CN115795968A - CMC material turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics - Google Patents
CMC material turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics Download PDFInfo
- Publication number
- CN115795968A CN115795968A CN202211564569.1A CN202211564569A CN115795968A CN 115795968 A CN115795968 A CN 115795968A CN 202211564569 A CN202211564569 A CN 202211564569A CN 115795968 A CN115795968 A CN 115795968A
- Authority
- CN
- China
- Prior art keywords
- cmc
- blade
- mesoscopic
- probability distribution
- thermal conductivity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
- Turbine Rotor Nozzle Sealing (AREA)
Abstract
The invention discloses a CMC turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics, which is characterized in that for a woven structure CMC material, XCT is adopted to obtain mesoscopic structure characteristic images of different sections in the material, the probability distribution characteristics of mesoscopic geometric parameters such as the section size of a fiber bundle, the space of the fiber bundle, the weaving angle and the like are statistically analyzed, the probability distribution characteristic of anisotropic equivalent thermal conductivity is obtained based on a woven structure CMC material mesoscopic structure parameterized model, on the basis, the spatial distribution change and the probability distribution of the anisotropic thermal conductivity are introduced into the woven structure CMC turbine blade thermal analysis model, and the probability characteristic of a blade temperature field is statistically analyzed by adopting a Monte Carlo random finite element method. The method obtains the random characteristics of the internal geometric structure of the CMC material and the corresponding anisotropic thermophysical property probability distribution based on the actual microscopic structure image, and can more truly and effectively obtain the potential high-temperature region characteristics of the turbine blade of the CMC material with the braided structure.
Description
Technical Field
The invention belongs to the technical field of engineering thermophysics, and particularly relates to a CMC material turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics.
Background
In the early research of Ceramic Matrix Composite (CMC) abroad, along with the gradual improvement of material performance and the gradual maturity of preparation process, europe, america and other countries have developed the simulation examination and even the engineering application of CMC typical parts and simulation parts, wherein corresponding engineering design methods have been established abroad in relation to the application research of CMC on aeroengine turbine blades. From the public data, the most representative of the data is the SiC-specific data developed by NASA Glenn research center in UEET program f And preparing and examining a/SiC turbine blade simulation piece. The center not only proves that the three-dimensional five-direction woven SiC is formed through tests f In the reliability calculation process, NASA researchers fully consider the influences of the dispersion of material mechanics and thermodynamic properties, the uncertainty of pressure loads inside and outside the blade, the fluctuation of blade structure parameters, the dispersion of material failure critical loads and the like on the reliability of the blade from the perspective of probability analysis, wherein the dispersion data of the material performance parameters are derived from physical property test results carried out on the material, in addition, in the finite element modeling of the blade, the phenomena of anisotropy of CMC physical properties, the spatial distribution of the material physical property main direction caused by the bending of the blade profile and the like are carefully considered, and finally, the probability that the design requirement cannot be met by the current blade design scheme is 1.6 percent through calculation. It has been proved that the above researchers have been working as SiC f Commercial transportation of/SiC turbine bladesAnd a solid foundation is laid.
As the research of ceramic matrix composite materials in China starts late, the research aiming at CMC is still in material level at present, the research on the engineering level application design method of the CMC turbine blade is less, and most of the research is only focused on certain specific technical difficult problems.
Sun Jie and the like combine the rigidity performance prediction of a plain weave composite material and the thermal-solid coupling analysis of a turbine guide vane based on the blade profile of an air-cooled turbine vane in the literature and a method for considering the anisotropy of physical properties of materials in the literature, combine the material optimization and the structure optimization, and establish a structure and material integrated optimization design method of the ceramic matrix weave composite turbine guide vane from the two scales of materials and structures. In the method, the material stress and the blade displacement are limited as constraint conditions, the minimum blade mass is taken as an optimization target, a good optimization effect is obtained, but the discreteness of the physical property parameters of the composite material is not considered in the calculation process of the method, so that the method needs to be improved to realize engineering application.
Xu Rui and the like, on the basis of the research of a unidirectional composite material heat conductivity coefficient calculation method, a Mark II turbine blade is taken as an object, a self-programming finite element and Fluent simulation method is adopted, the influences of anisotropy of the heat conductivity coefficient and random fluctuation of the heat conductivity coefficient on the temperature distribution of the blade, particularly on the high-temperature areas of the front edge and the tail edge are mainly researched, and the sensitivity of a blade temperature field to the heat conductivity coefficients in different main directions and the change rule of the high-temperature areas of the blade are obtained. The research results provide a referential technical scheme for considering the dispersion of material physical properties in the thermal analysis of the CMC turbine blade, but the research object in the text can be regarded as a Mark II turbine blade consisting of unidirectional fibers, which is greatly different from the structure of a three-dimensional woven CMC turbine blade which is commercially available internationally.
