CN117705616A - Method, system, equipment and storage medium for predicting fatigue life of structural material - Google Patents

Method, system, equipment and storage medium for predicting fatigue life of structural material Download PDF

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Publication number
CN117705616A
CN117705616A CN202311566701.7A CN202311566701A CN117705616A CN 117705616 A CN117705616 A CN 117705616A CN 202311566701 A CN202311566701 A CN 202311566701A CN 117705616 A CN117705616 A CN 117705616A
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structural material
fatigue life
strain rate
determining
strain
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初起宝
王仪美
房永刚
王庆
吕云鹤
曾珍
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Nuclear And Radiation Safety Center Ministry Of Ecology And Environment
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Nuclear And Radiation Safety Center Ministry Of Ecology And Environment
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N3/32—Investigating strength properties of solid materials by application of mechanical stress by applying repeated or pulsating forces
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N3/02—Details
    • G01N3/06—Special adaptations of indicating or recording means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N3/08—Investigating strength properties of solid materials by application of mechanical stress by applying steady tensile or compressive forces
    • G01N3/18—Performing tests at high or low temperatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0001—Type of application of the stress
    • G01N2203/0005—Repeated or cyclic
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0058—Kind of property studied
    • G01N2203/006—Crack, flaws, fracture or rupture
    • G01N2203/0067—Fracture or rupture
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0058—Kind of property studied
    • G01N2203/0069—Fatigue, creep, strain-stress relations or elastic constants
    • G01N2203/0073—Fatigue
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0058—Kind of property studied
    • G01N2203/0069—Fatigue, creep, strain-stress relations or elastic constants
    • G01N2203/0075—Strain-stress relations or elastic constants
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/02—Details not specific for a particular testing method
    • G01N2203/022—Environment of the test
    • G01N2203/0222—Temperature
    • G01N2203/0226—High temperature; Heating means

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
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Abstract

The invention provides a method, a system, equipment and a storage medium for predicting the fatigue life of a structural material, which are applied to the technical field of structural material application. The method for predicting the fatigue life of the structural material comprises the following steps: acquiring an actual strain amplitude of a dangerous part in a structural material, and determining an initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve; acquiring heat aging data and strain rate data of the structural material, determining a heat aging sensitivity coefficient based on the heat aging data, and determining a strain rate sensitivity coefficient based on the strain rate data; and adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material. The technical scheme of the invention aims to solve the technical problem that the prediction result is inaccurate in the traditional structure material life prediction method.

Description

Method, system, equipment and storage medium for predicting fatigue life of structural material
Technical Field
The invention relates to the technical field of structural material application, in particular to a method, a system, equipment and a storage medium for predicting the fatigue life of a structural material.
Background
In the long-period high-temperature service process of the common structural materials in the nuclear power plant, high-temperature heat aging phenomenon caused by the increased brittleness of the materials can occur, and the mechanical properties of the structural materials are affected. In addition, as the operation condition of the power plant is changed, the structural material can bear a changed load, and the strain rate can be correspondingly changed in the load change process, so that the structural material is superimposed with an alternating load to cause fatigue fracture. Therefore, it is necessary to predict the life of structural materials of a nuclear power plant so that the nuclear power plant can be operated normally.
At present, the service life of the structural material is usually predicted by taking a single influencing factor as a variable, however, the structural material is influenced by coupling of multiple influencing factors in addition to the single influencing factor in the operation process of the nuclear power plant, so that the traditional structural material service life prediction method has the technical problem of inaccurate prediction results.
Disclosure of Invention
The invention provides a method, a system, equipment and a storage medium for predicting the fatigue life of a structural material, and aims to solve the technical problem that a traditional method for predicting the life of the structural material has inaccurate prediction results.
In order to solve the above problems, the present invention provides a method for predicting fatigue life of a structural material, the method for predicting fatigue life of a structural material comprising:
acquiring an actual strain amplitude of a dangerous part in a structural material, and determining an initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve;
acquiring heat aging data and strain rate data of the structural material, determining a heat aging sensitivity coefficient based on the heat aging data, and determining a strain rate sensitivity coefficient based on the strain rate data;
and adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material.
Optionally, the thermal aging data comprises: the step of obtaining the thermal ageing data of the structural material comprises the following steps:
and when the strain amplitude of the structural material is detected to be equal to the preset strain amplitude, and the strain rate of the structural material is detected to be equal to the preset strain rate, obtaining the fatigue life corresponding to different heat aging time.
