A method, system and device for predicting high-temperature fatigue life of a martensitic heat-resistant steel, and a storage medium

Abstract

The present application relates to the technical field of high-temperature fatigue damage evaluation of metal materials, and relates to a martensitic heat-resistant steel high-temperature fatigue life prediction method, system, device and storage medium; wherein the martensitic heat-resistant steel high-temperature fatigue life prediction method comprises: obtaining low-cycle fatigue test data and microstructure parameters of a material to be evaluated; analyzing the low-cycle fatigue test data to obtain flow stress and yield stress of the material to be evaluated; inputting the flow stress and the microstructure parameters into a flow stress decomposition model to obtain dislocation density data; calculating a dislocation annihilation thermodynamic indicator to determine a dislocation annihilation judgment result; determining a cyclic softening dominant reason according to the dislocation annihilation judgment result, and using a life prediction model to analyze the determined cyclic softening dominant reason, the dislocation density data and the microstructure parameters to output a fatigue life prediction result of the material to be evaluated. The present application can improve the accuracy of high-temperature component fatigue life prediction results.

Description

Technical Field

[0001] This invention relates to the field of high-temperature fatigue damage assessment technology for metallic materials, and in particular to a method, system, equipment, and storage medium for predicting the high-temperature fatigue life of martensitic heat-resistant steel. Background Technology

[0002] 9–12%Cr ferritic-martensitic heat-resistant steel is widely used in high-temperature critical components such as those in ultra-supercritical power plants. These components are prone to cyclic softening under high-temperature cyclic loading, which reduces structural reliability and shortens fatigue life. Therefore, research on the cyclic softening mechanism and life assessment methods is of significant engineering importance.

[0003] Currently, the main methods for assessing the fatigue life of heat-resistant steel under high-temperature cyclic loading include strain-life test correlation models, hysteretic energy dissipation models, and constitutive models based on damage mechanics. Among these, the Coffin-Manson strain-life model and the hysteretic energy dissipation model are widely used in engineering life assessment due to their compact expressions and relatively simple parameter calibration and engineering implementation.

[0004] However, the aforementioned models, which primarily rely on correlation fitting, typically implicitly include the microscopic evolutionary process upon which high-temperature cyclic softening depends in the fitting coefficients. This results in the model's prediction accuracy largely depending on how the microscopic evolutionary process is represented. When changes in strain amplitude or temperature alter the dominant mechanism of cyclic softening and its weights, the migration and extrapolation of model parameters become sensitive, thereby affecting the stability and reliability of lifetime predictions.

[0005] Furthermore, while existing research has discussed the driving mechanisms of cyclic softening in 9–12% Cr martensitic heat-resistant steel from the perspectives of dislocation rearrangement and annihilation, subgrain and cellular structure refinement, and grain boundary migration, quantitative descriptions of the contributions of each mechanism to varying load conditions across engineering temperatures and wide strain ranges remain insufficient. In particular, due to the lack of a unified characterization method that correlates the energy barrier characteristics of dislocation annihilation with the kinetic intensity of dynamic recovery, existing models struggle to continuously predict the relative contribution evolution of these two types of dislocation processes with strain amplitude at 600℃, thus limiting the reliable assessment of cyclic softening and life degradation in high-temperature components. Summary of the Invention

[0006] This application aims to at least solve the technical problems existing in the prior art, and to provide a method, system, device and storage medium for predicting the high-temperature fatigue life of martensitic heat-resistant steel.

[0007] In a first aspect, the present invention provides a method for predicting the high-temperature fatigue life of martensitic heat-resistant steel, comprising: Acquire low-cycle fatigue test data and microstructure parameters of the material to be evaluated. The low-cycle fatigue test data is the cyclic stress-strain response data generated by strain-controlled low-cycle fatigue test of the material to be evaluated at a preset temperature. The low-cycle fatigue test data were analyzed to obtain the flow stress and yield stress of the material to be evaluated. The flow stress and the microstructure parameters are input into the flow stress decomposition model, and the flow stress decomposition model uses a preset algorithm to process the flow stress and the microstructure parameters to output dislocation density data. Based on the yield stress and microstructure parameters, the dislocation annihilation thermodynamic indicator is determined, and based on the dislocation annihilation thermodynamic indicator, it is determined whether dislocation annihilation can occur spontaneously thermodynamically, thus obtaining the dislocation annihilation judgment result. Based on the dislocation annihilation judgment results, the dominant cause of cyclic softening is determined. The determined dominant cause of cyclic softening, dislocation density data, and microstructure parameters are input into the life prediction model. The life prediction model analyzes the determined dominant cause of cyclic softening, dislocation density data, and microstructure parameters, and outputs the fatigue life prediction results of the material to be evaluated.

