Steel aging process numerical simulation method and system

Abstract

The invention provides a steel aging process numerical simulation method and system.The method comprises the steps that an initial constitutive model of creep fatigue of a to-be-tested steel material is built, and material parameters of the initial constitutive model comprise isotropic hardening parameters, follow-up hardening parameters and viscous stress parameters; the method comprises the following steps: performing a creep fatigue interaction experiment on a to-be-tested steel material at a preset temperature to obtain experimental data under the conditions of preset loading time, constant strain rate and constant strain amplitude, including a cumulative inelastic strain curve, a tensile curve and a stress relaxation curve; constructing a mechanical behavior model of the to-be-tested steel material according to the material parameters, and performing fitting by utilizing an accumulated inelastic strain curve, a tensile curve and a stress relaxation curve to obtain a target constitutive model; and utilizing the target constitutive model to perform aging numerical simulation on the to-be-tested steel material to obtain a change curve of stress along with time. And the aging process of the steel is simulated based on experimental data and a creep fatigue mechanism, so that the accuracy of aging evaluation and life prediction of the steel is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of material life prediction, and particularly to a numerical simulation method and system for the steel aging process. Background Art

[0002] With the continuous progress of industrial technology, higher requirements are put forward for materials in equipment in industries such as supercritical and ultrasupercritical thermal power generation and petrochemical industries. As a high-performance ferritic heat-resistant steel, the 9 chromium series steel has been widely used in these high-temperature and high-pressure environments due to its excellent high-temperature strength, creep resistance and good oxidation resistance, such as P92 steel in 9Cr-1Mo steel. However, in high-temperature and long-term complex stress environments, creep-fatigue interaction occurs in this series of steel, resulting in a significant decline in its mechanical properties and service life. Therefore, it is of great significance to conduct in-depth research and accurate simulation on the creep-fatigue behavior of this series of steel.

[0003] In related technologies, although various viscoplastic models and modified models can more accurately reflect the creep-fatigue characteristics of steel, the key challenge lies in whether accurate numerical values of model parameters can be obtained to accurately describe the creep and fatigue mechanical behaviors of steel materials. For the more complex alloy steel of the 9 chromium series, due to its complex composition and microstructure, diverse aging mechanisms are exhibited under different service conditions, and the relevant model parameters cannot be accurately obtained, making the simulation of its creep-fatigue deformation behavior still face great challenges and uncertainties, resulting in increased difficulty in predicting creep and fatigue behaviors, thus affecting the accuracy of aging assessment and life prediction of the 9 chromium series steel. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a comprehensive review, nor is it intended to identify key / important elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the subsequent detailed description.

[0005] In view of the above-mentioned disadvantages of the prior art, the present invention discloses a numerical simulation method and system for the steel aging process, which is used to solve the technical problem of low accuracy in steel aging assessment and life prediction in the prior art.

[0006] In a first aspect, the present application provides a numerical simulation method for the aging process of steel. The method includes: constructing an initial constitutive model for the creep fatigue of the steel material to be tested, where the material parameters of the initial constitutive model include an isotropic hardening parameter, a kinematic hardening parameter, and a viscous stress parameter; conducting a creep-fatigue interaction experiment on the steel material to be tested at a preset temperature to obtain experimental data under conditions of a preset loading time, a constant strain rate, and a constant strain amplitude, where the experimental data includes a cumulative inelastic strain curve, a tensile curve, and a stress relaxation curve; constructing a mechanical behavior model for the steel material to be tested based on the material parameters, and using the cumulative inelastic strain curve, the tensile curve, and the stress relaxation curve to fit the material parameters to obtain a target constitutive model; using the target constitutive model to perform an aging numerical simulation on the steel material to be tested to obtain a curve of stress varying with time.

[0007] In an embodiment of the present invention, the constructing a mechanical behavior model for the steel material to be tested based on the material parameters includes: constructing a first relationship model between the isotropic hardening parameter and the cumulative inelastic strain, where the isotropic hardening parameter includes an isotropic hardening variable and an isotropic hardening coefficient; constructing a second relationship model based on the kinematic hardening parameter, stress, and inelastic strain, where the kinematic hardening parameter includes a kinematic hardening variable, a first kinematic hardening coefficient, and a second kinematic hardening coefficient; constructing a third relationship model between the viscous stress and the cumulative inelastic strain rate based on the viscous stress parameter, where the viscous stress parameter includes a first viscous stress parameter and a second viscous stress parameter; and constituting the mechanical behavior model based on the first relationship model, the second relationship model, and the third relationship model.

[0008] In an embodiment of the present invention, the first relationship model is: R = Q(1 - e -bp ) + Hp, where R represents the isotropic hardening variable, p represents the cumulative inelastic strain, and Q, b, H represent the isotropic hardening coefficients; the second relationship model is: where ζ (i) represents the kinematic hardening variable, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, ε in represents the inelastic strain, σ represents the stress, and R represents the isotropic hardening variable; the third relationship model is: where σ v represents the viscous stress, represents the cumulative inelastic strain rate, Z represents the first viscous stress parameter, and A represents the second viscous stress parameter.

[0009] In an embodiment of the present invention, the fitting method of the isotropic hardening parameter includes: obtaining the peak stress, and constructing a first relationship between the isotropic hardening variable and the stress according to the peak stress, where the peak stress is the maximum stress within the first cycle of the cumulative inelastic strain curve; constructing a second relationship between the stress and the cumulative inelastic strain according to the first relationship and the first relationship model; using the second relationship to fit the cumulative inelastic strain curve to obtain the isotropic hardening coefficient, and substituting the isotropic hardening coefficient into the first relationship model to obtain the isotropic hardening variable, thereby completing the fitting of the isotropic hardening parameter. The horizontal axis and the vertical axis of the cumulative inelastic strain curve respectively represent the cumulative inelastic strain and the stress.

[0010] In an embodiment of the present invention, the fitting method of the kinematic hardening parameter includes: obtaining a pre-constructed third relationship and a fourth relationship, where the third relationship is the relationship between the principal strain and the stress, and the fourth relationship is the relationship between the inelastic strain and the stress; according to the third relationship and the fourth relationship, converting the tensile curve into a relationship curve between the inelastic strain and the stress, where the horizontal axis and the vertical axis of the tensile curve respectively represent the strain and the stress; using the inelastic strain equation in the second relationship model to fit the relationship curve between the inelastic strain and the stress to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient into the kinematic hardening variable equation in the second relationship model to obtain the kinematic hardening variable, thereby completing the fitting of the kinematic hardening parameter.

[0011] In an embodiment of the present invention, the fitting method of the kinematic hardening parameter further includes: based on the yield state of the steel material to be tested, dividing the hardening stage during the tensile process into an initial hardening stage and a later hardening stage; constructing a first inelastic strain equation for the initial hardening stage and a second inelastic strain equation for the later hardening stage; using the first inelastic strain equation to fit the relationship curve between the inelastic strain and the stress to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage into the kinematic hardening variable equation to obtain the kinematic hardening variable for the initial hardening stage; substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage into the second inelastic strain equation to fit the relationship curve between the inelastic strain and the stress to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient for the later hardening stage, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the later hardening stage into the kinematic hardening variable equation to obtain the kinematic hardening variable for the later hardening stage, thereby completing the fitting of the kinematic hardening parameter.

