Method and device for predicting long-term creep data based on short-term creep data

Abstract

This application belongs to the technical field of creep prediction, and specifically discloses a method and device for predicting long-term creep data based on short-term creep data. The method includes: obtaining steady-state creep rate data of a material at different stress levels through a multi-level stress step loading method based on the short-term creep data obtained from short-term creep tests; determining the first fitting parameter value of the creep deformation performance model through non-linear fitting; determining the creep stress exponent at different stress levels based on the steady-state creep rate data and creep stress; determining the creep damage parameter at different stress levels based on the creep stress exponent and the creep damage parameter model; determining the second fitting parameter value of the creep deformation prediction model through non-linear fitting to predict the long-term creep deformation of the material; and determining the third fitting parameter value of the creep life prediction model based on the second fitting parameter value to predict the long-term creep life of the material. This application realizes the prediction of long-term creep performance based on short-term creep data.

Description

Technical Field

[0001] This application belongs to the technical field of creep prediction, and more specifically, relates 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, the energy demand is increasing day by day. In order to increase the thermal efficiency and reduce the resource loss, modern energy equipment is constantly developing towards high temperature and high pressure, which puts forward higher requirements for the safety and reliability of the equipment. Under the service conditions of high temperature and high pressure, creep is the most main form of high temperature structure damage. Creep refers to the phenomenon that the strain of solid materials increases with the extension of time under the condition of constant stress.

[0003] The creep problem under high pressure has been well solved at present, but the creep deformation and damage at high temperature have not been well solved. Creep at high temperature is a complex phenomenon dependent on time. Using the creep data obtained in a relatively short time (experimental time <10000h) to predict the creep data of long time (>100000h) is of crucial significance for the design and evaluation of high temperature structures.

[0004] Currently, the commonly used creep life prediction methods include the isothermal line method, the parameter method based on the rate equation, and the empirical parameter method. The isothermal line method believes that the logarithmic life changes linearly with the logarithmic stress. This kind of extrapolation will lead to a dangerous overestimation tendency in the long-term creep life. The parameter method based on the rate equation assumes that creep is controlled by the rate process. However, experiments show that the rate process will fail with the decrease of stress, which limits the extrapolation degree of this method. The empirical parameter method often uses complex functions to determine the relationship between creep life and temperature or stress through non-linear fitting. However, this method requires more experimental data, and the reliability during extrapolation has not been verified. Therefore, it is urgent to develop a method for predicting 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 technology, the embodiments of this application provide a method and device for predicting long-term creep data based on short-term creep data, aiming to solve the problems of low extrapolation accuracy or dependence on the results of long-term creep tests in the related technology.

[0006] In a first aspect, the embodiments of this application provide a method for predicting long-term creep data based on short-term creep data, including:

[0007] Performing a short-term creep test by a multi-stage stress grading loading method to obtain the steady-state creep rate data of the material at different stress levels;

[0008] Determine the first fitting parameter value of the creep deformation performance model through non - linear 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, determine the creep stress exponent n at different stress levels;

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

[0011] Based on the short - term creep test data, the creep deformation performance model and the creep damage parameter β D , determine the second fitting parameter value of the creep deformation prediction model through non - linear fitting, and predict the long - term creep deformation of the material based on the creep deformation prediction model;

[0012] Based on the second fitting parameter value, determine the third fitting parameter value of the creep life prediction model, 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.

[0013] In a second aspect, the embodiments of the present application further provide a long - term creep data prediction device based on short - term creep data, including:

[0014] An acquisition module, configured to perform a short - term creep test through a multi - level stress - grading loading method to obtain the steady - state creep rate data of the material at different stress levels;

[0015] A first determination module, configured to determine the first fitting parameter value of the creep deformation performance model through non - linear 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, configured to determine the creep stress exponent n at different stress levels based on the steady - state creep rate data and creep stress;

[0017] A third determination module, configured 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 evolution law of the creep damage parameter β of the material D with the creep stress exponent n;

[0018] A first prediction module, configured to be based on the short - term creep test data, the creep deformation performance model and the creep damage parameter β D, determining the second fitting parameter value of the creep deformation prediction model through non - linear fitting, and predicting the long - term creep deformation of the material based on the creep deformation prediction model;

[0019] A second prediction module, configured to determine the 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 β D , for predicting the long - term creep life of the material.

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

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

[0022] In a fifth aspect, an embodiment of the present application further provides a computer program product. When the computer program product runs on a processor, the processor is caused to execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0023] The long - term creep data prediction method and device based on short - term creep data provided by the embodiments of the present application, through the multi - level stress loading method, consider the transformation of the creep damage evolution law under short - term creep conditions and long - term creep conditions, and can accurately describe the influence of the aggravation of creep damage under long - term creep conditions; only short - term creep test data are required in the process of predicting long - term creep deformation and life, without relying on the long - term creep test results of the material; it can predict the long - term creep deformation and life data under low stress levels, with high extrapolation accuracy and good prediction accuracy. Description of the Drawings

[0024] To more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the following - described drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 is a schematic flow chart of the long - term creep data prediction method based on short - term creep data provided by the embodiment of the present application;

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

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

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

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

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

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

[0032] Figure 8 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0033] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to 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 It is a schematic diagram of the process of a long - term creep - data prediction method based on short - term creep data provided by an embodiment of the present application. As Figure 1 shown, the method at least includes the following steps (Step):

[0035] S101. Conduct a short - term creep test by the multi - level stress - grading loading method to obtain the steady - state creep - rate data of the material at different stress levels.

