Prediction method for permeability of thermally resistant porous microstructures in high-temperature chemical reaction environments

Abstract

The present application belongs to the technical field of protection systems, and specifically discloses a method for predicting the permeability of a thermally resistant porous microstructure under a high-temperature chemical reaction environment, including determining the high-temperature gas environment parameters during a reentry process; establishing a three-dimensional geometric model of the thermally resistant porous microstructure; performing a DSMC gas flow simulation of the thermally resistant porous microstructure; calculating the permeability of the thermally resistant porous microstructure based on the gas flow parameters; considering the influence of a multi-component gas environment of a high-temperature chemical reaction on the permeability characteristics of a thermally resistant porous material, overcoming the limitation of the prior art that is limited to a single-component gas or room temperature air, considering the gas molecule dissociation effect under a high-temperature environment and the chemical reaction process of a mixed gas and a porous material solid, so that the analysis results are closer to the actual working conditions during the reentry process; a permeability analysis method and a permeability normalization method based on the Klinkenberg model, quantitatively characterizing the permeability characteristics of the material under different temperature and gas component conditions under a thermochemical non-equilibrium environment in which intramolecular energy is excited.

Description

Technical Field

[0001] The present invention belongs to the technical field of protection systems, and specifically is a method for predicting the permeability of a heat-resistant porous microstructure in a high-temperature chemical reaction environment by establishing a three-dimensional geometric model of the heat-resistant porous microstructure and performing DSMC gas flow simulation in a high-temperature chemical reaction environment, thereby calculating the permeability of the porous material. Background Art

[0002] During high-speed re-entry into the atmosphere, an extreme aerodynamic heating environment will be experienced, and thermal protection systems (TPS) are required for protection. Lightweight ablative materials are an important type of thermal protection material, and they have a complex porous microstructure inside during operation. Under high-temperature conditions, complex physical and chemical changes will occur inside the material, such as pyrolysis, oxidation, and ablation. These phenomena will affect the microstructure and gas transport properties of the material. The permeability of the material is a key parameter for quantifying the gas transport process. Therefore, accurately predicting and analyzing the permeability characteristics of porous thermal protection materials under high-temperature chemical reaction gas conditions is crucial to the design of thermal protection systems.

[0003] CN118468699A discloses a GDL absolute permeability prediction method based on a three-dimensional convolutional neural network, and a GDL reconstruction model is constructed based on the Porespy software platform. The SNOW algorithm is used to simplify the GDL model to generate a pore network model; a detailed calculation is performed through a unidirectional flow simulation method, and the absolute permeability corresponding to the GDL reconstruction model image is used as a key label, and further each image of the GDL reconstruction model and its corresponding absolute permeability are integrated into a sample data set, and the three-dimensional convolutional neural network is trained using this data set. Through the optimization algorithm, the optimal prediction model for the absolute permeability of GDL is finally obtained.

[0004] CN116680988A discloses a method for predicting the permeability of porous media based on a Transformer network. The flow field is simulated using a pore-scale simulation method to calculate the permeability of a three-dimensional porous medium; the three-dimensional structural image of the porous medium is regarded as a spatial sequence composed of two-dimensional slice images, and the physical properties of each two-dimensional slice image are extracted; the corresponding physical parameter matrix is ​​added to the three-dimensional structural image of the porous medium, and the sample set is constructed using the porous medium permeability as the label value corresponding to the image, and the training set, validation set and test set are determined, and then the convolutional neural network CNN and the Transformer network are used to construct and train the PhyCNN-Transformer neural network model, thereby achieving accurate prediction of the permeability of porous media.

[0005] However, the above prediction methods do not involve the processes of pyrolysis, oxidation and ablation in the aerodynamic heating environment, and are therefore not suitable for the permeability characteristics of porous heat-resistant materials under high-temperature chemical reaction gas conditions during high-speed re-entry into the atmosphere.