[0012] In an embodiment of the present invention, the fitting method of the viscous stress parameter includes: obtaining a pre-constructed fifth relationship and a sixth relationship, where the fifth relationship is the relationship between the cumulative inelastic strain rate and the loading time, and the sixth relationship is the relationship between the stress and the loading time; according to the fifth relationship and the sixth relationship, converting the stress relaxation curve into a relationship curve between the viscous stress and the cumulative inelastic strain rate, where the horizontal axis and the vertical axis of the stress relaxation curve respectively represent the stress and the loading time; using the third relationship model to fit the relationship curve between the viscous stress and the cumulative inelastic strain rate to obtain the first viscous stress parameter and the second viscous stress parameter, thus completing the fitting of the viscous stress parameter.

[0013] In an embodiment of the present invention, the expression of the target constitutive model is: where ε represents the principal strain, ε e represents the elastic strain, ε in represents the inelastic strain, σ represents the stress, D represents the stiffness tensor of the steel material to be measured, represents the inelastic strain rate, Z represents the first viscous stress parameter, A represents the second viscous stress parameter, F y represents the yield function, ζ represents the total kinematic hardening variable, s represents the stress deviator, R represents the isotropic hardening variable, k represents the initial elastic limit, M represents the number of iterations, ζ (i) represents the kinematic hardening variable, represents the kinematic hardening rate, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, represents the cumulative inelastic strain rate, represents the isotropic hardening rate, and b and Q represent the isotropic hardening coefficients.

[0014] In an embodiment of the present invention, the use of the target constitutive model to perform an aging numerical simulation on the steel material to be measured to obtain the stress-time change curve includes: discretizing the target constitutive model in terms of the loading time to obtain a discretized constitutive model; constructing a solution equation for the discretized constitutive model, performing numerical calculations on the discretized constitutive model, and continuously iterating the solution equation to simulate the numerical process of steel aging to obtain the stress-time change curve.

[0015] Second aspect, the present application provides a numerical simulation system for the steel aging process. The system includes: a model construction module for constructing an initial constitutive model of the creep fatigue of the steel material to be tested, where the material parameters of the initial constitutive model include isotropic hardening parameters, kinematic hardening parameters, and viscous stress parameters; an experimental data acquisition module for performing a creep-fatigue interaction experiment on the steel material to be tested at a preset temperature to obtain experimental data under conditions of a preset loading time, a constant strain rate, and a constant strain amplitude, where the experimental data includes a cumulative inelastic strain curve, a tensile curve, and a stress relaxation curve; a parameter fitting module for constructing a mechanical behavior model of the steel material to be tested according to the material parameters and using the cumulative inelastic strain curve, the tensile curve, and the stress relaxation curve to fit the material parameters to obtain a target constitutive model; and a numerical simulation module for using the target constitutive model to perform an aging numerical simulation on the steel material to be tested to obtain a curve of stress varying with time.

[0016] Third aspect, the present application provides an electronic device, characterized in that the electronic device includes: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the numerical simulation method for the steel aging process described in the first aspect.

[0017] Fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor of a computer, the computer executes the numerical simulation method for the steel aging process described in the first aspect.

[0018] As described above, a numerical simulation method and system for the steel aging process provided by the embodiments of the present invention have the following beneficial effects:

[0019] First, an initial constitutive model for the creep fatigue of the steel material to be tested is constructed, and its material parameters include isotropic hardening parameters, kinematic hardening parameters, and viscous stress parameters. Then, a creep-fatigue interaction experiment is conducted on the steel material to be tested at a preset temperature to obtain experimental data under the conditions of a preset loading time, a constant strain rate, and a constant strain amplitude, including the cumulative inelastic strain curve, the tensile curve, and the stress relaxation curve. Next, a mechanical behavior model of the steel material to be tested is constructed based on the material parameters, and the material parameters are fitted using the cumulative inelastic strain curve, the tensile curve, and the stress relaxation curve to obtain the target constitutive model. Finally, using the target constitutive model, an aging numerical simulation is performed on the steel material to be tested to obtain the curve of stress varying with time. First, an initial constitutive model for the creep fatigue of the steel material to be tested is constructed according to the creep-fatigue mechanism, and then the material parameters in the model are accurately fitted based on the creep-fatigue interaction experiment, solving the problem that the relevant parameters of the model cannot be accurately obtained. Thus, a target constitutive model with high accuracy can be obtained to achieve accurate numerical simulation of the creep-fatigue behavior. In this way, by combining experimental data and the creep-fatigue mechanism, the aging process of the steel is simulated, reducing the prediction difficulty of the creep-fatigue behavior and improving the accuracy of steel aging assessment and life prediction.

[0020] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. Brief Description of the Drawings

[0021] The accompanying drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application. Obviously, the accompanying drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0022] Figure 1 is a schematic diagram of the implementation environment of a numerical simulation system for the steel aging process shown in an exemplary embodiment of this application;

[0023] Figure 2 is a flowchart of a numerical simulation method for the steel aging process shown in an exemplary embodiment of this application;

[0024] Figure 3 is a flowchart of a specific numerical simulation method for the steel aging process shown in an exemplary embodiment of this application;

[0025] Figure 4 is a block diagram of a numerical simulation system for the steel aging process shown in an exemplary embodiment of this application;

[0026] Figure 5It is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present application. Detailed implementation manners

[0027] The following will describe the implementation manners of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0028] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0029] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0030] First of all, it should be noted that as a high-performance ferritic heat-resistant steel, the 9Cr series steel has been widely used in these high-temperature and high-pressure environments due to its excellent high-temperature strength, creep resistance, and good oxidation resistance. For example, P92 steel in 9Cr-1Mo steel. However, in high-temperature and long-term complex stress environments, creep and fatigue interaction will occur in this series of steels, resulting in a significant decline in their mechanical properties and service life. Therefore, it is of great significance to conduct in-depth research and accurate simulation on the creep-fatigue behavior of this series of steels. However, through the research of the inventors of the present application, it is found that although a variety of viscoplastic models and modified models can more accurately reflect the creep-fatigue characteristics of steel, for this more complex alloy steel of the 9Cr series, due to its complex composition and microstructure, diverse aging mechanisms are exhibited under different service conditions, and the relevant parameters of the models cannot be accurately obtained, making the simulation of its creep-fatigue deformation behavior still face great challenges and uncertainties, resulting in increased difficulty in predicting creep and fatigue behaviors, thereby affecting the accuracy of aging assessment and life prediction of the 9Cr series steel.

