Probabilistic analysis method for thermal structural strength of ceramic matrix composites based on progressive damage

Abstract

The present application belongs to the technical field of aerospace vehicle thermal protection technology, and relates to a ceramic matrix composite thermal structure strength probability analysis method based on progressive damage. Firstly, the present application predicts the damage process and failure strength of the thermal structure under typical load based on a progressive damage analysis method. On this basis, single progressive damage analysis is taken as single sampling calculation to carry out material uncertainty modeling. Through Latin hypercube sampling and Kriging surrogate model, failure probability analysis and parameter sensitivity evaluation of the thermal structure are realized. The ceramic matrix composite thermal structure strength probability analysis method based on progressive damage established by the present application can be applied to failure strength and probability analysis of various ceramic matrix composites in the fields of aerospace and aviation, and can support scheme design improvement.

Description

Technical Field

[0001] This invention relates to a probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage, belonging to the field of thermal protection technology for aerospace vehicles. Background Technology

[0002] Ceramic matrix composites, represented by C / SiC and SiC / SiC, possess advantages such as high temperature resistance, low density, and excellent high-temperature mechanical properties, making them promising for applications in reentry vehicle thermal protection structures and hot-end components of aero-engines. During operation, ceramic matrix composite components are subjected to aerodynamic loads, thermal loads, and centrifugal loads, leading to component failure controlled by complex loads. Furthermore, ceramic matrix composites exhibit significant nonlinearity and dispersion in their mechanical properties. Therefore, the design and failure analysis of ceramic matrix composite thermal structural components require the establishment of a probabilistic strength analysis method based on progressive damage. This method can accurately predict the damage process and failure probability of thermal structures under load, providing a foundation for the application of ceramic matrix composites in aerospace thermal structural components.

[0003] The dispersion of mechanical properties is a significant characteristic of ceramic matrix composites. Traditional thermal structural strength analysis of ceramic matrix composites generally involves linear or progressive damage analysis based on material modulus, strength, and other performance parameters to derive the thermal structural failure strength. However, most analytical methods do not consider the influence of material dispersion. Summary of the Invention

[0004] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage. This method can be applied to the failure strength and probability analysis of various ceramic matrix composites in the aerospace and aviation fields, and supports the improvement of scheme design.

[0005] The technical solution of this invention is: a probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage, comprising:

[0006] Based on the progressive damage analysis method, the damage process and failure intensity of the thermal structure under typical loads are predicted, and the results of a single progressive damage analysis are obtained.

[0007] Using the results of a single progressive damage analysis as the result of a single sampling calculation, we conduct material property uncertainty modeling and evaluate the failure probability analysis and parameter sensitivity of the thermal structure through Latin hypercube sampling and Kriging surrogate model.

[0008] Furthermore, the predicted damage process and failure intensity of the thermal structure under typical loads include:

[0009] Establish a material constitutive model describing the nonlinearity of the material based on the material parameters obtained from experimental testing;

[0010] Based on the material constitutive model, progressive damage analysis is carried out on the thermal structure of ceramic matrix composites to obtain the damage process and failure strength of the thermal structure, which are used as the results of a single progressive damage analysis.

[0011] Furthermore, the material parameters include in-plane tensile modulus E1 and E2, where E1 = E2, and interlaminar tensile modulus E z Poisson's ratio μ in the in-plane direction 12 Poisson's ratio μ in the interlayer direction 13 μ 23 μ 13 =μ 23 In-plane shear modulus G 12 Interlaminar shear modulus G 13 G 23 G 13 =G 23 In-plane tensile strength X t Y t X t =Y t In-plane compressive strength X c Y c X c =Y c Interlaminar tensile strength Z t Interlaminar compressive strength Z c In-plane shear strength S 12 Interlaminar shear strength S 13 S 23 S 13 =S 23 .

[0012] Furthermore, the material constitutive model includes a damage assessment criterion and a stiffness reduction criterion; the damage assessment criterion is the Tsai-Wu criterion, the Hoffman criterion, or a damage assessment criterion modified based on engineering test results; the stiffness reduction criterion is an instantaneous reduction criterion or a functional reduction criterion.

[0013] Furthermore, based on the sample data of each material parameter in the experimental test, a material performance uncertainty model is established by selecting a normal distribution or a Weibull distribution.

[0014] Furthermore, the failure probability analysis and parameter sensitivity of the evaluated thermal structure include:

[0015] Based on the material property uncertainty model, Latin hypercube sampling is performed on the input material parameters obtained from the test, with a sampling number of no less than 60.