Sun sets up a set of 2.5D C starting from two dimensions of materials and structures f A method for material-structure integrated optimization and reliability evaluation of a/SiC guide blade. The authors first used the Monte Carlo method on 2.5D C f The randomness of the mechanical properties of the/SiC composite material is researched and found2.5D C f The macroscopic mechanical property of the/SiC composite material is closely related to the randomness of the material components and the microstructure, and then a 2.5DC considering the material property dispersion is established f And finally, carrying out integral analysis on the distribution model of the blade mechanical performance calculation result to verify the reliability of the optimization result. In general, the method has strong engineering practicability, and although the method aims to optimize the blade structure and analyze the mechanical property, the method still has good reference significance for establishing a thermal analysis model of the CMC turbine blade.
However, the research on the thermal analysis is less directed to the inevitable randomness of the geometric characteristics of the CMC material during the weaving and compounding processes, thereby bringing about the overall anisotropy and the anisotropic characteristics of the material. This is because the thermal analysis modeling of the CMC material turbine blade needs to consider more influencing factors than the thermal analysis of the conventional metal turbine blade, wherein the two most prominent problems are the influence of the probability distribution of the geometrical features of the microstructure on the anisotropic thermal conductivity, and the influence of the probability distribution of the anisotropic thermal conductivity on the temperature field of the macroscopic blade. Aiming at the two problems, the key problem to be solved by the research is how to identify and post-process the geometric characteristics of the microscopic structure by the existing image identification technology and establish a database of geometric characteristic probability distribution; and how to introduce the probability distribution of the anisotropic thermal conductivity coefficient acquired by the microscopic structure into the macroscopic blade to solve the temperature field.
Most of the above researches on the anisotropic thermal conductivity probability distribution characteristics of the composite material are directed at the random change of the positions of the fiber bundles and the difference of the volume fractions, and the influence rule of the randomness of the geometric characteristic parameters of the microstructure of the woven CMC material has not been systematically researched. Therefore, the method is based on a Monte-Carlo simulation method, aims at obtaining the equivalent thermal conductivity of the material, and systematically calculates and analyzes the influence rule of the geometrical characteristic parameters of the warp width, the weft width, the fiber bundle gap and the weaving angle in the typical microscopic structure of the material on the probability distribution characteristic of the equivalent thermal conductivity of the CMC material of the woven structure.
Disclosure of Invention
The method comprises the steps of obtaining probability distribution characteristics of mesoscopic geometric parameters inside a CMC material with a braided structure based on XCT, obtaining probability distribution characteristics of anisotropic equivalent thermal conductivity of the material by combining a mesoscopic structure parameterized model, introducing spatial distribution change and probability distribution of the anisotropic thermal conductivity into a thermal analysis model of the CMC turbine blade with the braided structure on the basis, and statistically analyzing the probabilistic characteristics of a temperature field of the turbine blade by adopting a Monte Carlo random finite element method to obtain the potential high-temperature region characteristics of the turbine blade with the CMC material.
In order to realize the purpose, the invention adopts the technical scheme that:
the CMC material turbine blade probability thermal analysis method based on the mesoscopic structure feature statistics comprises the following steps:
the method comprises the following steps: carrying out mesoscopic structure test on a CMC material sample with a braided structure by adopting XCT (X-ray computer tomography), and obtaining mesoscopic structure images of sections of the material in different directions and positions;
step two: according to a certain number of microscopic structure images, the probability distribution characteristics of geometrical characteristic parameters such as the length and width of the cross sections of the warp yarns and the weft yarns in the material, the warp yarn spacing and the weaving angle are statistically analyzed, and corresponding probability distribution functions are adopted for representation;
step three: establishing a CMC material mesoscopic weaving structure parameterized model, inputting the geometric characteristic parameters in the second step into the CMC material mesoscopic weaving structure parameterized model, and calculating and acquiring the probability distribution characteristics of the anisotropic heat conductivity coefficient of the material based on a Monte Carlo random finite element method, wherein the parameterized geometric characteristics comprise the cross-sectional lengths and widths of the warps and the wefts, the warp spacing and the weaving angle;
step four: aiming at the CMC turbine blade, the thermophysical property characteristic of a woven structure CMC material is represented by adopting an anisotropic equivalent thermal conductivity coefficient, the direction of the anisotropic thermal conductivity coefficient is changed along with the molded surface of the blade, a curve coordinate system is adopted to realize the conversion from a main coordinate system of the anisotropic thermal conductivity coefficient to a space coordinate system, tetrahedral grids are used for generating blade grids in the calculation, a third type of convection heat transfer boundary condition is respectively applied to the inner wall surface and the outer wall surface of the blade, and the finite element calculation of a CMC blade temperature field is carried out;
step five: based on the Monte Carlo random finite element method, sampling is carried out in the material anisotropic thermal conductivity coefficient probability distribution obtained in the third step, the finite element calculation of the CMC blade temperature field in the fourth step is repeated, the probability distribution characteristics of the CMC blade temperature field are further obtained, and statistical analysis is carried out on key parameters such as the highest temperature of the blade.