[0008] The above technical solutions provide multi-scale, multi-physics input information for life prediction by acquiring low-cycle fatigue test data and microstructure parameters, overcoming the problem of weak physical foundation of the model caused by relying solely on macroscopic test data in traditional methods. By analyzing the test data to extract flow stress and yield stress, the mechanical state of the material under evaluation in the cyclic stable stage can be captured. By inputting flow stress and microstructure parameters into the flow stress decomposition model to back-calculate dislocation density data, the determination of dislocation density can be freed from excessive reliance on empirical fitting, improving the physical clarity and extrapolation robustness of the model. By determining the thermodynamic indicator of dislocation annihilation based on parameters such as yield stress, and judging the spontaneity of dislocation annihilation accordingly, thermodynamic feasibility criteria are introduced into engineering life assessment. By inputting the dominant cause of cyclic softening along with dislocation density data and microstructure parameters into the life prediction model, the prediction of the fatigue life of the material under evaluation fully considers macroscopic experimental data and microstructure description, thereby improving the accuracy of high-temperature fatigue life prediction results for martensitic heat-resistant steel.

[0009] Optionally, the step of analyzing the low-cycle fatigue test data to obtain the flow stress and yield stress of the material to be evaluated includes: Extract the flow stress and yield stress from the half-life hysteresis loop under the same strain amplitude condition; The value of the flow stress is the peak tensile stress value of the hysteresis loop corresponding to a specified lifetime ratio; the stress value at the end of the elastic deformation stage is taken as the yield stress based on the stress-strain data of the same hysteresis loop.

[0010] Optionally, the dislocation density data includes total dislocation density, initial dislocation density, and failed dislocation density; the microstructure parameters include at least one of Taylor factor, precipitate equivalent radius, precipitate volume fraction, dislocation segment annihilation length, and dislocation spacing within the cross-slip plane.

[0011] The above technical solution clarified the specific content of dislocation density data and microstructure parameters.

[0012] Optionally, the Taylor factor is calculated based on electron backscatter diffraction orientation data, and the equivalent radius of the precipitate, the volume fraction of the precipitate, the dislocation segment annihilation length, and the dislocation spacing in the cross-slip plane are measured and statistically analyzed based on transmission electron microscopy images.

[0013] The above technical solution clarifies the method for obtaining microstructure parameters.

[0014] Optionally, the formula for calculating the thermodynamic indicator of dislocation annihilation is: ; in, For dislocation formation energy, For migration energy, For vacancy auxiliary energy, Contribute to the entropy term; ; ; ; ; in, This represents the shear modulus of martensitic steel at the experimental temperature. The Burgers vector representing martensitic steel. Dislocation segments representing annihilation, Represents the cyclic yield stress that defines the initiation point of plastic flow. It represents the width of the distortion field near the dislocation core. This represents the spacing between parallel dislocations on the cross-slip surface. The temperature function representing vacancy-assisted annihilation. Represents the vacancy formation energy at high temperatures. Represents the Boltzmann constant. represents the vacancy concentration, T represents the experimental temperature, and S represents the statistical entropy of the system during dislocation slip and annihilation.

[0015] The above technical solution clarifies the calculation method for the thermodynamic indicator of dislocation annihilation.

[0016] Optionally, if the dislocation annihilation thermodynamic indicator is negative, the dominant cause of cyclic softening is determined to be the coupling of dynamic recovery and dislocation annihilation; if the dislocation annihilation thermodynamic indicator is positive, the dominant cause of cyclic softening is determined to be dynamic recovery, and dislocation annihilation is suppressed, thus obtaining the dislocation annihilation judgment result.

[0017] The above technical solutions clarify the rules for determining the primary cause of cyclic softening.

[0018] Optionally, the method further includes: Calculate dynamic recovery rate and dislocation migration rate parameters based on dynamic recovery model; The thermodynamic indicator and dynamic recovery rate of dislocation annihilation are used to determine whether dislocation annihilation can occur spontaneously in thermodynamics, and the result of the dislocation annihilation judgment is obtained. When the thermodynamic indicator of dislocation annihilation is negative and the dynamic recovery rate is positive, the dominant cause of cyclic softening is the coupling of dynamic recovery and dislocation annihilation; when the thermodynamic indicator of dislocation annihilation is positive and the dynamic recovery rate is negative, the dominant cause of cyclic softening is dynamic recovery, and dislocation annihilation is suppressed.

[0019] Secondly, the present invention provides a high-temperature fatigue life prediction system for martensitic heat-resistant steel, the system comprising: The acquisition module is used to acquire low-cycle fatigue test data and microstructure parameters of the material to be evaluated. The low-cycle fatigue test data is the cyclic stress-strain response data generated by the strain-controlled low-cycle fatigue test of the material to be evaluated at a preset temperature. The analysis module is used to analyze the low-cycle fatigue test data to obtain the flow stress and yield stress of the material to be evaluated; The processing module is used to input the flow stress and the microstructure parameters into the flow stress decomposition model, and the flow stress decomposition model uses a preset algorithm to process the flow stress and microstructure parameters to output dislocation density data. The dislocation annihilation judgment module is used to determine the dislocation annihilation thermodynamic indicator based on the yield stress and microstructure parameters, and to determine whether dislocation annihilation can occur spontaneously thermodynamically based on the dislocation annihilation thermodynamic indicator, so as to obtain the dislocation annihilation judgment result. The output module is used to determine the dominant cause of cyclic softening based on the dislocation annihilation judgment result, and input the determined dominant cause of cyclic softening, dislocation density data and microstructure parameters into the life prediction model. The life prediction model analyzes the determined dominant cause of cyclic softening, dislocation density data and microstructure parameters, and outputs the fatigue life prediction result of the material to be evaluated.

