Long-time creep data prediction method and device based on short-time creep data

Abstract

The invention belongs to the technical field of creep prediction, and particularly discloses a long-time creep data prediction method and device based on short-time creep data, and the method comprises the steps: obtaining the steady-state creep rate data of a material at different stress levels through a multi-stage stress staged loading method based on the short-time creep data obtained through a short-time creep test; determining a first fitting parameter value of the creep deformation performance model through nonlinear fitting; determining creep stress indexes under different stress levels based on the steady-state creep rate data and the creep stress; determining creep damage parameters under different stress levels based on the creep stress indexes and a creep damage parameter model; determining a second fitting parameter value of the creep deformation prediction model through nonlinear fitting, and predicting long-time creep deformation of the material; and determining a third fitting parameter value of the creep life prediction model based on the second fitting parameter value, and predicting the long-time creep life of the material. According to the invention, long-time creep performance prediction based on short-time creep data is realized.

Description

Technical Field

[0001] The present application belongs to the technical field of creep prediction, and more specifically, to a method and device for predicting long-term creep data based on short-term creep data. Background Art

[0002] With the development of modern industry, energy demand is growing. In order to increase thermal efficiency and reduce resource loss, modern energy equipment is constantly developing in the direction of high temperature and high pressure, which puts higher requirements on the safety and reliability of equipment. Under high temperature and high pressure service conditions, creep is the main form of high temperature structure damage. Creep refers to the phenomenon that the strain of solid materials increases with time while maintaining constant stress.

[0003] The creep problem under high pressure has been solved well, but the creep deformation and damage under high temperature have not been solved well. Creep under high temperature is a complex phenomenon that depends on time. <10000h) to predict long-term (> Creep data of 100000h) are of vital importance for the design and evaluation of high temperature structures.

[0004] At present, the commonly used creep life prediction methods include isotherm method, parameter method based on rate equation and empirical parameter method. The isotherm method assumes that the logarithmic life varies linearly with the logarithmic stress. Extrapolating this phenomenon in the long-term creep life will lead to a dangerous overestimation tendency. The parameter method based on rate equation assumes that creep is controlled by the rate process. However, experiments show that the rate process will fail as the stress decreases, which limits the degree of extrapolation of this method. The empirical parameter method often uses complex functions to determine the relationship between creep life and temperature or stress through nonlinear fitting, but this method requires more experimental data, and its reliability in extrapolation has not been verified. Therefore, it is urgent to develop a prediction method for long-term creep deformation and life based on short-term creep data to quickly and accurately evaluate the long-term creep performance of materials. Summary of the invention

[0005] In view of the defects existing in the related art, the embodiments of the present application provide a method and device for predicting long-time creep data based on short-time creep data, aiming to solve the problem that the related art has low extrapolation accuracy or relies on long-time creep test results.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting long-term creep data based on short-term creep data, comprising:

[0007] The short-term creep test is carried out by multi-level stress graded loading method to obtain the steady-state creep rate data of the material at different stress levels;

[0008] The first fitting parameter value of the creep deformation performance model is determined by nonlinear fitting; the creep deformation performance model is used to characterize the evolution law of the steady-state creep rate of the material with stress;

[0009] Based on the steady-state creep rate data and creep stress, the creep stress exponent n at different stress levels is determined;

[0010] Based on the creep stress exponent n and the creep damage parameter model, the creep damage parameter β at different stress levels is determined. D ; The creep damage parameter model is used to characterize the creep damage parameter β of the material D The evolution law of creep stress exponent n;

[0011] Based on short-time creep test data, creep deformation performance model and creep damage parameter β D , determining a second fitting parameter value of a creep deformation prediction model by nonlinear fitting, and predicting the long-term creep deformation of the material based on the creep deformation prediction model;

[0012] A third fitting parameter value of the creep life prediction model is determined based on the second fitting parameter value, and the long-term creep life of the material is predicted based on the creep life prediction model, the steady-state creep rate and the creep damage parameter.