Preferably: in the second step, a normal distribution function is adopted to represent the probability distribution functions of the section lengths and widths of the warp yarns and the weft yarns, the warp yarn spacing and the weaving angle.
Preferably: in the third step, the probability distribution process of the anisotropic thermal conductivity coefficient of the material calculated and obtained based on the Monte Carlo finite element method is as follows: aiming at a CMC material mesoscopic woven structure parameterized model generated by sampling geometric characteristic parameters every time, tetrahedral meshes are adopted to generate calculation meshes, constant temperature boundaries are applied to the upper surface and the lower surface of the model, periodic boundary conditions are applied to the periphery, temperature field finite element simulation is carried out, the temperature gradient and the heat flow density mean value of the model are obtained, and the equivalent thermal conductivity coefficient of the material is calculated based on a Fourier thermal conductivity equation.
Compared with the prior art, the invention has the following beneficial effects:
according to the probability thermal analysis method and the flow for the turbine blade of the CMC material with the braided structure, which are established by the invention, the random characteristics of the internal geometric structure of the CMC material and the corresponding anisotropic thermophysical property probability distribution are obtained based on the actual mesoscopic structure image, so that the potential high-temperature region characteristics of the turbine blade of the CMC material with the braided structure can be more truly and effectively obtained, the temperature field estimation precision of the turbine blade of the CMC material is improved, and a thermal analysis method support is provided for the engineering design application of the CMC blade.
Drawings
FIG. 1 is a sample of a 2.5-dimensional woven structure CMC material;
FIG. 2 is a CMC mesostructure XCT model;
FIG. 3 is a cross-sectional feature image of a CMC mesostructure;
FIG. 4 is a model of the CMC mesoscopic structure thermal conductivity coefficient prediction;
FIG. 5 is a CMC equivalent thermal conductivity normal distribution histogram;
FIG. 6 is a schematic view of the variation of the anisotropic thermal conductivity of a turbine blade of CMC material;
FIG. 7 is a schematic view of a CMC material turbine blade partition;
FIG. 8 is a cloud view of a temperature field distribution for a turbine blade of CMC material;
FIG. 9 is a schematic view of the range of thermal conductivity fluctuation of CMC materials;
FIG. 10 is a normal distribution histogram of the maximum temperature of a turbine blade made of CMC materials.
Detailed Description
The present invention will be further described with reference to the following examples.
Example (b): the invention discloses a probability thermal analysis method for a CMC (ceramic matrix composite) material turbine blade based on mesoscopic structure characteristic statistics, which is illustrated by taking a 2.5-dimensional woven structure ceramic matrix composite material turbine blade as an example.
The method aims at a 2.5-dimensional woven structure CMC material sample shown in figure 1, and adopts XCT (X-ray Computed Tomography) to carry out shooting and reconstruction of an internal mesostructure, so as to obtain a true characteristic three-dimensional model of the mesostructure of the woven structure CMC material shown in figure 2. Further to the mesostructured three-dimensional model in fig. 2, two-dimensional images of sections in different directions and positions are taken, as shown in fig. 3. The statistical analysis is performed on 1104 mesoscopic structure two-dimensional images represented in fig. 3, and the normal distribution characteristics and the characterization functions of the mesoscopic structure geometric characteristic parameters such as the weft length L1, the weft width L2, the warp length I1, the warp width I2, the warp pitch Id, the weaving angle a1 and the like are obtained, and the mean value and the standard deviation thereof are shown in table 1.
