A random spectrum embedding-based blade fatigue reliability analysis method and system

Abstract

The application belongs to the technical field of gas turbine reliability analysis, and discloses a blade disc fatigue reliability analysis method and system based on random spectrum embedding; wherein the blade disc fatigue reliability analysis method comprises: based on a plurality of gas turbine blade discs to be analyzed for reliability, acquiring blade disc characteristic parameters, and based on measured data of each blade disc characteristic parameter, determining the probability distribution of each blade disc characteristic parameter; inputting the acquired blade disc characteristic parameters and the determined probability distribution of each blade disc characteristic parameter into a blade disc fatigue reliability analysis model based on random spectrum embedding to obtain a failure probability. The application adopts a blade disc fatigue reliability analysis model based on random spectrum embedding to perform reliability calculation, greatly improves the accuracy and efficiency of gas turbine blade disc fatigue reliability analysis, and has the advantages of self-adaptation, accuracy, high efficiency and the like.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of gas turbine reliability analysis, and particularly relates to a blade disc fatigue reliability analysis method and system based on random spectrum embedding. BACKGROUND

[0002] The gas turbine is a key core equipment of clean power generation and power device, and represents the advanced technology level and the top manufacturing level of a country. The blade disc (blade wheel disc) system as the core component of heat and power conversion of the gas turbine often bears the coupling effect of factors such as thermal load, centrifugal load and aerodynamic load in the running process, and is extremely prone to fatigue failure; at the same time, in the manufacturing and running process of the gas turbine blade disc, multi-source uncertainty factors such as structure parameters and environmental parameters exist widely, which has a serious impact on the actual performance and safety of the blade disc. Therefore, the reliability analysis of the blade disc system has extremely important significance for guaranteeing the economy and safety of the operation and maintenance of the gas turbine.

[0003] In the prior art, the traditional gas turbine blade disc reliability analysis usually relies on a data-driven method, and the following defects still exist:

[0004] 1) The construction of the data model often needs to call the original simulation analysis program a lot, and there are technical problems such as high calculation cost and low calculation efficiency; in addition, the accuracy of the traditional data model for structure failure probability evaluation is also difficult to satisfy people;

[0005] 2) The reliability of the reliability analysis result is seriously restricted by the data quantity and the effectiveness of the simulation model; in addition, for the gas turbine blade disc with complex shape and unclear actual working characteristics, the effectiveness of the result when only relying on numerical simulation is also to be discussed. SUMMARY

[0006] The purpose of the present application is to provide a blade disc fatigue reliability analysis method and system based on random spectrum embedding, to solve one or more of the above technical problems. The technical scheme provided by the present application uses a blade disc fatigue reliability analysis model based on random spectrum embedding for reliability calculation, greatly improves the accuracy and efficiency of the gas turbine blade disc fatigue reliability analysis, and has the advantages of self-adaptation, accuracy, efficiency and the like.

[0007] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0008] The blade disc fatigue reliability analysis method based on random spectrum embedding provided by the present application comprises the following steps:

[0009] Based on a plurality of gas turbine blade discs to be analyzed for reliability, the blade disc characteristic parameters are obtained, and the probability distribution of each blade disc characteristic parameter is determined based on the measured data of each blade disc characteristic parameter;

[0010] The obtained blade characteristic parameters and the determined probability distribution of each blade characteristic parameter are input into a blade fatigue reliability analysis model based on random spectrum embedding to perform reliability calculation, and failure probabilities of the plurality of gas turbine blades to be reliability analyzed are obtained.

[0011] The method is further improved in that the step of obtaining blade characteristic parameters based on the plurality of gas turbine blades to be reliability analyzed comprises:

[0012] Based on the plurality of gas turbine blades to be reliability analyzed, structure parameters, environmental parameters and load parameters are selected, dimensionality reduction is performed by using a manifold learning method, and blade characteristic parameters are obtained.

[0013] The method is further improved in that the plurality of gas turbine blades are a plurality of gas turbine blades of the same type and batch.

