duplex steel can be obtained:

[0123] No. 1: ,

[0124] No. 2: ; where SRO is the degree of local enrichment of alloy components, which mainly represents the enrichment degree of Cr in this application.

[0125] For the specific fitting situation, refer to Figure 7 as shown, to obtain the curve of Charpy impact energy varying with SRO. The curve fitting result has a good agreement with the experimental measured result, with an accuracy ≥ 90%. The difference in the curve width range is caused by the range of the logarithm of time; according to thermodynamics and formula limits, the Charpy impact energy has a lower limit, has an upper limit.

[0126] S4: Calculate the damage defect state of the duplex steel during the thermal aging service stage according to the magnetic comprehensive parameters and thermoelectric potential parameters.

[0127] Specifically, the thermoelectric potential parameters are obtained through a thermoelectric potential parameter detector. In this application, the thermoelectric potential parameter detector as long as it can detect the thermoelectric potential parameters of materials can be any existing form of thermoelectric potential detector. 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 on hardness, phase transformation, irradiation embrittlement, thermal aging embrittlement, etc. of metal materials; 2) sorting and quality identification of metal materials.

[0128] After obtaining the thermoelectric potential parameters, fit the relationship between the experimentally measured thermoelectric potential (Seebeck coefficient V) and the aging time t:

[0129] , , where is the change in thermoelectric potential, is the change in saturated thermoelectric potential when t → ∞, is the degree of change of the thermoelectric potential with respect to the thermal aging time (τ can be regarded as the half-saturation time), F is the initial growth rate of the thermoelectric potential. Here is the general constant of the formula when fitting the relationship curve between the thermoelectric potential and time, used to represent the correlation degree between the fitting formula and the curve; thus, the thermoelectric potential fitting results of No. 1 and No. 2 refer to Figure 8 as shown, and correspond well with the experimental data results. In this application, is a relatively large negative number. For the convenience of formula simplification, F ≈ 1, ;

[0130] Then the relationship between the thermal aging time and the change in thermoelectric potential is:

[0131] , where , is obtained by deformation. Take the logarithm of both sides;

[0132] Substitute the relationship between the Cr enrichment degree and time: ;

[0133] Generate the relationship between the thermoelectric potential and the Cr enrichment degree: ; where is the natural logarithm.

[0134] For specific fitting reference Figure 9 as shown, Figure 9 shows the relationship diagram between the thermoelectric potential and the SRO.

[0135] Specifically, the magnetic comprehensive parameters are obtained by a magnetic parameter detector, and the magnetic parameter detector refers to Figure 10 as shown, Figure 10 shows the connection schematic diagram of the magnetic parameter detector of the present application.

[0136] The magnetic parameter detector mainly includes three major modules: a detection probe (multi-sensor component 300), box body instrument equipment, and a computer 304. Among them, the detection probe is the multi-sensor component 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 body, mainly to complete the acquisition and processing of analog signals. The computer 304 needs to complete relevant software operations such as machine learning detection implementation: including but not limited to controlling measurement operations, setting relevant parameters, data processing and evaluation, result visualization, etc. The software system is based on modular design and is programmed in the Python language in PyCharm. At the same time, the detection probe (multi-sensor component) is connected to the box body through a shielded coaxial cable; the power supply of the box body comes from the ordinary 220V alternating current of the wall; the box body sends the detection data to the computer 304 through the Ethernet, and the computer 304 also controls the circuit work in the box body through the Ethernet data cable; the computer 304 processes all detection data and associates the detection data with the physical characteristics of the detection object in the form of numbers and charts.