Optionally, the step of determining a thermal ageing susceptibility based on the thermal ageing data comprises:
determining a logarithmic heat aging time corresponding to each of the plurality of heat aging times, and determining a first logarithmic fatigue life corresponding to each of the plurality of fatigue lives;
and determining a plurality of heat ageing data points in a preset coordinate system based on the corresponding logarithmic heat ageing time and the first logarithmic fatigue life of the heat ageing time, and determining a heat ageing sensitivity coefficient based on the plurality of heat ageing data points.
Optionally, the step of determining the thermal ageing susceptibility based on a plurality of the thermal ageing data points comprises:
fitting a plurality of heat aging data points through a preset fitting strategy to obtain a fitting curve formula;
and obtaining the thermal aging sensitivity coefficients corresponding to the thermal aging times respectively based on the slopes in the fitting curve formula.
Optionally, the strain rate data comprises: the step of obtaining strain rate data of the structural material comprises the following steps of:
and when the strain amplitude of the structural material is detected to be equal to the strain amplitude, and the structural material is not subject to thermal aging, obtaining fatigue life corresponding to each of different strain rates.
Optionally, the step of determining a strain rate sensitivity coefficient based on the strain rate data comprises:
determining a logarithmic strain rate corresponding to each of the plurality of strain rates, and determining a second logarithmic fatigue life corresponding to each of the plurality of fatigue lives;
determining a plurality of strain rate data points in a preset coordinate system based on the corresponding logarithmic strain rate and the second logarithmic fatigue life of the strain rates, and determining a strain rate sensitivity coefficient based on the plurality of strain rate data points.
Optionally, the step of adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain a predicted life of the structural material includes:
determining a life impact factor from the product of the thermal aging coefficient of sensitivity and the strain rate coefficient of sensitivity;
a predicted lifetime of the structural material is determined by a product of the lifetime influencing factor and the initial fatigue lifetime.
In addition, in order to solve the above problems, the present invention also proposes a structural material fatigue life prediction system, including:
the first acquisition module is used for acquiring the actual strain amplitude of a dangerous part in the structural material and determining the initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve;
the second acquisition module is used for acquiring heat ageing data and strain rate data of the structural material, determining a heat ageing sensitivity coefficient based on the heat ageing data and determining a strain rate sensitivity coefficient based on the strain rate data;
and the service life determining module is used for adjusting the initial fatigue service life based on the thermal ageing sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted service life of the structural material.
In order to solve the above-mentioned problems, the present invention also proposes a structural material fatigue life prediction apparatus including: a memory, a processor, and a structural material fatigue life prediction program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the structural material fatigue life prediction method as described above.
In order to solve the above-mentioned problems, the present invention also proposes a storage medium having stored thereon a structural material fatigue life prediction program which, when executed by a processor, implements the steps of the structural material fatigue life prediction method as described above.
According to the embodiment of the invention, the actual strain amplitude of the dangerous part in the structural material is obtained, and the initial fatigue life corresponding to the actual strain amplitude is determined in the preset fitting curve, so that the fatigue life corresponding to the actual strain amplitude can be predicted according to the fitting curve obtained by actual fitting; then, the influence degree of the thermal ageing and the strain rate on the fatigue life can be obtained by acquiring the thermal ageing data and the strain rate data of the structural material, determining the thermal ageing sensitivity coefficient based on the thermal ageing data and determining the strain rate sensitivity coefficient based on the strain rate data; finally, the initial fatigue life is adjusted based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material, and the fatigue life of the structural material in the actual strain amplitude can be accurately predicted on the premise of considering the coupling influence of the thermal aging and the strain rate on the life of the material.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of a device architecture of a hardware operating environment of a structural material fatigue life prediction device according to an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of a method for predicting fatigue life of a structural material according to the present invention;
FIG. 3 is a schematic diagram of fatigue life data under initial conditions of an embodiment of a method for predicting fatigue life of a structural material according to the present invention;
FIG. 4 is a schematic diagram of thermal aging data for an embodiment of a method for predicting fatigue life of a structural material according to the present invention;
FIG. 5 is a diagram of strain rate data for one embodiment of a method for predicting fatigue life of a structural material according to the present invention;
FIG. 6 is a schematic diagram showing the comparison of predicted life and actual life of an embodiment of a method for predicting fatigue life of a structural material according to the present invention;
FIG. 7 is a schematic diagram of functional blocks of an embodiment of a fatigue life prediction system for structural materials according to the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
In the present invention, unless specifically stated and limited otherwise, the terms "connected," "affixed," and the like are to be construed broadly, and for example, "affixed" may be a fixed connection, a removable connection, or an integral body; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Furthermore, descriptions such as those referred to as "first," "second," and the like, are provided for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implying an order of magnitude of the indicated technical features in the present disclosure. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Referring to fig. 1, fig. 1 is a schematic diagram of an apparatus structure of a hardware operation environment of a fatigue life prediction apparatus for structural materials according to an embodiment of the present invention.