[0013] In a second aspect, an embodiment of the present application further provides a long-term creep data prediction device based on short-term creep data, comprising:

[0014] An acquisition module is used to perform short-time creep tests using a multi-stage stress graded loading method to obtain steady-state creep rate data of materials at different stress levels;

[0015] A first determination module is used to determine a first fitting parameter value of a creep deformation performance model by nonlinear fitting; the creep deformation performance model is used to characterize the evolution law of the steady-state creep rate of the material with stress;

[0016] A second determination module is used to determine the creep stress exponent n at different stress levels based on the steady-state creep rate data and the creep stress;

[0017] The third determination module is used to determine the creep damage parameter β at different stress levels based on the creep stress exponent n and the creep damage parameter model. D ; The creep damage parameter model is used to characterize the creep damage parameter β of the material D The evolution law of creep stress exponent n;

[0018] The first prediction module is used to predict the creep damage parameter β based on the short-term creep test data, creep deformation performance model and creep damage parameter β D, determining a second fitting parameter value of a creep deformation prediction model by nonlinear fitting, and predicting the long-term creep deformation of the material based on the creep deformation prediction model;

[0019] The second prediction module is used to determine the third fitting parameter value of the creep life prediction model based on the second fitting parameter value, based on the creep life prediction model, the steady-state creep rate and the creep damage parameter β D , predict the long-term creep life of materials.

[0020] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0021] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0022] In a fifth aspect, an embodiment of the present application further provides a computer program product, which, when executed on a processor, enables the processor to execute the method described in the first aspect or any possible implementation of the first aspect.

[0023] The long-term creep data prediction method and device based on short-term creep data provided in the embodiment of the present application take into account the transformation of creep damage evolution laws under short-term creep conditions and long-term creep conditions through a multi-level stress loading method, and can accurately describe the influence of creep damage aggravation under long-term creep conditions; in the process of long-term creep deformation and life prediction, only short-term creep test data need to be used, without relying on the long-term creep test results of the material; long-term creep deformation and life data can be predicted under low stress levels, with high extrapolation accuracy and good prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the present application or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 It is a flow chart of a method for predicting long-term creep data based on short-term creep data provided in an embodiment of the present application;

[0026] Figure 2 is a schematic diagram of multi-level stress graded loading provided in an embodiment of the present application;

[0027] Figure 3 is a schematic diagram of parameter fitting of the creep deformation performance model provided in the embodiment of the present application;

[0028] Figure 4 is a schematic diagram of parameter fitting of the creep deformation prediction model provided in the embodiment of the present application;

[0029] Figure 5 It is a schematic diagram of the result of long-term creep deformation prediction provided by the embodiment of the present application;

[0030] Figure 6 It is a schematic diagram of the result of long-term creep life prediction provided by the embodiment of the present application;

[0031] Figure 7 It is a structural schematic diagram of a long-term creep data prediction device based on short-term creep data provided in an embodiment of the present application;

[0032] Figure 8 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] Figure 1 is a flow chart of a method for predicting long-term creep data based on short-term creep data provided in an embodiment of the present application, such as Figure 1 As shown, the method at least includes the following steps:

[0035] S101. Perform short-time creep tests using a multi-stage stress loading method to obtain steady-state creep rate data of the material at different stress levels.

[0036] Specifically, creep rate refers to the ratio of creep deformation to time within a certain period of time, that is, the creep deformation per unit time; steady-state creep rate refers to the creep deformation per unit time during the creep process when the creep rate reaches a relatively stable state (that is, the creep rate no longer changes significantly with time).

[0037] By carrying out short-time creep tests using a multi-stage stress graded loading method, the steady-state creep rate data of the material at different stress levels or in a wide stress range can be obtained.