[0137] Thus, the basic electromagnetic hardware structure of the instrument consists of five modules: a multi-sensor component 300, a magnetic field excitation signal generator 301, a magnetic field induction signal collector 302, an eddy current impedance signal collector 303, and a computer 304 (for control, processing, and display). The basic functions of the instrument are as follows: The magnetic field excitation signal generator 301 module generates a magnetic field excitation signal to drive the multi-sensor component to work. The computer 304, through the magnetic field induction signal collector 302 module, relies on the multi-sensor component to obtain the magnetic field induction signal of the measured sample, collects the impedance information of the eddy current excitation induction coil through the eddy current impedance signal collector 303 module, forms a control closed-loop based on the excitation signal and the collected signal, and obtains various test parameters of the measured sample. The computer 304 performs intelligent signal data processing on various parameters, analyzes the correlation between the test data and the physical characteristics of the measured sample, and all test actions records and test data can be repeated, traced, exported, and formed into charts.

[0138] The Barkhausen noise signal, multi-frequency eddy current parameter signal, incremental permeability signal, and harmonic analysis signal of the measured sample collected by the magnetic parameter detector through the sensor all go through data preprocessing, feature parameter extraction, and feature parameter data processing, and then are sent into a 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 measured sample.

[0139] A total of 62 characteristic magnetic parameters are obtained through the magnetic parameter detector. The summary of the extracted magnetic characteristic parameters is shown in Table 2:

[0140] Table 2. Summary table of extracted characteristic magnetic parameters

[0141]

[0142] After the above-mentioned collection of characteristic magnetic parameters is completed, the 62 characteristic magnetic parameters are linearly combined into 6 main variables by using the PCA principal component analysis method, and then these 6 main variables are transformed to obtain the magnetic comprehensive parameter Y (the magnetic comprehensive parameter Y is equivalent to the thermal aging time t). The fitting results are shown in Figure 11 as shown, Figure 11 showing the relationship between the magnetic comprehensive parameters and the aging time of two types of Z3CN20.09M DSS duplex steels, namely No. 1 and No. 2.

[0143] Exemplarily, the magnetic comprehensive parameters of two types of Z3CN20.09M DSS duplex steels, No. 1 and No. 2, are as follows:

[0144] No. 1: Y = 4250 + 8.6173 * PC1 + 4.6244 * PC2 - 15.7019 * PC3 - 28.5064 * PC4 - 53.1757 * PC5,

[0145] No. 2: Y = 4250 - 8.8242*PC1 + 22.2514*PC2 - 2.4750*PC3 - 16.6443*PC4 + 6.2109*PC5, where PCn is only a variable substitution symbol, and the coefficients of the two materials are not the same. PCn is a linear combination of magnetic comprehensive parameters; the average value needs to be subtracted before substituting the magnetic comprehensive parameters.

[0146] The specific parameters are as follows:

[0147] 1. M max' = M max - average(M max ), refer to the following table:

[0148]

[0149] 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:

[0150]

[0151] Error estimation: There are 10 groups of data at each time. After calculating the standard deviation of 62 parameters respectively and then substituting, refer to the following table:

[0152]

[0153] Therefore, taking the No. 1 duplex steel as an example, the fitting equation of the main components is:

[0154] Y = 4250 + 8.6173087277922633*PC1 + 4.6244195880789114*PC2 + (-15.7018757914582583*PC3) + (-28.5063832494472287*PC4) + (-53.1757140490943740*PC5)

[0155] The composition of the main components refers to the following table:

[0156]

[0157] The correlation coefficient and its square between the main components and the dependent variable:

[0158] The correlation coefficient between PC1 and the dependent variable Y: 0.4352

[0159] The square of the correlation coefficient (R 2 ): 0.1894

[0160] The correlation coefficient between PC2 and the dependent variable Y: 0.1459

[0161] The square of the correlation coefficient (R 2 ): 0.0213

[0162] The correlation coefficient between PC3 and the dependent variable Y: -0.3341

[0163] The square of the correlation coefficient (R 2 ): 0.1116

[0164] The correlation coefficient between PC4 and the dependent variable Y: -0.4782

[0165] The square of the correlation coefficient (R 2 ): 0.2287

[0166] The correlation coefficient between PC5 and the dependent variable Y: -0.6701

[0167] The square of the correlation coefficient (R 2 ): 0.4490.