[0020] Thirdly, the present invention provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for predicting the high-temperature fatigue life of martensitic heat-resistant steel.

[0021] Fourthly, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the above-described method for predicting the high-temperature fatigue life of martensitic heat-resistant steel.

[0022] In summary, this application includes the following beneficial technical effects: By acquiring low-cycle fatigue test data and microstructure parameters, multi-scale and multi-physics input information is provided for life prediction, overcoming the problem of weak physical foundation of the model caused by relying solely on macroscopic test data in traditional methods. By analyzing the test data to extract flow stress and yield stress, the mechanical state of the material under evaluation in the cyclic stable stage can be captured. By inputting flow stress and microstructure parameters into the flow stress decomposition model to back-calculate dislocation density data, the determination of dislocation density can be freed from excessive reliance on empirical fitting, improving the physical clarity and extrapolation robustness of the model. By determining the thermodynamic indicator of dislocation annihilation based on parameters such as yield stress, and judging the spontaneity of dislocation annihilation accordingly, thermodynamic feasibility criteria are introduced into engineering life assessment. By inputting the dominant cause of cyclic softening along with dislocation density data and microstructure parameters into the life prediction model, the prediction of the fatigue life of the material under evaluation fully considers macroscopic experimental data and microstructure description, thereby improving the accuracy of high-temperature fatigue life prediction results for martensitic heat-resistant steel. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for predicting the high-temperature fatigue life of martensitic heat-resistant steel according to an embodiment of the present invention. Figure 2 This is a schematic diagram showing the dimensions and geometry of the experimental specimen of the material to be evaluated, provided in an embodiment of the present invention. Figure 3 For strain amplitude Taylor factor diagram, average orientation difference diagram and grain boundary distribution diagram of EBSD of the sample under the condition of 0.2%~0.8%; Figure 4 A schematic diagram of transmission electron microscopy images showing the interaction between lath boundaries and precipitates on dislocations and the evolution of dislocation substructures during cyclic softening. Figure 5 For different strain amplitudes stress amplitude A graph showing how the number of iterations changes. Figure 6 This is a comparison chart of the fatigue life prediction values ​​and measured values ​​of the life prediction model of this application, the Coffin-Manson model, and the hysteresis energy dissipation model. Figure 7 This is a schematic diagram of the structure of an electronic device for implementing the high-temperature fatigue life prediction method for martensitic heat-resistant steel according to an embodiment of the present invention.

[0024] Reference numerals: 10, processor; 11, memory; 12, communication bus; 13, communication interface.

[0025] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0026] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0027] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0028] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0029] Reference Figure 1 The diagram shown is a flowchart illustrating a method for predicting the high-temperature fatigue life of martensitic heat-resistant steel according to an embodiment of the present invention. In this embodiment, the method for predicting the high-temperature fatigue life of martensitic heat-resistant steel includes: S1. Obtain low-cycle fatigue test data and microstructure parameters of the material to be evaluated.

[0030] Low-cycle fatigue test data refers to the cyclic stress-strain response data generated by strain-controlled low-cycle fatigue tests on the material under evaluation at a preset temperature. Specifically, axial strain-controlled low-cycle fatigue tests are conducted on the material under evaluation at the target temperature, and the cyclic stress-strain hysteresis response and stress amplitude curves as a function of the number of cycles are recorded. The life criterion is determined during the fatigue test as follows: using the stable plateau segment of the stress amplitude curve as a benchmark, when the stress amplitude decreases by 10% relative to the plateau value, the corresponding number of cycles is defined as the experimental fatigue life. .

[0031] The microstructure parameters include at least one of the following: Taylor factor, precipitate equivalent radius, precipitate volume fraction, dislocation segment annihilation length, and dislocation spacing within the cross-slip plane. The Taylor factor is calculated based on electron backscatter diffraction orientation data, and the precipitate equivalent radius, precipitate volume fraction, dislocation segment annihilation length, and dislocation spacing within the cross-slip plane are measured and statistically analyzed based on transmission electron microscopy images.

[0032] The microstructure parameters of the material to be evaluated were determined based on characterization data from electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM), including: Taylor factor is calculated from EBSD crystal orientation data; precipitate-related parameters, such as precipitate equivalent radius. Volume fraction and dislocation segment annihilation length Dislocation spacing within the cross-slip plane The data are obtained from TEM images and can be statistically analyzed using ImageJ.

[0033] Reference Figure 2 This embodiment of the present application defines the experimental specimen size and geometry of the material to be evaluated; in this embodiment, the steps of the strain-controlled low-cycle fatigue test include: 1. Specimen Preparation: Martensitic heat-resistant steel P92 was selected as the evaluation object. Fatigue specimens were taken from the middle wall region of the pipe to avoid the influence of the decarburized layer on the inner surface and the oxide layer on the outer surface. The specimen axis was machined along the longitudinal direction of the pipe to obtain a consistent axial strain control low-cycle fatigue baseline. The specimen geometry can be machined according to GB / T 145-2001 B2 type round bar specimen.