[0016] Parametric modeling was completed based on the progressive damage analysis of the thermal structure of ceramic matrix composites, resulting in a parametric model; the parametric modeling includes structural geometric parametric modeling and material property parametric modeling.

[0017] Using the sampling results of the obtained material parameters as input, and based on the parameterized model, the progressive damage strength analysis of the thermal structure with the corresponding material parameter sampling quantity is completed, and the thermal structure strength calculation results with the corresponding material parameter sampling quantity are obtained.

[0018] Based on the obtained multiple sets of thermal structure strength calculation results, the normal distribution or Weibull distribution is selected to perform probability statistical distribution on the calculation results, and the probability density distribution histogram and cumulative probability distribution curve are obtained.

[0019] A Kriging proxy model is constructed based on the cumulative probability distribution curve, and a preset number of samples are calculated to obtain a large sample probability density distribution histogram and cumulative probability distribution curve. Based on this, the failure probability of the thermal structure under a specific ultimate load is obtained; the preset number is not less than 10,000.

[0020] A probabilistic analysis system for the thermal structural strength of ceramic matrix composites based on progressive damage includes:

[0021] The first module, based on the progressive damage analysis method, predicts the damage process and failure intensity of the thermal structure under typical loads and obtains the results of a single progressive damage analysis.

[0022] The second module uses the results of a single progressive damage analysis as a single sampling calculation to carry out material property uncertainty modeling. It evaluates the failure probability analysis and parameter sensitivity of the thermal structure through Latin hypercube sampling and Kriging surrogate model.

[0023] Furthermore, the predicted damage process and failure intensity of the thermal structure under typical loads include:

[0024] Establish a material constitutive model describing the nonlinearity of the material based on the material parameters obtained from experimental testing;

[0025] Based on the material constitutive model, progressive damage analysis is carried out on the thermal structure of ceramic matrix composites to obtain the damage process and failure intensity of the thermal structure, which are used as the results of a single progressive damage analysis.

[0026] The material parameters include in-plane tensile modulus E1 and E2, where E1 = E2, and interlaminar tensile modulus E z Poisson's ratio μ in the in-plane direction 12 Poisson's ratio μ in the interlayer direction 13 μ 23 μ 13 =μ 23 In-plane shear modulus G 12Interlaminar shear modulus G 13 G 23 G 13 =G 23 In-plane tensile strength X t Y t X t =Y t In-plane compressive strength X c Y c X c =Y c Interlaminar tensile strength Z t Interlaminar compressive strength Z c In-plane shear strength S 12 Interlaminar shear strength S 13 S 23 S 13 =S 23 ;

[0027] The material constitutive model includes a damage assessment criterion and a stiffness reduction criterion; the damage assessment criterion is the Tsai-Wu criterion, the Hoffman criterion, or a damage assessment criterion modified based on engineering test results; the stiffness reduction criterion is an instantaneous reduction criterion or a functional reduction criterion.

[0028] Based on the sample data of each material parameter in the test, a material performance uncertainty model is established by selecting the normal distribution or the Weibull distribution.

[0029] The failure probability analysis and parameter sensitivity of the evaluated thermal structure include:

[0030] Based on the material property uncertainty model, Latin hypercube sampling is performed on the input material parameters obtained from the test, with a sampling number of no less than 60.

[0031] Parametric modeling was completed based on the progressive damage analysis of the thermal structure of ceramic matrix composites, resulting in a parametric model; the parametric modeling includes structural geometric parametric modeling and material property parametric modeling.

[0032] Using the sampling results of the obtained material parameters as input, and based on the parameterized model, the progressive damage strength analysis of the thermal structure with the corresponding material parameter sampling quantity is completed, and the thermal structure strength calculation results with the corresponding material parameter sampling quantity are obtained.

[0033] Based on the obtained multiple sets of thermal structure strength calculation results, the normal distribution or Weibull distribution is selected to perform probability statistical distribution on the calculation results, and the probability density distribution histogram and cumulative probability distribution curve are obtained.

[0034] A Kriging proxy model is constructed based on the cumulative probability distribution curve, and a preset number of samples are calculated to obtain a large sample probability density distribution histogram and cumulative probability distribution curve. Based on this, the failure probability of the thermal structure under a specific ultimate load is obtained; the preset number is not less than 10,000.

[0035] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage.

[0036] The device for probabilistic analysis of the thermal structural strength of ceramic matrix composites based on progressive damage includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage.