[0014] The method is further improved in that, in the blade fatigue reliability analysis model based on random spectrum embedding,

[0015] The calculation expression of the failure probability of the gas turbine blade is

[0016]

[0017] In the formula, is the failure probability of the gas turbine blade; B is the total number of bootstrap repeated sampling, and b represents the bth sampling; is the failure probability of the gas turbine blade in the bth sampling;

[0018]

[0019] In the formula, is the probability mass of the kth sub-domain; is the conditional failure probability of the kth sub-domain in the bth sampling;

[0020] wherein the B sampling results constitute a set

[0021]

[0022] In the formula, the superscript (b) represents the bth sampling; is the random spectrum embedding approximation of the limit state function; is an index pair, wherein the element k=(l,p) in the index pair is used to mark the residual expansion of SSE, l∈{0,...,L} represents the expansion level of the residual, L is the total number of expansion levels, and p∈{1,···,P l} represents the residual expansion times index at a given level, P l represents the total expansion times at the lth level; D is the kth SSE sub-domain of X is the terminal domain set; the union of all terminal domains constitutes the original parameter space, denoted as is an indicator function, which is 1 in the subspace , otherwise 0; SSE is a random spectral embedding.

[0023] The further improvement of the method of the application is that, estimated by a probabilistic simulation method.

[0024] The further improvement of the method of the application is that, solved by a spectral expansion technique.

[0025] The further improvement of the method of the application is that, in the random spectral embedding-based blade fatigue reliability analysis model,

[0026] The sub-domain is obtained by an adaptive sequential domain segmentation strategy to realize active learning iterative approximation of the localized behavior of the model.

[0027] The further improvement of the method of the application is that, the adaptive sequential domain segmentation strategy is a subdivision domain screening strategy, a subdivision domain segmentation strategy and a sample enrichment strategy.

[0028] The further improvement of the method of the application is that, when training the random spectral embedding-based blade fatigue reliability analysis model, the calculation expression of the limit state function of the sample is,

[0029] g(X)=N-N0;

[0030] In the formula, g(X) is a limit state function; N0 is a preset safe life; N is a predicted life obtained by a digital-physical fusion finite element method driven gas turbine blade fatigue life prediction model;

[0031] The construction steps of the digital-physical fusion finite element method driven gas turbine blade fatigue life deterministic prediction model include:

[0032] ​The Latin hypercube sampling method is used to extract a group of characteristic parameters. In the physical space, the full-scale components of the gas turbine bladed disk corresponding to each group of characteristic parameters and the test conditions are provided. Thermo-fluid-structure interaction strength or vibration characteristic tests are carried out, and the actual working state response of the gas turbine bladed disk is obtained at the preset observation points. In the numerical space, a three-dimensional high-fidelity finite element analysis model corresponding to each group of characteristic parameters is established, and thermo-fluid-structure interaction nonlinear finite element analysis is carried out to obtain the numerical solution of the mechanical behavior at the observation points.

[0033] An optimization problem is constructed to correct errors in the finite element analysis process, making the numerical solution approximate the actual mechanical behavior of the structure. The optimization problem involves modifying the numerical space model by adjusting parameters based on the actual physical quantity information obtained from observation points in physical space. The mathematical expression is as follows:

[0034]

[0035] In the formula, e(Θ,Ω) represents the relative error of the observation point in numerical and physical space measurement information; Θ represents the decision variable for setting boundary conditions; and Ω represents the decision variable for calculation process parameters. These are the measurement information of the nth observation point under the mth set of characteristic parameters in the numerical and material spaces, respectively.

[0036] Solve the optimization problem to obtain the combination of decision variables that maximizes the similarity between the numerical and physical space results, and construct a high-precision numerical-physical fusion finite element model of the gas turbine bladed disk;

[0037] Based on a high-precision finite element model of gas turbine bladed disks, the physical field information of the bladed disks under a given combination of input characteristic parameters is calculated, and key fatigue failure sites and main failure modes are identified. A deterministic prediction model for the fatigue life of gas turbine bladed disks driven by the finite element method of fusion of input characteristic parameters and service life is constructed.

[0038] This invention provides a bladed disk fatigue reliability analysis system based on random spectrum embedding, comprising:

[0039] The data acquisition module is used to acquire the characteristic parameters of multiple gas turbine bladed disks to be analyzed for reliability, and to determine the probability distribution of each characteristic parameter based on the measured data of each bladed disk characteristic parameter.