TABLE 1 meso-structure geometric characteristic parameters Normal distribution homogeneity and Standard deviation
Establishing a parametric model of the mesoscopic weave structure of the CMC material with 2.5-dimensional weave structure as shown in FIG. 4, wherein the parametric geometric model parameters comprise weft length L1, weft width L2, warp length I1, warp width I2, warp spacing Id and weave angle a1, and the fluctuation of the parametric geometric parameters obeys the probability distribution function obtained in the previous paragraph. The established mesoscopic braided structure parameterized model makes the following assumptions: (1) the fiber bundle and the matrix are in complete contact; (2) The central position of the fiber bundle does not change randomly, only the geometric characteristics of the fiber change randomly, and a Monte-Carlo method is used, namely samples are randomly extracted according to the geometric characteristics to be researched, and a corresponding parameterized model is generated; (3) Cracks and pores do not exist in the material, and the whole material is considered to be a continuous body.
Aiming at the established heat conductivity coefficient estimation model, a tetrahedral mesh is adopted for mesh division, and local encryption is carried out on meshes at the junction of fibers and a matrix, wherein the number of the meshes is 2317707, the maximum size of a mesh unit is 0.660mm, the minimum size is 0.048mm, and the growth rate of the meshes is 1.4. In the calculation, constant-temperature boundary conditions are added to the upper surface and the lower surface in the thickness direction of the model, the temperatures of the upper surface and the lower surface are respectively set to be 283K and 273K, and periodic boundary conditions are adopted on the wall surfaces around the model. The CMC used in this example had an axial thermal conductivity of 8.63W/(m · K), a radial thermal conductivity of 1.175W/(m · K), and a matrix thermal conductivity of 4.25W/(m · K). And carrying out temperature field finite element simulation on the model, obtaining the heat flow density and temperature gradient distribution in the model, and calculating to obtain the equivalent thermal conductivity coefficient of the material according to a Fourier thermal conductivity equation. Based on a Monte Carlo random finite element method, a specific heat conductivity coefficient estimation model is randomly generated each time, and the normal distribution characteristic of the equivalent heat conductivity coefficient of the material can be obtained by repeating the calculation process, as shown in fig. 5, the mean value is 2.9123W/(m.k), and the standard deviation is 0.0305W/(m.k), wherein the Monte-Carlo method used for the 2.5-dimensional CMC material comprises the following specific steps: 1) Firstly, shooting a mesoscopic structure of a CMC sample, carrying out three-dimensional reconstruction on all slices, extracting probability distribution of geometric features and calculating a distribution function; 2) Constructing a parameterized model according to parameters with geometric characteristic fluctuation, introducing a random distribution function into the model, and randomly generating sample values until the maximum value of the samples is reached; 3) Applying a thermal boundary condition, and carrying out finite element thermal analysis on the model based on the Fourier law; 4) And calculating to obtain a group of equivalent heat conductivity coefficients in the thickness direction of the material, repeating the process to obtain the probability distribution of the equivalent heat conductivity coefficients of the material, and finally processing the data by using a probability statistical method.
On the basis, a 2.5-dimensional weaving structure CMC material turbine blade equivalent model is established, as shown in FIG. 6, the anisotropic equivalent thermal conductivity coefficient is adopted to represent the thermophysical characteristics of the CMC material, and the direction of the anisotropic thermal conductivity coefficient changes along the blade profile. Therefore, firstly, a contour fitting function of each region of the blade needs to be obtained, a basis is provided for calculating a spatial deflection angle of a local ETC main direction coordinate system of the blade relative to a blade temperature field calculation coordinate system, on the basis, the blade is divided into a front edge, a reinforcing rib and a blade body to carry out contour fitting, and the spatial deflection angle is solved, as shown in FIG. 6. And tetrahedral meshes are adopted for dividing the CMC turbine blade calculation model, and the number of the meshes is 239240. The blade temperature field calculation thermal boundary conditions adopt a third type of convection heat transfer boundary, and simultaneously, the blade surface is divided into 6 regions according to the heat transfer characteristics of different regions of the blade, as shown in fig. 7, and the specific heat transfer boundary of each region is shown in table 2.
TABLE 2CMC blade surface Heat exchange boundary conditions
The calculated blade temperature field cloud chart is shown in fig. 8, the highest temperature appears in the central part of the front edge of the blade, the temperature is gradually reduced along the pressure surface and the suction surface, and under the typical constant-value thermal conductivity coefficient working condition, the highest temperature and the lowest temperature of the surface of the blade are 2099.1K and 1028.1K respectively. As the thermal conductivity fluctuates randomly (fluctuation range 2.817 to 3.021W/(m.K), as shown in FIG. 9), the fluctuation range of the maximum temperature of the blade surface is 2099.1 + -3.3K, and follows a normal distribution, as shown in FIG. 10.