[0168] Thus, establish the relationship between the magnetic comprehensive parameter Y and the Cr enrichment degree :

[0169] ,

[0170] .

[0171] S5: Compare the mechanical property parameters and the damage defect state 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.

[0172] Specifically, compare the mechanical property parameters and the damage defect state with the mechanical property degradation curve of the dual-phase steel during the thermal aging service stage to respectively generate the relationship between the Charpy impact energy and the thermoelectric potential:

[0173] ; The fitting result is referred to Figure 12 as shown in Figure 12 , which shows the relationship curve between the Charpy impact energy and the thermoelectric potential;

[0174] Generate the relationship between the Charpy impact energy and the magnetic comprehensive parameter Y:

[0175] .

[0176] In summary, the method for predicting and evaluating the thermal aging damage of duplex steel in this application can accurately determine multiple detailed evaluation parameters such as the degradation status of the mechanical properties, the damage of internal micro-defects, and the remaining service life at the current service stage of the thermal aging of duplex steel from the perspective of basic theory, so as to accurately judge the current service state of duplex steel and improve the safety of its service system.

[0177] In some embodiments, refer to Figure 13 as shown Figure 13 is a block diagram of an electronic device for implementing the embodiments of this application. The electronic device includes: a memory 401 and a processor 402. The memory 401 stores a computer program that can run on the processor 402. When the processor 402 executes the computer program, the method in the above embodiments is implemented. The number of the memory 401 and the processor 402 can be one or more.

[0178] The electronic device further includes:

[0179] a communication interface 403, which is used to communicate with external devices and perform data interaction and transmission.

[0180] 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 the sake of convenience of representation, Figure 13 only a thick line is shown in

[0181] but it does not mean that there is only one bus or one type of bus. Optionally, in 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.

[0182] The embodiments of this application provide a computer-readable storage medium that stores a computer program, and when the program is executed by the processor 402, the method provided in the embodiments of this application is implemented.

[0183] An embodiment of the present application further provides a chip, which includes a processor 402, configured to call and run instructions stored in a memory 401 from the memory 401, so that a communication device installed with the chip executes the method provided by the embodiment of the present application.

[0184] An embodiment of the present application further 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 configured to execute code in the memory 401. When the code is executed, the processor 402 is configured to execute the method provided by the embodiment of the application.

[0185] It should be understood that the above-mentioned processor 402 may be a central processing unit (CPU), or may be other general-purpose processors 402, digital signal processors 402 (DSP), 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 that supports the advanced risc machines (ARM) architecture.

[0186] 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 volatile and non-volatile memories 401. Among them, the non-volatile memory 401 may include a read-only memory 401 (ROM), a programmable read-only memory 401 (PROM), an erasable programmable read-only memory 401 (EPROM), an electrically erasable programmable read-only memory 401 (EEPROM), or a flash memory. The volatile memory 401 may include a random access memory 401 (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static random access memory 401 (SRAM), dynamic random access memory 401 (DRAM), synchronous dynamic random access memory 401 (SDRAM), double data rate synchronous dynamic random access memory 401 (DDR SDRAM), enhanced synchronous dynamic random access memory 401 (ESDRAM), synchlink dynamic random access memory 401 (SLDRAM), and direct rambus random access memory 401 (DR RAM).