[0006] The porous microstructure of phenolic impregnated carbon ablator (PICA) has complex interconnected pores and fiber network characteristics. Its pore size is in the micrometer range, which is comparable to the mean free path of gas molecules under high-altitude flight conditions. This causes the flow of gas in the porous microstructure to exhibit significant discontinuous characteristics, and its permeability characteristics cannot be described by the traditional Darcy's law applicable to continuous flow. Therefore, the direct simulation Monte Carlo (DSMC) method is currently mainly used to study the permeability characteristics of porous materials under discontinuous flow gas conditions, and the Klinkenberg model is used to describe the relationship between permeability and internal pressure of porous materials.

[0007] In existing studies, gaseous media are mainly limited to single-component gases: the permeability characteristics of nitrogen are studied in the temperature range of 290–1200K; the permeability characteristics of helium are studied at room temperature of 290K; although the temperature range of argon permeability characteristics is extended to 298–1500K, it is still limited to single-component inert gases. The study of air is limited to room temperature conditions of 285–310K, and the dissociation and chemical reactions of air in high temperature environments are not considered. Even the study of gases such as CO and CO2 is only conducted at a temperature of 300K.

[0008] It can be seen that the existing technology mainly focuses on the study of the permeability characteristics of single-component gases and undissociated air, without considering the effects of internal energy excitation and chemical reactions under high-temperature reaction gas conditions. Specifically, (1) the research object is limited to single-component gases (argon, nitrogen, helium, etc.) or air at room temperature; (2) the dissociation effect of air in a high-temperature environment is not considered, such as the multi-component gas formed after oxygen molecules dissociate into atomic oxygen; (3) the effect of chemical reactions between multi-component gases (oxygen molecules and atomic oxygen) and thermally resistant porous microstructure materials on gas permeability under high-temperature conditions is not considered. In fact, the thermal protection system during reentry has to face an extreme environment with high temperature (1000K–3000K), multiple components and violent chemical reactions. Therefore, it is urgent to develop a method that can accurately analyze the permeability characteristics of thermally resistant porous microstructures under high-temperature chemical reaction gas conditions. Summary of the invention

[0009] The purpose of this application is to solve the technical problem that the prior art lacks a method for analyzing the permeability characteristics of heat-resistant materials under high-temperature, multi-component, and chemical reaction conditions, and to provide a method for predicting the permeability of heat-resistant porous microstructures under high-temperature chemical reaction environments.

[0010] To achieve the above object, the present application provides a method for analyzing the permeability of a heat-resistant porous microstructure to a high-temperature chemical reaction gas, which is characterized by comprising the following steps:

[0011] Determine the high temperature gas environment parameters during reentry;

[0012] Establish a three-dimensional geometric model of thermal-resistant porous microstructures;

[0013] Conduct DSMC gas flow simulations of thermally protected porous microstructures;

[0014] The permeability of porous materials is analyzed based on flow parameters.

[0015] Preferably, determining the high-temperature gas environment parameters during the re-entry process includes: based on the target re-entry orbit data, extracting the stagnation temperature and pressure data that change with time, and selecting different characteristic temperature points as analysis conditions, determining the pressure range corresponding to each characteristic temperature point, determining the Knudsen number range based on the stagnation temperature and pressure, and selecting different mixed gas components to perform flow simulation calculations.

[0016] Specifically, the stagnation temperature data is analyzed and statistically analyzed to obtain multiple quantiles and the temperature corresponding to each quantile as different temperature conditions for the reentry process. Each determined temperature condition corresponds to an upper and lower pressure limit. A series of pressures between the upper and lower pressure limits are taken as pressure conditions to determine the Knudsen number range, which is used as the pressure, temperature conditions and the corresponding Knudsen number Kn for the flow simulation example.

[0017] The different mixed gas components include: 100% O 2 , 50% O 2 + 50% O, 100% O, the above are mole fraction ratios.

[0018] Preferably, the establishment of a three-dimensional geometric model of a heat-resistant porous microstructure includes: using X-ray computed tomography technology to obtain a microstructure image of the heat-resistant porous material, and using image processing software to segment it according to a set grayscale threshold, reconstructing the three-dimensional structure, and generating a three-dimensional geometric model composed of triangular units.

[0019] Specifically, a three-dimensional needle-punched carbon preform is selected to establish a three-dimensional geometric model of a heat-resistant porous microstructure. The microstructure of the carbon preform is composed of carbon fibers with a diameter of about 7 μm and pores with a diameter distributed in the range of 10-100 μm; the carbon fibers are randomly distributed in the preform, and the porosity exceeds 70%.