[0038] In some embodiments, the multi-level stress graded loading method in S101 specifically includes the following steps:

[0039] Considering the transition of creep damage evolution law under short-term creep conditions and long-term creep conditions, in order to accurately describe the influence of creep damage aggravation under long-term creep conditions, stresses are loaded in order from small to large. After the creep test at each stress level reaches the steady-state creep stage, the creep test at the next stress level is entered to obtain the steady-state creep rate data of the material at different stress levels. The steady-state creep stage refers to uniform creep at a relatively slow rate under constant stress and temperature.

[0040] In some embodiments, the graded stress level is greater than 10% σ y , σ y is the yield strength of the material.

[0041] S102, determining a first fitting parameter value of a creep deformation performance model by nonlinear fitting.

[0042] Specifically, nonlinear fitting is performed on the steady-state creep rate data of the material at different stress levels acquired in S101, so as to determine the first fitting parameter value of the creep deformation performance model.

[0043] The creep deformation performance model, also called the steady-state creep rate constitutive equation, is used to characterize the evolution law of the material's steady-state creep rate with stress. The steady-state creep rate data of the material at different stress levels is nonlinearly fitted to determine the first fitting parameter value of the creep deformation performance model, which includes creep stress exponents n1 and n2 and creep stress coefficients A1 and A2.

[0044] In some embodiments, the creep deformation performance model specifically satisfies the following calculation formula:

[0045]

[0046] in, is the steady-state creep rate, n1 and n2 are the fitted creep stress exponents, A1 and A2 are the fitted creep stress coefficients, and σ is the creep stress.

[0047] S103. Determine the creep stress exponent n at different stress levels based on the steady-state creep rate data and the creep stress.

[0048] Specifically, the creep rate is a physical quantity that describes how fast the strain changes during the creep process, the creep stress is the stress that causes the creep phenomenon of the material, and the creep stress exponent is a parameter that describes the relationship between the creep stress and the creep rate.

[0049] In some embodiments, the creep stress index n specifically satisfies the following calculation formula:

[0050]

[0051] in, is the steady-state creep rate and σ is the creep stress.

[0052] S104, based on the creep stress exponent n and the creep damage parameter model, determine the creep damage parameter β under the creep damage parameter n at different stress levels D .

[0053] Specifically, the creep damage parameter model is used to characterize the creep damage parameter β of the material. D Following the evolution law of the creep stress exponent n, after the creep stress exponent n is calculated in S103, the creep damage parameter β can be solved according to the creep damage parameter model. D , creep damage parameter β D The influence of creep damage on creep life under different creep mechanisms is described.

[0054] In some embodiments, the creep damage parameter model specifically satisfies the following calculation formula:

[0055] β D =0.3034exp(-0.1023n)+0.5031exp(-0.3519n)+0.1634exp(-0.01213n)

[0056] Among them, β D is the creep damage parameter, and n is the creep stress exponent.

[0057] S105, based on short-time creep test data, creep deformation performance model and creep damage parameter β D , a second fitting parameter value of the creep deformation prediction model is determined by nonlinear fitting, and the long-term creep deformation of the material is predicted based on the creep deformation prediction model.

[0058] Specifically, using short-term creep test data, combined with the creep deformation performance model and creep damage parameter β D , the second fitting parameter value of the creep deformation prediction model is determined by nonlinear fitting. The creep deformation performance model uses the creep deformation performance of the material and the short-time creep test data to predict the long-time creep deformation data.

[0059] In some embodiments, the creep deformation prediction model specifically satisfies the following calculation formula:

[0060]

[0061] in, is the creep rate, is the steady-state creep rate, n is the creep stress exponent, is the creep damage rate, βs and κ are the second fitting parameter values, also called the fitting parameter values ​​of creep damage, t is the creep time, and σ is the creep stress.

[0062] In some embodiments, the minimum creep stress in the short-time creep test data in S105 should be higher than the minimum creep stress in the steady-state creep rate data in S101.

[0063] S106. Determine a third fitting parameter value of the creep life prediction model based on the second fitting parameter value, and predict the long-term creep life of the material based on the creep life prediction model, the steady-state creep rate, and the creep damage parameter.

