Fatigue reliability analysis test method and device based on feature simulation piece

Abstract

The invention provides a fatigue reliability analysis test method and device based on a characteristic simulation piece, and the method comprises the steps: carrying out the static strength calculation, determining a dangerous part and stress distribution, building a sensitivity analysis model according to the dangerous part, screening out a target size, reserving the target size on a to-be-designed simulation piece, building a geometric model and a finite element model, manufacturing a feature simulation piece and carrying out statistical analysis on the target size on the simulation piece to obtain distribution parameter information; determining a failure mode and a performance function of the characteristic simulation part, performing reliability analysis on the characteristic simulation part to obtain fatigue life analysis data of the simulation part, and generating a cumulative distribution function curve of the fatigue life of simulation analysis; carrying out a low-cycle fatigue test on the characteristic simulation part, obtaining fatigue life test data, and obtaining a fatigue life experience cumulative distribution function curve of the simulation part test; and comparing the fatigue life experience cumulative distribution curves obtained by simulation analysis with the fatigue life experience cumulative distribution curves obtained by tests, and calculating errors, thereby verifying the correctness of the reliability analysis method.

Description

Technical Field

[0001] This application relates to the technical field of aircraft component design, and particularly to a fatigue reliability analysis test method and device based on a feature simulation component. Background Art

[0002] Performing fatigue reliability test verification on aero-engine components is an essential task to ensure their reliability during service. Currently, the main method for fatigue reliability analysis test of such components is to first design a simulation component based on the force-bearing situation of the components to simulate the fatigue life of these components. Then, a small number of real component fatigue tests are carried out through simplified components or scaled-down components to evaluate the accuracy of the simulation component test results. There are certain limitations in such verification schemes, resulting in relatively low accuracy of fatigue reliability analysis. Summary of the Invention

[0003] This application aims to at least solve one of the technical problems in the related art to some extent.

[0004] To this end, the first object of this application is to propose a fatigue reliability analysis test method based on a feature simulation component.

[0005] The second object of this application is to propose a device.

[0006] The third object of this application is to propose an electronic device.

[0007] The fourth object of this application is to propose a computer-readable storage medium.

[0008] The fifth object of this application is to propose a computer program product.

[0009] To achieve the above object, the first aspect embodiment of this application proposes a fatigue reliability analysis test method based on a feature simulation component, including:

[0010] Establish a finite element analysis model of the component, perform static strength calculation, and generate calculation results;

[0011] Determine the dangerous part of the component according to the calculation results, perform geometric parameterization on the dangerous part to establish a sensitivity analysis model, and determine the target geometric dimensions according to the sensitivity analysis model;

[0012] Determine the stress distribution according to the force-bearing situation of the dangerous part;

[0013] Construct the geometric model and finite element model of the feature simulation component according to the target geometric dimensions and the stress distribution;

[0014] Manufacture the feature simulation piece and perform statistical analysis on the target set of dimensions on the feature simulation piece to obtain distribution parameter information;

[0015] Determine the failure mode and functional function of the feature simulation piece, substitute the obtained distribution parameter information of the target set of dimensions into the reliability analysis, establish a reliability analysis model of the feature simulation piece, and perform reliability analysis to obtain the reliability analysis result, so as to generate an analysis fatigue life cumulative distribution function curve;

[0016] Conduct low-cycle fatigue tests on each of the feature simulation pieces, statistically analyze the life distribution law of the feature simulation pieces, so as to generate an empirical cumulative distribution function curve of the test;

[0017] Compare the analysis fatigue life cumulative distribution function curve obtained by reliability analysis and the empirical cumulative distribution function curve of the test and calculate the error to verify the correctness of the reliability analysis method.

[0018] Optionally, the determining the target geometric dimensions according to the sensitivity analysis model includes:

[0019] Conduct a global sensitivity analysis based on the failure probability to obtain the correlation between each geometric dimension and the fatigue life;

[0020] Determine the geometric dimensions with the correlation greater than the preset threshold as the target geometric dimensions.