[0031] Therefore, Figure 1 It is a schematic diagram of the implementation environment of a numerical simulation system for the steel aging process shown in an exemplary embodiment of the present application. AsFigure 1 As shown, the implementation environment may include a numerical simulation system 110 for steel aging process and a computer device 120. The numerical simulation system 110 for steel aging process may be set within the computer device 120 and is used for performing numerical simulation of the steel aging process. Among them, the computer device 120 may be at least one of a desktop Graphic Processing Unit (GPU) computer, a GPU computing cluster, a neural network computer, etc. The numerical simulation system 110 for steel aging process first constructs an initial constitutive model for creep fatigue of the steel material to be tested according to the creep fatigue mechanism, and then accurately fits the material parameters in the model according to the creep fatigue interaction experiment, solving the problem that the relevant parameters of the model cannot be accurately obtained. Thus, a target constitutive model with higher accuracy can be obtained to achieve accurate numerical simulation of creep fatigue behavior. In this way, by combining experimental data and the creep fatigue mechanism, the steel aging process is simulated, reducing the prediction difficulty of creep fatigue behavior and improving the accuracy of steel aging assessment and life prediction.

[0032] Please refer to Figure 2 , Figure 2 FIG. is a flowchart of a numerical simulation method for steel aging process shown in an exemplary embodiment of the present application. This method can be applied to Figure 1 the implementation environment shown, and is specifically executed by the numerical simulation system for steel aging process in this implementation environment. It should be understood that this method can also be applicable to other exemplary implementation environments and is specifically executed by devices in other implementation environments. This embodiment does not limit the implementation environment applicable to this method.

[0033] As Figure 2 shown, in an exemplary embodiment, the numerical simulation method for steel aging process at least includes steps S210 to S240, which are introduced in detail as follows:

[0034] Step S210: Construct an initial constitutive model for creep fatigue of the steel material to be tested. The material parameters of the initial constitutive model include an isotropic hardening parameter, a kinematic hardening parameter, and a viscous stress parameter.

[0035] Among them, the isotropic hardening parameter includes an isotropic hardening variable and an isotropic hardening coefficient, the kinematic hardening parameter includes a kinematic hardening variable, a first kinematic hardening coefficient, and a second kinematic hardening coefficient; the viscous stress parameter includes a first viscous stress parameter and a second viscous stress parameter.

[0036] In addition, the steel material to be tested may be P92 steel. Of course, it may also be other types of steel in the 9 chromium series.

[0037] In this embodiment, an initial constitutive model for creep fatigue of the steel material to be tested is constructed according to the unified viscoplasticity theory. The initial constitutive model is:

[0038]

[0039] Among them, ε represents the principal strain, ε e represents the elastic strain, ε in represents the inelastic strain, σ represents the stress, D represents the stiffness tensor of the steel material to be measured, represents the inelastic strain rate, Z represents the first viscous stress parameter, A represents the second viscous stress parameter, F y represents the yield function, ζ represents the total kinematic hardening variable, s represents the stress deviator, R represents the isotropic hardening variable, k represents the initial elastic limit, M represents the number of iterations, ζ (i) represents the kinematic hardening variable, represents the kinematic hardening rate, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, represents the cumulative inelastic strain rate, represents the isotropic hardening rate, b and Q represent the isotropic hardening coefficients.

[0040] In the initial constitutive model, ε = ε e + ε in represents the decomposition function of the principal strain; σ = D:(ε - ε in ) represents the stress function, reflecting the relationship between the elastic response of the steel material to be measured and the strain state; Based on the Mises yield condition (i.e., the fourth strength theory), it is defined as F y represents the yield function, measuring the magnitude of the stress deviator s minus the kinematic hardening variable ζ, and considering the isotropic hardening variable R and the initial elastic limit k, is the total kinematic hardening variable equation; is the kinematic hardening rate equation; is the isotropic hardening rate equation.

[0041] The construction of this initial constitutive model uses the unified viscoplasticity theory to predict the mechanical response of the steel material to be measured under complex loading conditions. The various parameters and expressions in the model work together to accurately capture the behavioral characteristics of the steel material to be measured at different stress levels and loading times, which is conducive to the aging assessment and life prediction of the steel material to be measured.

[0042] It should be noted that this model is an initial constitutive model. To ensure the accuracy and robustness of this initial constitutive model, accurate numerical values of the material parameters in the model need to be obtained, that is, the isotropic hardening parameter R, the isotropic hardening coefficients b and Q, the kinematic hardening variable ζ (i) , the first kinematic hardening coefficient c i , the second kinematic hardening coefficient ri The exact numerical values of the first viscous stress parameter Z and the second viscous stress parameter A to accurately describe the creep-fatigue behavior of steel materials.

[0043] Step S220: Conduct a creep-fatigue interaction experiment on the steel material to be tested at a preset temperature, and obtain experimental data under the conditions of a preset loading time, a constant strain rate, and a constant strain amplitude. The experimental data includes a cumulative inelastic strain curve, a tensile curve, and a stress relaxation curve.

[0044] In this embodiment, the requirements for the experimental parameters are as follows: the temperature, strain rate, and strain amplitude are fixed values, and the loading time is a variable value. Among them, the ranges of temperature, stress rate, strain amplitude, and loading time can be specifically set according to requirements. Taking temperature as an example, if it is necessary to simulate the numerical value of the aging process of the steel material to be tested at a high temperature of 600 °C, the preset temperature is set to 600 °C to obtain the experimental data corresponding to 600 °C, and to realize the fitting of the material parameters corresponding to 600 °C and the numerical simulation of the aging process.

[0045] It should be noted that the horizontal axis and vertical axis of the cumulative inelastic strain curve represent cumulative inelastic strain and stress respectively, the horizontal axis and vertical axis of the tensile curve represent strain and stress respectively, and the horizontal axis and vertical axis of the stress relaxation curve represent stress and loading time respectively.

[0046] Step S230: Construct a mechanical behavior model of the steel material to be tested according to the material parameters, and use the cumulative inelastic strain curve, the tensile curve, and the stress relaxation curve to fit the material parameters to obtain the target constitutive model.

[0047] In this embodiment, by constructing a mechanical behavior model of the steel material to be tested and fitting the material parameters based on the experimental data, the exact numerical values of the material parameters are obtained, and a target constitutive model with higher accuracy is obtained, which can realize the accurate numerical simulation of the steel aging process.

[0048] In one embodiment, constructing a mechanical behavior model of the steel material to be tested according to the material parameters includes: constructing a first relationship model between the isotropic hardening parameter and the cumulative inelastic strain, where the isotropic hardening parameter includes an isotropic hardening variable and an isotropic hardening coefficient; constructing a second relationship model according to the kinematic hardening parameter, stress, and inelastic strain, where the kinematic hardening parameter includes a kinematic hardening variable, a first kinematic hardening coefficient, and a second kinematic hardening coefficient; constructing a third relationship model between the viscous stress and the cumulative inelastic strain rate according to the viscous stress parameter, where the viscous stress parameter includes a first viscous stress parameter and a second viscous stress parameter; and constituting a mechanical behavior model based on the first relationship model, the second relationship model, and the third relationship model.

[0049] In this embodiment, the first relationship model is used to determine the isotropic hardening variable and the isotropic hardening coefficient among the isotropic hardening parameters of the steel material to be measured, the second relationship model is used to determine the kinematic hardening variable, the first kinematic hardening coefficient and the second kinematic hardening coefficient among the kinematic hardening parameters of the steel material to be measured, and the third relationship model is used to determine the first viscous stress parameter and the second viscous stress parameter among the viscous stress parameters of the steel material to be measured.