[0040] The failure probability acquisition module is used to input the acquired bladed disk characteristic parameters and the determined probability distribution of each bladed disk characteristic parameter into a bladed disk fatigue reliability analysis model based on random spectrum embedding to perform reliability calculations and obtain the failure probability of the multiple gas turbine bladed disks to be reliability analyzed.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a method for fatigue reliability analysis of bladed disks based on random spectrum embedding. This method employs a fatigue reliability analysis model based on random spectrum embedding for reliability calculation, demonstrating broad applicability to complex gas turbine bladed disks. It significantly improves the efficiency and accuracy of fatigue reliability analysis for gas turbine bladed disks, offering advantages such as adaptability, accuracy, and high efficiency. Further, the invention utilizes a random spectrum embedding method for gas turbine bladed disk reliability analysis. This method combines global spectral representation with adaptive sequential domain decomposition, exhibiting strong global convergence and sufficient local approximation accuracy, thereby enhancing the accuracy and efficiency of reliability analysis.

[0043] In the method of this invention, the main feature parameters are extracted while maintaining the geometric topological relationship of the parameter space through manifold learning, which reduces the complexity of reliability analysis and improves the efficiency of reliability analysis.

[0044] In this invention, the method comprehensively considers multiple factors affecting the fatigue life of gas turbine bladed disks, mines key characteristic parameters, and establishes a data-physical fusion fatigue life prediction model. When predicting the fatigue life of gas turbine bladed disks based on the thermo-fluid-structure interaction finite element method, the measured data in physical space is used as a reference to construct an optimization problem. The numerical space is corrected by updating parameters, realizing full interaction between numerical simulation and physical experiment, thereby obtaining a simulation model with higher accuracy and reliability, and improving the accuracy of reliability analysis. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art are briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a method for analyzing the fatigue reliability of bladed disks based on random spectrum embedding, provided in an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of the algorithm flow of the random spectrum embedding model for reliability analysis in an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of a bladed disk fatigue reliability analysis system based on random spectrum embedding provided in an embodiment of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0051] The present invention will now be described in further detail with reference to the accompanying drawings:

[0052] Please see Figure 1 The present invention provides a gas turbine bladed disk reliability analysis method based on random spectrum embedding, comprising the following steps:

[0053] Step 1: Based on multiple gas turbine bladed disks to be reliably analyzed, obtain the characteristic parameters of the bladed disks, and determine the probability distribution of each characteristic parameter based on the measured data of each characteristic parameter. Specifically, in the process of obtaining the characteristic parameters of the multiple gas turbine bladed disks to be reliably analyzed, a manifold learning method is used to reduce the dimensionality and filter and determine the characteristic parameters of the bladed disks. The multiple gas turbine bladed disks are multiple gas turbine bladed disks of the same model and batch. The obtained characteristic parameters of the bladed disks are one or more of the following: structural parameters, environmental parameters, and load parameters.

[0054] Step 2: Input the bladed disk characteristic parameters obtained in Step 1 and the probability distribution of each bladed disk characteristic parameter into the bladed disk fatigue reliability analysis model based on random spectrum embedding to perform reliability calculation and obtain the failure probability of the multiple gas turbine bladed disks to be analyzed for reliability.

[0055] The technical solution provided by this invention utilizes a bladed disk fatigue reliability analysis model based on random spectrum embedding. By combining global spectrum representation with a random spectrum embedding method based on adaptive sequential domain decomposition for reliability calculation, it greatly improves the accuracy and efficiency of gas turbine bladed disk fatigue reliability analysis and has the advantages of being adaptive, accurate, and efficient.

[0056] In a specific and exemplary embodiment of the present invention, step 1 may include the following steps: During the entire lifecycle of the gas turbine from start-up to operation to shutdown, identify random input parameters affecting the fatigue life of the bladed disk, such as structural parameters, environmental parameters, and load parameters. Based on measured data (exemplary, but also based on past processing and operating experience), determine its probability distribution model (interpretive, such as normal distribution, uniform distribution, etc.). Subsequently, through manifold learning methods such as isometric feature mapping (ISOMAP), dimensionality reduction of the feature parameters is achieved while maintaining the geometric topological relationship of the parameter space. The main feature parameters extracted after dimensionality reduction are denoted as an M-dimensional vector X = (x1, x2, ..., x...). M The corresponding feature parameter space is denoted as . Then, through the Rosenblatt transformation, D is... X Equal probability mapping to the standard normal space D U The transformed feature parameters are denoted as U = (u1, u2, ..., u...). M ).

