Dynamic ablation prediction method based on microreaction molecular dynamics
By constructing an ablation model based on microscopic reaction molecular dynamics and combining it with data optimization methods, the problem of low computational efficiency in predicting the dynamic ablation of materials on aircraft surfaces was solved, and accurate prediction of the ablation behavior of composite materials was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-04-07
AI Technical Summary
Existing technologies have low computational efficiency in predicting the dynamic ablation of materials on aircraft surfaces, making it difficult to accurately predict the ablation behavior of composite materials under different temperatures and pressures, and thus failing to meet practical engineering needs.
A method based on microscopic reaction molecular dynamics was used to construct carbon-carbon, carbon-phenolic, and silicon carbide ablation models. Through simulation and data optimization, combined with the Bayesian maximum entropy data fusion method, the activation energy prediction was optimized to achieve accurate prediction of ablation results.
It improves the accuracy and computational efficiency of ablation prediction, enabling more accurate prediction of the ablation behavior of composite materials under different temperatures and pressures, and solves the problem of low computational efficiency in existing technologies.
Smart Images
Figure CN121811985A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a dynamic ablation prediction method based on microscopic reaction molecular dynamics, belonging to the field of high-speed aircraft technology. Background Technology
[0002] When an aircraft flies at high speed in the atmosphere, the viscosity of the air causes intense friction between airflow layers with large velocity gradients within the viscous boundary layer, leading to a sharp increase in temperature near the surface. The flow field development in localized ablation regions is disturbed by ablation products, resulting in severe dynamic ablation behavior of the aircraft surface materials. This makes the already challenging ablation of composite materials even more difficult to predict. Composite material ablation models are one of the important directions for evaluating the high-temperature ablation resistance of aircraft.
[0003] Domestic and international research has been conducted on relatively systematic and in-depth studies of coupled calculation methods for aircraft flow fields and structural temperature fields. Obtaining accurate multiphysics coupled calculation results depends on a thorough understanding of the Navier-Stokes equations numerical scheme, turbulence models, ablation models, and coupled calculation strategies. Current coupled calculation strategies primarily employ sequential coupling, meaning that flow field calculations are performed before calculations of structural temperature fields and ablation retreat. The development of sequential coupling methods is mainly due to the order-of-magnitude difference between the characteristic time steps of flow field changes and the time steps of structural temperature field changes, resulting in extremely low computational efficiency and failing to meet practical engineering needs. Therefore, this invention analyzes the chemical reaction kinetic parameters of existing thermal protection materials and fits them to obtain more accurate ablation results for carbon-carbon, carbon phenolic, and silicon carbide composite materials under different temperatures and pressures. This allows for the acquisition of the ablation performance and performance evolution laws of carbon-carbon composite materials under dynamic ablation conditions, thereby improving computational efficiency. Summary of the Invention
[0004] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a dynamic ablation prediction method based on microscopic reaction molecular dynamics.
[0005] This invention discloses a dynamic ablation prediction method based on microscopic reaction molecular dynamics, comprising:
[0006] Based on the microstructure of the surface material, carbon-carbon ablation model, carbon-phenolic ablation model and silicon carbide ablation model under dynamic conditions were constructed.
[0007] Simulation conditions were set to simulate carbon-carbon ablation model, carbon phenol ablation model and silicon carbide ablation model under dynamic conditions to obtain the number of small molecule products.
[0008] Based on the characteristics of different materials, the activation energy regression curves as a function of temperature were obtained;
[0009] Based on the regression curves of activation energy and the number of small molecule products, the pre-exponential factors and activation energies of the carbon-carbon ablation model, the carbon-phenolic ablation model, and the silicon carbide ablation model were obtained.
[0010] By extracting the data of reaction activation energy, the pre-exponential factor and activation energy of the carbon-carbon ablation model, the carbon-phenolic ablation model and the silicon carbide ablation model were optimized to obtain the optimized pre-exponential factor and activation energy.
[0011] Based on the optimized activation energy, reaction pre-exponential factor, and the set incoming flow height, Mach number, heat flux, and time, the ablation retreat rate and ablation retreat amount of the carbon-carbon ablation model, carbon phenolic ablation model, and silicon carbide ablation model under the corresponding incoming flow conditions are obtained.
[0012] Furthermore, in the above method, the carbon-carbon ablation model, the carbon-phenolic ablation model, and the silicon carbide ablation model are specifically as follows:
[0013] The carbon-carbon ablation model employs a woven graphene structure; the weft and warp yarns made of graphene composite material are interlocked by winding them together; the fiber bundles are interwoven at a certain angle in the thickness direction to form a woven graphene structure.
[0014] The carbon phenolic material ablation model employs a composite structure of phenolic single chains and carbon nanotubes; by using C 56 H 50 O8 phenolic single chains, carbon nanotubes, and phenolic resin were compressed at a temperature of 200K–300K to form a matrix model; then, a substrate was formed on the surface of the matrix model. The vacuum layer and oxygen-filled layer; the length of the carbon nanotubes is Diameter The density of phenolic resin is 1–1.5 g / cc;
[0015] The silicon carbide ablation model uses a silicon carbide gas-solid interface, which consists of a solid phase and a gas phase. The solid phase is 4H-SiC crystal, and the gas phase is oxygen molecules. The height of the gas phase is 3 to 4 times that of the solid phase.
[0016] Furthermore, in the above method, the simulation conditions include the time step and the simulation time; for the carbon-carbon ablation model, the time step is 0.10–0.20 fs, and the simulation time is not less than 200 ps; for the carbon phenolic ablation model, the time step is 0.10–0.20 fs, and the simulation time is not less than 1000 ps; for the silicon carbide ablation model, the time step is 0.20–0.25 fs, and the simulation time is not less than 200 ps.
[0017] Furthermore, in the above method, the regression curve of the activation energy as a function of temperature is specifically as follows:
[0018]
[0019] Where K is the ablation reaction rate, A refers to the pre-factor, and E... a It is the activation energy, R is the ideal gas constant, and T is the temperature.
[0020] Furthermore, in the above method, the pre-exponential factor and activation energy are specifically:
[0021] ln(K)=lnA-E a / RT
[0022] Where K is the ablation reaction rate, A refers to the pre-factor, and E... a It is the activation energy, R is the ideal gas constant, and T is the temperature.
[0023] Furthermore, in the above method, the step of obtaining the ablation retreat rate and ablation retreat amount of each ablation model under the corresponding incoming flow state based on the optimized activation energy, pre-exponential factor, and set incoming flow height, Mach number, heat flux, and time is specifically as follows:
[0024] Determine the input parameters, including flight altitude, Mach number, heat flux intensity, and ablation time;
[0025] Based on the time step, the ablation time t is decomposed into several individual times t1, t2, ... t. n ;
[0026] Based on the input parameters, the wall temperature T of carbon-carbon, carbon phenolic, and silicon carbide materials at different time steps under corresponding incoming flow conditions was obtained through simulation calculations. n-wall ;
[0027] For the carbon-carbon ablation model, the ablation retreat rate k calculated in a single run is... n-C for:
[0028]
[0029] ablation retreat H C for:
[0030]
[0031] in, t represents the average ablation retreat rate of carbon-carbon materials between the previous and current time points. n This is the step size for this event;
[0032] For the carbon phenolic ablation model, the ablation retreat rate calculated in a single run is: for:
[0033]
[0034] ablation retreat H H2O for:
[0035]
[0036] in, This represents the average ablation retreat rate of the carbon phenolic material between the previous and current time points.
[0037] For the silicon carbide ablation model, the ablation retreat rate calculated in a single run is k. n-SiC for:
[0038]
[0039] ablation retreat H SiC for:
[0040]
[0041] in, This represents the average ablation retreat rate of silicon carbide material between the previous and current time points.
[0042] The advantages of this invention over the prior art are as follows:
[0043] (1) This invention uses microscale simulation calculations based on the reaction molecular dynamics (RMD) method to realize the construction of carbon-carbon ablation models, carbon phenolic ablation models and silicon carbide ablation models under dynamic conditions, thereby improving the accuracy of ablation prediction.
[0044] (2) This invention introduces the Bayesian maximum entropy (BME) data fusion method, which optimizes the activation energy results of the ablation model by integrating the activation energy data obtained from the experiment, thereby generating more reliable activation energy prediction results;
[0045] (3) Based on prediction and experimental results, this invention can fit the ablation results of composite materials under different temperatures and pressures, and can give the ablation results of carbon-carbon, carbon phenolic and carbon silicon carbide composite materials under different temperatures and pressures more accurately. Compared with existing multi-physics field coupling calculations, it solves the problem of ablation calculation efficiency. Attached Figure Description
[0046] Figure 1 This is a schematic diagram of the carbon-carbon material ablation model construction of this invention;
[0047] Figure 2 This is a schematic diagram of the construction of the carbon phenolic material ablation model of the present invention;
[0048] Figure 3 This is a schematic diagram of the construction of the silicon carbide ablation model of the present invention;
[0049] Figure 4 This is a statistical diagram illustrating the ablation morphology of 2D woven graphene and the products of the ablation process in this invention.
[0050] Figure 5 This is a schematic diagram of the ablation product analysis, activation energy calculation, and simulated snapshot of the carbon nanotube phenolic composite material of the present invention;
[0051] Figure 6 This is a schematic diagram of the reaction process of the SiC material of this invention at four temperatures; (red represents oxygen atoms, yellow represents silicon atoms, and gray represents carbon atoms).
[0052] Figure 7 This is a schematic diagram of the solid phase quality change of the C / SiC material of the present invention. Detailed Implementation
[0053] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0054] This invention provides a dynamic ablation prediction method based on microscopic reaction molecular dynamics, comprising the following steps:
[0055] S1. Based on the microstructure of the surface materials, theoretical models for the ablation of carbon-carbon, carbon phenolic, and silicon carbide materials under dynamic conditions are constructed. The carbon-carbon ablation model employs a woven graphene structure; the carbon phenolic material ablation model employs a composite structure of phenolic single chains and carbon nanotubes; and the silicon carbide ablation model employs a carbon carbide gas-solid interface. Specifically:
[0056] like Figure 1 As shown, the carbon-carbon ablation model uses a woven graphene structure, which is formed by interlocking the weft and warp yarns made of graphene composite material; the fiber bundles are interwoven at a certain angle in the thickness direction to form a woven graphene structure.
[0057] like Figure 2 As shown, the ablation model of carbon phenolic materials uses a composite structure of phenolic single chains and carbon nanotubes, through C 56 H 50 O8 phenolic single chains, carbon nanotubes, and phenolic resin were compressed at a temperature of 200K–300K to form a matrix model; then, a substrate was formed on the surface of the matrix model. The vacuum layer and oxygen-filled layer; the length of the carbon nanotubes is Diameter The density of phenolic resin is 1 to 1.5 g / cc.
[0058] like Figure 3 As shown, the silicon carbide ablation model uses a silicon carbide gas-solid interface, which consists of a solid phase and a gas phase. The solid phase is 4H-SiC crystal, and the gas phase is oxygen molecules. The height of the gas phase is 3 to 4 times that of the solid phase.
[0059] S2. Simulation conditions were set, and the theoretical models of carbon-carbon, carbon phenolic, and silicon carbide ablation were relaxed for 10 ps using simulation software at a wall temperature of 100 K. Periodic boundary conditions were set in the xy direction and in the z direction. The reflection boundary conditions were set with appropriate parameters, and the number of iterations, time step, total simulation time, and atomic trajectory acquisition interval were configured for the numerical simulation. For the carbon-carbon ablation model, the time step was 0.10–0.20 fs, and the simulation time was at least 200 ps. For the carbon phenolic ablation model, the time step was 0.10–0.20 fs, and the simulation time was at least 1000 ps. For the silicon carbide ablation model, the time step was 0.20–0.25 fs, and the simulation time was at least 200 ps. Simulations of the carbon-carbon ablation model, carbon phenolic ablation model, and silicon carbide ablation model under dynamic conditions were performed to obtain the number of small molecule products.
[0060] (1) Carbon-carbon material ablation model
[0061] By processing the calculation results of the ablation model, the following was obtained: Figure 4 The simulation results are shown. Figure 4 (a) shows the morphology of four 2D braided graphenes with different porosities after ablation at 200ps. It can be seen that the morphology of model one with the smallest porosity is the most intact, while the structure of model four with the largest porosity is more severely damaged. This indicates that a smaller porosity is beneficial for braided graphene materials to maintain morphological integrity under high enthalpy conditions.
[0062] Figure 4 Figures (b) and (c) respectively statistically illustrate the evolution of the number and mass fraction of small molecule products in Model 1 over time during the simulation. As can be seen from the figures, in the first 150 ps of the simulation, the number of oxygen molecules generated is dominant, due to the catalytic recombination effect of graphene, which catalyzes the conversion of oxygen atoms into oxygen molecules. In the last 50 ps of the simulation, the number of CO molecules is greater, reflecting the continuous increase in system temperature and the continuous breaking of C / C bonds.
[0063] (2) Carbon phenolic material ablation model
[0064] Figure 5 (a) shows the formation of small molecule products in carbon nanotube phenolic composite material at a temperature of 2500K. It can be seen from the figure that water molecules are generated in the largest amount during pyrolysis, followed by hydrogen molecules. Figure 5 (c) shows the morphological evolution of the model body after ablation at 3000K. It can be seen that the carbon nanotube structure in the middle exhibits obvious stratification.
[0065] (3) Silicon carbide ablation model
[0066] from Figure 6 It can be seen that at 1500K and 2000K, SiC mainly undergoes passive oxidation, resulting in the formation of an oxide film, with the underlying SiC maintaining its original crystal structure. At 2500K, active oxidation begins, the oxide film exhibits an irregular flow pattern, and the underlying SiC can no longer maintain its crystal configuration. At 3000K, active oxidation dominates, the SiC structure is severely damaged, and carbon chains are generated in the gas phase. The mass change of the solid phase at different pressures at 3000K was calculated, such as... Figure 7 As shown.
[0067] S3, based on the characteristics of different materials, yields the activation energy regression curves as a function of temperature. Specifically:
[0068] For the carbon-carbon ablation model, the regression curve of the activation energy is:
[0069]
[0070] Where k C It refers to the ablation reaction rate of carbon-carbon composite materials, where A refers to the prefactor and E refers to the prefactor. a The activation energy is given by R, the ideal gas constant is given by T, and the temperature is given by k. The ablation reaction rate k of carbon-carbon composite materials is also given. C The quantification is defined as the ratio of the number of CC bond breaks per unit time to the total number of CC bonds in the original model. The total number of CC bonds in the original model is obtained by statistically analyzing the number of small molecule products C2 and C3 under the initial model conditions; the number of CC bond breaks is obtained by statistically analyzing the number of CO, CO2, C2O, and C3O in the small molecule products at a certain temperature, thereby obtaining the ablation reaction rate of carbon-carbon composite materials at this temperature.
[0071] For the carbon phenolic ablation model, the regression curve of the activation energy is:
[0072]
[0073] In the formula k H2O Let A be the reaction rate of water molecules, and E be the pre-exponential factor. a Let k be the activation energy, R be the ideal gas constant, and T be the temperature. H2O It is defined as the rate of H2O molecule formation per unit time. The reaction rate of water molecules at a certain temperature can be obtained by statistically analyzing the number of small molecule products H2O at that temperature.
[0074] For the silicon carbide ablation model, the regression curve of the activation energy is:
[0075]
[0076] In the formula k SiC Let A be the reaction rate of the SiC molecule, and E be the pre-exponential factor.a Let k be the activation energy, R be the ideal gas constant, and T be the temperature. SiC It is defined as the sublimation rate of solid-phase SiC molecules per unit time. The reaction rate of SiC molecules at a certain temperature can be obtained by statistically analyzing the amount of small-molecule SiC products that sublimate at a certain temperature.
[0077] S4. Based on the regression curves of activation energy and the number of small molecule products of different materials, the pre-exponential factors and activation energies of the carbon-carbon ablation model, the carbon-phenolic ablation model, and the silicon carbide ablation model are obtained. Specifically:
[0078] For the carbon-carbon ablation model, the pre-exponential factor and activation energy are:
[0079] Statistical analysis of the ablation reaction rate k of carbon-carbon composite materials at different temperatures C Fitting k C The curve of temperature variation is calculated using the logarithmic formula for activation energy, ln(k). C )=lnA C -E aC / RT can be used to calculate the pre-exponential factor A of the carbon-carbon ablation model. C and activation energy E aC .
[0080] This invention fitted carbon-carbon composite materials with porosities of 2.32%, 6.48%, 13.09%, and 17.25% to obtain the corresponding formulas for the ablation reaction rate versus temperature. The four models are as follows:
[0081] Porosity 2.32%
[0082] Porosity 6.48%
[0083] Porosity 13.09%
[0084] Porosity 17.25%
[0085] Table 1 Ablation rate of carbon-carbon composite materials
[0086]
[0087] For the carbon phenolic ablation model, the pre-exponential factor and activation energy are:
[0088] Statistical analysis of the reaction rate k of water molecules at different temperatures H2O Fitting k H2O The curve of temperature variation is calculated using the logarithmic formula for activation energy, ln(k). H2O )=lnA H2O -Ea H2O / RT can be used to calculate the pre-exponential factor A of the carbon phenolic ablation model. H2O and activation energy E aH2O .
[0089] This invention fitted a carbon fiber reinforced phenolic resin composite material, obtaining a pre-exponential factor of 9.53, an activation energy of 21.73 kJ / mol, and ablation rates at different temperatures as follows:
[0090] Table 2 Ablation rate of carbon phenolic materials
[0091]
[0092] For the silicon carbide ablation model, the pre-exponential factor and activation energy are:
[0093] By fitting the SiC sublimation rate with temperature, the relationship between the SiC sublimation rate and temperature can be obtained, according to the logarithmic formula of activation energy ln(k SiC )=lnA SiC -E aSiC / RT can be used to calculate the pre-exponential factor A of the silicon carbide ablation model. SiC and activation energy E aSiC .
[0094] By fitting the SiC sublimation rate with temperature, the relationship between the SiC sublimation rate and temperature can be obtained, k SiC =25.53exp(-106.42 / RT), activation energy is approximately 106.42 kJ / mol, and pre-exponential factor is 25.53.
[0095] Table 3 Ablation rate of silicon carbide materials
[0096]
[0097] S5 introduces the BME (Bayesian Maximum Entropy) data fusion method, extracts the reaction activation energy data measured in experiments as the true value, optimizes the activation energy of each ablation model, and obtains the optimized activation energy result that is closer to the real situation.
[0098] By integrating experimental data and optimizing existing results, the activation energies of SiC and graphite materials were combined to obtain more reliable data, resulting in the optimized activation energy of the silicon carbide target material.
[0099] Table 4 Comparison of BME optimization results with original activation energy
[0100]
[0101] S6, based on the optimized activation energy, pre-exponential factor, and set incoming flow height, Mach number, heat flux, and time, obtains the ablation retreat rate and ablation retreat amount of carbon-carbon, carbon phenolic, and silicon carbide materials under the corresponding incoming flow conditions, specifically:
[0102] Provide the calculation input parameters, including flight altitude, Mach number, heat flux intensity, and ablation time;
[0103] Let the ablation time be t, and the total time be decomposed into t1, t2, ... t. n Several individual time steps were used. Based on the input parameters, the wall temperature T of carbon-carbon, carbon phenolic, and silicon carbide materials at different time steps under corresponding inflow conditions was obtained through simulation calculations. n-wall ;
[0104] For the carbon-carbon ablation model, the ablation retreat rate k calculated in a single run is... n-C for:
[0105]
[0106] ablation retreat H C The ablation retreat rate is obtained by multiplying the average ablation retreat rate of the previous time node and the current time node by the current time step Δt and summing the results:
[0107]
[0108] For the carbon phenolic ablation model, the ablation retreat rate calculated in a single run is:
[0109]
[0110] ablation retreat H H2O The ablation retreat rate is obtained by multiplying the average ablation retreat rate of the previous time node and the current time node by the current time step Δt and summing the results:
[0111]
[0112] For the silicon carbide ablation model, the ablation retreat rate calculated in a single run is k. n-SiC :
[0113]
[0114] ablation retreat H SiC The ablation retreat rate is obtained by multiplying the average ablation retreat rate of the previous time node and the current time node by the current time step Δt and summing the results:
[0115]
[0116] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.
[0117] The contents not described in detail in this specification are common knowledge to those skilled in the art.
Claims
1. A dynamic ablation prediction method based on microscopic reaction molecular dynamics, characterized in that, include: Based on the microstructure of the surface material, carbon-carbon ablation model, carbon-phenolic ablation model and silicon carbide ablation model under dynamic conditions were constructed. Simulation conditions were set to simulate carbon-carbon ablation model, carbon phenol ablation model and silicon carbide ablation model under dynamic conditions to obtain the number of small molecule products. Based on the characteristics of different materials, the activation energy regression curves as a function of temperature were obtained; Based on the regression curves of activation energy and the number of small molecule products, the pre-exponential factors and activation energies of the carbon-carbon ablation model, the carbon-phenolic ablation model, and the silicon carbide ablation model were obtained. By extracting the data of reaction activation energy, the pre-exponential factor and activation energy of the carbon-carbon ablation model, the carbon-phenolic ablation model and the silicon carbide ablation model were optimized to obtain the optimized pre-exponential factor and activation energy. Based on the optimized activation energy, reaction pre-exponential factor, and the set incoming flow height, Mach number, heat flux, and time, the ablation retreat rate and ablation retreat amount of the carbon-carbon ablation model, carbon phenolic ablation model, and silicon carbide ablation model under the corresponding incoming flow conditions are obtained.
2. The dynamic ablation prediction method based on microscopic reaction molecular dynamics according to claim 1, characterized in that: The carbon-carbon ablation model, carbon-phenolic ablation model, and silicon carbide ablation model are specifically as follows: The carbon-carbon ablation model employs a woven graphene structure; the weft and warp yarns made of graphene composite material are interlocked by winding them together; the fiber bundles are interwoven at a certain angle in the thickness direction to form a woven graphene structure. The carbon phenolic material ablation model employs a composite structure of phenolic single chains and carbon nanotubes; by using C 56 H 50 O8 phenolic single chains, carbon nanotubes, and phenolic resin were compressed at a temperature of 200K–300K to form a matrix model; then, a substrate was formed on the surface of the matrix model. The vacuum layer and oxygen-filled layer; the length of the carbon nanotubes is Diameter The density of phenolic resin is 1–1.5 g / cc; The silicon carbide ablation model uses a silicon carbide gas-solid interface, which consists of a solid phase and a gas phase. The solid phase is 4H-SiC crystal, and the gas phase is oxygen molecules. The height of the gas phase is 3 to 4 times that of the solid phase.
3. The dynamic ablation prediction method based on microscopic reaction molecular dynamics according to claim 1, characterized in that: The simulation conditions include the time step and the simulation time; for the carbon-carbon ablation model, the time step is 0.10–0.20 fs and the simulation time is no less than 200 ps; for the carbon phenolic ablation model, the time step is 0.10–0.20 fs and the simulation time is no less than 1000 ps; for the silicon carbide ablation model, the time step is 0.20–0.25 fs and the simulation time is no less than 200 ps.
4. The dynamic ablation prediction method based on microscopic reaction molecular dynamics according to claim 1, characterized in that: The regression curve of the activation energy as a function of temperature is specifically as follows: Where K is the ablation reaction rate, A refers to the pre-factor, and E... a It is the activation energy, R is the ideal gas constant, and T is the temperature.
5. The dynamic ablation prediction method based on microscopic reaction molecular dynamics according to claim 1, characterized in that: The pre-exponential factor and activation energy of the reaction are specifically as follows: ln(K)lnA-E a / RT Where K is the ablation reaction rate, A refers to the pre-factor, and E... a It is the activation energy, R is the ideal gas constant, and T is the temperature.
6. The dynamic ablation prediction method based on microscopic reaction molecular dynamics according to claim 1, characterized in that, The ablation retreat rate and ablation retreat amount of each ablation model under the corresponding incoming flow state are obtained based on the optimized activation energy, pre-exponential factor, and set incoming flow height, Mach number, heat flux, and time. Specifically: Determine the input parameters, including flight altitude, Mach number, heat flux intensity, and ablation time; Based on the time step, the ablation time t is decomposed into several individual times t1, t2, ... t. n ; Based on the input parameters, the wall temperature T of carbon-carbon, carbon phenolic, and silicon carbide materials at different time steps under corresponding incoming flow conditions was obtained through simulation calculations. n-wall ; For the carbon-carbon ablation model, the ablation retreat rate k calculated in a single run is... n-C for: ablation retreat H C for: in, t represents the average ablation retreat rate of carbon-carbon materials between the previous and current time points. n This is the step size for this event; For the carbon phenolic ablation model, the ablation retreat rate calculated in a single run is: for: ablation retreat H H2O for: in, This represents the average ablation retreat rate of the carbon phenolic material between the previous and current time points. For the silicon carbide ablation model, the ablation retreat rate calculated in a single run is k. n-SiC for: ablation retreat H SiC for: in, This represents the average ablation retreat rate of silicon carbide material between the previous and current time points.