[0050] Specifically, the first relationship model is:

[0051] R = Q(1 - e -bp ) + Hp, (2)

[0052] where R represents the isotropic hardening variable, p represents the cumulative inelastic strain, and Q, b, and H represent the isotropic hardening coefficients;

[0053] The second relationship model is:

[0054]

[0055] where ζ (i) represents the kinematic hardening variable, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, ε in represents the inelastic strain, σ represents the stress, and R represents the isotropic hardening variable;

[0056] The third relationship model is:

[0057]

[0058] where σ v represents the viscous stress, represents the cumulative inelastic strain rate, Z represents the first viscous stress parameter, and A represents the second viscous stress parameter.

[0059] It should be noted that in Equation (4), is the partial derivative of σ with respect to ε, representing the rate of change of stress with the principal strain, is the partial derivative of R with respect to ε, representing the rate of change of the isotropic hardening variable with the principal strain. In the second relationship model, Equation (3) represents the kinematic hardening variable equation, and Equation (4) represents the inelastic strain equation.

[0060] In this embodiment, there are isotropic hardening variables and isotropic hardening coefficients in the constructed first relationship model, kinematic hardening variables, a first kinematic hardening coefficient, and a first kinematic hardening coefficient in the constructed second relationship model, and a first viscous stress parameter and a second viscous stress parameter in the constructed third relationship model. In this way, the values of each material parameter can be determined according to the constructed relationship model, solving the problem that the relevant material parameters in the constitutive model of the steel material to be measured cannot be accurately obtained.

[0061] In one embodiment, the fitting method for the isotropic hardening parameter includes: obtaining the peak stress, and constructing a first relationship between the isotropic hardening variable and the stress according to the peak stress, where the peak stress is the maximum stress within the first cycle of the cumulative inelastic strain curve; constructing a second relationship between the stress and the cumulative inelastic strain according to the first relationship and the first relationship model; using the second relationship to fit the cumulative inelastic strain curve to obtain the isotropic hardening coefficient, and substituting the isotropic hardening coefficient into the first relationship model to obtain the isotropic hardening variable, thus completing the fitting of the isotropic hardening parameter. The horizontal axis and the vertical axis of the cumulative inelastic strain curve respectively represent the cumulative inelastic strain and the stress.

[0062] In this embodiment, the first relationship is:

[0063] R = σ - σ m , Equation (6)

[0064] where R represents the isotropic hardening variable, σ represents the stress, and σ m represents the peak stress.

[0065] In this embodiment, according to the first relationship and the first relationship model, a second relationship between the stress and the cumulative inelastic strain is constructed, that is, substituting the first relationship into the first relationship model, replacing R with σ - σ m , and then transposing σ m to obtain the second relationship. The second relationship is:

[0066] σ = Q(1 - e -bp ) + Hp + σ m , Equation (7)

[0067] where σ represents the stress, p represents the cumulative inelastic strain, Q, b, and H represent the isotropic hardening coefficients, and σ m represents the peak stress.

[0068] In this embodiment, the second relationship is the relationship between stress and cumulative inelastic strain. The horizontal axis and the vertical axis of the cumulative inelastic strain curve represent cumulative inelastic strain and stress respectively. Therefore, the second relationship can be used to fit the cumulative inelastic strain curve, so as to obtain the isotropic hardening coefficients Q, b, and H. Substituting Q, b, and H into the first relationship model, the isotropic hardening variable R can be obtained.

[0069] It should be noted that in the first relationship model, since the cumulative inelastic strain changes with the loading time, the isotropic hardening variable also changes with the loading time.

[0070] In this way, by combining the first relationship model between the isotropic hardening parameters and the cumulative inelastic strain and the cumulative inelastic strain curve in the experimental data, the isotropic hardening parameters in the initial constitutive model of the steel material to be measured are accurately and effectively obtained.

[0071] In one embodiment, the fitting method of the kinematic hardening parameters includes: obtaining the pre-constructed third relationship and the fourth relationship. The third relationship is the relationship between the principal strain and the stress, and the fourth relationship is the relationship between the inelastic strain and the stress; according to the third relationship and the fourth relationship, the tensile curve is converted into a relationship curve between the inelastic strain and the stress. The horizontal axis and the vertical axis of the tensile curve represent strain and stress respectively; using the inelastic strain equation in the second relationship model, the relationship curve between the inelastic strain and the stress is fitted to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient into the kinematic hardening variable equation in the second relationship model to obtain the kinematic hardening variable, thus completing the fitting of the kinematic hardening parameters.

[0072] In this embodiment, the third relationship is:

[0073]

[0074] where σ represents stress, ε represents the principal strain, ε0 represents the initial strain, σ0 represents the initial stress, and n0 represents the power exponent. is the partial derivative of σ with respect to ε, representing the change rate of stress with the principal strain.

[0075] In this embodiment, the fourth relationship is:

[0076]

[0077] where σ represents stress, ε in represents the inelastic strain, ε represents the principal strain, ε0 represents the initial strain, σ0 represents the initial stress, and n0 represents the power exponent. is the partial derivative of σ with respect to ε, representing the change rate of stress with the principal strain.

[0078] Among them, σ represents stress, ε in represents inelastic strain, ε represents principal strain, E represents the elastic modulus of the steel material to be measured, represents the partial derivative of σ with respect to ε in which represents the rate of change of stress with respect to inelastic strain, is the partial derivative of σ with respect to ε, representing the rate of change of stress with respect to principal strain.

[0079] In this embodiment, the third relational expression is the relational expression between principal strain and stress, and further is the relational expression of, the fourth relational expression is the relational expression between inelastic strain and stress, and further is the relational expression of. The horizontal axis and the vertical axis of the tensile curve respectively represent strain and stress. Among them, the strain is the principal strain. Therefore, according to the third relational expression and the fourth relational expression, the tensile curve can be converted into a relationship curve between inelastic strain and stress. Therefore, the inelastic strain equation (i.e., Equation 4) in the second relationship model can be used to fit the relationship curve between inelastic strain and stress to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient, and substitute them into the kinematic hardening variable equation in the second relationship model to obtain the kinematic hardening variable ζ (i) .

[0080] In this way, by combining the second relationship model constructed according to the kinematic hardening parameters, stress and inelastic strain and the tensile curve in the experimental data, the kinematic hardening parameters in the initial constitutive model of the steel material to be measured are accurately and effectively obtained.

[0081] In a possible embodiment, the construction method of the third relational expression includes: constructing another relational expression between principal strain and stress, and this another relational expression is:

[0082]

[0083] Among them, ε represents principal strain, ε0 represents initial strain, σ represents stress, σ0 represents initial stress, and n0 represents the power exponent; using this another relational expression to fit the tensile curve to obtain the initial strain and the power exponent; in this another relational expression, taking the derivative of both sides with respect to the principal strain to obtain the third relational expression.

[0084] As a possible embodiment, this another relational expression is the relationship between principal strain and stress. The horizontal axis and the vertical axis of the tensile curve respectively represent strain and stress. Among them, the strain is the principal strain. Therefore, this another relational expression can be used to fit the tensile curve to obtain the initial strain and the power exponent.