[0057] Please see Figure 2 In this embodiment of the invention, in the bladed disk fatigue reliability analysis model based on random spectrum embedding,

[0058] The Random Spectral Embedding Surrogate (SSE) model combines global spectral representation with adaptive sequential domain decomposition. The former gives the algorithm strong global convergence, while the latter ensures sufficient local approximation accuracy. Specifically, a random sampling method is used to sample 2N... ref The set of feature parameters is input into the gas turbine bladed disk fatigue life prediction model driven by the finite element method of data fusion, and the corresponding limit state function value (LSF) is calculated to construct an initial sample set for model initialization; wherein each sample in the initial sample set includes the input parameters and their corresponding limit state function value.

[0059] Further illustratively, this embodiment of the invention establishes a limit state function (LSF) based on the obtained gas turbine bladed disk fatigue life prediction model driven by the fusion of numerical and physical finite element methods. The limit state function is as follows:

[0060] g(X) = N - N0;

[0061] In the formula, g(X) is the limit state function; N0 is the preset safe life; N is the predicted life obtained by the gas turbine bladed disk fatigue life prediction model driven by the finite element method of data fusion.

[0062] The steps involved in constructing a deterministic prediction model for the fatigue life of gas turbine bladed disks driven by the fusion of data and matter finite element methods include:

[0063] The Latin hypercube sampling method (LHS) is used to extract a set of feature parameters {X1, X2, ..., X...} a In the physical space, test conditions such as full-scale components and loading environment of the gas turbine bladed disk corresponding to each set of parameters are provided to carry out thermal-fluid-structure interaction strength or vibration characteristic tests. C representative and easily measurable parts are selected as observation points, and the actual working state response of the bladed disk is obtained through contact or non-contact measurement methods. In the numerical space, a three-dimensional high-fidelity finite element analysis model corresponding to each set of parameters is established, and thermal-fluid-structure interaction nonlinear finite element analysis is carried out to obtain the numerical solution of the mechanical behavior of the observation points.

[0064] To correct for errors in the finite element method and make the numerical calculation results approximate the actual mechanical behavior of the structure, an optimization problem is constructed as follows: Based on the real physical quantity information obtained from observation points in physical space, the numerical space model is corrected by adjusting parameters. The mathematical expression of the optimization problem is as follows:

[0065]

[0066] In the formula, e(Θ,Ω) represents the relative error between the numerical and physical space measurement information of the observation point; Θ represents the decision variable for setting boundary conditions, including displacement constraints, degree of freedom coupling, etc.; Ω represents the decision variable for calculation process parameters, including load steps, contact pair friction coefficient, nonlinear parameters, etc. and These are the measurement information of the nth observation point under the mth set of characteristic parameters in the numerical and physical spaces, including displacement, strain, stress, etc.

[0067] Various intelligent optimization algorithms and statistical prediction algorithms, such as simulated annealing and regression prediction, combined with engineering experience, can be used to solve the optimization problem, obtaining the combination of decision variables that maximizes the similarity between the numerical and physical space results. This allows for the construction of a high-precision finite element model of the gas turbine bladed disk. Based on this finite element model, the physical field information of the bladed disk under a given input parameter combination X is calculated, such as temperature distribution and stress-strain distribution. According to the temperature and strength distribution characteristics of the bladed disk and the actual operating state of the gas turbine, key fatigue failure sites and main failure modes are identified, and a theoretical life prediction model between the input parameter X and the service life is constructed.

[0068] In this embodiment of the invention, the theoretical life prediction model includes a low-cycle fatigue life prediction model, a high-cycle fatigue life prediction model, etc. For example, taking the low-cycle fatigue failure mode of a gas turbine bladed disk under a start-stop load spectrum as an example, the Manson-Coffin fatigue life prediction model can be used, and its expression is as follows:

[0069]

[0070] In the formula, ε a For the cyclic strain amplitude, σ' f Where E is the fatigue strength coefficient, E is the elastic modulus, and N is the fatigue strength coefficient. f To predict fatigue life, b is the fatigue strength index, ε' f is the fatigue ductility coefficient, and c is the fatigue ductility index.

