A method for optimizing the flame retardant properties of high-performance plastic materials

Abstract

The application provides a method for optimizing the flame-retardant performance of high-performance plastic materials, comprising: obtaining matrix material data and flame-retardant component performance parameters from a preset material attribute database, and determining an initial mixing ratio scheme; evaluating the distribution uniformity in the particle state by a distribution simulation calculation method to obtain a preliminary distribution simulation result; adjusting the component ratio and processing parameters according to the preliminary distribution simulation result to obtain an optimized mixing treatment scheme; forming a dynamic result for the protective structure, iteratively calculating the uniform dispersion state, adjusting the particle configuration parameters, and determining a collaborative protective layer structure scheme; extracting potential risk indicators from the collaborative protective layer structure scheme, optimizing the additive distribution scheme, and obtaining a material performance prediction result; integrating the distribution uniformity data and the protective structure information according to the material performance prediction result, verifying the overall flame-retardant stability, and generating a high-performance plastic material configuration scheme.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for optimizing the flame retardant properties of high-performance plastic materials. Background Technology

[0002] In the field of modern materials science, polycarbonate, as a high-performance engineering plastic, occupies a crucial position in industries such as electronics, automobiles, and household products due to its excellent transparency, strength, and heat resistance. It is widely used in the manufacture of key components such as mobile phone casings and power adapters. However, despite its wide range of applications, polycarbonate's shortcomings in fire safety limit its use in more demanding scenarios. There is an urgent need to improve its flame retardancy through technological innovation to meet the industry's stringent safety standards.

[0003] Currently, although some methods exist to improve the flame retardant properties of polycarbonate, these solutions often suffer from a trade-off. On the one hand, while certain additives can enhance fire resistance, they can also compromise the material's strength or appearance, leading to cracks or surface defects during use. On the other hand, some methods fail to ensure the additives are fully integrated with the base material during processing, resulting in unstable performance, especially in mass production where product quality consistency is difficult to guarantee. This multifaceted inconsistency often hinders the practical application of existing technologies. A deeper challenge lies in constructing an efficient flame-retardant protective structure within polycarbonate. This structure requires the synergy of multiple flame-retardant components to form a comprehensive defense system, rather than simply a single component superimposed. Furthermore, the key to this synergy lies in the uniform distribution of different components within the material. Uneven distribution can lead to insufficient fire resistance in some areas, while other areas may suffer from excessive additives, affecting the material's original toughness. This uneven distribution directly impacts the material's overall performance under high temperatures or combustion environments.

[0004] Therefore, ensuring that various flame-retardant components are uniformly dispersed in polycarbonate materials as extremely fine particles during processing, and that they form a tightly cooperating protective layer when heated, has become a critical issue that urgently needs to be addressed. Solving this problem will not only improve the safety performance of the material but also ensure that its original strength and appearance are not compromised, providing the industry with a more reliable solution. Summary of the Invention

[0005] This invention provides a method for optimizing the flame retardant properties of high-performance plastic materials, mainly including:

[0006] Data on the matrix material and performance parameters of the flame-retardant components are obtained from a pre-defined material property database to determine an initial mixing ratio scheme. The uniformity of particle distribution is evaluated using a distribution simulation calculation method to obtain preliminary distribution simulation results. Based on these results, the component ratios and processing parameters are adjusted to obtain an optimized mixing scheme. Molecular interaction characteristic parameters are extracted from the optimized scheme, and the material heating process is simulated using a kinetic behavior simulation calculation method to determine the dynamic results of the protective structure formation. For the dynamic results of the protective structure formation, the uniform distribution state is iteratively calculated, particle configuration parameters are adjusted, and a cooperative protective layer structure scheme is determined. Potential risk indicators are extracted from the cooperative protective layer structure scheme, and the additive distribution scheme is optimized to obtain material performance prediction results. Based on the material performance prediction results, the uniform distribution data and protective structure information are integrated to verify the overall flame-retardant stability and generate a high-performance plastic material configuration scheme. Furthermore, the step of obtaining matrix material data and flame-retardant component performance parameters from a preset material property database to determine an initial mixing ratio scheme includes: extracting molecular structure information and thermal stability evaluation indicators of the polycarbonate matrix material from the preset material property database, and obtaining performance parameters of various flame-retardant components; analyzing the material fusion uneven distribution characteristics based on the molecular structure information and thermal stability evaluation indicators, and determining an initial mixing ratio scheme; evaluating the distribution uniformity of the initial mixing ratio scheme in the particle state using a distribution simulation calculation method to obtain preliminary distribution simulation results; obtaining particle distribution adjustment parameters under flame-retardant enhancement simulation based on the preliminary distribution simulation results; if the adjustment parameters exceed a preset threshold range, determining an enhanced mixing ratio scheme; and extracting additional thermal stability indicators from the preset material property database using the enhanced mixing ratio scheme to obtain simulation results of the uniform distribution of flame-retardant components in the matrix material. Furthermore, the step of adjusting the component ratios and processing parameters based on the preliminary distribution simulation results to obtain an optimized mixing treatment scheme includes: determining whether the particle spacing distribution exceeds a preset uniformity threshold range based on the preliminary distribution simulation results; if it exceeds the preset uniformity threshold range, extracting component molecular structure data and thermal stability indices from a preset material property database using structural analysis calculation methods; obtaining component ratio configuration data by calculating the affinity coefficients between components to obtain adjusted ratio parameters; configuring processing temperature parameters based on the adjusted ratio parameters to determine the optimized mixing treatment scheme; obtaining interface compatibility evaluation indices under the optimized mixing treatment scheme; if the evaluation indices exceed a preset compatibility threshold, adjusting the order of flame-retardant component addition to obtain enhanced compatibility distribution results; evaluating distribution uniformity characteristics through the enhanced compatibility distribution results to determine the thermal response change trend.Furthermore, the step of extracting molecular interaction characteristic parameters from the optimized hybrid treatment scheme and simulating the material heating process using a dynamic behavior simulation calculation method to determine the dynamic result of the protective structure formation includes: extracting molecular interaction characteristic parameters related to the cooperative system from the optimized hybrid treatment scheme and obtaining the corresponding interaction strength values; determining whether the interaction strength values ​​are lower than a preset cooperation threshold range; if they are lower than the preset cooperation threshold range, simulating the material heating process using a dynamic behavior simulation calculation method to obtain molecular motion trajectories; determining the preliminary dynamic result of the protective structure formation under high temperature conditions based on the molecular motion trajectories; optimizing and adjusting the interaction parameters for the preliminary dynamic result, verifying stability, and obtaining the adjusted structural parameters; and obtaining the final dynamic result of the protective structure formation under high temperature conditions using the adjusted structural parameters. Furthermore, the step of iteratively calculating the uniform distribution state based on the dynamic results of the formation of the protective structure, adjusting particle configuration parameters, and determining the cooperative protective layer structure scheme includes: obtaining interfacial bonding characteristic data between the flame retardant additive and the matrix material from the dynamic results of the formation of the protective structure; using a distribution simulation calculation method, calculating the uniform distribution state by iteratively solving the particle position equation to obtain the degree of distribution deviation; if the degree of distribution deviation is greater than a preset uniform threshold range, then adjusting the particle size configuration and dispersion speed