As shown in fig. 1, in a hardware operating environment of a structural material fatigue life prediction device, the structural material fatigue life prediction device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structural material fatigue life prediction device structure shown in fig. 1 is not limiting of the terminal device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a structural material fatigue life prediction program may be included in a memory 1005, which is a type of computer storage medium.
In the device shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server, and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to invoke the structural material fatigue life prediction program stored in the memory 1005 and perform the following operations:
acquiring an actual strain amplitude of a dangerous part in a structural material, and determining an initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve;
acquiring heat aging data and strain rate data of the structural material, determining a heat aging sensitivity coefficient based on the heat aging data, and determining a strain rate sensitivity coefficient based on the strain rate data;
and adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material.
Based on the hardware structure, the overall conception of each embodiment of the method for predicting the fatigue life of the structural material is provided.
At present, in the long-period high-temperature service process of the common structural materials of the nuclear power plant, the phenomenon of high-temperature thermal aging caused by the increased brittleness of the materials can occur, and the mechanical properties of the structural materials are affected. In addition, as the operation condition of the power plant is changed, the structural material can bear a changed load, and the strain rate can be correspondingly changed in the load change process, so that the structural material is superimposed with an alternating load to cause fatigue fracture. Therefore, it is necessary to predict the life of structural materials of a nuclear power plant so that the nuclear power plant can be operated normally.
When predicting the fatigue life of a structural material, the life of the structural material is usually predicted by taking a single influencing factor as a variable, however, the structural material is influenced by coupling of multiple influencing factors besides the single influencing factor in the operation process of the nuclear power plant, so that the traditional structural material life prediction method has the technical problem of inaccurate prediction results.
In order to solve the problems, the invention provides a method for predicting the fatigue life of a structural material.
Based on the overall conception of the embodiments of the method for predicting the fatigue life of the structural material, the embodiments of the method for predicting the fatigue life of the structural material are provided.
It should be understood that the execution main body of the structural material fatigue life prediction method of the present invention is structural material fatigue life prediction equipment, and the structural material fatigue life prediction equipment may be a server, a microprocessor, or the like. In the following embodiments, description of execution bodies is omitted.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for predicting fatigue life of a structural material according to a first embodiment of the present invention. It should be noted that although a logical sequence is shown in the flow chart, in some cases, the individual steps of the structural material fatigue life prediction method of the present invention may of course be performed in a different order than that herein.
In this embodiment, the method for predicting fatigue life of a structural material according to the present invention includes:
step S10, acquiring an actual strain amplitude of a dangerous part in a structural material, and determining an initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve;
the dangerous portion refers to a portion of the structural material where fracture and embrittlement are likely to occur, and the preset fitted curve refers to a strain amplitude-fatigue life curve of the structural material obtained by a laboratory low cycle fatigue method.
Before the fatigue life of the structural material is predicted, a strain amplitude-fatigue life curve of the material to be predicted is required to be determined based on a low-cycle fatigue method, then the strain amplitude of a part which is easy to break and embrittle in the structural material is obtained by obtaining the strain of the structural material in a typical service environment (use environment) or carrying out finite element calculation on the structural material, so that an actual strain amplitude is obtained, and the actual strain amplitude is input into the strain amplitude-fatigue life curve obtained based on the low-cycle fatigue method, so that the fatigue life corresponding to the actual strain amplitude is obtained, and the initial fatigue life is obtained.
As an example, the expression of the strain amplitude-fatigue life curve obtained in the low cycle fatigue method is as follows, and after the actual strain amplitude is obtained, the fatigue life corresponding to each of the different strain amplitudes can be determined according to the following expression.
ε a ＝K 1 N K2 +K 3 ,
Wherein ε a For strain amplitude, N is fatigue life, and K1, K2, and K3 are constants.
Step S20, obtaining heat aging data and strain rate data of the structural material, determining a heat aging sensitivity coefficient based on the heat aging data, and determining a strain rate sensitivity coefficient based on the strain rate data;
after the initial fatigue life is obtained, the obtained initial fatigue life is also adjusted according to the thermal aging data and the strain rate data of the structural material. Therefore, fatigue life information of the structural material in different heat aging time periods and fatigue life information of different strain rates are obtained, and further, the heat aging sensitivity coefficient can be determined according to the fatigue life information of different heat aging time periods, and the strain rate sensitivity coefficient is determined according to the fatigue life information of different strain rates.
It should be noted that the thermal aging sensitivity coefficient refers to an influence factor of thermal aging time on the life of the material, and the strain rate sensitivity coefficient refers to an influence factor of strain rate on the life of the material.