[0034] 2. Test Preparation and Low-Cycle Fatigue Loading: The low-cycle fatigue test can be conducted on a servo-hydraulic fatigue testing machine equipped with a three-section resistance furnace; K-type thermocouples are used for multi-zone temperature feedback to stabilize the sample temperature within the set value ±2℃; the axial strain is controlled by a high-temperature contact extensometer in a closed loop, and the strain amplitude... According to ASME BPVC NB-3228, the strain rates were set to 0.2%, 0.4%, 0.6%, and 0.8%. The loading was performed using a triangular wave with a strain ratio R = -1 in an air environment at 600°C.

[0035] 3. Life criterion and key cycle stress extraction: (1) Life criterion: For each strain amplitude condition, record the stress amplitude change curve with the number of cycles. After the stress amplitude enters the stable plateau section, perform linear fitting. With the plateau value as the benchmark, when the stress amplitude in the subsequent softening section drops to less than 10% of the plateau value, the corresponding number of cycles is defined as fatigue life Nf; (2) Characteristic stress extraction: Extract the flow stress σflow and yield stress σY from the half-life hysteresis loop.

[0036] 4. Preparation and characterization of microstructure samples: (1) SEM and EBSD samples: Slices were cut along the axial direction from the gauge length of the initial and failed samples. After cold mounting, the samples were ground sequentially with 600#, 800#, 1000#, and 1500# sandpaper, followed by 1μm diamond polishing and 0.5μm silicon carbide polishing. EBSD samples were further vibratory polished with 0.02μm silicon carbide for 4 h to reduce the surface deformation layer. The EBSD scanning area was 300×300 μm. 2 The acquisition conditions were 20 kV, 70° tilt angle, and 0.1 μm step size; strain amplitude Taylor factor diagram, average nucleus orientation difference diagram, and grain boundary distribution diagram of the EBSD of the sample under the condition of 0.2%~0.8% are as follows: Figure 3 As shown; (2) TEM Samples: Samples were taken from near the tip of the main crack in the failed specimen to ensure the representativeness of the crack tip area. The main surface crack was located first, and a small piece containing the crack tip was cut out. After mechanical thinning, a 3 mm circular piece was punched out so that the crack tip was located near the center of the circular piece. The thin piece was ground to about 80 μm and then prepared by double-jet electrolytic polishing until a central perforation was formed. Bright-field images were obtained under 200 kV TEM. At least ≥10 locations were selected for each working condition to ensure statistical representativeness. Transmission electron microscopy images of the interaction between lath boundaries and precipitates on dislocations and the evolution of dislocation substructures during cyclic softening are shown in the figure. Figure 4 As shown.

[0037] S2. Analyze the low-cycle fatigue test data to obtain the flow stress and yield stress of the material to be evaluated.

[0038] Specifically, the characteristic stress extraction rule is as follows: extract the flow stress and yield stress from the half-life hysteresis loop under the same strain amplitude condition; specifically, analyze low-cycle fatigue test data to obtain the flow stress and yield stress of the material to be evaluated, including: Extract the flow stress and yield stress from the half-life hysteresis loop under the same strain amplitude condition; The flow stress is taken as the peak tensile stress value of the hysteresis loop corresponding to a specified life ratio; the stress value at the end of the elastic deformation stage is taken as the yield stress based on the stress-strain data of the same hysteresis loop.

[0039] S3. Input the flow stress and microstructure parameters into the flow stress decomposition model. The flow stress decomposition model uses a preset algorithm to process the flow stress and microstructure parameters and output dislocation density data.

[0040] Specifically, dislocation density data includes total dislocation density, initial dislocation density, and failed dislocation density.

[0041] Under fixed material and temperature conditions, flow stress can be decomposed into three parts: matrix strength, dislocation interaction strengthening, and precipitation strengthening. ; in The matrix strength constant is Characterizing dislocation interaction terms, Characterize the contribution of precipitation enhancement.

[0042] The dislocation interaction term adopts the Taylor relation: ; in The total dislocation density, α is the Taylor factor, μ is the geometric factor, μ is the shear modulus, and b is the Burgers vector.

[0043] The preferred expression for the extracted enhancement term is based on the Orowan mechanism: ; Where ν is the high-temperature driven dislocation migration rate, and the average radius of the randomly cut surface of the precipitate can be taken as: ; The average spacing λ is further determined by the precipitate radius r and volume fraction f: ; From this, the inverse expression for the total dislocation density can be obtained: ; By substituting the initial state and the failure state into the corresponding... The initial dislocation density is obtained. With failure dislocation density In GND density with EBSD inversion At the same time, it can also calculate the statistical storage dislocation density. : ; Statistical storage of dislocation density is used to assist in the interpretation of dislocation gradient contributions.

