Method and system for predicting progressive damage behavior of composite solid propellant

Abstract

The invention relates to a method and a system for predicting progressive damage behaviors of a composite solid propellant. The method comprises the following steps of: constructing a high-fidelity representative volume element (RVE) model to simulate a microstructure of the propellant; compressing the RVE model by adopting a self-consistent clustering analysis technology to form a reduced RVE model consisting of several clusters; performing parameterization processing on the reduced RVE model; a discrete Lippmann-Schwinger integral equation is utilized to calculate the mechanical response of the reduced RVE model; a calculation result is compared with experimental data, and the prediction precision is improved by iteratively optimizing model parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of solid propellants, and in particular relates to a method and system for predicting progressive damage behavior of composite solid propellants. Background Art

[0002] Composite solid propellants are an indispensable power source for modern spacecraft, including rockets and missiles. Research on their performance and reliability has long been a hot topic in aerospace engineering. With the rapid development of aerospace technology, solid propellants are required to operate under increasingly demanding conditions, placing higher demands on their performance. In particular, under the influence of extreme temperatures, pressures, and chemical environments, propellants are prone to progressive damage, a major cause of propellant performance degradation and even failure. Progressive damage typically involves microstructural changes, such as particle fracture, interfacial debonding, and crack propagation. The accumulation and evolution of these microdamages ultimately affect the macroscopic mechanical properties of the propellant. Therefore, in-depth research and accurate prediction of the progressive damage behavior of composite solid propellants are crucial for ensuring the safe operation of spacecraft and increasing their service life. Traditional damage prediction methods, such as finite element analysis (FEA), can theoretically simulate and predict propellant damage behavior. However, FEA has significant limitations in practical applications, particularly when processing complex models and large amounts of data, resulting in lengthy computational times and low efficiency. This problem of computational efficiency is particularly prominent in the rapid development and optimization of propellants. Traditional FEA methods often find it difficult to meet the high-efficiency requirements of modern spacecraft development. In addition, traditional FEA methods also face challenges in simulating microscopic damage mechanisms because it is difficult to accurately describe the complex microstructure and multi-scale effects inside the propellant. Therefore, finding a new and efficient damage prediction method that can not only accurately capture the damage behavior of the propellant but also significantly improve computational efficiency has become an urgent problem to be solved in the field of solid propellant research. The present invention is proposed to overcome these limitations of the existing technology and provide a new solution for the research and development of propellants. Summary of the Invention

[0003] The present invention aims to address the problem of damage prediction of composite solid propellants during loading. By combining advanced mathematical modeling, big data analysis, and computational physics methods, it aims to provide innovative technical means for the design, performance evaluation, safety monitoring, and life prediction of solid propellants. This method and system has important practical significance and application value for improving the performance and reliability of solid propellants and promoting technological progress in aerospace, military, and civilian blasting. A method and system for predicting the progressive damage behavior of composite solid propellants are proposed. The technical solution of the present invention is as follows:

[0004] A method for predicting progressive damage behavior of a composite solid propellant comprises the following steps:

[0005] -Build a high-fidelity representative volume element (RVE) model to simulate the propellant microstructure;

[0006] - using a self-consistent cluster analysis technique to compress the RVE model to form a reduced RVE model consisting of several clusters;

[0007] - parameterizing the reduced RVE model;

[0008] - Calculating the mechanical response of the reduced RVE model using the discretized Lippmann-Schwinger integral equation;

[0009] -Compare the calculated results with experimental data and iteratively optimize the model parameters to improve the prediction accuracy.

[0010] Furthermore, the construction of the high-fidelity RVE model includes consideration of the geometric characteristics, material properties, and interface characteristics of the propellant particles to ensure the accuracy and reliability of the model.

[0011] Furthermore, the self-consistent clustering analysis technology achieves effective compression of the model while retaining key mechanical information by intelligently identifying and merging nodes or elements with similar properties.

[0012] Furthermore, the parameterization process includes setting constitutive relations, applying boundary conditions, and developing specific calculation algorithms to meet the fast calculation requirements of the online stage.