As an example, a thermal aging data can be obtained by adjusting the thermal aging time of a structural material to be predicted for life, and a strain rate data can be obtained by similarly adjusting the strain rate of the structural material. The impact factor of the heat aging time on the life of the material can then be determined from the heat aging data and the impact factor of the strain rate on the life of the material can be determined from the strain rate data.
And step S30, adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material.
After the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient are obtained, the initial fatigue life can be adjusted through the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient. The adjustment mode can be to respectively control the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to adjust the initial fatigue life so as to obtain two intermediate lives, and further to superimpose the two intermediate lives so as to obtain the predicted life of the structural material.
In the embodiment, the fatigue life of the structural material in the actual strain amplitude can be accurately predicted by adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material under the premise of considering the coupling influence of the thermal aging and the strain rate on the material life.
Based on the above-described first embodiment of the structural material fatigue life prediction method of the present invention, a second embodiment of the structural material fatigue life prediction method of the present invention is proposed.
In this embodiment, the thermal aging data includes: the step S20 includes:
step S201, when it is detected that the strain amplitude of the structural material is equal to the preset strain amplitude, and the strain rate of the structural material is equal to the preset strain rate, fatigue life corresponding to different heat aging time is obtained.
And when the strain amplitude of the structural material is detected to be equal to the set strain amplitude and the strain rate of the structural material is detected to be equal to the set strain rate, the thermal ageing time of the structural material is adjusted, and the fatigue life of the structural material in different thermal ageing times is obtained, so that the fatigue life corresponding to the different thermal ageing times is obtained.
As an example, when the strain amplitude is set to 0.5%, the strain rate is set to 0.04%/s, and when the strain amplitude is detected to be 0.5%, and the strain rate is set to 0.04%/s, the thermal aging time of the structural material is adjusted, for example, the thermal aging times are controlled to 1000h, 5000h, 10000h, and 12000h, respectively, and fatigue lives of the different thermal aging times are recorded, thereby obtaining fatigue lives corresponding to the different thermal aging times, respectively.
Optionally, the step S201 includes:
step 2011, determining a plurality of logarithmic heat aging times corresponding to the heat aging times respectively, and determining a first logarithmic fatigue life corresponding to the fatigue life respectively;
step S2012, determining a plurality of thermal aging data points in a preset coordinate system based on the logarithmic thermal aging time and the first logarithmic fatigue life corresponding to each of the plurality of thermal aging times, and determining a thermal aging sensitivity coefficient based on the plurality of thermal aging data points.
After the fatigue life corresponding to each of the different heat aging times is obtained, carrying out logarithmic treatment on the plurality of heat aging times and the plurality of fatigue life respectively, obtaining the logarithmic heat aging time based on the heat aging time after logarithmic treatment, and obtaining the first logarithmic fatigue life based on the fatigue life after logarithmic treatment. And taking the logarithmic heat ageing time and the first logarithmic fatigue life corresponding to the same heat ageing time as numerical value pairs, thereby obtaining a plurality of numerical value pairs. And when the preset coordinate system is that the transverse axis represents logarithmic heat ageing time and the longitudinal axis represents the first logarithmic fatigue life, mapping a plurality of numerical pairs into the coordinate system, so as to obtain a plurality of heat ageing data points, and further determining a heat ageing sensitivity coefficient based on the plurality of heat ageing data points.
In the present embodiment, the logarithmic processing refers to a process of calculating a logarithmic value with 10 as a base and a certain value as a base. For example, the log heat aging time 1000 is subjected to log processing, and the obtained log heat aging time is 3. In addition, the preset coordinate system proposed by the invention is not limited to the horizontal representation of the heat aging time and the vertical representation of the fatigue life.
As an example, when the heat aging data is {10000 hours, 2000 times }, {12000 hours, 1000 times }, {1000 hours, 4000 times }, {5000 hours, 3000 times }, the corresponding logarithmic values are calculated with 1000 hours, 5000 hours, 10000 hours, 12000 hours as the bases, respectively, thereby obtaining logarithmic heat aging times; similarly, the corresponding logarithmic values can be calculated by taking 2000 times, 1000 times, 4000 times and 3000 times as the base numbers respectively, so as to obtain the first logarithmic fatigue life; then, the logarithmic heat aging time lg10000 corresponding to 10000 hours and the first logarithmic fatigue life lg2000 are taken as a numerical pair, and the logarithmic heat aging time lg12000 corresponding to 12000 hours and the first logarithmic fatigue life lg1000 are taken as a numerical pair, so that the numerical pairs corresponding to 1000 hours and 5000 hours can be obtained, and the detailed description is omitted. After obtaining the plurality of value pairs, mapping the plurality of value pairs into a coordinate system which transversely represents the logarithmic heat aging time and longitudinally represents the first logarithmic fatigue life, thereby obtaining a plurality of heat aging data points, and further determining a heat aging sensitivity coefficient based on the plurality of heat aging data points.