The temperature field distribution information of the parameterized basic model can be calculated by applying a third class of boundary conditions, a Monte Carlo random finite element method is used, the probability distribution characteristic of the anisotropic heat conductivity coefficient is introduced through multiple random sampling, the finite element calculation of the temperature field of the CMC material turbine blade is repeated for multiple times, so that the probability distribution characteristic of the temperature field of the CMC blade is obtained, the fluctuation characteristic of the temperature field is statistically analyzed, and the fluctuation of the highest temperature point of the blade and the accurate prediction of a potential high temperature area are realized.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (3)
1. The CMC material turbine blade probability thermal analysis method based on the mesoscopic structure feature statistics is characterized by comprising the following steps of:
the method comprises the following steps: carrying out mesoscopic structure test on a CMC material sample with a braided structure by adopting XCT (X-ray computer tomography), and obtaining mesoscopic structure images of sections of the material in different directions and positions;
step two: according to a certain number of microscopic structure images, the probability distribution characteristics of geometrical characteristic parameters such as the lengths and widths of the cross sections of the warps and the wefts in the material, the warp spacing and the weaving angle are statistically analyzed, and corresponding probability distribution functions are adopted for representing;
step three: establishing a CMC material mesoscopic weaving structure parameterized model, inputting the geometric characteristic parameters in the second step into the CMC material mesoscopic weaving structure parameterized model, and calculating and acquiring the probability distribution characteristics of the anisotropic heat conductivity coefficient of the material based on a Monte Carlo random finite element method, wherein the parameterized geometric characteristics comprise the cross-sectional lengths and widths of the warps and the wefts, the warp spacing and the weaving angle;
step four: aiming at the CMC turbine blade, the thermophysical property characteristic of a woven structure CMC material is represented by adopting an anisotropic equivalent thermal conductivity coefficient, the direction of the anisotropic thermal conductivity coefficient is changed along with the molded surface of the blade, a curve coordinate system is adopted to realize the conversion from a main coordinate system of the anisotropic thermal conductivity coefficient to a space coordinate system, tetrahedral grids are used for generating blade grids in the calculation, a third type of convection heat transfer boundary condition is respectively applied to the inner wall surface and the outer wall surface of the blade, and the finite element calculation of a CMC blade temperature field is carried out;
step five: based on the Monte Carlo random finite element method, sampling is carried out in the material anisotropic thermal conductivity coefficient probability distribution obtained in the third step, the finite element calculation of the CMC blade temperature field in the fourth step is repeated, the probability distribution characteristics of the CMC blade temperature field are further obtained, and statistical analysis is carried out on key parameters such as the highest temperature of the blade.
2. The CMC material turbine blade probabilistic thermal analysis method based on mesoscopic structural feature statistics as recited in claim 1, wherein: in the second step, a normal distribution function is adopted to represent the probability distribution functions of the section lengths and widths of the warp yarns and the weft yarns, the warp yarn spacing and the weaving angle.