[0071] Furthermore, based on the bootstrap repeated sampling strategy, the random spectral embedding approximation expression of the limiting state function is constructed as follows:

[0072]

[0073] In the formula, B represents the number of times the bootstrap is repeated, and the superscript indicates that the number of times the bootstrap is repeated is greater than the number of times it is repeated. (b) This represents the b-th sampling. For the random spectral embedding approximation of the limit state function; For index pairs, the element k = (l, p) is used to label the residual expansion of the SSE, where l ∈ {0, ..., L} represents the expansion level of the residual, L is the total number of expansion levels, and p ∈ {1, ..., P}. l} represents the index of the number of residual expansions at a given level, where P l This represents the total number of expansions at level l; D X The k-th SSE subdomain; Let be the set of terminal domains, i.e., any undivided subdomains. The union of all terminal domains constitutes the original parameter space, i.e. For indicator functions, their values ​​are in the subspace If the result is 1, then take 0; The SSE residuals can be solved using spectral expansion techniques, such as Fourier expansion and polynomial chaotic expansion (PCE). Taking the PCE method as an example, the calculation... The spectral expansion expression is:

[0074]

[0075] In the formula, The truncated set is determined by the highest degree of the PCE polynomial; The PCE basis functions are mutually orthogonal. For the corresponding coefficient.

[0076] The mathematical expressions for the conditional failure probability of each subdomain are as follows:

[0077]

[0078] In the formula, The probability of conditional failure of the k-th subdomain during the b-th sampling can be estimated using probabilistic simulation methods such as Monte Carlo simulation or subset simulation. The results of the B samplings constitute a set.

[0079] Therefore, the failure probability of the gas turbine bladed disk in the b-th sampling is... It can be evaluated by the following formula:

[0080]

[0081] In the formula, The probability mass of the k-th subdomain is given by the set of B sampling results.

[0082] In this embodiment of the invention, the failure probability of the gas turbine bladed disk It can be calculated using the following formula:

[0083]

[0084] In specific and exemplary embodiments of the present invention, SSE achieves active learning and iterative approximation of the model's localization behavior through the adaptive sequential domain segmentation idea, including subdivision domain selection strategy, subdivision domain segmentation strategy and sample enrichment strategy.

[0085] The subdivision domain filtering strategy is described as follows: At each domain split, based on the set... The importance of each terminal domain to the overall approximation is ranked among the terminal domains, and the terminal domains that are most helpful in improving the accuracy of the results are identified as subdivision domains, which are used for subsequent domain segmentation and sample enrichment; the specific mathematical expression is as follows:

[0086]

[0087] In the formula, k refine,q The subdivision index is selected for the q-th iteration; Var[·] is the variance.

[0088] Specifically, as exemplified in this embodiment of the invention, the subdivision domain segmentation strategy is described as follows: By quantitatively evaluating the accuracy of region prediction, the correctly predicted structural working state and the incorrectly predicted regions in the subdivision domain are separated; wherein, the region prediction accuracy metric is given by the following formula:

[0089]

[0090] In the formula, This is an estimate of the probability of misclassification. and This is an indicator function.

[0091] By defining in the standard normal space D U The auxiliary vector in the algorithm identifies the probability of accurate and misclassified classification in the current subdivision, as expressed below:

[0092]

[0093] In the formula, Z 0 To accurately classify and identify vectors, Z + Let be the misclassification identification vector, and let its edges in the i-th dimension be denoted as . and

[0094] Based on the classification and recognition vector, D is solved by constructing an optimization problem. U The optimal splitting position in each dimension, such that the Z-axis on both sides... + and Each possesses the highest probability mass, and their mathematical expression is as follows:

[0095] in

[0096] In the formula, D U The i-th dimension; v i The domain segmentation position for the i-th dimension; The optimal segmentation position; L i (v i ) is the objective function.

[0097] To select the splitting direction, the objective function values ​​corresponding to the optimal splitting positions in each dimension are compared, and the region is split along the dimension d that achieves the optimal split. Its mathematical representation is as follows:

[0098]

[0099] Specifically, in this embodiment of the invention, the sample enrichment strategy is described as follows: newly added sample points are uniformly and randomly placed in the misclassification subdomain of the refinement domain; wherein, the number of newly added sample points is calculated by the following formula:

[0100]

[0101] In the formula, N uni N represents the number of new samples. curr To refine the number of existing samples in the domain.