[0020] The image processing software processes the microstructure image and performs image segmentation by setting the gray value threshold. The carbon fibers and pores are then identified and distinguished. The marching cubes algorithm is then used to reconstruct the geometric morphology of the porous structure. Finally, a fiber surface mesh composed of triangular units is generated to generate a three-dimensional geometric model of the heat-resistant porous microstructure.

[0021] Preferably, the DSMC gas flow simulation of the heat-resistant porous microstructure includes: constructing a complete calculation domain including an upstream flow field, a three-dimensional geometric model of the heat-resistant porous microstructure and a downstream flow field, setting inlet and outlet pressure and temperature boundary conditions according to the working conditions, simulating the movement, collision and surface action processes of gas molecules, considering the internal energy excitation and chemical reaction effects under high temperature conditions, and calculating the volume flow parameters of the flow.

[0022] The volume flow parameters include at least: gas flow velocity, pressure, temperature, and inlet mass flow rate.

[0023] Specifically, the DSMC method is used to simulate the molecular motion process by solving the time-varying Boltzmann equation to simulate the gas flow.

[0024] The SPARTA program was used for simulation calculations, which separated the molecular motion and collision process, tracked the particle motion in virtual grid cells, and each simulated particle represented multiple actual molecules.

[0025] Furthermore, during simulation calculations, the grid size is smaller than the mean free path, and the time step is smaller than the average collision frequency;

[0026] During simulation calculation, after the flow field reaches a steady state, the volume flow parameters are obtained by statistical sampling of the microscopic particle properties in multiple time steps.

[0027] In terms of computational domain construction, the cubic thermal-protective porous microstructure 3D geometric model is placed at the center of the rectangular computational domain. The cross section of the computational domain has the same size as the 3D geometric model. Fluid cavity areas are set upstream and downstream of the 3D geometric model of the thermal-protective porous microstructure.

[0028] Furthermore, the gas inlet and outlet boundaries are subsonic boundaries, and their pressure and temperature conditions are set according to the simulation condition table; the boundaries around the calculation domain are symmetric boundary conditions.

[0029] In terms of intermolecular interactions, the variable hard sphere (VHS) model is used to describe particle collisions, and the diffuse reflection model is used to characterize the interaction between particles and surfaces.

[0030] For diatomic oxygen molecules, the Larsen-Borgnakke model is used to deal with the energy exchange between translational energy and internal energy (rotational and vibrational).

[0031] In terms of chemical reactions, considering the oxidation of carbon fiber by oxygen, there are two reactions and their reaction rates:

[0032]

[0033]

[0034] for Reaction rate, Its Reaction rate, T Represents the wall temperature.

[0035] Preferably, the calculation and analysis of the permeability of the porous material based on the gas flow parameters includes:

[0036] Penetration parameters S and average pressure P m When the linear relationship is satisfied, the Darcy permeability is obtained by linear fitting based on the Klinkenberg model. K and average pressure P m The corresponding functional relationship.

[0037] Furthermore, the above functional relationship is:

[0038]

[0039] Where: S * is the normalized permeability parameter, P m is the average pressure upstream and downstream of the material.

[0040] Penetration parameters S and average pressure P m When the linear relationship is not satisfied, the Darcy permeability is obtained by data analysis and fitting K and average pressure P m The corresponding functional relationship.

[0041] Furthermore, the above functional relationship is:

[0042]

[0043] In the formula, A, B, and C are fitting coefficients respectively.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The established analytical method for the permeability of heat-resistant porous microstructures to high-temperature chemical reaction gases systematically considers the influence of the multi-component gas environment of high-temperature chemical reactions on the permeability characteristics of heat-resistant porous materials, overcoming the limitation of existing technologies that are limited to single-component gases or room temperature air.

[0046] 2. By considering the gas molecule dissociation effect in a high temperature environment and the chemical reaction process between the mixed gas and the porous material solid, the analysis results are closer to the actual working conditions during the reentry process.