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

[0037] Conduct a short - term creep test by the multi - level stress - grading loading method to obtain the steady - state creep - rate data of the material at different stress levels or within a wide stress range.

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

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

[0040] In some embodiments, the stress levels of the stress grading are all greater than 10%σ y , σ y is the yield strength of the material.

[0041] S102. Determine the first fitting parameter values of the creep deformation performance model through non-linear fitting.

[0042] Specifically, non-linear fitting is performed on the steady-state creep rate data of the material obtained in S101 at different stress levels, so as to determine the first fitting parameter values 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 steady-state creep rate of the material with stress. Non-linear fitting is performed on the steady-state creep rate data of the material at different stress levels, and the first fitting parameter values of the creep deformation performance model are determined. The first fitting parameter values include the creep stress exponents n1 and n2 and the creep stress coefficients A1 and A2.

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

[0045]

[0046] Among them, 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 describing the speed of strain change during the creep process, the creep stress is the stress that causes the creep phenomenon of the material; the creep stress exponent is a parameter describing the relationship between the creep stress and the creep rate.

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

[0050]

[0051] Among them, 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 at different stress levels under the creep damage parameter n D .

[0053] Specifically, the creep damage parameter model is used to characterize the evolution law of the creep damage parameter β of the material. 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 . The creep damage parameter β D describes the influence of creep damage on creep life under different creep mechanisms. D In some embodiments, the creep damage parameter model specifically satisfies the following calculation formula:

[0054] β

[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 the short-term creep test data, the creep deformation performance model and the creep damage parameter β D , determine the second fitting parameter value of the creep deformation prediction model through nonlinear fitting, and predict the long-term creep deformation of the material based on the creep deformation prediction model.

[0058] Specifically, use the short-term creep test data, combine the creep deformation performance model and the creep damage parameter β D , and determine the second fitting parameter value of the creep deformation prediction model through nonlinear fitting. The creep deformation performance model uses the creep deformation performance of the material and the short-term creep test data to predict the long-term creep deformation data.

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

[0060]

[0061] Among them, is the creep rate, is the steady-state creep rate, n is the creep stress exponent, is the creep damage rate, βs α and κ are the second fitting parameter values, also known as 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-term 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 the 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, the creep life refers to the time length that the material can withstand creep deformation at high temperature.

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

[0066]

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

[0068] In some embodiments, the third fitting parameter value of the creep life prediction model determined based on the second fitting parameter value in S106 specifically satisfies 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 in 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 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 7This is a schematic structural diagram of a long-term creep data prediction device based on short-term creep data provided by an embodiment of the present application. As Figure 7 shown, the device at least includes:

[0079] An acquisition module 701, configured to obtain steady-state creep rate data of a material at different stress levels by a multi-level stress step loading method based on short-term creep data obtained from a short-term creep test;

[0080] A first determination module 702, configured to determine a first fitting parameter value of a creep deformation performance model by non-linear 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, configured to determine a creep stress exponent n at different stress levels based on the steady-state creep rate data and creep stress;

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

[0083] A first prediction module 705, configured to determine a second fitting parameter value of a creep deformation prediction model by non-linear fitting based on short-term creep test data, a creep deformation performance model, and the creep damage parameter β D , and predict the long-term creep deformation of the material based on the creep deformation prediction model;

[0084] A second prediction module 706, configured to determine a third fitting parameter value of a 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 β D .

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

[0086] Loading stresses in ascending order, and after the creep test at each stress level reaches the steady-state creep stage, entering the creep test at the next stress level to obtain the steady-state creep rate data of the material at different stress levels.

[0087] In some embodiments, the stepped 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] Among them, 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] Among them, 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] Among them, 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 fitted 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 are the third fitted parameter values, σ is the creep stress, is the steady-state creep rate, β D is the creep damage parameter.

[0103] In some embodiments, a 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:

[0104]

[0105] It can be understood that the detailed function implementation of each of the above units / modules can be referred to the introduction in the foregoing method embodiments, and will not be elaborated herein.

[0106] It should be understood that the above device is used to execute the method in the above embodiments. For the corresponding program modules in the device, their implementation principles and technical effects are similar to those described in the above method. The working process of the device can refer to the corresponding process in the above method, and will not be elaborated herein.

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

[0108] Figure 8 is a schematic structural diagram of the electronic device provided by the embodiment of the present application. As Figure 8 shown, the electronic device may include: a processor 801, a communication interface 802, a memory 803, and a communication bus 804. Among them, the processor 801, the communication interface 802, and the memory 803 complete mutual communication through the communication bus 804. The processor 801 can call software instructions in the memory 803 to execute the method described in the above embodiments.

[0109] In addition, when the logical instructions in the above-mentioned memory 803 are implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the related technology, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application.

[0110] Based on the method in the above embodiments, an embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program runs on a processor, the processor is caused to execute the method in the above embodiments.

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

[0112] It can be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), 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 may be implemented in a hardware manner or by a processor executing software instructions. The software instructions may be composed of corresponding software modules. The software modules may be stored in a random access memory (RAM), flash memory, read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium well 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 write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

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

[0116] Those skilled in the art can easily understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope 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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