[0085] In a possible embodiment, the construction method of the inelastic strain equation in the second relationship model includes: obtaining the stress equation during the stretching process, where the stress equation includes a kinematic hardening variable; substituting the stress equation into the kinematic hardening variable equation in the second relationship model, and taking the derivative and logarithmic processing of the inelastic strain to obtain the inelastic strain equation.

[0086] As a possible embodiment, the stress equation during the stretching process is:

[0087] σ = ζ (i) + R + k, Equation (11)

[0088] where σ represents stress, ζ (i) represents the kinematic hardening variable, R represents the isotropic hardening variable, and k represents the initial elastic limit.

[0089] Substitute the stress equation into the kinematic hardening variable equation in the second relationship model, that is, use Equation (11) to replace ζ in Equation (3) (i) , to obtain:

[0090]

[0091] where σ represents stress, R represents the isotropic hardening variable, k represents the initial elastic limit, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, ε in represents the inelastic strain.

[0092] In Equation (12), take the derivative of both sides with respect to ε in , to obtain:

[0093]

[0094] where σ represents stress, ε in represents the inelastic strain, R represents the isotropic hardening variable, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, represents the partial derivative of σ with respect to ε in , representing the rate of change of stress with respect to inelastic strain, is the partial derivative of R with respect to ε in , representing the rate of change of the isotropic hardening variable with respect to inelastic strain.

[0095] Finally, perform logarithmic processing on Equation (13) to obtain the inelastic strain equation, that is, Equation (4).

[0096] Further, the fitting method of the kinematic hardening parameters further includes: based on the yield state of the steel material to be measured, dividing the hardening stage during the tensile process into an initial hardening stage and a later hardening stage; constructing a first inelastic strain equation for the initial hardening stage and a second inelastic strain equation for the later hardening stage; using the first inelastic strain equation to fit the relationship curve between the inelastic strain and the stress, obtaining the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage into the kinematic hardening variable equation to obtain the kinematic hardening variable for the initial hardening stage; substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage into the second inelastic strain equation to fit the relationship curve between the inelastic strain and the stress, obtaining the first kinematic hardening coefficient and the second kinematic hardening coefficient for the later hardening stage, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the later hardening stage into the kinematic hardening variable equation to obtain the kinematic hardening variable for the later hardening stage, thus completing the fitting of the kinematic hardening parameters.

[0097] It should be noted that the yield state includes unyielded and yielded. When the yield state is unyielded, the hardening stage during the tensile process is defined as the initial hardening stage, and when the yield state is yielded, the hardening stage during the tensile process is defined as the later hardening stage.

[0098] It should also be noted that in the initial hardening stage, the stress equation is:

[0099] σ = ζ (1) +R + k, Equation (14)

[0100] where σ represents stress, ζ (1) represents the kinematic hardening variable for the initial hardening stage, R represents the isotropic hardening variable, and k represents the initial elastic limit.

[0101] In the later hardening stage, the stress equation is:

[0102] σ = ζ (1) +ζ (2) +R + k, Equation (15)

[0103] where represents stress, ζ (1) represents the kinematic hardening variable for the initial hardening stage, ζ (2) represents the kinematic hardening variable for the later hardening stage, R represents the isotropic hardening variable, and k represents the initial elastic limit.

[0104] Based on the above construction method of the inelastic strain equation, the first inelastic strain equation for the initial hardening stage and the second inelastic strain equation for the later hardening stage constructed are shown in Equations (16) and (17) respectively:

[0105]

[0106] Among them, σ represents stress, ε in represents inelastic strain, R represents the isotropic hardening variable, c1 represents the first kinematic hardening coefficient in the initial hardening stage, and r1 represents the second kinematic hardening coefficient in the initial hardening stage;

[0107]

[0108] Among them, σ represents stress, ε in represents inelastic strain, R represents the isotropic hardening variable, c2 represents the first kinematic hardening coefficient in the later hardening stage, r2 represents the second kinematic hardening coefficient in the later hardening stage, and ζ (2) represents the kinematic hardening variable in the later hardening stage.

[0109] In this embodiment, the relationship curve between inelastic strain and stress is fitted using Equation (16) to obtain the first kinematic hardening coefficient c1 and the second kinematic hardening coefficient r1 in the initial hardening stage, and substituting them into Equation (3) to obtain the kinematic hardening variable ζ (1) in the initial hardening stage; substituting Equation (3) into Equation (17), and substituting c1 and r1 into Equation (17), the relationship curve between inelastic strain and stress is fitted to obtain the first kinematic hardening coefficient c2 and the second kinematic hardening coefficient r2 in the later hardening stage, and then substituting c2 and r2 into Equation (3) to obtain the kinematic hardening variable ζ (2) .

[0110] In this way, considering the influence on the kinematic hardening parameters in different hardening stages, which will result in different kinematic hardening parameters in different hardening stages, the first inelastic strain equation in the initial hardening stage and the second inelastic strain equation in the later hardening stage are constructed. In the second inelastic strain equation, the first kinematic hardening coefficient and the second kinematic hardening coefficient in the initial hardening stage are also introduced to describe the complex deformation mechanism caused by the internal damage accumulation of the material in the later hardening stage. In addition, by constructing the first inelastic strain equation in the initial hardening stage and the second inelastic strain equation in the later hardening stage, and then combining them with the relationship curve between inelastic strain and stress obtained by converting the tensile curve in the experimental data respectively, the kinematic hardening parameters in the initial constitutive model of the steel material to be measured are accurately and effectively obtained. When using the constitutive model, different kinematic hardening parameters can be selected based on different hardening stages to more accurately perform the aging numerical simulation of the steel material to be measured.

[0111] In one embodiment, the fitting method of the viscous stress parameter includes: obtaining a pre-constructed fifth relation and a sixth relation, where the fifth relation is the relation between the cumulative inelastic strain rate and the loading time, and the sixth relation is the relation between the stress and the loading time; according to the fifth relation and the sixth relation, converting the stress relaxation curve into a relation curve between the viscous stress and the cumulative inelastic strain rate, where the horizontal axis and the vertical axis of the stress relaxation curve respectively represent the stress and the loading time; using the third relation model to fit the relation curve between the viscous stress and the cumulative inelastic strain rate to obtain a first viscous stress parameter and a second viscous stress parameter, thus completing the fitting of the viscous stress parameter.

[0112] In this embodiment, the constructed third relation model is the result of logarithmic transformation, and the original relation is:

[0113]

[0114] where, σ v represents the viscous stress, represents the cumulative inelastic strain rate, Z represents the first viscous stress parameter, and A represents the second viscous stress parameter. In this way, the relation between the viscous stress and the loading time is logarithmically transformed, which is more conducive to the fitting of the first viscous stress parameter and the second viscous stress parameter and improves the fitting rate.

[0115] In this embodiment, the fifth relation is:

[0116]

[0117] where, represents the cumulative inelastic strain rate, t represents the loading time, E represents the elastic modulus of the steel material to be measured, and α, β represent the parameters related to the stress-time curve;

[0118] The sixth relation is:

[0119] σ = σ0 - βln(αt + 1), Equation (20)

[0120] where, σ represents the stress, σ0 represents the initial stress, t represents the loading time, and α, β represent the parameters related to the stress-time curve.