[0037] The advantages of this invention compared to the prior art are:

[0038] 1) By combining progressive damage analysis with probabilistic analysis, a probabilistic strength analysis method for the thermal structure of ceramic matrix composites based on progressive damage was established. Through progressive damage analysis, the damage process and failure strength of the thermal structure of ceramic matrix composites under complex loads can be obtained. Combining steps such as quantitative characterization of material property uncertainties, performance parameter sampling, structural parameterization modeling, high-fidelity calculation with small samples, and surrogate model calculation, the probability distribution characteristics of the maximum load-bearing capacity of the thermal structure and the failure probability results are given.

[0039] 2) In the progressive damage analysis process, a damage judgment criterion based on engineering test results is proposed. Using this criterion to carry out progressive damage strength analysis of typical thermal protection structures can accurately predict the location of structural failure. Under the premise that the calculated failure strength value is less than the test result, the prediction error is within 9%. Attached Figure Description

[0040] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0041] Figure 1 This invention provides a probabilistic analysis process for the thermal structural failure strength based on progressive damage.

[0042] Figure 2 This is a schematic diagram of the simulation results of the thermal structural damage process of the present invention;

[0043] Figure 3 This is a schematic diagram illustrating the extraction of thermal structural failure strength according to the present invention;

[0044] Figure 4 This is a schematic diagram of Latin hypercube sampling for the material performance parameters of this invention;

[0045] Figure 5 This is the probability density distribution histogram for a small sample size in this invention;

[0046] Figure 6 This is the cumulative probability distribution curve for a small sample size in this invention;

[0047] Figure 7 This is the probability density distribution histogram for a large sample size in this invention.

[0048] Figure 8 This is the cumulative probability distribution curve for a large sample size in this invention. Detailed Implementation

[0049] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0050] The following description, in conjunction with the accompanying drawings, provides a more detailed explanation of the probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage provided in this application. Specific implementation methods may include (e.g.) Figure 1 As shown):

[0051] First, based on the progressive damage analysis method, the damage process and failure strength of the thermal structure under typical loads are predicted.

[0052] Based on this, material uncertainty modeling is carried out by taking single progressive damage analysis as a single sampling calculation, and failure probability analysis and parameter sensitivity assessment of thermal structures are realized by Latin hypercube sampling and Kriging surrogate model.

[0053] Furthermore, the present invention includes the following steps:

[0054] 1) The material parameters required for the analysis process include the in-plane tensile modulus E1 and E2 (E1 = E2), and the interlaminar tensile modulus E z Poisson's ratio μ in the in-plane direction 12 Poisson's ratio μ in the interlayer direction 13 μ 23 (μ 13 =μ 23 In-plane shear modulus G12 Interlaminar shear modulus G 13 G 23 (G 13 =G 23 In-plane tensile strength X t Y t (X t =Y t In-plane compressive strength X c Y c (X c =Y c Interlaminar tensile strength Z t Interlaminar compressive strength Z c In-plane shear strength S 12 Interlaminar shear strength S 13 S 23 (S 13 =S 23 ).

[0055] 2) Based on the UMAT subroutine of the ABAQUS finite element software, a material constitutive model describing the nonlinearity of the material is established. The constitutive model includes two parts: damage judgment criteria and stiffness reduction criteria. The material parameters required to establish the material constitutive model are the material parameters given in step 1). The material parameters are obtained through experimental testing. The number of test samples for a single parameter is generally not less than 30. Parameters that cannot be obtained through experimental testing due to limitations such as equipment conditions can be obtained through literature.

[0056] 3) Damage assessment criteria may include the Tsai-Wu criterion, the Hoffman criterion, or damage assessment criteria modified based on engineering test results.

[0057] Tsai-Wu Criterion:

[0058]

[0059] In the formula, σ1 and σ2 represent the in-plane normal stress at the element node, σ3 represents the inter-story normal stress at the element node, and τ 12 τ represents the in-plane shear stress at the element node. 13 τ 23 This represents the inter-story shear stress at the element node. The expressions for other coefficients are:

[0060]

[0061] Hoffman's Criterion:

[0062] The Hoffman criterion is also a polynomial criterion, with the same polynomial form as the Tsai-Wu criterion (see formula (1)). The difference lies in some coefficients of the Hoffman criterion, which differ from those of the Tsai-Wu criterion:

[0063]

[0064] Damage assessment criteria revised based on engineering test results:

[0065] Damage assessment criteria, modified by engineers in related fields during practical application, are categorized into three types based on the present invention: in-plane tensile / compressive damage and shear damage, and interlaminar tensile / compressive damage and interlaminar shear damage. The expressions for these types are as follows:

[0066] In-plane tensile / compressive and shear failures:

[0067]

[0068] Interlaminar tensile-compressive failure:

[0069]

[0070] Interlaminar shear failure:

[0071]

[0072] in:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] The expressions for each coefficient are as follows:

[0080]

[0081] This invention uses this criterion to conduct progressive damage strength analysis of typical thermal protection structures, which can accurately predict the location of structural failure. Under the premise that the calculated failure strength is less than the experimental result, the prediction error is within 9%.

[0082] 4) Stiffness reduction criteria can be instantaneous reduction, functional reduction, etc.

[0083] Instantaneous reduction:

[0084] Instantaneous reduction usually sets a failure coefficient d (d is a constant and d < 1). When the conditions of the failure criterion are met, the coefficients of the stiffness matrix are reduced as follows:

[0085]

[0086] Usually, let 0.9 < d < 1, and the damaged part instantly loses its bearing capacity.

[0087] Functional reduction:

[0088] This type of reduction mode is similar to the instantaneous reduction. A failure coefficient d is also set to reduce the stiffness. The difference is that d is a function value. For example, an expression for the failure coefficient is:

[0089]

[0090] Where F is the value of the left - hand side expression of the damage judgment criterion formula in step 3), and R and m are constants obtained by fitting through engineering tests. The reduction of the stiffness matrix is shown in formula (13).

[0091] 5) Based on the UMAT subroutine of the ABAQUS finite - element software, progressive damage analysis is carried out for the thermal structure of ceramic - matrix composites, and the damage process and failure strength of the thermal structure can be obtained. In the subsequent failure probability analysis, the result of one calculation in this step is a single - sampling result.

[0092] 6) Based on the sub - sample data of each material parameter in step 1), mathematical models such as normal distribution or Weibull distribution are selected to establish a material property uncertainty model. This process can be realized through commercial software with data - processing functions such as Matlab and SPSS.

[0093] 7) Based on the material property uncertainty model established in step 6), Latin - hypercube sampling is carried out for the input of the material property parameters required in the example of step 5). The number of samplings in this step is not less than 60.

[0094] 8) Based on the thermal - structure finite - element analysis model established in step 5), parametric modeling is completed, including structural geometric parameterization and material property parameterization.

[0095] 9) Taking the material property sampling results obtained in step 7) as the input, based on the parametric model established in step 8), progressive damage strength analysis of the thermal structure corresponding to the number of samplings in step 7) is automatically completed, and the thermal - structure strength calculation results corresponding to the number of samplings in step 7) are obtained.

[0096] 10) Based on the multiple groups of thermal - structure strength calculation results obtained in step 9), mathematical models such as normal distribution or Weibull distribution are selected to perform probability statistical distribution on the calculation results, and the probability density distribution histogram and cumulative probability distribution curve are obtained.

[0097] 11) Based on the cumulative probability distribution curve obtained in step 10), construct the Kriging surrogate model to carry out a large number of sample calculations. The number of samples in this step is no less than 10,000. Obtain the large sample probability density distribution histogram and cumulative probability distribution curve. Based on this, obtain the failure probability of the thermal structure under a specific ultimate load.

[0098] The solution provided in the embodiments of this application includes:

[0099] Based on the load-bearing characteristics of ceramic matrix composites in the aerospace and aviation fields, and the significant nonlinearity and dispersion of ceramic matrix composites, it is necessary to predict the damage process and failure intensity of the thermal structure during thermal structure design using progressive damage analysis methods. This is combined with probabilistic analysis based on material property sample data to obtain the failure probability of the thermal structure under specific ultimate loads. This invention ultimately forms a set of methods and procedures for predicting the failure intensity probability of ceramic matrix composite thermal structures, providing technical support for design and analysis work in related fields.