[0044] S4. Determine the thermodynamic indicator of dislocation annihilation based on the yield stress and microstructure parameters, and determine whether dislocation annihilation can occur spontaneously thermodynamically based on the thermodynamic indicator of dislocation annihilation, thus obtaining the dislocation annihilation judgment result.

[0045] A total energy barrier model for dislocation segment slip and annihilation is established, synthesizing the dislocation formation energy Edf, migration energy, vacancy-assisted energy, and entropy contribution: The formula for calculating the thermodynamic indicator of dislocation annihilation is as follows: ; in, For dislocation formation energy, For migration energy, For vacancy auxiliary energy, Contribute to the entropy term; ; ; ; ; in, This represents the shear modulus of martensitic steel at the experimental temperature. The Burgers vector representing martensitic steel. Dislocation segments representing annihilation, Represents the cyclic yield stress that defines the initiation point of plastic flow. It represents the width of the distortion field near the dislocation core. This represents the spacing between parallel dislocations on the cross-slip surface. The temperature function representing vacancy-assisted annihilation. Represents the vacancy formation energy at high temperatures. Represents the Boltzmann constant. represents the vacancy concentration, T represents the experimental temperature, and S represents the statistical entropy of the system during dislocation slip and annihilation.

[0046] when At this time, dislocation annihilation can occur spontaneously in thermodynamics; the microscopic characterization results show that the dislocation density decreases, the flow stress decreases more easily, and the cyclic softening is accelerated; the purpose of the invention is to determine that the main cause of cyclic softening is "dynamic recovery + annihilation coupling".

[0047] when At that time, annihilation was suppressed (non-spontaneous) under the load conditions; the microscopic characterization results showed that dislocations mainly rearranged or polarized, forming dislocation cells and low-angle grain boundaries; the invention is used to determine that the dominant cause of cyclic softening is "dynamic recovery".

[0048] S5. Based on the dislocation annihilation judgment results, determine the dominant cause of cyclic softening, and input the determined dominant cause of cyclic softening, dislocation density data, and microstructure parameters into the life prediction model. The life prediction model analyzes the determined dominant cause of cyclic softening, dislocation density data, and microstructure parameters, and outputs the fatigue life prediction results of the material to be evaluated.

[0049] Specifically, if the dislocation annihilation thermodynamic indicator is negative, the dominant cause of cyclic softening is determined to be the coupling of dynamic recovery and dislocation annihilation; if the dislocation annihilation thermodynamic indicator is positive, the dominant cause of cyclic softening is determined to be dynamic recovery, and dislocation annihilation is suppressed, thus obtaining the dislocation annihilation judgment result.

[0050] A lifetime model based on dislocation density evolution driven by dynamic recovery is established: the relationship between recovery rate and dislocation density is combined, and by integrating the initial dislocation density and the failure dislocation density, the relationship with the maximum plastic strain is obtained. The relevant expressions; then, by substituting and rearranging them with the Coffin-Manson relation, we obtain the following: and Lifetime prediction expression for key variables: .

[0051] To further quantitatively characterize the intensity of the dislocation recovery process beyond the thermodynamic criterion (ΔG), and to avoid the inability of ΔG alone to fully explain the essential mechanism of cyclic softening, this invention introduces a dynamic recovery kinetic index to characterize the cyclic softening phenomenon caused by the decrease in matrix material strength due to dislocation rearrangement. This provides necessary input for identifying the dominant mechanism of cyclic softening and selecting branches for the life prediction model. In another embodiment of this application, the high-temperature fatigue life prediction method for martensitic heat-resistant steel further includes: Calculate dynamic recovery rate and dislocation migration rate parameters based on dynamic recovery model; The thermodynamic indicator and dynamic recovery rate of dislocation annihilation are used to determine whether dislocation annihilation can occur spontaneously in thermodynamics, and the result of the dislocation annihilation judgment is obtained. Specifically, the relationship between rearranged dislocation density ρ and strain ε is described using a Kocks–Mecking type relationship, and the dynamic restitution coefficient of body-centered cubic (BCC) alloys is introduced. Meanwhile, the dislocation migration rate driven by high-temperature diffusion is defined. Among them, σ can be determined by the half-life tensile peak stress. Instead, it reflects the stable cyclic stress level.

[0052] Further define the dynamic recovery rate as a characterization of the strength of the recovery kinetics. It can be calculated from the dislocation migration rate, Taylor factor, and total dislocation density: ; The magnitude is used to quantify the strength of recovery: The larger the value, the stronger the dynamic tendency of dislocations to rearrange into lower-energy structures.

[0053] Based on the above two types of indicators, a mechanism for discrimination rules is established, using the positive and negative signs of ∆G and The numerical value is used as the basis for distinction: When the thermodynamic indicator of dislocation annihilation is negative and the dynamic recovery rate is positive, the dominant cause of cyclic softening is the coupling of dynamic recovery and dislocation annihilation; when the thermodynamic indicator of dislocation annihilation is positive and the dynamic recovery rate is negative, the dominant cause of cyclic softening is dynamic recovery, and dislocation annihilation is suppressed. That is, ∆G<0 and >0: Dynamic recovery and dislocation annihilation are the dominant coupling factors; ∆G>0 and >0: Dynamic recovery dominates, dislocation annihilation is suppressed.