[0013] Furthermore, the iterative optimization process adjusts the material constitutive parameters and interface characteristic parameters based on the comparison between the calculated results and the experimental data until the predicted results are highly consistent with the experimental data.

[0014] A composite solid propellant progressive damage behavior prediction system includes a data acquisition module, a data processing module, a prediction module, and an output module. The data processing module uses a self-consistent cluster analysis technique to compress and parameterize a high-fidelity RVE model. The prediction module uses the compressed RVE model to calculate the mechanical response using the Lippmann-Schwinger integral equation and predicts damage behavior based on the calculated mechanical response. The output module intuitively displays the prediction results in the form of graphics, charts, and numerical data, facilitating a user's rapid understanding of the propellant's damage status and performance changes.

[0015] Furthermore, the system continuously improves the prediction accuracy through an iterative optimization process to meet the damage prediction needs of composite solid propellants under different working conditions.

[0016] The advantages and beneficial effects of the present invention are as follows:

[0017] The core advantage of this invention lies in its innovative combination of a data-driven approach and a multiscale mechanical model. This technological breakthrough enables a comprehensive analysis of the mechanical behavior of composite solid propellants from the microscopic to the macroscopic level. This multiscale analysis strategy not only deepens our understanding of propellant performance but also provides a solid scientific foundation for performance optimization. By precisely adjusting the propellant's composition and content, this invention can significantly enhance the propellant's mechanical properties while meeting diverse practical application requirements. This not only improves material performance but also enhances its adaptability in different environments.

[0018] Secondly, the high simulation accuracy and computational efficiency of this invention are another major highlight. Leveraging advanced simulation techniques and algorithms, this invention can quickly and accurately process large amounts of data and provide precise performance predictions, which directly shortens R&D cycles and significantly reduces costs. This increased computational efficiency means researchers can complete complex simulation analyses in a shorter time, which is crucial for accelerating propellant R&D and reducing experimental costs.

[0019] Finally, the application of this invention is not limited to improving performance; its contributions to sustainability and safety are equally significant. The optimized design results in more environmentally friendly material use and a safer application environment. Furthermore, the technology of this invention has broad interdisciplinary influence, and its multi-scale mechanical model and data analysis methods can provide new research paths in multiple fields, such as materials science and chemical engineering. This not only promotes technological innovation in the propellant industry, but also provides powerful tools for researchers in related fields, helping to shorten R&D cycles, improve R&D efficiency, and ultimately achieve large-scale cost reductions and efficient product iteration. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a method for predicting progressive damage behavior of composite solid propellants according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will describe the technical solutions in the embodiments of the present invention in detail with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention.

[0022] The technical solution of the present invention to solve the above technical problems is:

[0023] In order to solve the low computational efficiency and other limitations of the existing technology in predicting the progressive damage behavior of composite solid propellants, the present invention proposes an innovative method and system, namely a prediction method based on data-driven self-consistent cluster analysis (SCA). This method effectively compresses the complex representative volume element (RVE) model by combining data acquisition of high-fidelity models with self-consistent cluster analysis technology, greatly reducing the computational complexity of the model. While maintaining the accuracy of the prediction, the calculation speed is significantly improved, meeting the urgent need for high efficiency in the rapid development and optimization of propellants. The present invention not only covers the implementation steps of the method, but also includes the system design for realizing the method, which has brought revolutionary progress to the field of propellant damage prediction. The following is a detailed description of the method and system:

[0024] method

[0025] Offline stage:

[0026] Step 1: Construct a high-fidelity representative volume element (RVE) model capable of simulating the microstructure of the composite solid propellant. In this step, we used sophisticated experimental and image processing techniques to construct a high-fidelity RVE model that accurately reflects the microstructure of the composite solid propellant. This model not only incorporates the geometric characteristics of the propellant particles but also considers material properties and interface characteristics, ensuring high accuracy and reliability during simulations.

[0027] Step 2: Using self-consistent cluster analysis, we compress the high-fidelity RVE model into a reduced RVE model consisting of several clusters. This process involves cluster analysis of the data to ensure the representativeness of the clusters. Using self-consistent cluster analysis, we effectively compress the high-fidelity RVE model, simplifying the complex microstructure into a few representative clusters. This compression not only significantly reduces the model's computational complexity but also preserves the key mechanical properties of the original model, laying the foundation for subsequent rapid calculations.