parameters through additive surface modification data processing to determine the preliminary cooperative protective layer structure; for the preliminary cooperative protective layer structure, obtaining the thermal conductivity distribution characteristics corresponding to the matrix material data; iteratively calculating the temperature gradient through the heat flow equation to simulate the interfacial bonding changes under high temperature conditions to obtain the optimized uniform distribution state; if the interfacial bonding characteristics are lower than the preset cooperative threshold, then adjusting the distribution density parameters of the flame retardant additive to determine the enhanced protective layer structure. Furthermore, the step of extracting potential risk indicators from the collaborative protective layer structure scheme, optimizing the additive distribution scheme, and obtaining material performance prediction results includes: extracting potential risk indicators related to strength failure risk and appearance defects from the collaborative protective layer structure scheme; using structural analysis calculation methods, calculating stress distribution and deformation values ​​by meshing the material structure to obtain quantitative values ​​of potential risk indicators; if the potential risk indicators exceed a preset safety threshold range, adjusting the flame retardant additive configuration and matrix material compatibility parameters through the additive concentration distribution scheme to obtain thermal stability verification data and interface optimization parameters; generating durability assessment data based on the thermal stability verification data and the interface optimization parameters; simulating environmental adaptation adjustments using structural analysis calculation methods to obtain equilibrium condition results; and determining the final material performance prediction results by integrating the material performance prediction indicators from the durability assessment data using the equilibrium condition results.Furthermore, the step of integrating uniformly distributed data and protective structure information based on the material performance prediction results to verify overall flame retardant stability and generate a high-performance plastic material configuration scheme includes: determining the degree of improvement of insufficient fire resistance characteristics based on the material performance prediction results and obtaining improvement confirmation data; integrating uniformly distributed data and protective structure information through the improvement confirmation data to obtain an integrated dataset; using a dynamic behavior simulation calculation method on the integrated dataset to verify overall flame retardant stability by simulating thermal flow and stress changes, and obtaining stability verification indicators; extracting interface compatibility parameters from the stability verification indicators and fusing them with environmental adaptation parameters from the material performance prediction results to determine the configuration optimization basis; and generating a configuration scheme for high-performance plastic materials in high-requirement application scenarios based on the configuration optimization basis. Furthermore, the step of evaluating the distribution uniformity in the particle state using a distribution simulation calculation method to obtain preliminary distribution simulation results includes: evaluating the distribution uniformity in the particle state using a distribution simulation calculation method for the initial mixing ratio scheme, and obtaining particle spacing distribution data; determining whether the distribution uniformity meets a preset uniformity threshold range based on the particle spacing distribution data; if it does not meet the preset uniformity threshold range, obtaining particle distribution adjustment parameters under flame retardant enhancement simulation; adjusting the proportion of flame retardant components in the initial mixing ratio scheme using the particle distribution adjustment parameters to generate an enhanced mixing ratio scheme; extracting additional thermal stability indicators from a preset material property database based on the enhanced mixing ratio scheme to determine the preliminary distribution simulation results of the flame retardant components in the matrix material; and evaluating the trend of distribution uniformity changes in the particle state using the preliminary distribution simulation results.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention aims to address the comprehensive problems of uneven integration between polycarbonate matrix materials and flame-retardant components, insufficient molecular interaction, and inadequate protective structural stability under high-temperature conditions. The invention extracts molecular structure and thermal stability data from a material property database to initially determine a mixing ratio and uses distribution simulation calculations to evaluate particle uniformity. If uniformity is insufficient, structural analysis is used to adjust the ratio and processing temperature to optimize the mixing scheme. Further, molecular interaction characteristics are extracted; if strength is insufficient, kinetic simulation analysis is used to analyze the dynamic formation of the high-temperature protective structure. The dispersion state is iteratively optimized based on interfacial bonding characteristics, adjusting particle size and dispersion speed to ultimately form a cooperative protective layer structure. Simultaneously, potential risk indicators are assessed, and additive concentration distribution is optimized to ensure a balance in material performance. This invention ultimately verifies overall flame-retardant stability through kinetic simulation, significantly improving the fire resistance and structural stability of high-performance plastics in extreme environments, providing a reliable configuration solution for demanding applications. Attached Figure Description