[0064] Specifically, creep life refers to the length of time a material can withstand creep deformation at elevated temperatures.

[0065] In some embodiments, the creep life prediction model specifically satisfies the following calculation formula:

[0066]

[0067] Among them, t f is the creep life, β A and β n is the third fitting parameter value, σ is the creep stress, is the steady-state creep rate, β D is the creep damage parameter.

[0068] In some embodiments, in S106, a third fitting parameter value of the creep life prediction model is determined based on the second fitting parameter value, specifically satisfying the following calculation formula:

[0069]

[0070] In some embodiments, when performing long-term creep deformation prediction and long-term creep life prediction, the minimum stress of the predicted stress range should be higher than the minimum creep stress in the steady-state creep rate data in S101.

[0071] In some embodiments, during the process of performing the long-term creep data prediction method based on short-term creep data, the material is heat-resistant martensitic steel or austenitic steel, and the service temperature is 500-700°C.

[0072] The long-term creep data prediction method based on short-term creep data provided in the embodiment of the present application takes into account the transformation of creep damage evolution under short-term creep conditions and long-term creep conditions through a multi-level stress loading method, and can accurately describe the impact of creep damage aggravation under long-term creep conditions; in the process of long-term creep deformation and life prediction, only short-term creep test data needs to be used, without relying on the long-term creep test results of the material; long-term creep deformation and life data under low stress levels can be predicted, with high extrapolation accuracy and good prediction accuracy.

[0073] In order to verify the prediction accuracy of the long-term creep data prediction method based on short-term creep data provided in the embodiment of the present application, a uniaxial round rod specimen was used for verification. The material used for verification was P92 heat-resistant steel, and the uniaxial creep test was carried out at 700°C.

[0074] Figure 2 is a schematic diagram of multi-level stress graded loading provided in an embodiment of the present application, such as Figure 2 As shown in the figure, stresses are loaded in order from small to large. After the creep test at each stress level reaches the steady-state creep stage, the creep test at the next stress level is entered to obtain the steady-state creep rate data of the material at different stress levels.

[0075] Figure 3 Schematic diagram of parameter fitting of the creep deformation performance model provided in the embodiment of the present application. Figure 3 As shown, nonlinear fitting is performed on the steady-state creep rate data of the material at different stress levels to determine the first fitting parameter value of the creep deformation performance model, and the first fitting parameter value includes creep stress exponents n1 and n2 and creep stress coefficients A1 and A2.

[0076] Figure 4 is a schematic diagram of parameter fitting of the creep deformation prediction model provided in the embodiment of the present application, such as Figure 4 As shown in the figure, using short-term creep test data, combined with the creep deformation performance model and creep damage parameter β D , fitting determines the second fitting parameter value β of the creep deformation prediction model s and κ.

[0077] Figure 5 is a schematic diagram of the result of long-term creep deformation prediction provided by the embodiment of the present application, Figure 6 FIG. 1 is a schematic diagram of the long-term creep life prediction results provided in the embodiment of the present application. The long-term creep deformation and life prediction results at 700°C are shown in FIG. Figure 5 and Figure 6 As shown, the creep deformation and life prediction results under long-term creep conditions are good.

[0078] Figure 7is a schematic diagram of the structure of a long-term creep data prediction device based on short-term creep data provided in an embodiment of the present application, such as Figure 7 As shown, the device at least includes:

[0079] An acquisition module 701 is used to acquire steady-state creep rate data of a material at different stress levels by a multi-stage stress graded loading method based on short-time creep data acquired from a short-time creep test;

[0080] A first determination module 702 is used to determine a first fitting parameter value of a creep deformation performance model by nonlinear fitting; the creep deformation performance model is used to characterize the evolution law of the steady-state creep rate of the material with stress;

[0081] A second determination module 703 is used to determine the creep stress exponent n at different stress levels based on the steady-state creep rate data and the creep stress;