[0028] Step 3: Parameterize the reduced RVE model to facilitate online calculations. After model compression, we performed detailed parameterization of the reduced RVE model, including setting constitutive relations, applying boundary conditions, and developing computational algorithms. This process makes the reduced RVE model an efficient computational tool, providing a convenient interface and computational framework for online damage behavior prediction.

[0029] Online stage:

[0030] Step 1: Using the reduced RVE model obtained in the offline stage, the mechanical response is calculated by solving the discrete Lippmann-Schwinger integral equation. In this step, we use the reduced RVE model constructed in the offline stage to calculate the mechanical response. Specifically, the reduced RVE model is first loaded into specific boundary conditions to simulate the stress state of the actual propellant. Then, by solving the discrete Lippmann-Schwinger integral equation, the mechanical parameters such as stress and strain of the model under different loading conditions are calculated. This integral equation is an effective numerical method that can handle the mechanical problems of complex materials, especially in multi-scale simulations. Through this method, we can quickly obtain the mechanical response of the propellant at the microscale and provide data support for the prediction of damage behavior.

[0031] Step 2: Compare the calculated results with the experimental data, and improve the prediction accuracy by iteratively optimizing the model parameters. After obtaining the preliminary mechanical response calculation results, the key is to conduct an in-depth comparative analysis of these results with the experimental data. This process includes comparing the calculated and experimental stress-strain curves, peak stresses, and damage initiation and expansion modes, etc., one by one, to identify any differences. Based on these comparative analyses, the material constitutive parameters and interface characteristic parameters in the reduced RVE model are iteratively adjusted to minimize the differences between the calculated results and the experimental data. The optimization goal of this process is to improve the prediction accuracy of the model. Therefore, after each parameter adjustment, the mechanical response needs to be recalculated and compared with the experimental data for verification, and it is continuously iterated until the model's prediction results are highly consistent with the experimental data, thereby ensuring the accuracy and reliability of the model's predictions.

[0032] system

[0033] Here are the components of the system:

[0034] ●Data acquisition module: This module is responsible for collecting experimental data and finite element analysis data, and provides basic data for cluster analysis in the offline stage. As a key part of the system of the present invention, the data acquisition module undertakes the important task of collecting experimental data and finite element analysis data. This module uses high-precision experimental equipment and advanced measurement technology to comprehensively collect mechanical properties data such as stress, strain, displacement, etc. of the propellant under different loading conditions, and simultaneously obtains the microstructure scanning and simulation results required for finite element analysis. To ensure the reliability and representativeness of the data, the module adopts a variety of experimental methods and equipment, and has data preprocessing functions to clean, denoise and normalize the collected data. The design focuses on the repeatability and automation of the experiment to reduce human errors, improve the efficiency and quality of data acquisition, and provide solid basic data support for cluster analysis in the offline stage.

[0035] ●Data processing module: The data processing module is the core of the system of the present invention. It is responsible for the task of efficiently compressing and accurately parameterizing the representative volume element (RVE) model. This module intelligently processes the large amount of data obtained from the data acquisition module by executing the self-consistent cluster analysis (SCA) algorithm. Specifically, the data processing module first performs cluster analysis on the data in the RVE model, and divides the nodes or elements with similar properties into different clusters, thereby achieving model compression. This compression not only significantly reduces the computational complexity of the model, but also retains key structural and mechanical information. Subsequently, the module parameterizes each cluster, giving it representative material properties and mechanical parameters, to ensure that the compressed model can accurately reflect the mechanical behavior of the original model. Through this sophisticated data processing, the data processing module lays a solid foundation for rapid calculation and accurate prediction in the online stage.

[0036] ●Prediction module: The prediction module is the key link in realizing damage behavior prediction in the system of the present invention. It uses the mechanical response data obtained by online stage calculation, and through advanced algorithms and models, predicts the damage initiation, development and final failure behavior of the composite solid propellant during the loading process. This module comprehensively considers the constitutive relationship of the material, the damage evolution mechanism and the changes in macroscopic mechanical properties, and can provide a comprehensive analysis and forward-looking prediction of the propellant damage process. The prediction results not only include the location, size and shape of the damage, but also evaluate the impact of the damage on the macroscopic performance of the propellant, providing an important theoretical basis and technical support for the design optimization and safety assessment of the propellant. Through this module, the system of the present invention can help researchers and engineers better understand the damage mechanism of the propellant, thereby guiding the development and application of safer and more efficient new propellants.

[0037] ●Output module: The output module is the final link of the system of the present invention. Its function is to output the analysis results of the prediction module to the user in an intuitive and easy-to-understand form. This module can output prediction results of key mechanical parameters including stress distribution, damage distribution, stiffness and strength. Specifically, the output module not only provides numerical data, but also intuitively displays the stress state of the propellant under different loading conditions, the process of damage development, and changes in mechanical properties in the form of graphs and charts. This output form enables researchers and engineers to quickly grasp the overall performance and local damage of the propellant, and provide direct decision-making support for material design, structural optimization and safety assessment. In addition, the output module also supports exporting results into multiple formats, which is convenient for use in academic exchanges, report writing and engineering applications.

[0038] Invention Advantages

[0039] The following are the advantages of the present invention:

[0040] ● High efficiency: High efficiency is a notable feature of the system of the present invention, which is mainly due to the application of the self-consistent cluster analysis (SCA) method. Compared with traditional finite element analysis (FEA), the SCA method greatly reduces the computational complexity of the model through intelligent data compression and clustering technology. This not only reduces the calculation time, but also significantly improves the efficiency of the prediction. The advantage of this high efficiency is particularly evident in the process of rapid development and optimization of materials or structures. It allows researchers and engineers to complete multiple iterations and optimizations in a shorter period of time, thereby speeding up the product development cycle, reducing costs, and improving the accuracy of the design. Therefore, the system of the present invention is particularly suitable for application scenarios that require fast response and efficient calculations, such as the research and development and performance evaluation of new composite solid propellants.

[0041] ● Accuracy: Accuracy is another important indicator for evaluating the performance of the system of the present invention. Experimental verification shows that the system using the self-consistent cluster analysis (SCA) method can accurately capture the stress and damage distribution of composite solid propellants. This method ensures a high degree of consistency between the predicted results and the actual physical phenomena through its intelligent processing capabilities for complex data. The high degree of agreement between the experimental data and the predicted results not only proves the accuracy of the SCA method in simulating the mechanical behavior of the propellant, but also reflects the reliability of the system of the present invention in predicting material properties and assessing damage. This accuracy is crucial for the design, manufacture, and safety performance evaluation of propellants, because it provides researchers and engineers with a solid theoretical basis and decision-making support, so that they can more confidently adopt the system's predictions and suggestions in practical applications.

[0042] ● Practicality: Practicality is a highlight of the method and system of the present invention. Thanks to the significant improvement in computing efficiency, the method and system have shown broad prospects in practical applications. It can efficiently perform multi-scale analysis of propellants, which is crucial for understanding the relationship between the microstructure and macroscopic performance of propellants. Since complex multi-scale simulations can be completed in a relatively short period of time, the method and system of the present invention can be used not only for the research and development of new propellants, but also for performance optimization and fault diagnosis of existing propellants. This practicality makes the method and system have broad application potential in the design and manufacture of propellants in the fields of aerospace, military defense, and civil blasting, providing powerful tools and support for technological progress and product upgrades in related industries.

[0043] Example

[0044] The following are specific embodiments of the present invention:

[0045] ● Experiments were conducted on a certain type of composite solid propellant to collect damage data under different loading conditions: In this step, researchers designed a series of experiments to simulate the different loading conditions that composite solid propellants would experience in actual working environments. Mechanical properties of propellant samples were tested using advanced experimental equipment, such as a universal testing machine and an acoustic emission detection system. The experiments recorded in detail the damage evolution of the propellant under different stress levels, including key data such as the initiation, expansion, and ultimate failure of the damage. This data includes, but is not limited to, stress-strain curves, the morphology and size of the damaged area, and the energy release during the damage process.

[0046] Constructing a high-fidelity RVE model and applying self-consistent cluster analysis to compress the model: Based on experimental data, the researchers constructed a high-fidelity representative volume element (RVE) model that accurately reflects the complexity of the propellant microstructure and the anisotropy of its material properties. Subsequently, the RVE model was compressed using self-consistent cluster analysis (SCA). This method identifies and merges similar elements or nodes in the model, reducing the model's degrees of freedom and improving computational efficiency. This process ensures that the model maintains the accuracy of its mechanical behavior after compression.

[0047] Predicting propellant damage behavior through online calculations and comparing with experimental data: During the online calculation phase, the compressed RVE model is used to calculate the mechanical response. Based on these calculation results, the prediction module predicts the propellant damage behavior. The predictions include the location, size, shape, and key mechanical parameters associated with the damage. These predictions are then compared with previously collected experimental data to assess the accuracy of the predictions.

[0048] Optimize model parameters until predictions match experimental data: Based on the results of the comparative analysis, researchers iteratively optimize the parameters in the RVE model. This may involve adjusting the material's constitutive parameters, interface properties, or damage evolution criteria. After each parameter adjustment, online calculations are rerun and compared again with experimental data. This process is repeated until the predictions achieve a satisfactory level of agreement with the experimental data, ensuring the model's predictive power and practicality.

[0049] in conclusion

[0050] The proposed method and system utilize data-driven self-consistent cluster analysis (SCA) technology to successfully achieve rapid and accurate prediction of the progressive damage behavior of composite solid propellants. This groundbreaking technology significantly improves prediction efficiency while ensuring accuracy, which is of great significance in the field of propellant research and development. This method enables efficient processing of complex multi-scale data, providing a powerful tool for propellant design, performance evaluation, and safety analysis.

[0051] The method's high efficiency, accuracy, and practicality give it broad application prospects in a variety of fields. In particular, in industries such as aerospace, military defense, and civilian blasting, which require extremely high propellant performance, the method and system of this invention are expected to significantly shorten propellant R&D cycles, improve material performance, reduce costs, and enhance product safety and reliability.

[0052] Overall, the methods and systems of this invention revolutionize propellant research and development, providing new perspectives and tools for current research while also laying a solid foundation for future advances in materials science and technology. With further development and application, this technology is expected to have a profound impact on propellants and related fields.

[0053] Specific embodiment supplement:

[0054] Example: Prediction of Progressive Damage Behavior of Composite Solid Propellants Based on Data-Driven Self-Consistent Cluster Analysis

[0055] 1. Experimental Design and Data Collection

[0056] A comprehensive set of experiments was designed for a specific composite solid propellant, covering a variety of environmental conditions from low to high temperatures, from low to high pressures, and different stress rates from slow to rapid loading. The experiments used advanced electronic universal material testing machines and in-situ acoustic emission detection systems to record the stress-strain curves, damage evolution (including changes in damage location, size, and shape), and energy release of the propellant under different loading conditions. In addition, scanning electron microscopy (SEM) and X-ray tomography (XCT) techniques were used to obtain high-resolution images of the propellant's microstructure for the construction of an accurate RVE model.

[0057] 2. Construction of high-fidelity RVE model

[0058] Based on the experimental data and microscopic images, a detailed RVE model was constructed. This model not only incorporates the geometric distribution of the propellant particles but also accurately simulates the interparticle interface properties, such as bond strength and fracture toughness. The size and complexity of the RVE model were tailored to the actual size and microstructural characteristics of the experimental samples, ensuring that the model reflects the actual physical properties of the propellant.

[0059] 3. Application of Self-Consistent Cluster Analysis

[0060] Based on the constructed high-fidelity RVE model, the self-consistent cluster analysis (SCA) method was applied to automatically cluster elements with similar characteristics by analyzing the mechanical responses and properties of the elements in the model. The clustering process takes into account the multiphase material properties and anisotropy of the propellant, ensuring that each cluster represents a specific physical state or damage mechanism in the model. The model compressed by SCA significantly reduces the computational degrees of freedom while retaining key mechanical information and damage-sensitive areas, thereby significantly improving computational efficiency.

[0061] 4. Online damage behavior prediction

[0062] During the online calculation phase, a compressed RVE model is used to rapidly calculate the stress field distribution, strain energy density, and damage accumulation of the propellant under various loading conditions by solving the discrete Lippmann-Schwinger integral equation. Based on these calculation results and incorporating damage evolution criteria, the prediction module predicts the propellant's damage initiation point, damage path, and its impact on macroscopic mechanical properties. Prediction results include stress-strain curves of the damaged area, visualization of the damage pattern, and a quantitative assessment of the damage's impact on the propellant's stiffness and strength.

[0063] 5. Model parameter optimization and verification

[0064] The online prediction of damage behavior is compared with experimental data to assess its accuracy. Any discrepancies between the predicted results and experimental data are corrected through iterative optimization of model parameters, including adjustments to material constants, interfacial adhesion properties, and damage evolution parameters. This optimization process, based on a machine learning algorithm, automatically searches for the optimal combination of parameters until the predicted results closely match the experimental data. Furthermore, comparisons with other established prediction models, such as FEA, further validate the superiority of the proposed prediction method.

[0065] 6. System integration and application expansion

[0066] The prediction method and system of the present invention ensure efficient collaboration between modules through modular design. The tight integration of the data acquisition module and the data processing module ensures a seamless transition from experiment to model compression. The linkage between the prediction module and the output module enables the prediction results to be presented in an intuitive form, which is convenient for engineers and researchers to quickly understand. In addition, the system is also scalable and can adjust the model parameters and algorithms according to the different types of propellants and damage mechanisms to adapt to a wider range of application scenarios, such as the development of high-performance propellants, monitoring of propellant aging processes, and safety assessment of propellants.

[0067] Through the detailed implementation steps described above, the prediction method and system of this invention demonstrate not only high efficiency and accuracy in practical applications, but also high practicality and flexibility. It provides a novel technical approach for predicting the progressive damage behavior of composite solid propellants, and is expected to have a profound impact in fields such as aerospace, military, and civilian blasting, driving the continued innovation and development of propellant technology.

[0068] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0069] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0070] The above embodiments should be understood as merely illustrating the present invention and not as limiting the scope of protection of the present invention. After reading the contents of the present invention, technicians may make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for predicting progressive damage behavior of composite solid propellant, characterized in that: The following steps are involved: -Build a high-fidelity representative volume element (RVE) model to simulate the propellant microstructure; - using a self-consistent cluster analysis technique to compress the RVE model to form a reduced RVE model consisting of several clusters; - parameterizing the reduced RVE model; - Calculating the mechanical response of the reduced RVE model using the discretized Lippmann-Schwinger integral equation; -Compare the calculated results with experimental data and iteratively optimize the model parameters to improve the prediction accuracy.

2. The method according to claim 1, characterized in that The construction of the high-fidelity RVE model includes consideration of the geometric characteristics, material properties, and interface characteristics of the propellant particles to ensure the accuracy and reliability of the model.

3. The method according to claim 1, characterized in that The self-consistent clustering analysis technology achieves effective compression of the model while retaining key mechanical information by intelligently identifying and merging nodes or elements with similar properties.

4. The method according to claim 1, wherein The parameterization process includes setting constitutive relations, applying boundary conditions, and developing specific calculation algorithms to meet the fast calculation requirements of the online stage.

5. The method according to claim 1, wherein The iterative optimization process is based on the comparison of the calculated results with the experimental data, and adjusts the material constitutive parameters and interface characteristic parameters until the predicted results are highly consistent with the experimental data.

6. A composite solid propellant progressive damage behavior prediction system, characterized in that: The system includes a data acquisition module, a data processing module, a prediction module and an output module. The data processing module uses self-consistent cluster analysis technology to compress and parameterize the high-fidelity RVE model. The prediction module uses the compressed RVE model to calculate the mechanical response through the Lippmann-Schwinger integral equation and predicts the damage behavior based on this. The output module intuitively displays the prediction results in the form of graphics, charts and numerical data, so that users can quickly understand the damage status and performance changes of the propellant.

7. The system according to claim 6, characterized in that The system continuously improves prediction accuracy through an iterative optimization process to meet the damage prediction needs of composite solid propellants under different working conditions.

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