[0047] 3. The permeability analysis method and permeability normalization method based on the Klinkenberg model can quantitatively characterize the permeability characteristics of materials under different temperatures and gas composition conditions in a thermochemical non-equilibrium environment with intramolecular energy excitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the method flow of an embodiment of the present application.

[0049] Figure 2 Schematic diagram of the stagnation point pressure and temperature of the reentry trajectory of an embodiment of the present application.

[0050] Figure 3 Schematic diagram of the geometric structure and computational setup for DSMC gas flow simulation according to an embodiment of the present application.

[0051] Figure 4 This is a schematic diagram of the permeability parameter and average pressure change under non-reaction conditions of the embodiment of the present application; wherein, Figure 4 (a) in the figure is at 1000K; Figure 4 (b) in the figure is at 1600K; Figure 4 (c) in the figure is at 2850K.

[0052] Figure 5 This is a flow chart of the normalization process of the permeability parameters and slip coefficients according to the embodiment of the present application.

[0053] Figure 6 This is a schematic diagram of the changes in permeability parameters and average pressure under reaction conditions of 2850K in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The present invention is described in detail below in conjunction with the embodiments and drawings, but it should be understood that the embodiments and drawings are only used to exemplify the present invention and do not constitute any limitation on the protection scope of the present invention. All reasonable changes and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0055] like Figure 1 As shown, it is a schematic diagram of the method flow of an embodiment of the present application, and the steps include:

[0056] S1. Determine the parameters of the high temperature gas environment during reentry.

[0057] In this embodiment, the high-temperature gas environment parameters that need to be determined during the reentry process mainly include the stagnation temperature and stagnation pressure, as well as the concentration characteristics of the gas components. These high-temperature gas parameters are used as state parameters for subsequent gas flow calculations. Specifically: based on the reentry orbit data, the corresponding parameters are obtained as they change during the reentry process. This embodiment selects a certain return capsule and determines the high-temperature gas environment based on its orbital data during the Earth's atmosphere reentry process. The changes in the stagnation temperature and stagnation pressure over time during the reentry of the return capsule are shown in the figure below. Figure 2 As shown in Figure 2, the maximum temperature reaches 3409K and the maximum pressure reaches 38223Pa.

[0058] By analyzing the data of stagnation temperature, the following three statistics can be obtained: 25% quantile, 50% quantile (median), 75% quantile. The temperatures corresponding to the three quantiles are 1024K, 1598K, and 2864K. Based on these three typical temperature quantiles, three temperature values ​​can be determined, representing the low temperature, medium temperature, and high temperature conditions of the reentry process. The temperature values ​​are selected as 1000K, 1600K, and 2850K. Each temperature is determined in Figure 2 In the equation, there are two pressures, one large and one small, and a series of pressures between the two pressures are taken as pressure conditions. Finally, the pressure, temperature conditions and the corresponding Knudsen number Kn of the flow simulation example are obtained as shown in Table 1. Pm is the average pressure upstream and downstream of the material, Tin is the inlet gas temperature, Pin is the upstream inlet pressure, Pout is the downstream outlet pressure, and the Knudsen number Kn is calculated as follows:

[0059] (1)

[0060] in λ is the mean free path of the gas particles, is the characteristic length of the porous structure (50 μm). The Knudsen number range is 0.06–1.4, covering the slip flow region and part of the transition flow region.

[0061] Table 1 Pressure and temperature conditions of flow simulation examples and the corresponding Knudsen number Kn

[0062]

[0063] Under the extreme conditions of the reentry process, the high-temperature gas will chemically react with the carbon fibers in the porous material to generate multi-component gas. In order to simulate this gas environment, this embodiment considers a mixed gas composed of molecular oxygen (O2) and atomic oxygen (O), and sets three different molar fraction ratios:

[0064] (1) 100% O2 ( X O2: X O = 1:0), representing a completely undissociated state;

[0065] (2) 50% O2 + 50% O ( X O2: X O = 0.5 : 0.5), representing a partially dissociated state;

[0066] (3) 100% O ( X O2: X O = 0:1), representing a completely dissociated state.

[0067] For each component ratio, the temperature and pressure in Table 1 were selected for flow simulation calculations.

[0068] S2. Establish a three-dimensional geometric model of thermal-protective porous microstructure.