[0187] In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may 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 processes or functions according to the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0188] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for predicting and evaluating the thermal aging damage of duplex steel, characterized in that, The method includes: Calculating and generating a mechanical property degradation curve for the thermal aging service stage of the duplex steel based on the composition and initial mechanical property parameters of the duplex steel material; Taking the degree of local enrichment of Cr in ferrite as a characteristic defect quantitative parameter and calculating the change curve of the degree of local enrichment of Cr element during the thermal aging service stage of the duplex steel using the Fe-Cr EAM potential function; Establishing a thermal aging damage mechanics model of the duplex steel that correlates microstructural characteristic defects and Charpy impact energy and calculating the Charpy impact energy mechanical property parameters for the thermal aging service stage of the duplex steel; Calculating the damage defect state of the duplex steel during the thermal aging service stage based on magnetic comprehensive parameters and thermoelectric potential parameters; Comparing the mechanical property parameters and damage defect state with the mechanical property degradation curve of the duplex steel during the thermal aging service stage to generate the remaining service life of the duplex steel material; Among them, when establishing a thermal aging damage mechanics model of the duplex steel that correlates microstructural characteristic defects and Charpy impact energy, the method includes: Substituting the calculation method of the change curve of the degree of local enrichment of Cr element during the thermal aging service stage of the duplex steel into the calculation method of the mechanical property degradation curve during the thermal aging service stage of the duplex steel to generate a thermal aging damage mechanics model of the duplex steel that correlates microstructural characteristic defects and Charpy impact energy; Among them, when modeling the thermal aging damage mechanics model of the duplex steel, the method includes: Fitting the relationship between the room-temperature Charpy impact energy and the degree of enrichment of Cr element; , generate the Charpy impact energy - Cr enrichment degree, Cr element enrichment degree - aging time, where is the Charpy impact energy at room temperature, is the saturated Charpy impact energy, SRO is the local enrichment degree of alloy components, and SRO -1 is the reciprocal of SRO, is the maximum value of the system at thermal equilibrium when t → ∞, is the reciprocal of; Generating the curve change relationship between the Charpy impact energy and the degree of enrichment of Cr element during the thermal aging service stage of the duplex steel according to the saturation node time.

2. The evaluation method according to claim 1, wherein When calculating the mechanical property degradation curve during the thermal aging service stage of the duplex steel, the method includes: Calculating the saturated Charpy impact energy according to the elemental mass percentage of the duplex steel; Calculating the shape factor of the change curve of the room-temperature Charpy impact energy and half of the maximum change in 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 duplex steel according to the chemical composition of the duplex 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 for the thermal aging service stage of the duplex steel.

3. The evaluation method according to claim 2, wherein When generating the mechanical property degradation curve for the thermal aging service stage of the duplex steel, the method further includes: Calculating the mechanical property degradation curve for the thermal aging service stage of the duplex steel according to the change of the room-temperature Charpy impact energy with the thermal aging time; ， Thermal aging at 400 °C: , where is the Charpy impact energy at room temperature, is the saturated Charpy impact energy, is half of the maximum change in the saturated Charpy impact energy, is the shape factor of the change curve of the Charpy impact energy at room temperature, is the logarithm of time, and P is the thermal aging coefficient.

4. The evaluation method according to claim 3, wherein When calculating the mechanical property degradation curve for the thermal aging service stage of the duplex steel, the method further includes: Judging whether the Charpy impact energy drops to a preset threshold. If so, it is defined as the failure of the duplex steel material, and the saturation node time is generated.

5. The evaluation method according to claim 1, wherein When calculating the degree of local enrichment of Cr, the method includes: Calculating the degree of local enrichment of Cr in ferrite using the Fe-Cr EAM potential function; , where is the enrichment degree of the k-th nearest neighbor of Cr atoms, is the concentration of Fe atoms in the neighborhood of Cr atoms, is the global Cr atom concentration; Simulating the relationship between the degree of local enrichment of Cr and the thermal aging time using the Monte Carlo method; Fitting the relationship between the degree of enrichment of Cr element and the thermal aging time; , , wherein, is the degree of local enrichment of Cr, T is the service temperature of thermal aging, t is the thermal aging time, is the maximum value of the system at thermal equilibrium when t → ∞, and H is the growth rate with respect to the thermal aging time t.

6. The evaluation method according to claim 5, wherein After calculating the local enrichment degree 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 duplex steel is in a solid solution state; if , it means that most of the atoms in the neighborhood of Cr atoms are Cr atoms, and the duplex steel is in a phase separation state.