Optionally, step S2012 above further includes:
step A, fitting a plurality of heat aging data points through a preset fitting strategy to obtain a fitting curve formula;
and step B, obtaining the thermal aging sensitivity coefficients corresponding to the thermal aging times respectively based on the slopes in the fitting curve formula.
The fitting strategy includes a plurality of functional formulas, and the plurality of heat aging data points can be fitted through the plurality of functional formulas, so that the functional formula with the best fitting effect is used as a fitting curve formula. The fitted curve formula may be a formula representing a straight line or a formula representing a curve.
After obtaining the plurality of heat aging data points, the plurality of heat aging data points can be fitted according to a plurality of preset functional formulas, and the fitting deviation of each functional formula after fitting is calculated, so that the functional formula with the smallest fitting deviation value is used as a fitting curve formula. And then obtaining the slope of a graph formed by heat aging data points based on a fitting curve formula, and taking the slope as a power and taking a power function formed by heat aging time as a base as a heat aging sensitivity coefficient.
It will be appreciated that, when the heat aging time is fixed, the above power function is a constant since the slope is a constant, resulting in a heat aging susceptibility.
For example, when the slope is a and the thermal aging time is t, the thermal aging sensitivity coefficient corresponding to the thermal aging time t is:
K T ＝t A ,
wherein K is T Representing the thermal ageing susceptibility.
Alternatively, in one possible embodiment, the strain rate data comprises: the step S20 further includes:
step S202, when the strain amplitude of the structural material is detected to be equal to the strain amplitude, and the structural material is not subject to thermal aging, fatigue life corresponding to each of different strain rates is obtained.
In this embodiment, when strain rate data is obtained, it is required to ensure that no thermal aging phenomenon occurs in the structural material and keep the strain amplitude of the structural material consistent with the strain amplitude when the thermal aging data is obtained, so that fatigue lives corresponding to different strain rates can be obtained by using the strain rate as an independent variable. Therefore, when the strain amplitude of the structural material is detected to be the same as that when the thermal aging data is acquired, and the structural material is not thermally aged, fatigue lives corresponding to the different strain rates are acquired.
As an example, on the premise that the strain amplitude of the obtained heat aging data is 0.5%, when it is detected that the structural material is not heat aged and the strain amplitude is 0.5%, fatigue lives of the structural material corresponding to different strain rates are obtained. Thereby obtaining strain rate data.
Optionally, in a possible embodiment, step S20 above further includes:
step X, determining the logarithmic strain rate corresponding to each of the strain rates, and determining the second logarithmic fatigue life corresponding to each of the fatigue life;
and step Y, determining a plurality of strain rate data points in a preset coordinate system based on the corresponding logarithmic strain rate and the second logarithmic fatigue life of the strain rates, and determining a strain rate sensitivity coefficient based on the plurality of strain rate data points.
After the fatigue life corresponding to each of the different strain rates is obtained, the plurality of strain rates and the fatigue life corresponding to each of the plurality of strain rates are subjected to logarithmic treatment respectively, the logarithmic strain rate is obtained based on the strain rate after the logarithmic treatment, and the second logarithmic fatigue life is obtained based on the fatigue life after the logarithmic treatment. And then taking the logarithmic strain rate and the second logarithmic fatigue life corresponding to the same strain rate as numerical pairs, thereby obtaining a plurality of numerical pairs. And when the preset coordinate system is a transverse axis representing logarithmic strain rate and the longitudinal axis represents second logarithmic fatigue life, mapping a plurality of numerical pairs into the coordinate system, so as to obtain a plurality of strain rate data points, and further determining a thermal aging sensitivity coefficient based on the plurality of strain rate data points.
As an example, when strain rate data is {0.0001%/s,2000 times }, {0.001%/s,8000 times }, {0.4%/s,9000 times }, respective corresponding logarithmic values are calculated with 0.0001%/s, 0.001%/s, and 0.4%/s as bases, respectively, to obtain logarithmic strain rates; similarly, the corresponding logarithmic values can be calculated with 2000 times, 8000 times and 9000 times as the base numbers, respectively, to obtain a second logarithmic fatigue life; then, the logarithmic strain rate lg0.0001%/s corresponding to 0.0001%/s and the second logarithmic fatigue life lg2000 are taken as a numerical pair, and the logarithmic strain rate lg0.001%/s corresponding to 0.001%/s and the second logarithmic fatigue life lg8000 are taken as a numerical pair, so that the numerical pair corresponding to 0.4%/s can be obtained, which is not described herein. After the plurality of value pairs are obtained, the plurality of value pairs are mapped into a coordinate system which transversely represents the logarithmic strain rate and longitudinally represents the second logarithmic fatigue life, so that a plurality of strain rate data points are obtained, and the strain rate sensitivity coefficient is determined based on the plurality of strain rate data points.