[0021] Optionally, the determining the stress distribution according to the force condition of the dangerous part includes:

[0022] Determine the force condition of the dangerous part, and extract the data of the change of the first principal stress along its maximum stress gradient direction with distance from the point of maximum stress as the stress distribution.

[0023] Optionally, the constructing the geometric model and finite element model of the feature simulation piece according to the target geometric dimensions and the stress distribution includes:

[0024] Take the stress distribution as the design goal of the stress gradient and stress distribution of the feature simulation piece;

[0025] Determine the geometric form of the feature simulation piece, and preliminarily establish the geometric model of the feature simulation piece according to the target geometric dimensions;

[0026] Establish a finite element model of the unidirectional compression feature simulation piece, and perform iterative calculations on the finite element model to obtain the model of the simulation piece that meets the design goal.

[0027] Optionally, the performing statistical analysis on the target set of dimensions on the feature simulation piece to obtain distribution parameter information includes:

[0028] Perform a hypothesis test to observe and determine the distribution characteristics that match the data of the target set size on the feature simulation part;

[0029] Determine the distribution parameter information according to the distribution characteristics..

[0030] Optionally, determine the failure mode and functional function of the feature simulation part, substitute the obtained distribution parameter information of the target set size into the reliability analysis, establish a reliability analysis model of the feature simulation part, and perform reliability analysis to obtain the reliability analysis result, so as to generate the fatigue life cumulative distribution function curve of the analysis, including:

[0031] Select a corresponding life model according to the structure of the feature simulation part, define the failure mode, and determine the functional function according to the failure mode.

[0032] To achieve the above object, an embodiment of the second aspect of the present application proposes a fatigue reliability analysis test device based on a feature simulation part, including:

[0033] A calculation module, configured to establish a finite element analysis model of a component, perform static strength calculation, and generate a calculation result;

[0034] A target size determination module, configured to determine the dangerous part of the component according to the calculation result, perform geometric parameterization on the dangerous part to establish a sensitivity analysis model, and determine the target geometric size according to the sensitivity analysis model;

[0035] A stress distribution determination module, configured to determine the stress distribution according to the force condition of the dangerous part;

[0036] A model construction module, configured to construct a geometric model and a finite element model of the feature simulation part according to the target geometric size and the stress distribution;

[0037] A parameter acquisition module, configured to manufacture the feature simulation part and perform statistical analysis on the target set size on the feature simulation part to obtain distribution parameter information;

[0038] A first analysis module, configured to determine the failure mode and functional function of the feature simulation part, substitute the obtained distribution parameter information of the target set size into the reliability analysis, establish a reliability analysis model of the feature simulation part, and perform reliability analysis to obtain the reliability analysis result, so as to generate the fatigue life cumulative distribution function curve of the analysis;

[0039] A second analysis module, configured to perform a low-cycle fatigue test on each of the feature simulation parts, statistically analyze the life distribution law of the feature simulation parts, so as to generate an empirical cumulative distribution function curve of the test;

[0040] A comparison module, configured to compare the cumulative distribution function curve of the analyzed fatigue life obtained from the reliability analysis and the empirical cumulative distribution function curve of the test, calculate the error, and verify the correctness of the reliability analysis method.

[0041] To achieve the above object, an embodiment of the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0042] The memory stores computer-executable instructions;

[0043] The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of the first aspect.

[0044] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of the first aspect.

[0045] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, and when the computer program is executed by a processor, it implements the method according to any one of the first aspect.

[0046] The fatigue reliability analysis test method, device, electronic device and storage medium based on the feature simulation part provided by the present application batch-process the simulation part by using the same or similar processing technology as the parts, measure the distribution law of each geometric dimension and input it into the reliability analysis of the simulation part to obtain the reliability analysis result of the simulation part. The analysis result of the simulation part is compared with the test result, thereby improving the accuracy of the fatigue reliability analysis.