[0054] As a parallel kinetic intensity index, it is used to describe the speed of the recovery process during softening behavior. >0 is always true.

[0055] At 600℃, strain amplitude In the embodiments of low-cycle fatigue with a life range of approximately 102 to 104 cycles under antisymmetric axial strain control of 0.2%, 0.4%, 0.6%, and 0.8%, the predicted life and the measured life of the model of the present invention show good consistency in logarithmic coordinates. The predicted points are closely distributed around the 1:1 line and all fall within the ±20% deviation band, demonstrating high accuracy and low dispersion.

[0056] This application introduces the thermodynamic index ∆G of dislocation annihilation and the kinetic index of dislocation recovery. This transforms the qualitative judgment of the shift in cyclic softening behavior from dislocation annihilation-dominated to dislocation recovery-dominated into a calculable quantitative output. For example... Figure 5 As shown, Figure 5 For different strain amplitudes stress amplitude A graph showing how the number of iterations changes, (a) =0.2%; (b) =0.4%; (c) =0.6%; (d) =0.8%; such as Figure 3 As shown, the black curves represent different strain amplitudes. stress amplitude The fatigue life shows a decreasing trend with the evolution of the number of cycles N, indicating that the material undergoes typical cyclic softening under high-temperature strain-controlled cyclic loading. The "10% plateau descent criterion" is used to determine the plateau reference line (red dashed line) by linearly fitting the stable plateau segment (Stage II). Then, a criterion line (blue dashed line) is drawn parallel to this reference line and shifted downwards by 10%. The number of cycles corresponding to the intersection of the N curve and the blue dashed line is defined as follows: .

[0057] This embodiment will The –N curve is divided into three stages: Stage I is the early rapid softening stage; Stage II is the relatively stable softening stage (used to fit the plateau slope k); and Stage III is the accelerated failure stage (stress amplitude decreases rapidly and eventually fails). The boundary between the three stages is based on the degree of deviation of the instantaneous softening rate from the plateau slope k: when the instantaneous softening rate enters and remains within ±5% of k, it is defined as Stage II; when its deviation exceeds 5% and becomes further negative, it enters Stage III.

[0058] A lifetime prediction expression is established based on dislocation density evolution driven by dynamic recovery, and a calibration + verification strategy is adopted: selecting =0.2%, 0.6%, and 0.8% are the calibration point identification model parameters. =0.4% was used as an independent validation point; and the Coffin-Manson model and the hysteresis energy dissipation model could be used as controls. In this embodiment, the comparison chart of predicted lifetime and actual lifetime shows that the predicted points of the model of this invention are distributed around a 1:1 line, and the reference line gives the deviation bands of ±20% and ±50%, reflecting the prediction consistency and stability of this invention under the operating conditions of this embodiment. Based on Figure 5 Chinese annotation results Fatigue life at 0.2%, 0.4%, 0.6%, and 0.8% These are weeks 12096, 2245, 843, and 587 respectively. With... Increase The significant decrease and increase in the slope |k| of the plateau segment indicate an accelerated softening rate; at the same time, the proportion of Stage I in the lifetime increases and the proportion of Stage II decreases, indicating that early softening is more severe under high strain amplitude and the process of accelerated failure is accelerated more quickly.

[0059] It can be seen that ∆G changes from positive to negative with the change of strain amplitude ( When the percentage is 0.2%, ∆G = +36.319. When the percentages are 0.4%, 0.6%, and 0.8%, ∆G = -164.76, -400.22, and -696.04, respectively. The value increases with increasing strain amplitude, rising from 2.846 to 6.790, thus providing a clearer basis for judging the dominant mechanism of cyclic softening and its evolution with load conditions.

[0060] refer to Figure 6 , Figure 6 The graph shows a comparison between the fatigue life prediction model (blue sphere), the Coffin-Manson model (green square), and the hysteresis energy dissipation model (red triangle) of this application and the measured values. Compared with traditional models, the high-temperature fatigue life prediction method for martensitic heat-resistant steel in this application is more reliable in the long life range and reduces the risk of extrapolation error in the low strain amplitude region. Compared with the Coffin-Manson model and the hysteresis energy dissipation model, this invention can still maintain a prediction closer to the experiment in the long life range (about 104 cycles), while the traditional model has a significant overestimation phenomenon in this range, with a deviation of more than ±50%. Therefore, this invention is more suitable for the life assessment needs of high-temperature components such as power plants in the low strain amplitude and long service range.

[0061] This application uses the "10% decrease in relative saturation plateau" life definition method to determine fatigue life. This criterion is clear and repeatable, which is beneficial for life comparison and model calibration / verification between different strain amplitudes or different material batches.

[0062] This application is applicable to low-cycle fatigue assessment under constant amplitude, fully antisymmetric (R=-1) high-temperature strain control. When extended to non-fully antisymmetric loading (R≠-1), the cyclic yield stress and half-life tensile peak stress at the plastic flow initiation point in the stable hysteresis loop can be defined by selecting appropriate equivalent cyclic stress to consider the influence of mean stress. When extended to other high-temperature conditions, temperature-related parameters can be re-identified to adapt to thermal activation kinetics, thus enabling further promotion and application. The application has clear engineering applicability and scalability, facilitating practical application.