[0121] In this embodiment, the sixth relationship is the relationship between stress and loading time. The horizontal axis and the vertical axis of the stress relaxation curve represent stress and loading time respectively. Therefore, the sixth relationship can be used to fit the stress relaxation curve, so as to obtain the parameters α and β related to the stress-time curve, and then the parameters α and β related to the stress-time curve can be substituted into the fifth relationship. Further, the fifth relationship is the relationship between the cumulative inelastic strain rate and the loading time. The horizontal axis and the vertical axis of the stress relaxation curve represent stress and loading time respectively. Among them, the stress is regarded as the viscous stress. Therefore, according to the fifth relationship, the loading time in the stress relaxation curve can be converted into the cumulative inelastic strain rate, so as to obtain the relationship curve between the viscous stress and the cumulative inelastic strain rate. The third relationship model is the relationship model between the viscous stress and the cumulative inelastic strain rate. Therefore, the third relationship model can be used to fit the relationship curve between the viscous stress and the cumulative inelastic strain rate to obtain the first viscous stress parameter Z and the second viscous stress parameter A.

[0122] In this way, combining the third relationship model between the viscous stress and the cumulative inelastic strain rate and the stress relaxation curve in the experimental data, the viscous stress parameters in the initial constitutive model of the steel material to be measured are accurately and effectively obtained.

[0123] Step S240: Use the target constitutive model to perform aging numerical simulation on the steel material to be measured, and obtain the curve of stress changing with time.

[0124] Among them, the aging numerical value is reflected as the curve of stress changing with time.

[0125] In this embodiment, the material parameters in the target constitutive model have been obtained by fitting based on experimental data. Therefore, by simulating the steel aging process according to this target constitutive model, the curve of stress changing with time can be obtained, so that the steel aging evaluation and life prediction can be carried out according to the stress change situation in the curve, improving the accuracy of the steel aging evaluation and life prediction.

[0126] In one embodiment, the expression of the target constitutive model is:

[0127]

[0128] Among them, ε represents the principal strain, ε e represents the elastic strain, ε in represents the inelastic strain, σ represents the stress, D represents the stiffness tensor of the steel material to be measured, represents the inelastic strain rate, Z represents the first viscous stress parameter, A represents the second viscous stress parameter, F y represents the yield function, ζ represents the total kinematic hardening variable, s represents the stress deviator, R represents the isotropic hardening variable, k represents the initial elastic limit, M represents the number of iterations, ζ(i) Denotes the kinematic hardening variable, Denotes the kinematic hardening rate, c i Denotes the first kinematic hardening coefficient, r i Denotes the second kinematic hardening coefficient, Denotes the cumulative inelastic strain rate, Denotes the isotropic hardening rate, b and Q denote the isotropic hardening coefficients.

[0129] It should be noted that at this time, the isotropic hardening parameter R, the isotropic hardening coefficients b and Q, and the kinematic hardening variable ζ (i) , the first kinematic hardening coefficient c i , the second kinematic hardening coefficient r i , the first viscous stress parameter Z and the second viscous stress parameter A in the target constitutive model have been accurately fitted based on experimental data.

[0130] In one embodiment, using the target constitutive model, an aging numerical simulation is performed on the steel material to be tested, and a stress-time curve is obtained, including: discretizing the target constitutive model in terms of loading time to obtain a discretized constitutive model; constructing a solution equation for the discretized constitutive model, numerically calculating the discretized constitutive model, and continuously iterating the solution equation to simulate the numerical process of steel aging and obtain a stress-time curve.

[0131] In this embodiment, the target constitutive model is discretized using the backward Euler method and integrated over time, so that the loading time ranges from time t n to time t n+1 , and the discretized equation can meet the needs of finite element numerical simulation.

[0132] t n+1 The discretized constitutive model at time is:

[0133]

[0134] where ε n+1 denotes the principal strain at time t n+1 , denotes the elastic strain at time t n+1 , denotes the inelastic strain at time t n , denotes the principal strain at time t n , denotes the difference in inelastic strain between time t n+1 and time t n ; σ n+1 denotes the stress at time t n+1 , D denotes the stiffness tensor of the steel material to be tested, Δpn+1 Denote t n+1 The cumulative inelastic strain difference between the moment and t n is s n+1 Denote t n+1 The stress deviator at the moment is ζ n+1 Denote t n+1 The total kinematic hardening variable at the moment. Z represents the first viscous stress parameter, A represents the second viscous stress parameter, F y(n+1) Denote t n+1 The yield function at the moment is Δt n+1 Denote t n+1 The time difference between the moment and t n is R n+1 Denote t n+1 The isotropic hardening variable at the moment. k represents the initial elastic limit, ζ n+1 (i) Denote t n+1 The kinematic hardening variable at the moment is ζ n (i) Denote t n The kinematic hardening variable at the moment is c i Denote the first kinematic hardening coefficient as r i Denote the second kinematic hardening coefficient as ΔR n+1 Denote t n+1 The difference in the isotropic hardening variable between the moment and t n is Q, b, and H represent the isotropic hardening coefficients.

[0135] It should be understood that in the numerical simulation of the aging process of the steel material to be tested, during the initial hardening stage, c i takes c1, r i takes r1, ζ n (i) is ζ n (1) is ζ n+1 (i) is ζ n+1 (1) During the later hardening stage, c i takes c2, r i takes r2, ζ n (i) is ζ n (2) is ζ n+1 (i) is ζ n+1 (2) .

[0136] Exemplarily, the solution equation of the discretized constitutive model is constructed using the implicit stress integration method, that is, the discretized constitutive model is numericalized. The steps are as follows:

[0137] S1, t n At the moment, σ n , ε n , ζ n , R n are known, and Δε n+1 , Δt n+1 are set according to specific requirements. When Δε n+1 is all elastic strain, the stress at t n+1 can be solved according to Equation (23), is the trial elastic stress, and this yield function is Equation (24), is the stress deviator of , if , then the steel material to be tested has not reached the yield state. At this time, n+1 the stress at t

[0138]

[0139] S2. When the material enters the yield state, at this time σ n+1 is calculated by Equation (25);

[0140]

[0141] S3. When the material enters the inelastic stage (i.e., the plastic stage), S n+1 is calculated by Equation (26), then s n+1 - ζ n+1 is calculated by Equation (27), where G is the shear modulus of the steel material to be tested;

[0142]

[0143] S4. Construct and substitute it into the discretized kinematic hardening variable formula to obtain Equation (28);

[0144]

[0145] S5. Substitute Equation (18) and into Equation (27) to get Equation (29);

[0146]

[0147] S6. Substitute Δp n+1 , F y(n+1) in the discretized constitutive model into Equation (29) to construct Obtain Equation (30) for iterative solution;

[0148]

[0149] In Equation (30), G represents the shear modulus of the steel material to be measured, M represents the number of iterations, c i is the first kinematic hardening coefficient, R n+1 represents the isotropic hardening variable at time t n+1 , k represents the initial elastic limit, Z represents the first viscous stress parameter, A represents the second viscous stress parameter, s n+1 represents the stress deviator at time t n+1 , ζ n+1 represents the kinematic hardening variable at time t n+1 .