[0047] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and / or additional aspects and advantages of the present application will become apparent and be easily understood from the following description of the embodiments in conjunction with the drawings, where:

[0049] Figure 1 is a schematic flowchart of a fatigue reliability analysis test method based on a feature simulation part provided by an embodiment of the present application;

[0050] Figure 2 is a schematic diagram of the geometric parameterization of the booster cylinder mounting seat;

[0051] Figure 3For the installation seat of the afterburner cylinder body based on the results of global sensitivity analysis of failure probability;

[0052] Figure 4 Schematic diagram of the key dimensions of the installation seat;

[0053] Figure 5 Comparison diagram of stress gradients;

[0054] Figure 6 Distribution diagram of the key dimension rules;

[0055] Figure 7 Comparison diagram of the empirical cumulative distribution curves of the installation seat of the afterburner cylinder body;

[0056] Figure 8 Schematic diagram of the structure of a fatigue reliability analysis test device based on a feature simulation part provided by an embodiment of the present application. Detailed implementation manners

[0057] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0058] Carrying out fatigue reliability test verification for aero-engine components is an essential work to ensure their reliability during service. At present, the main methods for fatigue reliability analysis test verification of such components are to first design a simulation part according to the stress conditions of the components to simulate the fatigue life of these components. Then, a small number of real part fatigue tests are carried out through simplified parts or scaled-down parts to evaluate the accuracy of the simulation part test results. There are certain limitations in such verification schemes, mainly manifested as: one is that when designing the simulation part, only the simulation of characteristics such as the stress-strain distribution, stress gradient, and stress concentration coefficient of local dangerous parts is considered, without considering the influence of geometric uncertainty factors. The other is that some components such as turbine casings not only have very complex geometric shapes, but also are subjected to various loads such as temperature, high-temperature air pressure, and load transfer from other components. Using real parts for test verification, on the one hand, it needs to be simplified and the cost is relatively high, and on the other hand, it is very difficult to simulate the real load conditions through traditional loading devices. Therefore, a fatigue reliability analysis test verification method based on a feature simulation part that fully considers the influence of geometric uncertainty and incorporates it into the design criteria of the simulation part is required.

[0059] The present invention first designs a feature simulation component considering geometric uncertainty according to the local force conditions and local geometric characteristic dimensions of aero-engine components, then formulates fatigue reliability test conditions and life reliability evaluation schemes based on the actual working conditions during service, and gives a fatigue reliability analysis test verification scheme based on the feature simulation component, providing a technical approach for carrying out fatigue reliability analysis test verification of aero-engine components.

[0060] For the fatigue reliability analysis test verification of dangerous components of aero-engines, the existing simulation component design methods do not consider the influence of geometric uncertainty factors and the test verification methods are difficult to implement. The present invention aims to propose a fatigue reliability analysis test verification scheme based on feature simulation components. First, a parametric model of the real structure is established and a global sensitivity analysis based on the failure probability is carried out to screen out the geometric dimensions that have a greater impact on fatigue reliability. When designing the feature simulation component, not only the local stress distribution characteristics are considered, but also the screened geometric dimensions (such as chamfers) are retained in the feature simulation component. In this way, after batch processing of the simulation components, the uncertainty information of these dimensions can be obtained by measurement and taken into account in the subsequent reliability analysis, so as to be able to consider the influence of the geometric uncertainty of the real structure on the fatigue life, and further realize the fatigue reliability analysis test verification of dangerous components based on feature simulation components.

[0061] The embodiment of the present application provides a fatigue reliability analysis test method based on feature simulation components. Figure 1 It is a schematic flow chart of a fatigue reliability analysis test method provided by an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0062] Step 101, establish a finite element analysis model of the component, perform static strength calculation, and generate calculation results;

[0063] Step 102, determine the dangerous part of the component according to the calculation results, perform geometric parameterization on the dangerous part to establish a sensitivity analysis model, and determine the target geometric dimensions according to the sensitivity analysis model;

[0064] Step 103, determine the stress distribution according to the force condition of the dangerous part;

[0065] Step 104, construct the geometric model and finite element model of the feature simulation component according to the target geometric dimensions and the stress distribution;

[0066] Step 105, manufacture the feature simulation component and perform statistical analysis on the target set dimensions on the feature simulation component to obtain distribution parameter information; in this embodiment, the designed simulation components are batch processed by the same or similar processes, and the key dimensions retained on each simulation component are measured and recorded.