[0069] The heat-resistant porous material selected in this embodiment is a three-dimensional needle-punched carbon preform, which is mainly used as a matrix for low-density carbon-based ablative materials. The microstructure of the carbon preform consists of carbon fibers with a diameter of about 7 μm and pores with a diameter ranging from 10 to 100 μm. The carbon fibers are randomly distributed in the preform to form a complex porous network structure. The porosity of the material sample exceeds 70%.

[0070] In order to obtain accurate characterization of the internal microstructure of the material, X-ray computed tomography (micro-CT) technology was used for imaging. Specifically, a Zeiss Xradia 520 Versa 3D X-ray microscope was used for scanning, with a spatial resolution of 5μm and a voxel size of 2μm to ensure that the microstructural features of the material can be clearly captured.

[0071] In terms of image processing and reconstruction, PuMA software was used to process micro-CT images and generate 3D models of porous materials. Image segmentation was performed by setting the grayscale value threshold of 127 to identify and distinguish carbon fibers and pores, where black represents voids (pores) and white represents solids (carbon fibers). After calculation, the porosity obtained by this threshold segmentation method was 0.762. The marching cube algorithm was then used to reconstruct the complex geometric morphology of the porous structure. The algorithm approximated the isosurface of the geometric body obtained by grayscale threshold segmentation, and finally generated a fiber surface mesh composed of triangular units, providing a geometric basis for subsequent DSMC simulations. The established three-dimensional geometric model of the porous microstructure is shown in the figure. Figure 3 shown.

[0072] S3. DSMC gas flow simulation of thermally protective porous microstructures.

[0073] Since the internal flow of heat-resistant porous materials has discontinuous characteristics and microscale spatial characteristics, this embodiment uses the DSMC method to simulate gas flow. The DSMC method is a random simulation method based on kinetic theory, which simulates the molecular motion process by solving the time-varying Boltzmann equation. This method is particularly suitable for discontinuous flows. Specifically, the SPARTA program is used for simulation calculations. The DSMC algorithm separates the molecular motion and collision processes and tracks the particle motion in virtual grid cells. Each simulated particle represents multiple actual molecules. To ensure the calculation accuracy, the grid size must be smaller than the mean free path and the time step must be smaller than the average collision frequency.

[0074] In the specific settings, 20-50 simulated particles are set in each computational grid unit to reduce statistical fluctuations and ensure the accuracy of collision simulation. After 30,000 time steps of calculation, the flow field reaches a steady state. Macroscopic physical quantities (such as temperature, pressure, and number density) are obtained by statistical sampling of microscopic particle properties within 30,000 time steps. In terms of computational domain construction, a cubic porous material sample with a side length of 100 μm is placed in the center of a larger rectangular computational domain. The cross-section of the computational domain is the same as the sample size, such as Figure 3As shown. Fluid cavity regions are set upstream and downstream of the porous material, each with a length of 50μm. The purpose of setting these fluid cavities is to ensure that the airflow can be fully developed before entering the porous material, while preventing the boundary conditions from having unnecessary effects on the internal flow field of the porous material. The gas inlet and outlet boundaries are subsonic boundaries, and their pressure and temperature conditions are set according to the simulation operating table. The boundaries around the calculation domain are symmetric boundary conditions. In terms of intermolecular interactions, the variable hard sphere (VHS) model is used to describe particle collisions, and the diffuse reflection model is used to describe the interaction between particles and surfaces. For diatomic oxygen molecules, the Larsen-Borgnakke model is used to deal with the energy exchange between translational energy and internal energy (rotation and vibration) to accurately characterize the high-temperature gas dynamics. In terms of chemical reactions, the oxidation of oxygen on carbon fiber is mainly considered, including the following two reactions and their reaction rates:

[0075] (2)

[0076] (3)

[0077] for Reaction rate, Its Reaction rate, medium T represents the wall temperature. The simulation conditions of the reactive case are the same as those of the non-reactive case, the only difference being that the chemical reaction between the gas mixture and the carbon fiber is considered.

[0078] S4. Calculate and analyze the permeability of porous materials based on flow parameters.