[0063] Based on the same inventive concept, one embodiment of the present invention provides a high-temperature fatigue life prediction system for martensitic heat-resistant steel.

[0064] The high-temperature fatigue life prediction system for martensitic heat-resistant steel described in this invention can be installed in electronic devices. According to the functions to be implemented, the high-temperature fatigue life prediction system for martensitic heat-resistant steel includes: The acquisition module is used to acquire low-cycle fatigue test data and microstructure parameters of the material to be evaluated. The low-cycle fatigue test data is the cyclic stress-strain response data generated by strain-controlled low-cycle fatigue test of the material to be evaluated at a preset temperature. The analysis module is used to analyze low-cycle fatigue test data to obtain the flow stress and yield stress of the material to be evaluated. The processing module is used to input flow stress and microstructure parameters into the flow stress decomposition model. The flow stress decomposition model uses a preset algorithm to process the flow stress and microstructure parameters and output dislocation density data. The dislocation annihilation judgment module is used to determine the thermodynamic indicator of dislocation annihilation based on yield stress and microstructure parameters, and to determine whether dislocation annihilation can occur spontaneously thermodynamically based on the thermodynamic indicator of dislocation annihilation, so as to obtain the dislocation annihilation judgment result. The output module is used to determine the dominant cause of cyclic softening based on the dislocation annihilation judgment result, and input the determined dominant cause of cyclic softening, dislocation density data and microstructure parameters into the life prediction model. The life prediction model analyzes the determined dominant cause of cyclic softening, dislocation density data and microstructure parameters, and outputs the fatigue life prediction result of the material to be evaluated.

[0065] The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0066] The various variations and specific examples of the high-temperature fatigue life prediction method for martensitic heat-resistant steel provided in the above embodiments are also applicable to the high-temperature fatigue life prediction system for martensitic heat-resistant steel in this embodiment. Through the foregoing detailed description of the high-temperature fatigue life prediction method for martensitic heat-resistant steel, those skilled in the art can clearly understand the implementation method of the high-temperature fatigue life prediction system for martensitic heat-resistant steel in this embodiment. For the sake of brevity, it will not be described in detail here.

[0067] This application also discloses an electronic device, such as Figure 7 The diagram shown is a schematic representation of an electronic device for a method of predicting the high-temperature fatigue life of martensitic heat-resistant steel according to an embodiment of the present invention. The electronic device may include at least one processor 10, a memory 11 communicatively connected to the at least one processor, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for predicting the high-temperature fatigue life of martensitic heat-resistant steel.

[0068] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., methods for predicting the high-temperature fatigue life of martensitic heat-resistant steel), and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0069] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a method for predicting the high-temperature fatigue life of martensitic heat-resistant steel, but also to temporarily store data that has been output or will be output.

[0070] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0071] Communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0072] Figure 7 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 7 The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0073] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to at least one processor 10 via a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0074] It should be understood that the embodiments are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0075] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile.

[0076] This application provides a computer-readable storage medium, including, for example, any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM). The computer-readable storage medium stores a computer program that can be loaded by a processor and execute the high-temperature fatigue life prediction method for martensitic heat-resistant steel described in the above embodiments.

[0077] In the description of this specification, the references to terms such as "an embodiment," "some embodiments," "example," "specific example," "a implementation," "a preferred implementation," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0078] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for predicting the high-temperature fatigue life of martensitic heat-resistant steel, characterized in that, The method includes: Acquire low-cycle fatigue test data and microstructure parameters of the material to be evaluated. The low-cycle fatigue test data is the cyclic stress-strain response data generated by strain-controlled low-cycle fatigue test of the material to be evaluated at a preset temperature. The low-cycle fatigue test data were analyzed to obtain the flow stress and yield stress of the material to be evaluated. The flow stress and the microstructure parameters are input into the flow stress decomposition model, and the flow stress decomposition model uses a preset algorithm to process the flow stress and the microstructure parameters to output dislocation density data. Based on the yield stress and microstructure parameters, the dislocation annihilation thermodynamic indicator is determined, and based on the dislocation annihilation thermodynamic indicator, it is determined whether dislocation annihilation can occur spontaneously thermodynamically, thus obtaining the dislocation annihilation judgment result. Based on the dislocation annihilation judgment results, the dominant cause of cyclic softening is determined. The determined dominant cause of cyclic softening, dislocation density data, and microstructure parameters are input into the life prediction model. The life prediction model analyzes the determined dominant cause of cyclic softening, dislocation density data, and microstructure parameters, and outputs the fatigue life prediction results of the material to be evaluated.

2. The method for predicting the high-temperature fatigue life of martensitic heat-resistant steel as described in claim 1, characterized in that, The analysis of the low-cycle fatigue test data yields the flow stress and yield stress of the material to be evaluated, including: Extract the flow stress and yield stress from the half-life hysteresis loop under the same strain amplitude condition; The value of the flow stress is the peak tensile stress value of the hysteresis loop corresponding to a specified lifetime ratio; the stress value at the end of the elastic deformation stage is taken as the yield stress based on the stress-strain data of the same hysteresis loop.