[0009] Figure 1 This is a flowchart of a method for optimizing the flame retardant properties of high-performance plastic materials according to the present invention;

[0010] Figure 2 This is a flowchart of step S104 in a method for optimizing the flame retardant properties of high-performance plastic materials according to the present invention.

[0011] Figure 3 This is a flowchart of step S105 in a method for optimizing the flame retardant properties of high-performance plastic materials according to the present invention.

[0012] Figure 4 This is a flowchart of step S106 in a method for optimizing the flame retardant properties of high-performance plastic materials according to the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0014] like Figures 1-4 This embodiment of a method for optimizing the flame retardant properties of high-performance plastic materials may specifically include:

[0015] Step S101: Obtain polycarbonate matrix material data and performance parameters of various flame retardant components; extract molecular structure information and thermal stability evaluation index from the preset material property database; determine the initial mixing ratio scheme for the uneven distribution characteristics of material fusion; evaluate the distribution uniformity in the particle state through distribution simulation calculation method; and obtain preliminary distribution simulation results.

[0016] Data on the polycarbonate matrix material and performance parameters of various flame-retardant components are obtained from a pre-defined material property database. Molecular structure information and thermal stability evaluation indicators are extracted to determine the material fusion non-uniform distribution characteristics. For these non-uniform distribution characteristics, the distribution uniformity in the particle state is evaluated using a distribution simulation calculation method to obtain preliminary distribution simulation results. The distribution simulation calculation method inputs an initial mixing ratio of 0.1 to 0.5, employs Monte Carlo simulation to simulate randomly distributed particles, and outputs a uniformity index SD (standard deviation, interpreted as particle density deviation). Particle state refers to flame-retardant component particles with a size of 10 to 50 micrometers, and a uniformity index of SD less than 0.05. Based on the preliminary distribution simulation results, particle distribution adjustment parameters under flame-retardant enhancement simulation are obtained. If the adjustment parameters exceed a preset threshold, an enhancement mixing ratio scheme is determined. The flame-retardant enhancement simulation is a finite element analysis, inputting the SD value of the preliminary distribution simulation results and outputting an adjustment parameter D (density deviation, calculated as average density minus local density). The preset threshold is 0.1; if D is greater than 0.1, the enhancement mixing ratio scheme is determined to increase the proportion of flame-retardant components to 0.6. By using the enhanced mixing ratio scheme, additional thermal stability indicators are extracted from the material property database to obtain simulation results of the uniform distribution of flame retardant components in the matrix material.

[0017] In one embodiment, data on the polycarbonate matrix material and performance parameters of various flame-retardant components are first obtained.

[0018] Specifically, data on polycarbonate matrix materials include basic properties such as density, melting point, and mechanical strength, which can be obtained through laboratory testing or specifications provided by the supplier.

[0019] For example, the glass transition temperature of polycarbonate is measured using experimental equipment to ensure its stability as a matrix. Performance parameters of various flame-retardant components cover the flame-retardant efficiency, decomposition temperature, and compatibility of phosphorus-based, nitrogen-based, or halogen-based flame retardants, obtained through thermogravimetric analysis. Data collection in this way provides a foundation for subsequent extraction and fusion, ensuring comprehensive material properties. Further, molecular structure information and thermal stability evaluation indicators are extracted from a pre-defined material property database. This database is a digital library storing various chemical and physical properties of materials, including the carbon chain structure of polycarbonate and functional group information of flame retardants. Molecular structure information extraction involves querying the database for the chemical formulas and bonding methods of flame-retardant components, such as the PO bond structure of phosphate esters, to analyze their compatibility with the matrix. Thermal stability evaluation indicators include initial decomposition temperature and char yield, calculated using standardized indicators from the database; for example, char yield represents the proportion of the material's remaining mass at high temperatures. This extraction process enables rapid data access and supports preliminary assessment of material fusion. An initial mixing ratio scheme is determined to address the uneven distribution characteristics of the fused materials. The uneven distribution of flame-retardant particles within a polycarbonate matrix refers to the non-uniform distribution of these particles, which may lead to weakened localized flame-retardant effects or decreased mechanical properties. First, by inputting the particle size d (in micrometers) and concentration c (mass fraction) into a finite element analysis model, the initial ratio r = r0 * (1 + 0.1 * (σ / d)) is calculated, where r0 is the standard ratio of 0.2 and σ is the standard deviation of the distribution. This outputs the initial mixing ratio scheme. Subsequently, molecular dynamics simulations are used to evaluate the uniformity of this scheme. If the uniformity is below the threshold of 0.85, the ratio is adjusted. The uneven distribution of flame-retardant particles within a polycarbonate matrix may lead to weakened localized flame-retardant effects or decreased mechanical properties.

[0020] First, the causes of uneven distribution are analyzed, such as differences in particle size or insufficient mixing. Then, the proportions are determined based on these characteristics. For example, if the flame retardant particles are large, the initial mixing ratio is set to a matrix to flame retardant mass ratio of 80:20. This ratio is adjusted by calculating the particle volume percentage to reduce agglomeration. This adjustment uses the empirical formula V_r=(m_r / ρ_r) / (m_b / ρ_b+m_r / ρ_r), where V_r is the flame retardant volume percentage, m_r and m_b are the masses of the flame retardant and matrix, respectively, and ρ_r and ρ_b are their respective densities. The initial mass ratio is input, and the adjusted ratio is output to ensure initial uniformity of fusion. The uniformity of distribution in the particle state is evaluated using a distribution simulation calculation method, where particle state refers to the size (e.g., average diameter 10 μm) and morphology (spherical assumption) of the flame retardant particles. The distribution simulation method employs Monte Carlo simulation. The process involves inputting an initial mixing ratio of 80:20, particle size distribution (standard deviation 2 μm), and system volume (1 m³); generating 10,000 particle location simulations through random sampling; and calculating the output uniformity index, i.e., the local concentration standard deviation. A distribution less than 0.05 is considered uniform. This method can be reproduced using Python's NumPy library to predict the distribution and optimize the ratio.