[0150] After obtaining Equation (30), continuously perform iteration on stress and strain as follows:

[0151] S1. At time t n , σ n , ε n , ζ n , R n are known, Δε n+1 , Δt n+1 are set according to specific requirements. If the material has not reached the yield state, then

[0152] S2. If successively solve When the number of iterations continuously increases, the value tends to be stable and convergence is achieved, then output σ n+1 , otherwise perform iteration again to obtain the stress-time curve.

[0153] Please refer to Figure 3 , Figure 3 which is a flowchart of a specific numerical simulation method for the steel aging process shown in an exemplary embodiment of the present application. As Figure 3 shown, the specific numerical simulation method for the steel aging process at least includes steps S310 to S370, which are described in detail as follows:

[0154] Step S310: Construct an initial constitutive model for creep fatigue of the steel material to be measured. The material parameters in the initial constitutive model include isotropic hardening parameters, kinematic hardening parameters, and viscous stress parameters;

[0155] Step S320: Conduct a creep-fatigue interaction experiment on the steel material to be tested, and obtain the cumulative inelastic strain curve, tensile curve, and stress relaxation curve.

[0156] Step S330: Construct a mechanical behavior model of the steel material to be tested according to the material parameters, and use the cumulative inelastic strain curve, tensile curve, and stress relaxation curve to fit the material parameters to obtain the target constitutive model.

[0157] Step S340: Discretize the target constitutive model in terms of loading time to obtain the discretized constitutive model.

[0158] Step S350: Perform iterative solution according to the discretized constitutive model.

[0159] Step S360: Determine whether the convergence condition is satisfied. If not, continue to execute Step S350. If satisfied, enter Step S370.

[0160] Step S370: Output the curve of stress versus time.

[0161] In this way, by using the unified viscoplastic model and combining with the writing of specific subroutines, the accurate numerical simulation of the creep-fatigue behavior of 9Cr series steel is realized. And by comparing with the actual test results, this method has effectiveness and applicability, providing a powerful tool for the aging assessment and life prediction of 9Cr series steel. At the same time, this method has the advantages of strong generality, high calculation efficiency, and easy implementation, and can be widely applied.

[0162] The above numerical simulation method for the steel aging process first constructs an initial constitutive model for the creep-fatigue of the steel material to be tested. The material parameters of this model include the isotropic hardening parameter, kinematic hardening parameter, and viscous stress parameter. Then, a creep-fatigue interaction experiment is conducted on the steel material to be tested at a preset temperature to obtain experimental data under the same stress rate, the same strain amplitude, and different loading times, including the cumulative inelastic strain curve, tensile curve, and stress relaxation curve. Next, a mechanical behavior model of the steel material to be tested is constructed, and according to the mechanical behavior model, cumulative inelastic strain curve, tensile curve, and stress relaxation curve, the material parameters are fitted to obtain the target constitutive model. Finally, using the target constitutive model, the aging numerical simulation of the steel material to be tested is carried out to obtain the curves of strain and strain rate versus time. First, an initial constitutive model for the creep-fatigue of the steel material to be tested is constructed according to the creep-fatigue mechanism, and then the material parameters in the model are accurately fitted according to the creep-fatigue interaction experiment, solving the problem that the relevant parameters of the model cannot be accurately obtained, so as to be able to obtain a target constitutive model with higher accuracy to realize the accurate numerical simulation of the creep-fatigue behavior. In this way, by combining experimental data and the creep-fatigue mechanism, the steel aging process is simulated, reducing the prediction difficulty of the creep-fatigue behavior and improving the accuracy of the steel aging assessment and life prediction.

[0163] Please refer to Figure 4 , Figure 4 which is a block diagram of a numerical simulation system for the steel aging process shown in an exemplary embodiment of the present application. This system can be applied to Figure 1 the implementation environment shown. It should be understood that this system can also be applicable to other exemplary implementation environments, and this embodiment does not limit the implementation environment applicable to this system.

[0164] As Figure 4 shown, in an exemplary embodiment, the numerical simulation system 400 for the steel aging process at least includes a model construction module 410, an experimental data acquisition module 420, a parameter fitting module 430, and a numerical simulation module 440, which are introduced in detail as follows:

[0165] The model construction module 410 is used to construct an initial constitutive model for the creep fatigue of the steel material to be tested. The material parameters of the initial constitutive model include isotropic hardening parameters, kinematic hardening parameters, and viscous stress parameters;

[0166] The experimental data acquisition module 420 is used to perform a creep-fatigue interaction experiment on the steel material to be tested at a preset temperature, and obtain experimental data under the same stress rate, the same strain amplitude, and different loading times. The experimental data includes a cumulative inelastic strain curve, a tensile curve, and a stress relaxation curve;

[0167] The parameter fitting module 430 is used to construct a mechanical behavior model for the steel material to be tested, and fit the material parameters according to the mechanical behavior model, the cumulative inelastic strain curve, the tensile curve, and the stress relaxation curve to obtain a target constitutive model;

[0168] The numerical simulation module 440 is used to perform an aging numerical simulation on the steel material to be tested by using the target constitutive model, and obtain the curves of strain and strain rate varying with time.

[0169] It should be noted that the numerical simulation system for the steel aging process provided in the above embodiment and the numerical simulation method for the steel aging process provided in the above embodiment belong to the same concept. The content of the operations performed by each module has been described in detail in the method embodiment, and will not be repeated here.

[0170] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present application. Figure 5 shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. It should be noted that Figure 5 the computer system 500 of the electronic device shown is only an example, and should not bring any limitation to the functions and usage scope of the embodiments of the present application.

[0171] As Figure 5 shown, computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage section 508 into the random access memory (RAM) 503, such as executing the method in the above embodiments. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0172] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read from it can be installed into the storage section 508 as needed.

[0173] Specifically, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 509, and / or installed from the removable medium 511. When the computer program is executed by a central processing unit (CPU) 601, various functions defined in the system of the present application are executed.

[0174] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer is caused to execute the numerical simulation method for the steel aging process as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist alone without being assembled into the electronic device.

[0175] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the above two. A computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable computer program is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program included on the computer-readable medium may be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0177] The units involved in the embodiments of the present invention can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation to the units themselves in some cases.

[0178] The above embodiments are only used to exemplarily illustrate the principles and effects of the present invention, rather than to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A numerical simulation method for the steel aging process, characterized in that, The method includes: Constructing an initial constitutive model for the creep fatigue of the steel material to be tested, where the material parameters of the initial constitutive model include an isotropic hardening parameter, a kinematic hardening parameter, and a viscous stress parameter; Conducting a creep-fatigue interaction experiment on the steel material to be tested at a preset temperature to obtain experimental data under the conditions of a preset loading time, a constant strain rate, and a constant strain amplitude. The experimental data includes a cumulative inelastic strain curve, a tensile curve, and a stress relaxation curve; Constructing a mechanical behavior model for the steel material to be tested according to the material parameters, and using the cumulative inelastic strain curve, the tensile curve, and the stress relaxation curve to fit the material parameters to obtain a target constitutive model; Using the target constitutive model to perform an aging numerical simulation on the steel material to be tested to obtain a curve of the change of stress with time.