[0067] Step 106: Determine the failure mode and functional function of the feature simulation part, substitute the obtained distribution parameter information of the target set size into the reliability analysis, establish a reliability analysis model for the feature simulation part, and perform reliability analysis to obtain the reliability analysis result, so as to generate an analysis fatigue life cumulative distribution function curve;

[0068] Step 107: Conduct a low-cycle fatigue test on each of the feature simulation parts, statistically analyze the life distribution law of the feature simulation parts, so as to generate an empirical cumulative distribution function curve of the test;

[0069] Step 108: Compare the analysis fatigue life cumulative distribution function curve obtained by reliability analysis with the empirical cumulative distribution function curve of the test and calculate the error to verify the correctness of the reliability analysis method.

[0070] In this embodiment, the error calculation is as follows.

[0071]

[0072] Among them, A s is the area under the empirical cumulative distribution function curve of the experimental data, and A m is the area under the empirical cumulative distribution function curve calculated by reliability analysis.

[0073] Optionally, step 102 determines the target geometric dimensions according to the sensitivity analysis model, including:

[0074] Conduct a global sensitivity analysis based on the failure probability to obtain the correlation between each geometric dimension and the fatigue life;

[0075] Determine the geometric dimensions with the correlation greater than the preset threshold as the target geometric dimensions.

[0076] Optionally, step 103 determines the stress distribution according to the force condition of the dangerous part, including:

[0077] Determine the force condition of the dangerous part, and extract the data of the first principal stress changing with distance along its maximum stress gradient direction from the point of maximum stress as the stress distribution.

[0078] Optionally, step 104 constructs the geometric model and finite element model of the feature simulation part according to the target geometric dimensions and the stress distribution, including:

[0079] Use the stress distribution as the design target of the stress gradient and stress distribution of the feature simulation part;

[0080] Determine the geometric form of the feature simulation part, and preliminarily establish the geometric model of the feature simulation part according to the target geometric dimensions;

[0081] Establish a finite element model of the unidirectional compression feature simulation part, and perform iterative calculations on the finite element model to obtain the model of the simulation part that meets the design objectives.

[0082] Optionally, step 105 statistically analyzes the target set dimensions on the feature simulation part to obtain distribution parameter information, including:

[0083] Perform hypothesis testing observations to determine the distribution characteristics that the data of the target set dimensions on the feature simulation part conform to;

[0084] Determine the distribution parameter information according to the distribution characteristics. In this embodiment, statistical analysis is performed on the recorded key dimension data, hypothesis testing is performed to observe what distribution these data conform to, and the obtained distribution parameter information is imported into the reliability analysis of the simulation part. For example, when conforming to a normal distribution, there are two distribution parameters, namely the mean and the variance.

[0085] Optionally, step 106 determines the failure mode and performance function of the feature simulation part, substitutes the obtained distribution parameter information of the target set dimensions into the reliability analysis, establishes a reliability analysis model of the feature simulation part, and performs reliability analysis to obtain the reliability analysis result, so as to generate the fatigue life cumulative distribution function curve of the analysis, including:

[0086] Select the corresponding life model according to the structure of the feature simulation part, define the failure mode, and determine the performance function according to the failure mode.

[0087] In a possible embodiment, the method steps are as follows:

[0088] Taking the afterburner barrel of an aeroengine as an example, according to the fatigue reliability analysis test verification method proposed in this article, perform fatigue reliability test verification on the afterburner barrel mounting seat based on the feature simulation part. The specific work content is as follows:

[0089] Step 1: Establish a finite element calculation model of the afterburner barrel and carry out static strength calculation.