[0102] During the adaptive sequential domain partitioning process, the following convergence criterion is used to determine whether the algorithm can terminate: whether the structural reliability changes significantly with iteration.

[0103]

[0104] In the formula, β=-Φ -1 (P f ) represents structural reliability; Φ -1 (·) is the inverse standard normal cumulative distribution function; This is a reliability estimate; The termination threshold; and They are respectively The upper and lower boundaries of the confidence interval.

[0105] When reliability Upon convergence, a bladed disk fatigue reliability analysis model based on random spectrum embedding is obtained.

[0106] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0107] Please see Figure 3 In another embodiment of the present invention, a bladed disk fatigue reliability analysis system based on random spectrum embedding is provided, comprising:

[0108] The data acquisition module is used to acquire the characteristic parameters of multiple gas turbine bladed disks to be analyzed for reliability, and to determine the probability distribution of each characteristic parameter based on the measured data of each bladed disk characteristic parameter.

[0109] The failure probability acquisition module is used to input the acquired bladed disk characteristic parameters and the determined probability distribution of each bladed disk characteristic parameter into a bladed disk fatigue reliability analysis model based on random spectrum embedding to perform reliability calculations and obtain the failure probability of the multiple gas turbine bladed disks to be reliability analyzed.

[0110] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a bladed disk fatigue reliability analysis method based on random spectrum embedding.

[0111] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the bladed disk fatigue reliability analysis method based on random spectrum embedding in the above embodiments.

[0112] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for fatigue reliability analysis of bladed disks based on random spectrum embedding, characterized in that, Includes the following steps: Based on multiple gas turbine bladed disks to be reliably analyzed, the characteristic parameters of the bladed disks are obtained, and based on the measured data of the characteristic parameters of each bladed disk, the probability distribution of each characteristic parameter is determined. The acquired bladed disk characteristic parameters and the determined probability distribution of each bladed disk characteristic parameter are input into the bladed disk fatigue reliability analysis model based on random spectrum embedding to perform reliability calculations and obtain the failure probability of the multiple gas turbine bladed disks to be analyzed for reliability. in, In the bladed disk fatigue reliability analysis model based on random spectrum embedding, The formula for calculating the failure probability of a gas turbine bladed disk is as follows: ； In the formula, The failure probability of the gas turbine bladed disk; For the total number of repeated samplings of bootstrap, b Representing the b Second sampling; For the gas turbine blade disk in the first b The failure probability of the next sampling; ； In the formula, For the first The probabilistic quality of each subdomain; For the first b During the second sampling, the first The probability of conditional failure in each subdomain; ;in, The results of the sampling constitute a set. ; ； In the formula, superscript Representing the b Second sampling; For the random spectral embedding approximation of the limit state function; For indexed pairs, the elements are... Residual expansion used to label SSE Indicates the order of residual expansion. L To expand the total number of levels, This represents the index of the number of residual expansions at a given level. Indicates the first Total number of unfolds at each level; for The k One SSE subdomain; Let the set of terminal fields be denoted as ; the union of all terminal fields constitutes the original parameter space, denoted as . ; For indicator functions, in subspace If the result is 1, then take 0; is the SSE residual; SSE stands for random spectral embedding.

2. The method for fatigue reliability analysis of bladed disks based on random spectrum embedding according to claim 1, characterized in that, The steps for obtaining characteristic parameters of multiple gas turbine bladed disks based on reliability analysis include: Based on multiple gas turbine bladed disks to be reliably analyzed, structural parameters, environmental parameters, and load parameters are selected, and the dimensionality reduction method of manifold learning is used to obtain the characteristic parameters of the bladed disks.

3. The method for fatigue reliability analysis of bladed disks based on random spectrum embedding according to claim 1, characterized in that, The multiple gas turbine bladed disks are multiple gas turbine bladed disks of the same model and batch.

4. The method for fatigue reliability analysis of bladed disks based on random spectrum embedding according to claim 1, characterized in that, Estimation is performed using probabilistic simulation methods.

5. The method for fatigue reliability analysis of bladed disks based on random spectrum embedding according to claim 1, characterized in that, The solution is obtained using spectral expansion techniques.

6. The method for fatigue reliability analysis of bladed disks based on random spectrum embedding according to claim 1, characterized in that, In the bladed disk fatigue reliability analysis model based on random spectrum embedding, Subdomains are obtained through an adaptive sequential domain segmentation strategy to achieve proactive learning and iterative approximation of the model's localized behavior.