3. The method for predicting the high-temperature fatigue life of martensitic heat-resistant steel as described in claim 1, characterized in that, The dislocation density data includes total dislocation density, initial dislocation density, and failed dislocation density; the microstructure parameters include at least one of Taylor factor, precipitate equivalent radius, precipitate volume fraction, dislocation segment annihilation length, and dislocation spacing within the cross-slip plane.

4. The method for predicting the high-temperature fatigue life of martensitic heat-resistant steel as described in claim 3, characterized in that, Taylor factor was calculated based on electron backscatter diffraction orientation data, and the equivalent radius of precipitates, volume fraction of precipitates, dislocation segment annihilation length, and dislocation spacing in cross-slip planes were measured and statistically analyzed based on transmission electron microscopy images.

5. The method for predicting the high-temperature fatigue life of martensitic heat-resistant steel as described in claim 3, characterized in that, The formula for calculating the thermodynamic indicator of dislocation annihilation is: ； in, It is a thermodynamic indicator of dislocation annihilation. For dislocation formation energy, For migration energy, For vacancy auxiliary energy, Contribute to the entropy term; ； ； ； ； in, This represents the shear modulus of martensitic steel at the experimental temperature. The Burgers vector representing martensitic steel. Dislocation segments representing annihilation, Represents the cyclic yield stress that defines the initiation point of plastic flow. It represents the width of the distortion field near the dislocation core. This represents the spacing between parallel dislocations on the cross-slip surface. The temperature function representing vacancy-assisted annihilation. Represents the vacancy formation energy at high temperatures. Represents the Boltzmann constant. represents the vacancy concentration, T represents the experimental temperature, and S represents the statistical entropy of the system during dislocation slip and annihilation.

6. The method for predicting the high-temperature fatigue life of martensitic heat-resistant steel as described in any one of claims 1 to 5, characterized in that, If the dislocation annihilation thermodynamic indicator is negative, the dominant cause of cyclic softening is determined to be the coupling of dynamic recovery and dislocation annihilation; if the dislocation annihilation thermodynamic indicator is positive, the dominant cause of cyclic softening is determined to be dynamic recovery, and dislocation annihilation is suppressed, thus obtaining the dislocation annihilation judgment result.

7. The method for predicting the high-temperature fatigue life of martensitic heat-resistant steel as described in any one of claims 1 to 5, characterized in that, The method further includes: Calculate dynamic recovery rate and dislocation migration rate parameters based on dynamic recovery model; The thermodynamic indicator and dynamic recovery rate of dislocation annihilation are used to determine whether dislocation annihilation can occur spontaneously in thermodynamics, and the result of the dislocation annihilation judgment is obtained. When the thermodynamic indicator of dislocation annihilation is negative and the dynamic recovery rate is positive, the dominant cause of cyclic softening is the coupling of dynamic recovery and dislocation annihilation. When the thermodynamic indicator of dislocation annihilation is positive and the dynamic recovery rate is negative, the dominant cause of cyclic softening is dynamic recovery, and dislocation annihilation is suppressed.

8. A high-temperature fatigue life prediction system for martensitic heat-resistant steel, used to implement the high-temperature fatigue life prediction method for martensitic heat-resistant steel according to any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire low-cycle fatigue test data and microstructure parameters of the material to be evaluated. The low-cycle fatigue test data is the cyclic stress-strain response data generated by the strain-controlled low-cycle fatigue test of the material to be evaluated at a preset temperature. The analysis module is used to analyze the low-cycle fatigue test data to obtain the flow stress and yield stress of the material to be evaluated; The processing module is used to input the flow stress and the microstructure parameters into the flow stress decomposition model, and the flow stress decomposition model uses a preset algorithm to process the flow stress and microstructure parameters to output dislocation density data. The dislocation annihilation judgment module is used to determine the dislocation annihilation thermodynamic indicator based on the yield stress and microstructure parameters, and to determine whether dislocation annihilation can occur spontaneously thermodynamically based on the dislocation annihilation thermodynamic indicator, so as to obtain the dislocation annihilation judgment result. The output module is used to determine the dominant cause of cyclic softening based on the dislocation annihilation judgment result, and input the determined dominant cause of cyclic softening, dislocation density data and microstructure parameters into the life prediction model. The life prediction model analyzes the determined dominant cause of cyclic softening, dislocation density data and microstructure parameters, and outputs the fatigue life prediction result of the material to be evaluated.

9. An electronic device, characterized in that, The electronic device includes: At least one processor (10); and, A memory (11) communicatively connected to the at least one processor (10); The memory (11) stores a computer program that can be executed by the at least one processor (10) to enable the at least one processor (10) to perform the high-temperature fatigue life prediction method for martensitic heat-resistant steel as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program; when the computer program is executed by a processor, it implements the high-temperature fatigue life prediction method for martensitic heat-resistant steel as described in any one of claims 1 to 7.

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