[0090] Step 2: According to the finite element calculation results, determine that the dangerous part occurs at the afterburner barrel mounting seat. For this part, parametric modeling of the afterburner barrel mounting seat is carried out. The specific geometric dimension parameters are as Figure 2, there are a total of 4 geometric dimensions, namely the overall rib height h1, rib width h2, track seat width h3, and chamfer radius h4. To make the analysis more comprehensive, when performing global sensitivity analysis based on the failure probability, the uncertainty information of materials and loads is also added. The results of the global sensitivity analysis are as shown in Figure 3 shown.

[0091] It can be seen from the analysis results that the two dimensions of the overall rib height h1 and rib width h2 have a greater impact on the fatigue life. Therefore, these two dimensions are retained as key dimensions in the subsequent simulation part design.

[0092] Step 3: According to the force condition at the installation seat of the afterburner cylinder, extract the data of the first principal stress varying with distance along its maximum stress gradient direction from the point of maximum stress as the design goal of the stress gradient and stress distribution of the characteristic simulation part. Determine the geometric form of the characteristic simulation part, retain the geometric dimensions screened in Step 2, and preliminarily establish the geometric model of the characteristic simulation part. Establish the finite element model of the unidirectional compression characteristic simulation part and perform iterative calculations on the finite element model to obtain the configuration of the simulation part that meets the design requirements. The schematic diagram of the characteristic simulation part of the afterburner cylinder installation seat and the stress gradient comparison diagram are as follows.

[0093] Step 4: Batch process the designed simulation parts using the same or similar processes, and measure and record the key dimensions retained on each simulation part.

[0094] Step 5: Batch process the simulation parts, measure the retained dimensions and conduct statistical analysis to obtain the distribution law of dimensional uncertainty. Both of the two key dimensions follow a normal distribution, and the distribution law is as shown in Figure 6 shown.

[0095] Step 6: Select a suitable life model according to the actual situation, define the failure mode, and determine the performance function. Since the installation seat of the afterburner cylinder is subjected to large forces and conforms to the characteristics of low-cycle fatigue, the elastic-plastic total strain-fatigue life prediction model combining the Basquin formula and the Manson-Coffin formula is adopted. The low-cycle fatigue life prediction formula is as follows:

[0096]

[0097] where, Δε t is the total strain; ε e is the elastic strain; ε p is the plastic strain; σ' f is the fatigue strength coefficient, which may refer to the stress value at which the material fails under fatigue loading; E is the elastic modulus, indicating the stiffness of the material in the elastic deformation stage; N fis the fatigue life, i.e., the number of cycles that the material can withstand under fatigue loading; b and c are exponents related to the material properties, which describe the rates of change of the fatigue strength coefficient and fatigue strain with the fatigue life; ε' f is the fatigue strain coefficient, representing the strain level of the material under fatigue loading.

[0098] The fatigue life threshold of the afterburner barrel under the service environment is 12,000 cycles. Therefore, the performance function of the afterburner barrel is defined as:

[0099] g(X) = N f - 12,000

[0100] That is, when the calculated fatigue life N f is greater than 12,000, the structure is considered safe. When the calculated fatigue life N f is less than 12,000, the structure is considered to have failed.

[0101] Step Six: Substitute the distribution law of dimensional uncertainty into the reliability analysis, establish the reliability analysis and sensitivity analysis models of the afterburner barrel mounting seat, and conduct fatigue reliability analysis on the afterburner barrel mounting seat to obtain the reliability analysis results and life distribution law of the simulation parts.

[0102] Step Seven: Conduct low-cycle fatigue experiments on the afterburner barrel mounting seat, record the life at the cracking of each simulation part, and analyze to obtain the experimental results of the life distribution law of the simulation parts.

[0103] Step Eight: Compare the experimental results with the analysis results, observe the error analysis between the two. The comparison results of the afterburner barrel mounting seat are as Figure 7 shown, and the error between the test and the analysis is 8.4%.

[0104] Among them, the calculation method of the error is as follows

[0105]

[0106] E is the error, A s is the area under the curve of the empirical cumulative distribution function of the experimental data, and A m is the area under the curve of the empirical cumulative distribution function calculated by the reliability analysis.