7. The method for fatigue reliability analysis of bladed disks based on random spectrum embedding according to claim 6, characterized in that, The adaptive sequential domain segmentation strategy includes a subdivision domain filtering strategy, a subdivision domain segmentation strategy, and a sample enrichment strategy.

8. The method for fatigue reliability analysis of bladed disks based on random spectrum embedding according to claim 1, characterized in that, When training the bladed disk fatigue reliability analysis model based on random spectral embedding, the expression for calculating the limit state function of the sample is: ； In the formula, It is the limit state function; The preset safe lifespan; The predicted life is obtained by the gas turbine bladed disk fatigue life prediction model driven by the data-physical fusion finite element method. The construction steps of the deterministic prediction model for the fatigue life of gas turbine bladed disks driven by the fusion of data and matter finite element method include: Latin hypercube sampling method is used to extract a The system provides a set of characteristic parameters, along with the corresponding full-scale components and test conditions of the gas turbine bladed disk in the physical space. It conducts thermal-fluid-structure interaction strength or vibration characteristic tests and obtains the actual working state response of the gas turbine bladed disk at the preset observation points. In the numerical space, it establishes a three-dimensional high-fidelity finite element analysis model corresponding to each set of characteristic parameters, conducts thermal-fluid-structure interaction nonlinear finite element analysis, and obtains the numerical solution of the mechanical behavior at the observation points. An optimization problem is constructed to correct errors in the finite element analysis process, making the numerical solution approximate the actual mechanical behavior of the structure. The optimization problem involves modifying the numerical space model by adjusting parameters based on the actual physical quantity information obtained from observation points in physical space. The mathematical expression is as follows: ； In the formula, The relative error of the numerical and physical space measurement information of the observation point; Set decision variables for boundary conditions; These are the decision variables for the calculation process parameters; , The number and object spaces are respectively the first The first group of feature parameters Measurement information from each observation point; Solve the optimization problem to obtain the combination of decision variables that maximizes the similarity between the numerical and physical space results, and construct a high-precision numerical-physical fusion finite element model of the gas turbine bladed disk; Based on a high-precision finite element model of gas turbine bladed disks, the physical field information of the bladed disks under a given combination of input characteristic parameters is calculated, and key fatigue failure sites and main failure modes are identified. A deterministic prediction model for the fatigue life of gas turbine bladed disks driven by the finite element method of fusion of input characteristic parameters and service life is constructed.

9. A fatigue reliability analysis system for bladed disks based on random spectrum embedding, characterized in that, include: The data acquisition module is used to acquire the characteristic parameters of multiple gas turbine bladed disks to be analyzed for reliability, and to determine the probability distribution of each characteristic parameter based on the measured data of each bladed disk characteristic parameter. The failure probability acquisition module is used to input the acquired bladed disk characteristic parameters and the determined probability distribution of each bladed disk characteristic parameter into the bladed disk fatigue reliability analysis model based on random spectrum embedding to perform reliability calculation and obtain the failure probability of the multiple gas turbine bladed disks to be reliability analyzed. in, In the bladed disk fatigue reliability analysis model based on random spectrum embedding, The formula for calculating the failure probability of a gas turbine bladed disk is as follows: ； In the formula, The failure probability of the gas turbine bladed disk; For the total number of repeated samplings of bootstrap, b Representing the b Second sampling; For the gas turbine blade disk in the first b The failure probability of the next sampling; ； In the formula, For the first The probabilistic quality of each subdomain; For the first b During the second sampling, the first The probability of conditional failure in each subdomain; ;in, The results of the sampling constitute a set. ; ； In the formula, superscript Representing the b Second sampling; For the random spectral embedding approximation of the limit state function; For indexed pairs, the elements are... Residual expansion used to label SSE Indicates the order of residual expansion. L To expand the total number of levels, This represents the index of the number of residual expansions at a given level. Indicates the first Total number of unfolds at each level; for The k One SSE subdomain; Let the set of terminal fields be denoted as ; the union of all terminal fields constitutes the original parameter space, denoted as . ; For indicator functions, in subspace If the result is 1, then take 0; is the SSE residual; SSE stands for random spectral embedding.

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