7. The evaluation method according to claim 5, characterized in that When simulating the relationship between the degree of local enrichment of Cr and the thermal aging time using the Monte Carlo method, the method includes: Initialize the Fe-Cr alloy model: The Fe and Cr atoms are completely randomly distributed, and the simulation box is set to the preset size; Use the single-vacancy diffusion mechanism to change the positions of Fe and Cr atoms; Determine whether to accept the new configuration according to the Metropolis criterion, and determine the physical time according to the KMC time step; Execute the loop until the preset number of simulation steps is reached.

8. The evaluation method according to claim 1, wherein When calculating the damage defect state in the thermal aging service stage of duplex steel, the method includes: Fit the relationship between the measured thermoelectric potential and the thermal aging time; , , wherein, is the change in thermoelectric potential, is the change in saturated thermoelectric potential, is the degree of change in thermoelectric potential with respect to the rate of thermal aging time, F is the initial growth rate of thermoelectric potential, and C is the general constant of the formula when fitting the relationship curve between thermoelectric potential and time, which is used to represent the degree of correlation between the fitting formula and the curve; According to the relationship between the Cr enrichment degree and the thermal aging time, fit the relationship between the thermoelectric potential and the Cr enrichment degree; Use the PCA data statistical processing method to fit several magnetic parameters to generate a magnetic comprehensive parameter; Fit the relationship between the magnetic comprehensive parameter and the Cr enrichment degree: , , where Y is the magnetic comprehensive parameter, T is the thermal aging service temperature, and t is the thermal aging time; Generate the damage defect state in the thermal aging service stage of duplex steel according to the relationships between the thermoelectric potential and the magnetic comprehensive parameter and the Cr enrichment degree respectively.

9. The evaluation method according to claim 8, characterized in that When fitting the relationship between the thermoelectric potential and the Cr enrichment degree, the method includes: Calculate the relationship between the thermal aging time and the change in thermoelectric potential: , where ; Substitute the relationship between Cr enrichment degree and time: ; Relationship between the generated thermoelectric potential and the degree of Cr enrichment: .

10. The evaluation method according to claim 8, characterized in that, When generating the remaining service life of duplex steel materials, the method includes: Calculate the relationship between the Charpy impact energy and the thermoelectric potential: 。 11. The evaluation method according to claim 8, wherein When generating the remaining service life of duplex steel materials, the method includes: Establish the relationship between the magnetic comprehensive parameter Y and the Cr enrichment degree: ， ； Calculate the relationship between the Charpy impact energy and the magnetic comprehensive parameter: 。 12. A duplex stainless steel thermal aging damage prediction and evaluation device for performing the prediction and evaluation method according to any one of claims 1-11, characterized in that, Include: A mechanical curve calculation module for calculating and generating the mechanical property degradation curve in the thermal aging service stage of duplex steel according to the composition and initial mechanical property parameters of the duplex steel material; A characteristic defect evolution module for using the local Cr enrichment degree in ferrite as a characteristic defect quantitative parameter and calculating the change curve of the local Cr enrichment degree in the thermal aging service stage of duplex steel by using the Fe-Cr EAM potential function; A mechanical property calculation module for establishing a thermal aging damage mechanics model of duplex steel that correlates microscopic characteristic defects and Charpy impact energy and calculating the Charpy impact energy mechanical property parameters in the thermal aging service stage of duplex steel; A characteristic defect calculation module for calculating the damage defect state in the thermal aging service stage of duplex steel according to the magnetic comprehensive parameter and the thermoelectric potential parameter; A damage prediction and evaluation module for comparing the mechanical property parameters and the damage defect state with the mechanical property degradation curve in the thermal aging service stage of duplex steel to generate the remaining service life of duplex steel materials.

13. A dual-phase steel thermal aging damage prediction and evaluation device equipped with the prediction evaluation device described in claim 12, characterized in that, It also includes: A magnetic parameter detector for collecting the magnetic property data of duplex steel materials and integrating the collected magnetic property data into a magnetic comprehensive parameter; A thermoelectric potential detector for collecting the thermoelectric potential data of the magnetic force of duplex steel.

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