[0107] To implement the above embodiments, the present application also proposes a fatigue reliability analysis test device based on characteristic simulation parts. Figure 8 FIG. is a schematic structural diagram of a fatigue reliability analysis test device based on characteristic simulation parts provided by an embodiment of the present application. As Figure 8 shown, the device includes:

[0108] A calculation module 810 for establishing a finite element analysis model of parts, performing static strength calculations, and generating calculation results;

[0109] A target dimension determination module 820 for determining the dangerous parts of the parts according to the calculation results, geometrically parameterizing the dangerous parts to establish a sensitivity analysis model, and determining the target geometric dimensions according to the sensitivity analysis model;

[0110] A stress distribution determination module 830 for determining the stress distribution according to the force conditions of the dangerous parts;

[0111] A model construction module 840 for constructing a geometric model and a finite element model of the feature simulation part according to the target geometric dimensions and the stress distribution;

[0112] A parameter acquisition module 850 for manufacturing the feature simulation part and statistically analyzing the target set dimensions on the feature simulation part to obtain distribution parameter information; A first analysis module for determining the failure mode and functional function of the feature simulation part, substituting the distribution parameter information of the target set dimensions into the reliability analysis, establishing a reliability analysis model of the feature simulation part, and performing reliability analysis to obtain a reliability analysis result, so as to generate a cumulative distribution function curve of the life;

[0113] A first analysis module 860 for determining the failure mode and functional function of the feature simulation part, substituting the obtained distribution parameter information of the target set dimensions into the reliability analysis, establishing a reliability analysis model of the feature simulation part, and performing reliability analysis to obtain a reliability analysis result, so as to generate an analysis of the cumulative distribution function curve of the fatigue life;

[0114] A second analysis module 870 for performing low-cycle fatigue tests on each of the feature simulation parts, statistically analyzing the life distribution law of the feature simulation parts, so as to generate an empirical cumulative distribution function curve of the test;

[0115] A comparison module 880 for comparing the cumulative distribution function curve of the analyzed fatigue life obtained by reliability analysis and the empirical cumulative distribution function curve of the test and calculating the error to verify the correctness of the reliability analysis method.

[0116] To implement the above embodiments, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0117] To implement the above embodiments, the present application also provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method provided by the foregoing embodiments when executed by a processor.

[0118] To implement the above embodiments, the present application also provides a computer program product including a computer program, which implements the method provided by the foregoing embodiments when executed by a processor.

[0119] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0120] It should be noted that personal information from users should be collected for legal and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and signing an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0121] The present application anticipates providing embodiments for users to selectively block the use or access of personal information data. That is, the present disclosure anticipates providing hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0122] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0123] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0124] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0125] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0126] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0127] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0128] In addition, in each embodiment of the present application, the functional units can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0129] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A fatigue reliability analysis test method based on a feature simulation component, characterized in that, It includes the following steps: Establish a finite element analysis model of the component, conduct static strength calculation and generate calculation results; Determine the dangerous part of the component according to the calculation results, parameterize the geometry of the dangerous part to establish a sensitivity analysis model, and determine the target geometric dimensions according to the sensitivity analysis model; Determine the stress distribution according to the force condition of the dangerous part; Construct the geometric model and finite element model of the characteristic simulation part according to the target geometric dimensions and the stress distribution; Manufacture the characteristic simulation part and conduct statistical analysis on the target set dimensions on the characteristic simulation part to obtain distribution parameter information; Determine the failure mode and function function of the characteristic simulation part, substitute the obtained distribution parameter information of the target set dimensions into the reliability analysis, establish a reliability analysis model of the characteristic simulation part, and conduct reliability analysis to obtain the reliability analysis result, so as to generate the fatigue life cumulative distribution function curve of the analysis; Conduct low-cycle fatigue tests on each of the characteristic simulation parts, statistically analyze the life distribution law of the characteristic simulation parts, so as to generate the empirical cumulative distribution function curve of the test; Compare the fatigue life cumulative distribution function curve of the analysis obtained by the reliability analysis and the empirical cumulative distribution function curve of the test, calculate the error, and verify the correctness of the reliability analysis method.