2. The numerical simulation method for the steel aging process according to claim 1, characterized in that The constructing of the mechanical behavior model for the steel material to be tested according to the material parameters includes: Constructing a first relationship model between the isotropic hardening parameter and the cumulative inelastic strain. The isotropic hardening parameter includes an isotropic hardening variable and an isotropic hardening coefficient; Constructing a second relationship model according to the kinematic hardening parameter, the stress, and the inelastic strain. The kinematic hardening parameter includes a kinematic hardening variable, a first kinematic hardening coefficient, and a second kinematic hardening coefficient; Constructing a third relationship model between the viscous stress and the cumulative inelastic strain rate according to the viscous stress parameter. The viscous stress parameter includes a first viscous stress parameter and a second viscous stress parameter; Based on the first relationship model, the second relationship model, and the third relationship model, constituting the mechanical behavior model.

3. The numerical simulation method for the steel aging process according to claim 2, wherein The first relationship model is: R = Q(1 - e -bp ) + Hp, where R represents the isotropic hardening variable, p represents the cumulative inelastic strain, and Q, b, and H represent the isotropic hardening coefficients; The second relationship model is: Among them, ζ (i) represents the kinematic hardening variable, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, ε in represents the inelastic strain, σ represents the stress, and R represents the isotropic hardening variable; The third relationship model is: Among them, σ v represents the viscous stress, represents the cumulative inelastic strain rate, Z represents the first viscous stress parameter, and A represents the second viscous stress parameter.

4. The numerical simulation method for the steel aging process according to claim 3, characterized in that The fitting method of the isotropic hardening parameter includes: Obtaining the peak stress, and constructing a first relationship formula between the isotropic hardening variable and the stress according to the peak stress. The peak stress is the maximum stress within the first cycle of the cumulative inelastic strain curve; Constructing a second relationship formula between the stress and the cumulative inelastic strain according to the first relationship formula and the first relationship model; Using the second relationship formula to fit the cumulative inelastic strain curve to obtain the isotropic hardening coefficient, and substituting the isotropic hardening coefficient into the first relationship model to obtain the isotropic hardening variable, thereby completing the fitting of the isotropic hardening parameter. The horizontal axis and the vertical axis of the cumulative inelastic strain curve respectively represent the cumulative inelastic strain and the stress.

5. The numerical simulation method for the steel aging process according to claim 3, wherein The fitting method of the kinematic hardening parameter includes: Obtaining a pre-constructed third relationship formula and a fourth relationship formula. The third relationship formula is the relationship formula between the principal strain and the stress, and the fourth relationship formula is the relationship formula between the inelastic strain and the stress; According to the third relationship formula and the fourth relationship formula, converting the tensile curve into a relationship curve between the inelastic strain and the stress. The horizontal axis and the vertical axis of the tensile curve respectively represent the strain and the stress; Using the inelastic strain equation in the second relationship model, fitting the relationship curve between the inelastic strain and the stress to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient into the kinematic hardening variable equation in the second relationship model to obtain the kinematic hardening variable, thus completing the fitting of the kinematic hardening parameter.

6. The numerical simulation method for the steel aging process according to claim 5, wherein, The fitting method of the kinematic hardening parameter further includes: Based on the yield state of the steel material to be measured, dividing the hardening stage during the tensile process into an initial hardening stage and a later hardening stage; Constructing a first inelastic strain equation for the initial hardening stage and a second inelastic strain equation for the later hardening stage; Using the first inelastic strain equation to fit the relationship curve between the inelastic strain and the stress to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage into the kinematic hardening variable equation to obtain the kinematic hardening variable for the initial hardening stage; Substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the initial hardening stage into the second inelastic strain equation, fitting the relationship curve between the inelastic strain and the stress to obtain the first kinematic hardening coefficient and the second kinematic hardening coefficient for the later hardening stage, and substituting the first kinematic hardening coefficient and the second kinematic hardening coefficient for the later hardening stage into the kinematic hardening variable equation to obtain the kinematic hardening variable for the later hardening stage, thus completing the fitting of the kinematic hardening parameter.

7. The numerical simulation method for the steel aging process according to claim 3, wherein The fitting method of the viscous stress parameter includes: Obtaining a pre-constructed fifth relationship and a sixth relationship, where the fifth relationship is the relationship between the cumulative inelastic strain rate and the loading time, and the sixth relationship is the relationship between the stress and the loading time; According to the fifth relationship and the sixth relationship, converting the stress relaxation curve into a relationship curve between the viscous stress and the cumulative inelastic strain rate, where the horizontal axis and the vertical axis of the stress relaxation curve represent the stress and the loading time respectively; Using the third relationship model to fit the relationship curve between the viscous stress and the cumulative inelastic strain rate to obtain the first viscous stress parameter and the second viscous stress parameter, thus completing the fitting of the viscous stress parameter.

8. The numerical simulation method for the steel aging process according to any one of claims 1 to 7, characterized in that, The expression of the target constitutive model is: Among them, ε represents the principal strain, ε e represents the elastic strain, ε in represents the inelastic strain, σ represents the stress, D represents the stiffness tensor of the steel material to be measured, represents the inelastic strain rate, Z represents the first viscous stress parameter, A represents the second viscous stress parameter, F y represents the yield function, ζ represents the total kinematic hardening variable, s represents the stress deviator, R represents the isotropic hardening variable, k represents the initial elastic limit, M represents the number of iterations, ζ (i) represents the kinematic hardening variable, represents the kinematic hardening rate, c i represents the first kinematic hardening coefficient, r i represents the second kinematic hardening coefficient, represents the cumulative inelastic strain rate, represents the isotropic hardening rate, b and Q represent the isotropic hardening coefficients.

9. The numerical simulation method for the steel aging process according to claim 8, wherein Using the target constitutive model to perform an aging numerical simulation on the steel material to be measured to obtain the stress-time change curve includes: Discretizing the target constitutive model in terms of the loading time to obtain a discretized constitutive model; Constructing a solution equation for the discretized constitutive model, performing numerical calculation on the discretized constitutive model, and continuously iterating the solution equation to simulate the numerical process of steel aging to obtain the stress-time change curve.

10. A numerical simulation system for the steel aging process, characterized in that, The system includes: A model construction module for constructing an initial constitutive model of the creep fatigue of the steel material to be tested, wherein the material parameters of the initial constitutive model include isotropic hardening parameters, kinematic hardening parameters and viscous stress parameters; An experimental data acquisition module for performing a creep-fatigue interaction experiment on the steel material to be tested at a preset temperature to obtain experimental data under conditions of a preset loading time, a constant strain rate and a constant strain amplitude, wherein the experimental data includes a cumulative inelastic strain curve, a tensile curve and a stress relaxation curve; A parameter fitting module for constructing a mechanical behavior model of the steel material to be tested according to the material parameters, and using the cumulative inelastic strain curve, the tensile curve and the stress relaxation curve to fit the material parameters to obtain a target constitutive model; A numerical simulation module for performing an aging numerical simulation on the steel material to be tested by using the target constitutive model to obtain a curve of stress varying with time.

Images