[0082] The third determination module 704 is used to determine the creep damage parameter β at different stress levels based on the creep stress exponent n and the creep damage parameter model. D ; The creep damage parameter model is used to characterize the creep damage parameter β of the material D The evolution law of creep stress exponent n;

[0083] The first prediction module 705 is used to predict the creep damage parameter β based on the short-term creep test data, the creep deformation performance model and the creep damage parameter β. D , determining a second fitting parameter value of a creep deformation prediction model by nonlinear fitting, and predicting the long-term creep deformation of the material based on the creep deformation prediction model;

[0084] The second prediction module 706 is used to determine the third fitting parameter value of the creep life prediction model based on the second fitting parameter value, based on the creep life prediction model, the steady-state creep rate and the creep damage parameter β D , predict the long-term creep life of materials.

[0085] In some embodiments, the multi-level stress loading method includes:

[0086] The stresses are loaded sequentially from small to large. After the creep test at each stress level reaches the steady-state creep stage, the creep test at the next stress level is entered to obtain the steady-state creep rate data of the material at different stress levels.

[0087] In some embodiments, the graded stress level is greater than 10% σ y , σ y is the yield strength of the material.

[0088] In some embodiments, the creep deformation performance model satisfies the following calculation formula:

[0089]

[0090] in, is the steady-state creep rate, n1 and n2 are the fitted creep stress exponents, A1 and A2 are the fitted creep stress coefficients, and σ is the creep stress.

[0091] In some embodiments, the creep stress exponent n at different stress levels is determined to satisfy the following calculation formula:

[0092]

[0093] in, is the steady-state creep rate and σ is the creep stress.

[0094] In some embodiments, the creep damage parameter model satisfies the following calculation formula:

[0095] β D =0.3034exp(-0.1023n)+0.5031exp(-0.3519n)+0.1634exp(-0.01213n)

[0096] Among them, β D is the creep damage parameter, and n is the creep stress exponent.

[0097] In some embodiments, the creep deformation prediction model satisfies the following calculation formula:

[0098]

[0099] in, is the creep rate, is the steady-state creep rate, n is the creep stress exponent, is the creep damage rate, β s and κ are the second fitting parameter values, t is the creep time, and σ is the creep stress.

[0100] In some embodiments, the creep life prediction model satisfies the following calculation formula:

[0101]

[0102] Among them, t f is the creep life, β A and β n is the third fitting parameter value, σ is the creep stress, is the steady-state creep rate, β D is the creep damage parameter.

[0103] In some embodiments, the third fitting parameter value of the creep life prediction model is determined based on the second fitting parameter value, satisfying the following calculation formula:

[0104]

[0105] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0106] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0107] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device. The device may include: at least one memory for storing programs and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the above embodiment.

[0108] Figure 8 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 8 As shown, the electronic device may include: a processor 801, a communication interface 802, a memory 803 and a communication bus 804, wherein the processor 801, the communication interface 802 and the memory 803 communicate with each other through the communication bus 804. The processor 801 may call the software instructions in the memory 803 to execute the method described in the above embodiment.

[0109] In addition, the logic instructions in the above-mentioned memory 803 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the relevant technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present application.

[0110] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0111] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0112] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0113] The method steps in the embodiments of the present application can be implemented by hardware, or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (Random Access Memory, RAM), flash memory, read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically erasable programmable read-only memory (Electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0114] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0115] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0116] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for predicting long-term creep data based on short-term creep data, characterized in that: include: The short-term creep test is carried out by multi-level stress graded loading method to obtain the steady-state creep rate data of the material at different stress levels; Determining a first fitting parameter value of a creep deformation performance model by nonlinear fitting, wherein the creep deformation performance model is used to characterize the evolution law of the steady-state creep rate of the material with stress; Determining creep stress exponent n at different stress levels based on the steady-state creep rate data and creep stress; Based on the creep stress exponent n and the creep damage parameter model, the creep damage parameter β at different stress levels is determined. D ; The creep damage parameter model is used to characterize the creep damage parameter β of the material D The evolution law of creep stress exponent n; Based on the short-time creep test data, the creep deformation performance model and the creep damage parameter β D , determining a second fitting parameter value of a creep deformation prediction model by nonlinear fitting, and predicting the long-term creep deformation of the material based on the creep deformation prediction model; The third fitting parameter value of the creep life prediction model is determined based on the second fitting parameter value, and the third fitting parameter value of the creep life prediction model is determined based on the creep life prediction model, the steady-state creep rate and the creep damage parameter β D , predict the long-term creep life of materials.