2. The method according to claim 1, wherein The determination of the target geometric dimensions according to the sensitivity analysis model includes: Conduct global sensitivity analysis based on the failure probability to obtain the correlation between each geometric dimension and the fatigue life; Determine the geometric dimensions with the correlation greater than the preset threshold as the target geometric dimensions.

3. The method according to claim 2, wherein The determination of the stress distribution according to the force condition of the dangerous part includes: Determine the force condition of the dangerous part, and extract the data of the change of the first principal stress with distance along its maximum stress gradient direction from the stress maximum point as the stress distribution.

4. The method according to claim 3, wherein The construction of the geometric model and finite element model of the characteristic simulation part according to the target geometric dimensions and the stress distribution includes: Take the stress distribution as the design target of the stress gradient and stress distribution of the characteristic simulation part; Determine the geometric form of the characteristic simulation part, and preliminarily establish the geometric model of the characteristic simulation part according to the target geometric dimensions; Establish a finite element model of the unidirectional compression characteristic simulation part, and conduct iterative calculation on the finite element model to obtain the model of the simulation part that meets the design target.

5. The method according to claim 4, characterized in that, The statistical analysis of the target set dimensions on the characteristic simulation part to obtain distribution parameter information includes: Conduct hypothesis test observation to determine the distribution characteristics that the data of the target set dimensions on the characteristic simulation part conform to; Determine the distribution parameter information according to the distribution characteristics.

6. The method according to claim 5, characterized in that, The determination of the failure mode and function function of the characteristic simulation part, substituting the obtained distribution parameter information of the target set dimensions into the reliability analysis, establishing a reliability analysis model of the characteristic simulation part, and conducting reliability analysis to obtain the reliability analysis result, so as to generate the fatigue life cumulative distribution function curve of the analysis, includes: Select a corresponding life model according to the structure of the feature simulation part, define the failure mode, and determine the function function according to the failure mode.

7. A fatigue reliability analysis test device based on a feature simulation component, characterized in that It includes: A calculation module, configured to establish a finite element analysis model of the component, perform static strength calculation, and generate calculation results; A target dimension determination module, configured to determine the dangerous part of the component according to the calculation results, perform geometric parameterization on the dangerous part to establish a sensitivity analysis model, and determine the target geometric dimension according to the sensitivity analysis model; A stress distribution determination module, configured to determine the stress distribution according to the force condition of the dangerous part; A model construction module, configured to construct a geometric model and a finite element model of the feature simulation part according to the target geometric dimension and the stress distribution; A parameter acquisition module, configured to manufacture the feature simulation part and perform statistical analysis on the target set dimensions on the feature simulation part to obtain distribution parameter information; A first analysis module, configured to determine the failure mode and function function of the feature simulation part, substitute the obtained distribution parameter information of the target set dimensions into the reliability analysis, establish a reliability analysis model of the feature simulation part, and perform reliability analysis to obtain a reliability analysis result, so as to generate an analysis fatigue life cumulative distribution function curve; A second analysis module, configured to perform low-cycle fatigue tests on each of the feature simulation parts, statistically analyze the life distribution law of the feature simulation parts, so as to generate an empirical cumulative distribution function curve of the test; A comparison module, configured to compare the analysis fatigue life cumulative distribution function curve obtained by reliability analysis and the empirical cumulative distribution function curve of the test, calculate the error, and verify the correctness of the reliability analysis method.

8. An electronic device, characterized in that, It includes: A processor, and a memory communicatively connected to the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Computer execution instructions are stored in the computer-readable storage medium, and when the computer execution instructions are executed by the processor, they are used to implement the method according to any one of claims 1-6.

10. A computer program product, characterized in that, It includes a computer program, which when executed by the processor implements the method according to any one of claims 1-6.

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