[0079] Through the gas flow simulation of S3 of this embodiment, the gas flow parameters inside and outside the thermal protection porous microstructure, such as gas flow velocity, pressure, temperature, inlet mass flow rate, etc., can be obtained. Based on these flow parameters, the permeability of the gas flowing through the porous material can be further calculated. When the thermal protection porous material works under actual flight conditions, its internal flow exhibits a discontinuous slip effect. In this case, the permeability characteristics of the material can be described by the following formula:

[0080] (4)

[0081] in, Ke is the effective permeability, K is the Darcy (continuous) permeability, α is the slip coefficient, P is the gas pressure. Here we consider the case of a one-dimensional isothermal layer flowing through a porous material. By combining equation (4) with the mass conservation equation, the ideal gas state equation, the geometric properties of the material and Darcy's law, we obtain the following relationship:

[0082] (5)

[0083] in, S Defined as the penetration parameter (N), μ is the gas dynamic viscosity coefficient (kg / m·s), is the gas mass flow rate (kg / s), T is the gas temperature (K), L is the length of the material along the flow direction (m), A is the cross-sectional area of ​​the material (m2), M is the molar mass of the gas (kg / mol), Δ P is the pressure difference between the inlet and outlet of the material (Pa).

[0084] It can be seen that the penetration parameter S The gas flow physical parameters obtained by flow simulation and the material sample size can be calculated, and the penetration parameters can be used later S To characterize the permeability characteristics of thermal porous microstructures. S and average pressure Pm When the linear relationship (i.e., Klinkenberg model) is satisfied, the Darcy permeability can be obtained by linear fitting: K and slip coefficient α .like S and Pm When the linear relationship is not satisfied, the corresponding functional relationship can be obtained through data analysis and fitting.

[0085] For a multi-component gas mixture, the gas dynamic viscosity coefficient μ and molar mass M It is closely related to the gas composition. Under high temperature conditions, the dynamic viscosity coefficient of the mixed gas is calculated using Wilke's semi-empirical mixing law:

[0086] (6)

[0087] in, Xi is the molar concentration of the different components, μi is the viscosity coefficient of the different components, Φi is the scaling factor for the different components. The molar mass of the gas mixture M The molar mass of each component Mi calculate:

[0088] (7)

[0089] The following is a detailed analysis of the permeability parameters of high-temperature non-reactive gases and gases considering gas-solid reaction. S With average pressure Pm relationship.

[0090] Non-reactive gas under different temperature and gas concentration conditions S and Pm The relationship is as Figure 4 As shown in Figure 2, it can be seen that the relationship between permeability and average pressure satisfies a good linear relationship, and the S-Pm The slope of the curve (i.e. K ) are basically the same, the slip coefficient α This is because K Mainly related to the structure itself, α It is mainly affected by the gas flow conditions. α The relationship with gas parameters is as follows:

[0091] (8)

[0092] in, c is a constant, dp is the average pore size, which remains unchanged for the same material, and R is the ideal gas constant. μ , T and M All are related to gas conditions. Considering the characteristics of the slip parameter, the following normalized scaling method is used to select a suitable reference state to obtain the reference slip coefficient: α *, eliminate the effects of different temperatures and gas compositions α Impact:

[0093] (9)

[0094] In the formula, the parameters with superscript * are the corresponding parameters under the gas reference state conditions. Finally, the normalized permeability parameter that does not change with temperature and composition can be obtained by the following formula:

[0095] (10)

[0096] Figure 5 The specific steps to obtain the normalized permeability parameters and average pressure under different temperature and gas type conditions are shown. Pure oxygen at room temperature (298K) is selected as the reference state, including the reference temperature T * = 298K, corresponding reference viscosity μ and reference molar mass M The normalization process first converts the actual working temperature into T , Viscosity μ , molar mass M and slip coefficient α Convert to normalized parameters and then calculate the normalized slip parameter α *, combined with the obtainedK 0 Value and average pressure Pm , calculate the normalized permeability parameter S *.

[0097] The data points obtained under different gas components and temperatures show a good linear relationship. The following results are obtained by fitting, and the goodness of fit reaches 0.966:

[0098] (11)

[0099] Which contains Superscript Parameters and are the coefficients after fitting.

[0100] The results with reactive gases are as follows Figure 6 As shown. It can be found that when the gas curing reaction occurs, the relationship between the permeability parameter and the average pressure does not satisfy a good linear relationship. The characteristics of the data can be analyzed and different relationships can be obtained using fitting methods. Figure 6 The results of different components at 2850K can be obtained by data fitting, and the following quadratic polynomial relationship can be obtained:

[0101] (12)

[0102] in, A , B , C are the fitting coefficients respectively. For different specific gas components, the results are as follows:

[0103] (1) X O2: X O = 1 : 0, S = 3.2×10-16 Pm 2–4.1×10-11 Pm + 3.0×10-11

[0104] (2) X O2: X O = 0.5 : 0.5, S = 6.2×10-15 Pm 2–4.2×10-10 Pm –5.6×10-7

[0105] (3) X O2: X O = 0 : 1, S = 1.9×10-14 Pm 2–1.3×10-9 Pm –2.3×10-6

[0106] The fitting effect is good, and the goodness of fit is greater than 0.997.

[0107] The embodiments described above are only descriptions of the preferred methods of the present application, and are not intended to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements made to the technical solutions of the present application by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present application.

Claims

1. A method for predicting the permeability of a heat-resistant porous microstructure in a high-temperature chemical reaction environment, characterized in that: The following steps are involved: Step (1): Based on the target's re-entry orbit data, extract the stagnation temperature and pressure data that change with time, select different characteristic temperature points as analysis conditions, determine the pressure range corresponding to each characteristic temperature point, determine the Knudsen number range based on the stagnation temperature and pressure range, and select different mixed gas components; determine the pressure, temperature and corresponding Knudsen number for gas flow simulation calculation; specifically: analyze the stagnation temperature data and perform statistics to obtain multiple quantiles and the temperatures corresponding to each quantile as different temperature conditions of the re-entry process, each determined temperature condition corresponds to an upper pressure limit and a lower pressure limit, take a series of pressures between the upper pressure limit and the lower pressure limit as pressure conditions, determine the Knudsen number, and use it as the pressure, temperature condition and corresponding Knudsen number for gas flow simulation calculation; Step (2): establishing a three-dimensional geometric model of the heat-resistant porous microstructure; including: obtaining a material microstructure image by X-ray computed tomography, segmenting the material according to a set grayscale threshold using image processing software, reconstructing the three-dimensional structure, and generating a three-dimensional geometric model composed of triangular units; Step (3): using the pressure, temperature and corresponding Knudsen number obtained in step (1) for gas flow simulation calculation and the three-dimensional geometric model obtained in step (2) to perform DSMC gas flow simulation of the thermal protection porous microstructure, and obtain gas flow parameters; the DSMC gas flow simulation of the thermal protection porous microstructure includes: constructing a complete calculation domain including an upstream flow field, a three-dimensional geometric model of the thermal protection porous microstructure and a downstream flow field, setting inlet and outlet pressure and temperature boundary conditions according to working conditions, simulating gas molecular motion, collision and surface action processes, considering internal energy excitation and chemical reaction effects under high temperature conditions, and calculating gas flow parameters; Step (4): Calculate the permeability of the thermally protective porous microstructure based on the gas flow parameters obtained in step (3).

2. The prediction method according to claim 1, characterized in that: A three-dimensional needle-punched carbon preform was selected to establish a three-dimensional geometric model of the thermally protective porous microstructure.

3. The prediction method according to claim 2, characterized in that: In step (4), the permeability of the thermal protection porous microstructure is calculated based on the gas flow parameters, including: when the permeability parameter S and the average pressure of upstream and downstream P m When the linear relationship is satisfied, the Darcy permeability is obtained by linear fitting based on the Klinkenberg model. K and the average pressure of upstream and downstream P m The corresponding functional relationship.

4. The prediction method according to claim 3, characterized in that: In step (4), the permeability of the thermal protection porous microstructure is calculated based on the gas flow parameters, including: when the permeability parameter S and the average pressure of upstream and downstream P m When the linear relationship is not satisfied, the Darcy permeability is obtained by data analysis and fitting K and the average pressure of upstream and downstream P m The corresponding functional relationship.

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