3. The CMC material turbine blade probabilistic thermal analysis method based on mesoscopic structural feature statistics as recited in claim 1, wherein: in the third step, the probability distribution process of the anisotropic thermal conductivity coefficient of the material calculated and obtained based on the Monte Carlo finite element method is as follows: aiming at a CMC material mesoscopic woven structure parameterized model generated by sampling geometric characteristic parameters every time, tetrahedral meshes are adopted to generate calculation meshes, constant temperature boundaries are applied to the upper surface and the lower surface of the model, periodic boundary conditions are applied to the periphery, temperature field finite element simulation is carried out, the temperature gradient and the heat flow density mean value of the model are obtained, and the equivalent thermal conductivity coefficient of the material is calculated based on a Fourier thermal conductivity equation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211564569.1A CN115795968A (en) | 2022-12-07 | 2022-12-07 | CMC material turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211564569.1A CN115795968A (en) | 2022-12-07 | 2022-12-07 | CMC material turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115795968A true CN115795968A (en) | 2023-03-14 |
Family
ID=85418879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211564569.1A Pending CN115795968A (en) | 2022-12-07 | 2022-12-07 | CMC material turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115795968A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116502469A (en) * | 2023-06-25 | 2023-07-28 | 中国航发四川燃气涡轮研究院 | Turbine blade temperature correction method and device based on temperature test data |
| CN117574527A (en) * | 2023-10-23 | 2024-02-20 | 南京航空航天大学 | CMC material turbine blade anisotropic thermal analysis method based on Fluent UDF |
| CN117874998A (en) * | 2023-11-16 | 2024-04-12 | 南京航空航天大学 | Collaborative design method for gradient distribution and braiding structure of thermophysical parameters of CMC (CMC) material |
-
2022
- 2022-12-07 CN CN202211564569.1A patent/CN115795968A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116502469A (en) * | 2023-06-25 | 2023-07-28 | 中国航发四川燃气涡轮研究院 | Turbine blade temperature correction method and device based on temperature test data |
| CN116502469B (en) * | 2023-06-25 | 2023-09-05 | 中国航发四川燃气涡轮研究院 | Turbine blade temperature correction method and device based on temperature test data |
| CN117574527A (en) * | 2023-10-23 | 2024-02-20 | 南京航空航天大学 | CMC material turbine blade anisotropic thermal analysis method based on Fluent UDF |
| CN117874998A (en) * | 2023-11-16 | 2024-04-12 | 南京航空航天大学 | Collaborative design method for gradient distribution and braiding structure of thermophysical parameters of CMC (CMC) material |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115795968A (en) | CMC material turbine blade probability thermal analysis method based on mesoscopic structure characteristic statistics | |
| Chen et al. | Analysis of the damage initiation in a SiC/SiC composite tube from a direct comparison between large-scale numerical simulation and synchrotron X-ray micro-computed tomography | |
| CN106250575B (en) | A kind of woven composite Turbine Blade Temperature Field field computation method considering fiber orientation | |
| Vanaerschot et al. | Stochastic framework for quantifying the geometrical variability of laminated textile composites using micro-computed tomography | |
| CN106093108B (en) | Unidirectional fibre toughening composition Equivalent Thermal Conductivities predictor method based on interstitial defect identification | |
| WO2022078130A1 (en) | Ceramic matrix composite turbine blade thermal analysis method taking microscopic braided structure and fiber bundle direction change into consideration | |
| Forna-Kreutzer et al. | Full-field characterisation of oxide-oxide ceramic-matrix composites using X-ray computed micro-tomography and digital volume correlation under load at high temperatures | |
| CN110909495A (en) | Method for estimating equivalent thermal conductivity of woven CMC (carboxyl methyl cellulose) material thin-wall component based on full-size microstructure model | |
| CN112949153B (en) | Rapid prediction method for heat transfer characteristic of periodic structure composite material at high temperature | |
| Flisch et al. | Industrial computed tomography in reverse engineering applications | |
| Delerue et al. | Pore network modeling of permeability for textile reinforcements | |
| CN112149235B (en) | Micro-scale temperature field information correction-based thermal analysis method for woven structure ceramic matrix composite material | |
| Wentorf et al. | Automated modeling for complex woven mesostructures | |
| Creveling et al. | 4D Imaging of ceramic matrix composites during polymer infiltration and pyrolysis | |
| Balokas et al. | Stochastic modeling techniques for textile yarn distortion and waviness with 1D random fields | |
| Gao et al. | X-ray computed tomography based microstructure reconstruction and numerical estimation of thermal conductivity of 2.5 D ceramic matrix composite | |
| Fang et al. | Improved unit cells to predict anisotropic thermal conductivity of three-dimensional four-directional braided composites by Monte-Carlo method | |
| Zhao et al. | Thermal-oxidation coupled analysis method for unidirectional fiber-reinforced C/SiC composites in air oxidizing environments below 1000° C | |
| CN115620841A (en) | Method for predicting damage of three-dimensional woven ceramic matrix composite material containing random gradual change pore defects | |
| CN106651891B (en) | Surface parameter measurement method for three-dimensional braided fabric composite material prefabricated member | |
| Turpin et al. | Quantitative thermomechanical characterisation of 3D-woven SiC/SiC composites from in-situ tomographic and thermographic imaging | |
| Turpin et al. | In situ tomographic study of a 3D-woven SiC/SiC composite part subjected to severe thermo-mechanical loads | |
| CN115630540A (en) | Prediction model for probability distribution characteristics of heat conductivity coefficient of composite material with woven structure | |
| Straumit et al. | Quantification of micro-CT images of textile reinforcements | |
| CN118629555A (en) | Ceramic matrix composite part damage analysis method considering internal real pore characteristics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |