A method and system for predicting ablation rate of reaction-infiltrated modified carbon-carbon materials

Abstract

The application discloses a reaction infiltration modified carbon / carbon material ablation rate prediction method and system, and the method comprises the following steps: obtaining the infiltration element composition information, pore structure parameters, preparation process parameters, density parameters before and after deposition, and ablation environment parameters of high-temperature ceramic modified carbon / carbon composite materials; based on the obtained parameters, a physical heuristic feature for representing the post-infiltration density is constructed, and a post-infiltration density prediction model is used to predict the post-infiltration density; based on the post-infiltration density prediction value and the infiltration element composition information and the ablation environment parameters, a physical heuristic feature for representing the ablation protection behavior is constructed, and a linear ablation rate prediction model is used to predict the linear ablation rate of the high-temperature ceramic modified carbon / carbon composite material. The application can start from the mechanism of the high-temperature ceramic modified carbon / carbon composite material, construct the physical heuristic features for representing the post-infiltration density and the ablation protection behavior, and predict the ablation rate in layers, so that the ablation rate prediction is accurate.

Description

Technical Field

[0001] This invention belongs to the field of computer materials information technology, specifically relating to a method and system for predicting the ablation rate of reactive melt-infiltration modified carbon materials. Background Technology

[0002] Reactively infiltrated carbon / carbon composites have broad application prospects in thermal protection, propulsion, and extreme environment service due to their low density, excellent high-temperature load-bearing capacity, and designable resistance to oxidation and ablation. These materials are typically produced by pyrolytic carbon deposition and subsequent reactive infiltration modification of carbon fiber preforms. This allows multiple infiltrated components to penetrate the porous carbon matrix, undergoing a series of coupled processes under high-temperature conditions, including infiltration, carbonization, pore filling, and oxidation product generation. The final ablation rate exhibited by the material is influenced not only by the types and proportions of elements but also by the degree of densification after deposition, post-infiltration density, pore structure, elemental increments, oxidation product phase composition, and the external thermo-oxidative environment.

[0003] In existing technologies, the prediction of ablation rate for high-temperature modified carbon / carbon composites typically involves directly inputting the original element types, element ratios, density, porosity, and process conditions into a machine learning model for empirical fitting. While this method can establish a certain statistical mapping relationship, it generally suffers from the following problems:

[0004] 1. Lack of physical process constraints. Existing methods typically predict ablation rates directly from the original process or compositional variables, without fully considering the continuous physical processes during reactive melting and infiltration, namely, "liquid phase infiltration—interfacial reaction—pore shrinkage / blocking—formation of post-melting and infiltration state—evolution of high-temperature oxidation protective layer."

[0005] 2. Insufficient model interpretability. The black-box modeling approach, which directly inputs elemental atomic ratios and process parameters, makes it difficult to reveal which physical mechanisms dominate the ablation behavior and provides clear guidance for material design and process optimization.

[0006] 3. Lack of key intermediate state variables. Ablation behavior is not directly determined by the initial input, but is primarily influenced by the degree of densification after melting and infiltration, the elemental distribution after melting and infiltration, and the potential for the formation of the oxide protective phase. Without introducing intermediate state variables such as density after melting and infiltration, the model's ability to represent the true mechanism chain is easily weakened.

[0007] 4. Limited stability under small sample conditions. Materials experimental data usually have a limited number of samples. If high-dimensional original features are used directly for modeling, problems such as information redundancy, model overfitting, and insufficient generalization ability are likely to occur.

[0008] Therefore, it is necessary to propose an ablation rate prediction method that can construct physical heuristic features by using post-melting density as an intermediate bridging variable and combining reactive melting and ablation mechanisms with ablation protection mechanisms, thereby improving prediction accuracy and model interpretability. Summary of the Invention

[0009] To address the issues of insufficient mechanism expression and poor interpretability in existing ablation rate prediction methods, this invention provides a method and system for predicting the ablation rate of reactive melt-infiltration modified carbon materials.

[0010] To achieve the above technical objectives, the present invention adopts the following technical solution: A method for predicting the ablation rate of reactive melt-infiltration modified carbon-carbon materials includes: Step 100: Obtain information on the elemental composition of the high-temperature ceramic-modified carbon / carbon composite material, pore structure parameters, preparation process parameters, density parameters before and after deposition, and ablation environment parameters. Step 200: Based on the melt-infiltrated elemental composition information, pore structure parameters, preparation process parameters, and density parameters before and after deposition, construct a physical-inspired feature to characterize the density after melt-infiltration; Step 300: Input the physical heuristic features used to characterize the density after melting and infiltration into the trained density prediction model after melting and infiltration, and output the predicted density value after melting and infiltration. Step 400: Based on the predicted density value after melting and infiltration, the information on the elemental composition of melting and infiltration, and the parameters of the ablation environment, construct physical heuristic features to characterize the ablation protection behavior; Step 500: Input the physical heuristic features used to characterize the ablation protection behavior into the trained linear ablation rate prediction model, and output the linear ablation rate prediction results of the high-temperature ceramic modified carbon / carbon composite material.

[0011] Furthermore, the composition information of the infiltration elements includes the types of high-temperature ceramic modification additives, their atomic ratios, and physical property parameters; wherein, the high-temperature ceramic modification additives are selected from one or more combinations of silicon (Si), zirconium (Zr), hafnium (Hf), vanadium (V), molybdenum (Mo), aluminum (Al), tantalum (Ta), titanium (Ti), and copper (Cu); the physical property parameters include metal melting point, electronegativity, atomic weight, atomic radius, cohesive energy, carbide melting point, and carbide volume expansion.

[0012] The pore structure parameters include the porosity; The preparation process parameters include the reaction melting and infiltration preparation temperature; The density parameters before and after deposition include pre-deposition density, post-deposition density, and post-CVI density. The ablation environment parameters include oxygen flow rate, gas flow rate, ablation time, and spray gun distance.

[0013] Furthermore, the physical heuristic features used to characterize the density after melting and infiltration include one or more of the following: pyrolytic carbon deposition densification features, carbonization reaction enthalpy weighted features, transport-reaction coupling features, and infiltration driving force features.

[0014] Furthermore, the pyrolytic carbon deposition densification characteristics are characterized by calculating the following features: pyrolytic carbon deposition increment characteristics. Relative densification characteristics of pyrolytic carbon Pyrolysis carbon filling efficiency characteristics : ; ; ; In the formula, Density before deposition Density after deposition This refers to the open area ratio.

[0015] Furthermore, the enthalpy-weighted characteristic of the carbonization reaction is characterized by calculating the following feature: the total component enthalpy weighted value of the carbonization reaction. Normalized total component carbonization reaction enthalpy characteristics Enthalpy-weighted values ​​of carbonization reactions of non-Cu active carbide elements Average enthalpy characteristics of non-Cu active carbide elements Ratio of active carbide forming elements : ; ; ; ; ;

[0016] In the formula, For infiltration elements atomic ratio fraction, For infiltration elements Enthalpy of carbonization reaction; The element is Cu.

[0017] Furthermore, the transport-reaction coupling characteristic is a composite characteristic calculated from porosity, carbonization volume expansion, average atomic radius of active elements, melting point of the corresponding carbide of active elements, and preparation temperature. ; In the formula, It is a composite feature; The weighted average of the carbonization volume expansion of multi-element modification for high-temperature ceramics. This is the weighted average of the average atomic radii of non-Cu reactive elements. This is the weighted average of the melting points of the carbides corresponding to non-Cu reactive elements. The preparation temperature.

[0018] Furthermore, the permeation driving force characteristic is denoted as... The average cohesive energy, average electronegativity difference, average atomic weight, and average melting point were calculated based on the weighted average atomic ratios of the infiltrating elements. ; In the formula, The average cohesive energy is obtained by weighting the atomic ratio of the infiltrating elements. The average electronegativity difference of elements relative to carbon atoms, obtained by weighting according to the atomic ratio. The average atomic weight is obtained by weighting the atomic ratios of the elements. The average melting point is obtained by weighting the atomic ratios of the elements; where the average cohesive energy is... Poor average electronegativity Average atomic weight Average melting point They are uniformly denoted as equivalent physical properties. The weighted average of the physical properties of each element is obtained based on the atomic ratio of the infiltrating elements. ; In the formula, For infiltration elements atomic ratio, For infiltration elements Property characteristics to be determined The corresponding physical property parameters.

[0019] Furthermore, the physical heuristic features used to characterize ablation protection behavior include one or more of the following: basic protection features, liquid phase protection features, solid phase protection features, and liquid-solid equilibrium features; The basic protective features are used to characterize the coupling effect of the material's initial skeleton density, the degree of melt infiltration and densification retention, and the effectiveness of pore filling; The liquid phase protection feature is used to characterize the formation, flow, spreading, and blocking ability of liquid or viscous oxidation products under high-temperature ablation conditions; The solid-phase protection features are used to characterize the volume reserve, thermal stability, and resistance to high-temperature instability of the high-melting-point solid-phase oxide framework. The liquid-solid equilibrium characteristics are used to characterize the relative dominance between liquid-phase protection mechanisms and solid-phase protection mechanisms.

[0020] Furthermore, the formula for calculating the basic protection feature is: ; ; ; ; in, Represents basic protection features. Indicates density before deposition. For melt penetration retention rate, To improve melt infiltration filling efficiency, This is the predicted density value after melt infiltration. Density after CVI The equivalent mixing density of the melt-infiltrated phase, For infiltration elements mass fraction, For element infiltration metal density, Indicates the open area ratio;

[0021] The formula for calculating the liquid phase protection feature is: ; ; ; ; ; in, Represents liquid phase protection characteristics, This represents the total volume of the liquid-phase oxidation products. The average optical alkalinity in the liquid phase. The average ion field strength in the liquid phase; For the normalized characteristics of heat load, For the original heat load, This is the average of the heat load samples. Oxygen flow rate, For gas flow rate, For ablation time, This refers to the distance between the spray gun and the spray gun. This represents the normalized characteristic of the oxidative load. For the original oxidative load, This represents the mean of the oxidative load samples. The formula for calculating the solid-phase protection characteristics is: ; in, Represents solid-phase protection characteristics. This represents the total volume of the solid-phase oxidation products. This is an indicator of solid-phase stability.

[0022] The formula for calculating the liquid-solid equilibrium characteristics is: ; in, Represents liquid-solid equilibrium characteristics. To prevent extremely small positive numbers with a denominator of zero.

[0023] A linear ablation rate prediction system for high-temperature ceramic-modified carbon-carbon composite materials includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor implements the linear ablation rate prediction method described above.

[0024] Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention divides the ablation rate prediction process into three levels: "density prediction after melting and infiltration - state characterization after melting and infiltration - ablation rate prediction", which is more consistent with the actual evolution path of materials from preparation to service than the existing one-step black box prediction. 2. This invention introduces pyrolytic carbon deposition densification characteristics, carbonization reaction enthalpy weighted characteristics, transport-reaction coupling characteristics, and liquid-phase / solid-phase protection characteristics, which can more accurately reflect the melting and densification behavior and ablation protection mechanism; 3. This invention enhances the physical interpretability of the model and its generalization ability under small sample conditions by replacing simple original features with physically inspired features; 4. In this invention, the density after melting and infiltration is used as an intermediate bridge variable, which not only improves the accuracy of ablation rate prediction, but also further serves the incremental inversion of elements after melting and infiltration and the process optimization design. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the steps of the method described in the embodiments of this application. Detailed Implementation

[0026] The embodiments of the present invention will be described in detail below. These embodiments are based on the technical solutions of the present invention and provide detailed implementation methods and specific operation processes to further explain the technical solutions of the present invention.

[0027] Example 1

[0028] This embodiment provides a method for predicting the ablation rate of reactive infiltration modified carbon-carbon materials, referring to... Figure 1 As shown, it includes the following steps.

[0029] Step 100: Obtain information on the elemental composition of the high-temperature ceramic-modified carbon / carbon composite material, pore structure parameters, preparation process parameters, density parameters before and after deposition, and ablation environment parameters.

[0030] (1) Information on the composition of the melting and infiltration elements.

[0031] The composition information of the infiltration elements includes the types of high-temperature ceramic modification additives, their atomic ratios, and physical property parameters. The high-temperature ceramic modification additives include one or more of Zr, Hf, Si, V, Ti, Ta, Mo, Al, and Cu. The physical property parameters include metal melting point, electronegativity, atomic weight, atomic radius, cohesive energy, carbide melting point, and carbide volume expansion.

[0032] (2) Pore structure parameters.

[0033] The pore structure parameters include the porosity. In this embodiment, the porosity is converted to a decimal form; if the input value is greater than 1, it is converted by dividing the percentage by 100.

[0034] (3) Preparation process parameters.

[0035] The preparation process parameters include the reaction melting and infiltration preparation temperature, and in other embodiments, the heating rate, holding time, and pressure may also be included.

[0036] (4) Density parameters before and after deposition.

[0037] The density parameters before and after deposition include pre-deposition density, post-deposition density, and post-CVI density.

[0038] (5) The ablation environment parameters include oxygen flow rate, gas flow rate, ablation time and spray gun distance.

[0039] Step 200: Based on the melt-infiltrated elemental composition information, pore structure parameters, preparation process parameters, and density parameters before and after deposition, construct a physical-inspired feature to characterize the density after melt-infiltration.

[0040] The physical heuristic features used to characterize density after melting and infiltration include one or more of the following: pyrolytic carbon deposition densification features, carbonization reaction enthalpy weighted features, transport-reaction coupling features, infiltration driving force features, and system-level equivalent physical property features.

[0041] The pyrolytic carbon deposition densification characteristics include pyrolytic carbon deposition increment characteristics. Relative densification characteristics of pyrolytic carbon Pyrolysis carbon filling efficiency characteristics These are used to characterize the absolute density, relative density, and filling efficiency per unit open space of the pyrolysis carbon deposition process, respectively, and are calculated based on the pre-deposition density, post-deposition density, and open area ratio. ; ; ; In the formula, Density before deposition Density after deposition This refers to the open area ratio.

[0042] The enthalpy-weighted characteristic of carbonization reaction includes the total component carbonization reaction enthalpy weighted value. Normalized total component carbonization reaction enthalpy characteristics Enthalpy-weighted values ​​of carbonization reactions of non-Cu active carbide elements Average enthalpy characteristics of non-Cu active carbide elements Ratio of active carbide forming elements This is used to characterize the overall carbonization reaction driving force of the system and the contribution of active carbonizing elements to the melt infiltration reaction. Each characteristic is obtained by weighting the melt infiltration elemental composition information and the carbonization reaction enthalpy of each element: ; ; ; ; ; In the formula, For infiltration elements atomic ratio fraction, For infiltration elements Enthalpy of carbonization reaction; The element is Cu.

[0043] The transport-reaction coupling characteristic is a composite feature calculated from porosity, carbonization volume expansion, average atomic radius of active elements, melting point of the corresponding carbide of active elements, and preparation temperature. It is used to characterize the competitive relationship between liquid phase infiltration, reaction generation, and pore shrinkage or blockage during the melting and infiltration process, reflecting the nonlinear mechanism of transport and reaction working together during density formation after melting and infiltration. The calculation formula is: ; In the formula, It is a composite feature; The weighted average value of the carbonization volume expansion of a multi-element system (i.e., high-temperature ceramic modification with the addition of multiple elements, selected from silicon (Si), zirconium (Zr), hafnium (Hf), vanadium (V), molybdenum (Mo), aluminum (Al), tantalum (Ta), and titanium (Ti)). This is the weighted average of the average atomic radii of non-Cu reactive elements. This is the weighted average of the melting points of the carbides corresponding to non-Cu reactive elements. The preparation temperature.

[0044] Besides the pore structure and reaction product characteristics, the densification process after melt infiltration is also affected by the physicochemical properties of the infiltrating components themselves. To comprehensively characterize the effective infiltration capacity of multi-component components at a given preparation temperature, this embodiment calculates the infiltration driving force characteristics based on the average cohesive energy, average electronegativity difference, average atomic weight, and average melting point obtained by weighting the atomic ratios of the infiltrating elements. : ; In the formula, The average cohesive energy is obtained by weighting the atomic ratio of the infiltrating elements. The average electronegativity difference of elements relative to carbon atoms, obtained by weighting according to the atomic ratio. The average atomic weight is obtained by weighting the atomic ratios of the elements. The average melting point is obtained by weighting the atomic ratios of elements. The average cohesive energy... Poor average electronegativity Average atomic weight Average melting point They are uniformly denoted as equivalent physical properties. The weighted average of the physical properties of each element is obtained based on the atomic ratio of the infiltrating elements. ;

[0045] In the formula, For infiltration elements atomic ratio, For infiltration elements Property characteristics to be determined The corresponding physical property parameters.

[0046] Since a greater electronegativity difference between the infiltrating element and carbon generally implies stronger interfacial chemistry and a greater tendency to react, while higher cohesive energy characterizes a more significant interatomic binding energy scale; conversely, a larger average atomic weight and a higher average melting point raise the threshold for system flow, wetting, and reaction. Therefore, this embodiment uses... The introduction of a thermal activation inhibition effect reflects the decrease in penetration propulsion capability when the process temperature is below the characteristic melting point of the component. Therefore, this penetration driving force characteristic... This essentially characterizes the competitive relationship between "interfacial chemical driving" and "thermal property threshold inhibition" in a multi-component melt infiltration system, and can serve as an important physical inspiration feature affecting melt infiltration densification behavior.

[0047] Step 300: Input the physical heuristic features used to characterize the density after melting and infiltration into the trained density prediction model after melting and infiltration, and output the predicted density value after melting and infiltration.

[0048] The density prediction model after melting and infiltration can be one or more of the following: random forest regression model, extreme random tree regression model, gradient boosting regression model, support vector regression model, XGBoost model, or neural network model.

[0049] Step 400: Based on the predicted density value after melting and infiltration, the information on the elemental composition of melting and infiltration, and the parameters of the ablation environment, construct a physical heuristic feature to characterize the ablation protection behavior.

[0050] The physical heuristic features used to characterize ablation protection behavior include one or more of the following: basic protection features, liquid phase protection features, solid phase protection features, and liquid-solid equilibrium features.

[0051] The aforementioned basic protective features characterize the coupling effect of the material's initial skeleton density, the degree of retention of melt infiltration densification, and the effectiveness of pore filling. These features are calculated based on the density after melt infiltration, the density after CVI, the density before deposition, the porosity, and the equivalent mixed density of the melt-infiltrated phase. ; ; ; ;

[0052] in, Represents basic protection features. Indicates density before deposition; For melt penetration retention rate, To improve melt infiltration filling efficiency, This is the predicted density value after melt infiltration. Density after CVI The equivalent mixing density of the melt-infiltrated phase, For infiltration elements mass fraction, For infiltration elements metal density, This indicates the open area ratio.

[0053] The liquid phase protection characteristics are used to characterize the formation, flow, spreading, and blocking ability of liquid or viscous oxidation products under high-temperature ablation conditions. These characteristics are calculated based on the volume of the liquid phase oxidation products, the average optical alkalinity of the liquid phase, the average ionic field strength of the liquid phase, and the environmental thermal and oxygen load. ; ; ; ; ; in, Represents liquid phase protection characteristics, This represents the total volume of the liquid-phase oxidation products. The average optical alkalinity in the liquid phase. The average ion field strength in the liquid phase; For the normalized characteristics of environmental heat load, As the original heat load of the environment, This represents the average environmental heat load sample. Oxygen flow rate, For gas flow rate, For ablation time, This refers to the distance between the spray gun and the spray gun. This represents the normalized characteristics of environmental oxidative load. As the original oxidative load of the environment, This represents the sample mean of the environmental oxidative load.

[0054] The solid-phase protection characteristics are used to characterize the volume reserve, thermal stability, and resistance to high-temperature instability of the high-melting-point solid-phase oxide framework. They are calculated based on the volume of solid-phase oxidation products, solid-phase stability indices, and environmental thermal and oxygen loads. ; in, Represents solid-phase protection characteristics. This represents the total volume of the solid-phase oxidation products. This is an indicator of solid-phase stability.

[0055] The liquid-solid equilibrium characteristics are used to characterize the relative dominance between the liquid-phase protection mechanism and the solid-phase protection mechanism, and are calculated based on the volumes of liquid-phase oxidation products and solid-phase oxidation products. ; in, Represents liquid-solid equilibrium characteristics. To prevent extremely small positive numbers with a denominator of zero.

[0056] Step 500: Input the physical heuristic features used to characterize the ablation protection behavior into the trained linear ablation rate prediction model, and output the linear ablation rate prediction results of the high-temperature ceramic modified carbon / carbon composite material.

[0057] The linear ablation rate prediction model can employ one or more of the following: random forest regression model, extreme random tree regression model, gradient boosting regression model, support vector regression model, XGBoost model, or neural network model.

[0058] Before using the trained post-melting density prediction model and linear ablation rate prediction model to predict the post-melting density and linear ablation rate respectively in this embodiment, both prediction models are trained using corresponding training samples to optimize model parameters. Cross-validation is then used to evaluate the performance of the trained post-melting density prediction model and linear ablation rate prediction model. Based on the evaluation results, the optimal model and its parameters are selected for use in step 300 to predict the post-melting density and in step 500 to predict the linear ablation rate. The parameter space of the prediction model can be optimized using one or more methods, such as grid search, random search, or Bayesian optimization.

[0059] Example 2

[0060] This embodiment provides a linear ablation rate prediction system for high-temperature ceramic-modified carbon-carbon composite materials, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor implements the linear ablation rate prediction method described in Embodiment 1.

[0061] In practical applications, candidate compositions and process parameters can be input in batches. The system first predicts the density after melting and infiltration, then constructs the characteristics of the ablation protection mechanism and predicts the linear ablation rate. Finally, it selects the composition and process parameter combinations that meet the target ablation performance requirements to guide the optimization design and preparation of reactive melting and infiltration modified carbon / carbon composite materials.

[0062] The above embodiments are preferred embodiments of this application. Those skilled in the art can make various changes or improvements based on them. Without departing from the overall concept of this application, these changes or improvements should fall within the scope of protection claimed in this application.

Claims

1. A method for predicting the ablation rate of reactive melt-infiltration modified carbon-carbon materials, characterized in that, include: Step 100: Obtain information on the elemental composition of the high-temperature ceramic-modified carbon / carbon composite material, pore structure parameters, preparation process parameters, density parameters before and after deposition, and ablation environment parameters. Step 200: Based on the melt-infiltrated elemental composition information, pore structure parameters, preparation process parameters, and density parameters before and after deposition, construct a physical-inspired feature to characterize the density after melt-infiltration; Step 300: Input the physical heuristic features used to characterize the density after melting and infiltration into the trained density prediction model after melting and infiltration, and output the predicted density value after melting and infiltration. Step 400: Based on the predicted density value after melting and infiltration, the information on the elemental composition of melting and infiltration, and the parameters of the ablation environment, construct physical heuristic features to characterize the ablation protection behavior; Step 500: Input the physical heuristic features used to characterize the ablation protection behavior into the trained linear ablation rate prediction model, and output the linear ablation rate prediction results of the high-temperature ceramic modified carbon / carbon composite material. The physical heuristics used to characterize the density after melting and infiltration include: pyrolytic carbon deposition densification characteristics, carbonization reaction enthalpy weighted characteristics, transport-reaction coupling characteristics, and infiltration driving force characteristics; The enthalpy-weighted characteristic of the carbonization reaction is characterized by calculating the following feature: the total component carbonization reaction enthalpy weighted value. Normalized total component carbonization reaction enthalpy characteristics Enthalpy-weighted values ​​of carbonization reactions of non-Cu active carbide elements Average enthalpy characteristics of non-Cu active carbide elements Ratio of active carbide forming elements : ； ； ； ； ； In the formula, For infiltration elements atomic ratio fraction, For infiltration elements Enthalpy of carbonization reaction; The element is Cu; The transport-reaction coupling characteristic is a composite characteristic calculated from porosity, carbonization volume expansion, average atomic radius of active elements, melting point of the corresponding carbide of active elements, and preparation temperature. ； In the formula, It is a composite feature; The weighted average of the carbonization volume expansion of multi-element modification for high-temperature ceramics. This is the weighted average of the average atomic radii of non-Cu reactive elements. This is the weighted average of the melting points of the carbides corresponding to non-Cu reactive elements. For preparation temperature; Open area ratio; The permeation driving force characteristic is denoted as The average cohesive energy, average electronegativity difference, average atomic weight, and average melting point were calculated based on the weighted average atomic ratios of the infiltrating elements. ； In the formula, The average cohesive energy is obtained by weighting the atomic ratio of the infiltrating elements. The average electronegativity difference of elements relative to carbon atoms, obtained by weighting according to the atomic ratio. The average atomic weight is obtained by weighting the atomic ratios of the elements. The average melting point is obtained by weighting the atomic ratios of the elements; where the average cohesive energy is... Poor average electronegativity Average atomic weight Average melting point They are uniformly denoted as equivalent physical properties. The weighted average of the physical properties of each element is obtained based on the atomic ratio of the infiltrating elements. ； In the formula, For infiltration elements atomic ratio, For infiltration elements Property characteristics to be determined The corresponding physical property parameters.

2. The method for predicting the ablation rate of reactive melt-infiltration modified carbon-carbon materials according to claim 1, characterized in that, The composition information of the infiltration elements includes the types of high-temperature ceramic modification additives, their atomic ratios, and physical property parameters; the high-temperature ceramic modification additives include one or more combinations of Si, Zr, Hf, V, Mo, Al, Ta, Ti, and Cu; the physical property parameters include metal melting point, electronegativity, atomic weight, atomic radius, cohesive energy, carbide melting point, and carbide volume expansion; The pore structure parameters include the porosity; The preparation process parameters include the reaction melting and infiltration preparation temperature; The density parameters before and after deposition include pre-deposition density, post-deposition density, and post-CVI density. The ablation environment parameters include oxygen flow rate, gas flow rate, ablation time, and spray gun distance.

3. The method for predicting the ablation rate of reactive melt-infiltration modified carbon-carbon materials according to claim 1, characterized in that, The pyrolysis carbon deposition densification characteristics are characterized by calculating the following features: pyrolysis carbon deposition increment characteristics. Relative densification characteristics of pyrolytic carbon Pyrolysis carbon filling efficiency characteristics : ； ； ； In the formula, Density before deposition Density after deposition This refers to the open area ratio.

4. The method for predicting the ablation rate of reactive melt-infiltration modified carbon-carbon materials according to claim 1, characterized in that, The physical heuristic features used to characterize ablation protection behavior include one or more of the following: basic protection features, liquid phase protection features, solid phase protection features, and liquid-solid equilibrium features. The basic protective features are used to characterize the coupling effect of the material's initial skeleton density, the degree of melt infiltration and densification retention, and the effectiveness of pore filling; The liquid phase protection feature is used to characterize the formation, flow, spreading, and blocking ability of liquid or viscous oxidation products under high-temperature ablation conditions; The solid-phase protection features are used to characterize the volume reserve, thermal stability, and resistance to high-temperature instability of the high-melting-point solid-phase oxide framework. The liquid-solid equilibrium characteristics are used to characterize the relative dominance between liquid-phase protection mechanisms and solid-phase protection mechanisms.

5. The method for predicting the ablation rate of reactive melt-infiltration modified carbon-carbon materials according to claim 4, characterized in that, The formula for calculating the basic protection features is: ； ； ； ； in, Represents basic protection features. Indicates density before deposition. For melt penetration retention rate, To improve melt infiltration filling efficiency, This is the predicted density value after melt infiltration. Density after CVI The equivalent mixing density of the melt-infiltrated phase, For infiltration elements mass fraction, For element infiltration metal density, Indicates the open area ratio; The formula for calculating the liquid phase protection feature is: ； ； ； ； ； in, Represents liquid phase protection characteristics, This represents the total volume of the liquid-phase oxidation products. The average optical alkalinity in the liquid phase. The average ion field strength in the liquid phase; For the normalized characteristics of heat load, For the original heat load, This is the average of the heat load samples. Oxygen flow rate, For gas flow rate, For ablation time, This refers to the distance between the spray gun and the spray gun. This represents the normalized characteristic of the oxidative load. For the original oxidation load, This represents the mean of the oxidative load samples. The formula for calculating the solid-phase protection characteristics is: ； in, Represents solid-phase protection characteristics. This represents the total volume of the solid-phase oxidation products. This is an indicator of solid-phase stability. The formula for calculating the liquid-solid equilibrium characteristics is: ； in, Represents liquid-solid equilibrium characteristics. To prevent extremely small positive numbers with a denominator of zero.

6. A linear ablation rate prediction system for high-temperature ceramic-modified carbon-carbon composite materials, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, the processor enables the processor to implement the method for predicting the ablation rate of reactive infiltration modified carbon-carbon materials as described in any one of claims 1 to 5.

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