After a plurality of strain rate data points are obtained, fitting the plurality of strain rate data points according to a preset functional formula, so that the slope of a curve formed by the plurality of strain rate data points is obtained, and then the slope is used as a power function of power by taking the strain rate as a base. When the strain rate is constant, the strain rate sensitivity coefficient may be:
K ε ＝ε B ,
wherein K is ε Epsilon is the strain rate and B is the slope obtained by fitting the strain rate data points.
In the embodiment, the method and the device enable the obtained thermal ageing data and strain rate data to only receive the influence of a single factor (thermal ageing time or strain rate) by limiting the acquisition premise of the thermal ageing data and the acquisition premise of the strain rate data, so that accurate data is provided for the subsequent calculation of the thermal ageing sensitivity coefficient and the strain rate sensitivity coefficient. And then, by means of logarithmic processing and data fitting of the thermal ageing data and the strain rate data, the invention obtains the calculation factors of the thermal ageing on the service life (the slope obtained by fitting the thermal ageing data points) and the calculation factors of the strain rate on the service life (the slope obtained by fitting the strain rate data points) based on the thermal ageing data points and the strain rate data points, thereby quantifying the influence degree of the thermal ageing and the strain rate on the service life and further improving the accuracy of service life prediction.
Based on the above-described first and second embodiments of the structural material fatigue life prediction method of the present invention, a third embodiment of the structural material fatigue life prediction method of the present invention is proposed.
In this embodiment, the step S30 further includes:
step S301, determining a life impact factor according to the product of the thermal ageing sensitivity coefficient and the strain rate sensitivity coefficient;
step S302, determining the predicted life of the structural material through the product of the life influencing factor and the initial fatigue life.
After the thermal ageing sensitivity coefficient and the strain rate sensitivity coefficient are obtained, the thermal ageing nameplate sensitivity coefficient and the strain rate sensitivity coefficient can be multiplied to obtain a product, and the product is used as a life influence factor. The life-affecting factor is then multiplied by the initial fatigue life to obtain the predicted life of the structural material.
As one example, the lifetime impact factor may be expressed as:
α＝K T *K ε ,
wherein alpha is a life-affecting factor, K T For the thermal ageing sensitivity coefficient, K ε Is a strain rate sensitivity coefficient. Wherein KT > 1 represents an increase in fatigue life after heat aging, KT<1 indicates a decrease in fatigue life after heat aging, kt=1 indicates no change in fatigue life after heat aging; k epsilon is the strain rate sensitivity coefficient>1 represents a strain rate change to increase fatigue life, K ε<1 indicates a change in strain rate and decreases fatigue life, and K epsilon=1 indicates no change in strain rate and fatigue life.
After the initial predicted lifetime is obtained, the predicted lifetime can be calculated by the following formula:
wherein,to predict life, N a For initial predicted lifetime, α is a lifetime influencing factor.
As another example, in predicting the fatigue life of a stainless steel, a strain amplitude versus fatigue life curve ε of an unheated aged stainless steel is first obtained a =f (N), the expression of the curve is as follows:
ε a ＝K 1 N K2 +K 3 ，
where k1= 9.988, k2= -0.323, k3=0.065, the curve was fitted with fatigue life data at a strain rate of 0.4%/s and a strain amplitude of between 0.2% and 1.0% in a room temperature environment. Wherein fatigue life data is shown in fig. 3, the horizontal axis in fig. 3 represents fatigue life, and the vertical axis represents strain amplitude.
And then comparing the fatigue life data with the fatigue life data of the unaged stainless steel according to the fatigue life data of the stainless steel with strain amplitude of 0.6% and strain rate of 0.4%/s after heat aging for 1000 hours, 5000 hours, 10000 hours and 12000 hours under the high-temperature environment of 380 ℃. Wherein the heat aging data are shown in fig. 4, wherein the horizontal axis represents heat aging time and the vertical axis represents fatigue life, and the fatigue life corresponding to each of the different heat aging durations is shown in fig. 4. After the logarithmic processing and fitting processing were performed on the data in fig. 4, the obtained slope was 0.185. Thus, kt=t-0.185, where t is the heat aging time (t=1 indicates no heat aging), this example shows an increase in the heat aging time of the stainless steel and a decrease in the fatigue life.
Referring to fig. 5, fig. 5 shows fatigue life for each of the different strain rates. After analysis (log processing, data fitting) of fatigue life data of unaged stainless steel at strain amplitude of 0.6% and strain rate of 0.4%/s to 0.0004%/s, the resulting slope is 0.222, and hence k epsilon = epsilon 0.222, where epsilon is the strain rate, and in this example the maximum strain rate is 0.4%/s and the minimum strain rate is 0.0004%/s, in which range fatigue life decreases as the strain rate decreases.
After the strain rate sensitivity coefficient and the thermal aging sensitivity coefficient are obtained, the service life can be predicted according to the preset thermal aging time, strain amplitude and strain rate. Referring to FIG. 6, FIG. 6 shows the comparison of predicted and measured life at a strain of 0.5% and a strain rate of 0.04%/s for 8000h after heat aging of stainless steel. Wherein, the horizontal axis in fig. 6 represents the test life (measured life), the vertical direction represents the predicted life, and as can be obtained from fig. 6, the fatigue life prediction method provided by the invention can well predict the fatigue life of the structural material considering the influence of the thermal aging and the strain rate after the thermal aging at 380 ℃.
In this embodiment, the present invention can accurately predict the lifetime of the structural material by determining the lifetime influencing factor and determining the predicted lifetime by the product of the lifetime influencing factor and the initial fatigue lifetime.
In addition, the invention also provides a structural material fatigue life prediction system.
Referring to fig. 7, the structural material fatigue life prediction system includes:
the first obtaining module 10 is configured to obtain an actual strain amplitude of a dangerous portion in a structural material, and determine an initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve;
a second acquisition module 20 for acquiring thermal ageing data and strain rate data of the structural material, determining a thermal ageing susceptibility based on the thermal ageing data, and determining a strain rate susceptibility based on the strain rate data;
the life determining module 30 is configured to adjust the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain a predicted life of the structural material.
Optionally, the thermal aging data comprises: the heat aging time and the fatigue life corresponding to the heat aging time, the second acquisition module 20 includes:
and the thermal ageing data acquisition unit is used for acquiring fatigue lives corresponding to different thermal ageing times when the strain amplitude of the structural material is detected to be equal to the preset strain amplitude and the strain rate of the structural material is detected to be equal to the preset strain rate.
Optionally, the second acquisition module 20 includes:
the first logarithmic processing unit is used for determining the logarithmic heat aging time corresponding to each of the plurality of heat aging times and determining the first logarithmic fatigue life corresponding to each of the plurality of fatigue lives;
and the first logarithmic data processing unit is used for determining a plurality of thermal ageing data points in a preset coordinate system based on the logarithmic thermal ageing time and the first logarithmic fatigue life corresponding to each of the plurality of thermal ageing times, and determining a thermal ageing sensitivity coefficient based on the plurality of thermal ageing data points.
Optionally, the logarithmic data processing unit comprises:
the fitting subunit is used for fitting the plurality of heat aging data points through a preset fitting strategy to obtain a fitting curve formula;
and the first coefficient calculating subunit is used for obtaining the thermal ageing sensitivity coefficients corresponding to the thermal ageing times respectively based on the slopes in the fitting curve formula.
Optionally, the strain rate data comprises: the strain rate and fatigue life corresponding to the strain rate, the second acquisition module 20 includes:
and the strain rate data acquisition unit is used for acquiring fatigue life corresponding to each of different strain rates when the strain amplitude of the structural material is detected to be equal to the strain amplitude and the structural material is not subject to thermal aging.
Optionally, the second acquisition module 20 further includes:
a second logarithmic processing unit configured to determine a logarithmic strain rate corresponding to each of the plurality of strain rates, and determine a second logarithmic fatigue life corresponding to each of the plurality of fatigue lives;
and the second logarithmic data processing unit is used for determining a plurality of strain rate data points in a preset coordinate system based on the logarithmic strain rates and the second logarithmic fatigue life corresponding to the plurality of strain rates respectively, and determining a strain rate sensitivity coefficient based on the plurality of strain rate data points.
Optionally, the lifetime determination module 30 includes:
a first product unit for determining a life impact factor from a product of the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient;
a second product unit for determining a predicted lifetime of the structural material from a product of the lifetime influencing factor and the initial fatigue lifetime.
The function implementation of each module in the structural material fatigue life prediction system corresponds to each step in the structural material fatigue life prediction method embodiment, and the function and implementation process of each module are not described in detail herein.
In addition, the invention also provides a structural material fatigue life prediction device, which comprises: the method comprises the steps of a memory, a processor and a structural material fatigue life prediction program stored in the memory and capable of running on the processor, wherein the structural material fatigue life prediction program is executed by the processor to realize the structural material fatigue life prediction method.
The specific embodiment of the fatigue life predicting device for structural materials is basically the same as that of the above-mentioned method for predicting the fatigue life of structural materials, and will not be described herein.
The present invention also proposes a storage medium having stored thereon a structural material fatigue life prediction program which, when executed by a processor, implements the steps of the structural material fatigue life prediction method of the present invention as described above.
The specific embodiment of the storage medium of the present invention is substantially the same as the embodiments of the method for predicting fatigue life of structural material described above, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a car-mounted computer, a smart phone, a computer, or a server, etc.) to perform the method described in the embodiments of the present application.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.

Claims (10)

1. A method for predicting fatigue life of a structural material, the method comprising:
acquiring an actual strain amplitude of a dangerous part in a structural material, and determining an initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve;
acquiring heat aging data and strain rate data of the structural material, determining a heat aging sensitivity coefficient based on the heat aging data, and determining a strain rate sensitivity coefficient based on the strain rate data;
and adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material.
2. The method for fatigue life prediction of structural material according to claim 1, wherein the thermal aging data comprises: the step of obtaining the thermal ageing data of the structural material comprises the following steps:
and when the strain amplitude of the structural material is detected to be equal to the preset strain amplitude, and the strain rate of the structural material is detected to be equal to the preset strain rate, obtaining the fatigue life corresponding to different heat aging time.
3. The method of claim 2, wherein the step of determining a thermal aging susceptibility based on the thermal aging data comprises:
determining a logarithmic heat aging time corresponding to each of the plurality of heat aging times, and determining a first logarithmic fatigue life corresponding to each of the plurality of fatigue lives;
and determining a plurality of heat ageing data points in a preset coordinate system based on the corresponding logarithmic heat ageing time and the first logarithmic fatigue life of the heat ageing time, and determining a heat ageing sensitivity coefficient based on the plurality of heat ageing data points.
4. A method of predicting fatigue life of a structural material as recited in claim 3, wherein the step of determining a thermal aging susceptibility based on a plurality of the thermal aging data points comprises:
fitting a plurality of heat aging data points through a preset fitting strategy to obtain a fitting curve formula;
and obtaining the thermal aging sensitivity coefficients corresponding to the thermal aging times respectively based on the slopes in the fitting curve formula.
5. The method of claim 2, wherein the strain rate data comprises: the step of obtaining strain rate data of the structural material comprises the following steps of:
and when the strain amplitude of the structural material is detected to be equal to the strain amplitude, and the structural material is not subject to thermal aging, obtaining fatigue life corresponding to each of different strain rates.
6. The method of claim 5, wherein the step of determining a strain rate sensitivity coefficient based on the strain rate data comprises:
determining a logarithmic strain rate corresponding to each of the plurality of strain rates, and determining a second logarithmic fatigue life corresponding to each of the plurality of fatigue lives;
determining a plurality of strain rate data points in a preset coordinate system based on the corresponding logarithmic strain rate and the second logarithmic fatigue life of the strain rates, and determining a strain rate sensitivity coefficient based on the plurality of strain rate data points.
7. The method for predicting the fatigue life of a structural material according to any one of claims 1 to 6, wherein the step of adjusting the initial fatigue life based on the thermal aging sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted life of the structural material comprises:
determining a life impact factor from the product of the thermal aging coefficient of sensitivity and the strain rate coefficient of sensitivity;
a predicted lifetime of the structural material is determined by a product of the lifetime influencing factor and the initial fatigue lifetime.
8. A structural material fatigue life prediction system, the structural material fatigue life prediction system comprising:
the first acquisition module is used for acquiring the actual strain amplitude of a dangerous part in the structural material and determining the initial fatigue life corresponding to the actual strain amplitude in a preset fitting curve;
the second acquisition module is used for acquiring heat ageing data and strain rate data of the structural material, determining a heat ageing sensitivity coefficient based on the heat ageing data and determining a strain rate sensitivity coefficient based on the strain rate data;
and the service life determining module is used for adjusting the initial fatigue service life based on the thermal ageing sensitivity coefficient and the strain rate sensitivity coefficient to obtain the predicted service life of the structural material.
9. A structural material fatigue life prediction apparatus, characterized by comprising: a memory, a processor and a structural material fatigue life prediction program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the structural material fatigue life prediction method of any of claims 1 to 7.
10. A storage medium having stored thereon a structural material fatigue life prediction program which, when executed by a processor, implements the steps of the structural material fatigue life prediction method according to any of claims 1 to 7.
CN202311566701.7A 2023-11-22 2023-11-22 Method, system, equipment and storage medium for predicting fatigue life of structural material Pending CN117705616A (en)

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