[0100] like Figure 1 The diagram illustrates the implementation flow of a probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage, according to the present invention. The invention first inputs the performance parameters and sample data of the ceramic matrix composites required for analysis; secondly, it establishes a material constitutive model describing the material's nonlinearity based on the UMAT subroutine of the ABAQUS finite element software, selects appropriate damage judgment and stiffness reduction criteria, and completes a single progressive damage analysis of the thermal structure; thirdly, it establishes a material performance uncertainty model based on the material sample data and performs Latin hypercube sampling; on this basis, it completes parametric modeling and performs progressive damage strength analysis on all material performance sampling results to obtain the calculated thermal structural strength results, and acquires the probability density distribution histogram and cumulative probability distribution curve; finally, it constructs a surrogate model to perform calculations on a large number of samples, acquires a large sample probability density distribution histogram and cumulative probability distribution curve, and obtains the failure probability of the thermal structure under a specific ultimate load. The specific method includes the following steps:

[0101] 1) Input material performance parameters and sample data, including in-plane tensile modulus, interlaminar tensile modulus, in-plane Poisson's ratio, interlaminar Poisson's ratio, in-plane shear modulus, interlaminar shear modulus, in-plane tensile strength, in-plane compressive strength, interlaminar tensile strength, interlaminar compressive strength, in-plane shear strength, and interlaminar shear strength.

[0102] 2) Based on the UMAT subroutine of the ABAQUS finite element software, a material constitutive model describing the nonlinearity of the material is established. The constitutive model includes two parts: damage judgment criteria and stiffness reduction criteria.

[0103] 3) Based on the UMAT subroutine of the ABAQUS finite element software, progressive damage analysis is performed on the thermal structure of ceramic matrix composites to obtain the damage process of the thermal structure. Figure 2 ) and failure strength ( Figure 3 In subsequent failure probability analysis, the result of one calculation in this step is a single sampling result.

[0104] 4) Establish an uncertainty model for material properties based on material sample data, and then conduct Latin hypercube sampling of material properties, such as... Figure 4 As shown.

[0105] 5) Establish a parameterized model, and automatically complete the progressive damage strength analysis of the corresponding thermal structure sample quantity based on the material property sampling results, and obtain the thermal structure strength calculation results for the corresponding material property sampling quantity.

[0106] 6) Use mathematical models such as normal distribution or Weibull distribution to perform probability statistical distribution on the intensity calculation results, and obtain the probability density distribution histogram under small sample conditions. Figure 5 ) and cumulative probability distribution curve ( Figure 6 Based on this, a Kriging surrogate model was constructed to perform calculations on a large number of samples, obtaining the probability density distribution histogram under large sample sizes. Figure 7 ) and cumulative probability distribution curve ( Figure 8 ), thus obtaining the failure probability of the thermal structure under a specific ultimate load.

[0107] Based on the realization of single progressive damage simulation of thermal structure, this invention establishes a material uncertainty characterization model for the dispersion characteristics of mechanical properties of ceramic matrix composites, and uses appropriate sampling and surrogate model methods to realize the probabilistic analysis process of thermal structure, thus completing the probabilistic design of thermal structure based on progressive damage strength analysis. It fully considers the dispersion characteristics of mechanical properties of ceramic matrix composites and supports the design and application of thermal structures of ceramic matrix composites.

[0108] This application provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform... Figure 1 The method described.

[0109] 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 implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0114] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0115] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A probabilistic analysis method for the thermal structural strength of ceramic matrix composites based on progressive damage, characterized in that, include: Based on the progressive damage analysis method, the damage process and failure intensity of the thermal structure under typical loads are predicted, and the results of a single progressive damage analysis are obtained. Using the results of a single progressive damage analysis as the results of a single sampling calculation, we carried out material property uncertainty modeling, and evaluated the failure probability analysis and parameter sensitivity of the thermal structure through Latin hypercube sampling and Kriging surrogate model. The predicted damage process and failure intensity of the thermal structure under typical loads include: Establish a material constitutive model describing the nonlinearity of the material based on the material parameters obtained from experimental testing; Based on the material constitutive model, progressive damage analysis is carried out on the thermal structure of ceramic matrix composites to obtain the damage process and failure intensity of the thermal structure, which are used as the results of a single progressive damage analysis. The material parameters include in-plane tensile modulus, including... , ,in interlaminar tensile modulus Poisson's ratio in the in-plane direction Poisson's ratio in the interlayer direction includes , ,in In-plane shear modulus Interlaminar shear modulus includes , ,in In-plane tensile strength includes , ,in In-plane compressive strength includes , ,in Interlaminar tensile strength interlaminar compressive strength In-plane shear strength Interlaminar shear strength includes , ,in ; The material constitutive model includes a damage assessment criterion and a stiffness reduction criterion; the damage assessment criterion is the Tsai-Wu criterion, the Hoffman criterion, or a damage assessment criterion modified based on engineering test results; the stiffness reduction criterion is an instantaneous reduction criterion or a functional reduction criterion. Based on the sample data of each material parameter in the test, a material performance uncertainty model is established by selecting the normal distribution or the Weibull distribution. The failure probability analysis and parameter sensitivity of the evaluated thermal structure include: Based on the material property uncertainty model, Latin hypercube sampling is performed on the input material parameters obtained from the test, with a sampling number of no less than 60. Parametric modeling was completed based on the progressive damage analysis of the thermal structure of ceramic matrix composites, resulting in a parametric model; the parametric modeling includes structural geometric parametric modeling and material property parametric modeling. Using the sampling results of the obtained material parameters as input, and based on the parameterized model, the progressive damage strength analysis of the thermal structure with the corresponding material parameter sampling quantity is completed, and the thermal structure strength calculation results with the corresponding material parameter sampling quantity are obtained. Based on the obtained multiple sets of thermal structure strength calculation results, the normal distribution or Weibull distribution is selected to perform probability statistical distribution on the calculation results, and the probability density distribution histogram and cumulative probability distribution curve are obtained. A Kriging surrogate model is constructed based on the cumulative probability distribution curve, and a preset number of samples are calculated to obtain a large sample probability density distribution histogram and cumulative probability distribution curve. Based on this, the failure probability of the thermal structure under a specific ultimate load is obtained; the preset number is not less than 10,000; the parameter sensitivity is obtained based on the material performance uncertainty model and parameterized modeling.

2. A probabilistic analysis system for the thermal structural strength of ceramic matrix composites based on progressive damage, characterized in that, include: The first module, based on the progressive damage analysis method, predicts the damage process and failure intensity of the thermal structure under typical loads and obtains the results of a single progressive damage analysis. The second module uses the results of a single progressive damage analysis as a single sampling calculation to carry out material property uncertainty modeling, and evaluates the failure probability analysis and parameter sensitivity of the thermal structure through Latin hypercube sampling and Kriging surrogate model. The predicted damage process and failure intensity of the thermal structure under typical loads include: Establish a material constitutive model describing the nonlinearity of the material based on the material parameters obtained from experimental testing; Based on the material constitutive model, progressive damage analysis is carried out on the thermal structure of ceramic matrix composites to obtain the damage process and failure intensity of the thermal structure, which are used as the results of a single progressive damage analysis. The material parameters include in-plane tensile modulus. , , interlaminar tensile modulus Poisson's ratio in the in-plane direction Poisson's ratio in the interlayer direction , , In-plane shear modulus interlaminar shear modulus , , In-plane tensile strength , , In-plane compressive strength , , Interlaminar tensile strength interlaminar compressive strength In-plane shear strength Interlaminar shear strength , , ; The material constitutive model includes a damage assessment criterion and a stiffness reduction criterion; the damage assessment criterion is the Tsai-Wu criterion, the Hoffman criterion, or a damage assessment criterion modified based on engineering test results; the stiffness reduction criterion is an instantaneous reduction criterion or a functional reduction criterion. Based on the sample data of each material parameter in the test, a material performance uncertainty model is established by selecting the normal distribution or the Weibull distribution. The failure probability analysis and parameter sensitivity of the evaluated thermal structure include: Based on the material property uncertainty model, Latin hypercube sampling is performed on the input material parameters obtained from the test, with a sampling number of no less than 60. Parametric modeling was completed based on the progressive damage analysis of the thermal structure of ceramic matrix composites, resulting in a parametric model; the parametric modeling includes structural geometric parametric modeling and material property parametric modeling. Using the sampling results of the obtained material parameters as input, and based on the parameterized model, the progressive damage strength analysis of the thermal structure with the corresponding material parameter sampling quantity is completed, and the thermal structure strength calculation results with the corresponding material parameter sampling quantity are obtained. Based on the obtained multiple sets of thermal structure strength calculation results, the normal distribution or Weibull distribution is selected to perform probability statistical distribution on the calculation results, and the probability density distribution histogram and cumulative probability distribution curve are obtained. A Kriging surrogate model is constructed based on the cumulative probability distribution curve, and a preset number of samples are calculated to obtain a large sample probability density distribution histogram and cumulative probability distribution curve. Based on this, the failure probability of the thermal structure under a specific ultimate load is obtained; the preset number is not less than 10,000; the parameter sensitivity is obtained based on the material performance uncertainty model and parameterized modeling.

3. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in claim 1.

4. A probabilistic analysis device for the thermal structural strength of ceramic matrix composites based on progressive damage, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method as described in claim 1.

Images