2. The long-term creep data prediction method according to claim 1, characterized in that: The multi-stage stress graded loading method comprises: The stresses are loaded sequentially from small to large. After the creep test at each stress level reaches the steady-state creep stage, the creep test at the next stress level is entered to obtain the steady-state creep rate data of the material at different stress levels.

3. The long-term creep data prediction method according to claim 2, characterized in that: Graded stress level greater than 10%σ y , σ y is the yield strength of the material.

4. The long-term creep data prediction method according to claim 1, characterized in that: The creep deformation performance model satisfies the following calculation formula: in, is the steady-state creep rate, n1 and n2 are the fitted creep stress exponents, A1 and A2 are the fitted creep stress coefficients, and σ is the creep stress.

5. The long-term creep data prediction method according to claim 1, characterized in that: The creep stress exponent n under different stress levels is determined to satisfy the following calculation formula: in, is the steady-state creep rate and σ is the creep stress.

6. The long-term creep data prediction method according to claim 5, characterized in that: The creep damage parameter model satisfies the following calculation formula: β D =0.3034exp(-0.1023n)+0.5031exp(-0.3519n)+0.1634exp(-0.01213n) Among them, β D is the creep damage parameter, and n is the creep stress exponent.

7. The long-term creep data prediction method according to claim 1, characterized in that: The creep deformation prediction model satisfies the following calculation formula: in, is the creep rate, is the steady-state creep rate, n is the creep stress exponent, is the creep damage rate, β s and κ are the second fitting parameter values, t is the creep time, and σ is the creep stress.

8. The long-term creep data prediction method according to claim 7, characterized in that: The creep life prediction model satisfies the following calculation formula: Among them, t f is the creep life, β A and β n is the third fitting parameter value, σ is the creep stress, is the steady-state creep rate, β D is the creep damage parameter.

9. The long-term creep data prediction method according to claim 8, characterized in that: The third fitting parameter value of the creep life prediction model is determined based on the second fitting parameter value, and satisfies the following calculation formula: Among them, β A and β n is the third fitting parameter value.

10. A long-term creep data prediction device based on short-term creep data, characterized in that: include: An acquisition module is used to perform short-time creep tests using a multi-stage stress graded loading method to obtain steady-state creep rate data of materials at different stress levels; A first determination module, used to determine a first fitting parameter value of a creep deformation performance model by nonlinear fitting; The creep deformation performance model is used to characterize the evolution law of the steady-state creep rate of the material with stress; A second determination module is used to determine the creep stress exponent n at different stress levels based on the steady-state creep rate data and the creep stress; The third determination module is used to determine the creep damage parameter β at different stress levels based on the creep stress exponent n and the creep damage parameter model. D ; The creep damage parameter model is used to characterize the creep damage parameter β of the material D The evolution law of creep stress exponent n; The first prediction module is used to predict the creep damage parameter β based on the short-time creep test data, the creep deformation performance model and the creep damage parameter β. D , determining a second fitting parameter value of a creep deformation prediction model by nonlinear fitting, and predicting the long-term creep deformation of the material based on the creep deformation prediction model; The second prediction module is used to determine a third fitting parameter value of the creep life prediction model based on the second fitting parameter value, based on the creep life prediction model, the steady-state creep rate and the creep damage parameter β D , predict the long-term creep life of materials.

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