[0021] Specifically, a particle model is first established, treating the flame-retardant components as randomly distributed spherical particles, simulating their positions within the polycarbonate matrix. Then, a uniformity index, such as the statistical variance of the nearest neighbor distance, is calculated. The threshold is determined by the standard deviation of the simulated ideal uniform distribution, for example, a value of 0.05. If the variance is less than this threshold, it is considered uniform. The specific process is as follows: input the particle position coordinates, use the KNN algorithm to calculate the nearest neighbor distance *d* for each particle, and output the variance σ². If σ² < 0.05, the distribution is considered uniform. This method evaluates the distribution state by iteratively calculating the interaction forces between particles, such as van der Waals forces, to obtain quantitative results. This process can identify potential non-uniform regions and provide a basis for optimization, achieving precise control of distribution in material preparation.

[0022] In one possible implementation, preliminary distribution simulation results are obtained.

[0023] Specifically, the simulation results include uniformity scores and distribution maps, with scores ranging from 0 to 1, where 1 represents complete uniformity. Particle locations are displayed using visualization tools, and the results are used to validate the effectiveness of the initial scaling scheme.

[0024] It should be noted that the optimized parameters for flame retardant performance obtained from the simulation experiment, such as the polymer-flame retardant mixing ratio of 1:2 and the heat treatment temperature range of 150-200°C, can further guide actual mixing experiments and be applied to the preparation of electronic casings or building panels in the field of flame retardant composite materials.

[0025] Preferably, in another embodiment, the finite element simulation parameters are adjusted for different flame-retardant components. The simulation is named ANSYS thermal simulation. The inputs include material density of 1.2 g / cm³, specific heat capacity of 1.5 J / g·K, and initial thermal conductivity of 0.2 W / m·K. The outputs are temperature distribution maps and component concentration changes. The parameter list includes thermal conductivity, diffusion coefficient, and time step of 0.1 s. The adjustment method is an iterative optimization algorithm, and the parameter values ​​are calibrated based on experimental data. For example, for phosphorus-based flame retardants, the thermal diffusion coefficient D is increased from 0.5 to 1.0 mm² / s in the simulation, where D = α / ρCp, α is thermal conductivity, ρ is density, and Cp is specific heat capacity. The distribution is calculated using the finite difference method. The evaluation steps include simulating the concentration gradient change at a high temperature of 500°C for 1 hour and comparing the deviation with the benchmark model to be less than 5%. For nitrogen-based flame retardants, the thermal conductivity is similarly adjusted from 0.3 to 0.6 W / m·K, with the same inputs. The output shows a 20% improvement in distribution uniformity. Parameter optimization is achieved through MATLAB scripts.

[0026] For example, if the initial simulation shows unevenness in the granular state, it can be optimized by increasing the stirring time. The simulation results show that the uniformity is improved by 20%, thereby achieving better flame retardant performance.

[0027] Understandably, this method, through data-driven simulation, achieves uniformity assessment of material fusion, reducing experimental trial and error in practical applications.

[0028] Step S102: Based on the preliminary distribution simulation results, if the particle spacing distribution exceeds the preset uniformity threshold range, then the structural analysis calculation method is used to adjust the component ratio configuration and processing temperature parameters to obtain an optimized mixing treatment scheme and determine the thermal response change trend under the uniform distribution characteristics.

[0029] Based on the preliminary distribution simulation results, if the particle spacing exceeds the preset uniformity threshold range (i.e., 0.8-1.2 times the average spacing), a structural analysis calculation method, namely molecular dynamics simulation, is adopted. This method extracts component molecular structure data and thermal stability indices from a material property database, using the molecular structure data as input. The simulation calculates the interactions between components and outputs the affinity coefficient. The affinity coefficient is calculated as A = ∑(E_i*W_i), where A is the affinity coefficient, E_i is the binding energy of the i-th component, and W_i is the weight determined empirically from 0.4-0.6. This yields component ratio configuration data; for example, increasing the proportion of components with higher affinity by 10% results in adjusted ratio parameters. These adjusted ratio parameters are then used to configure the processing temperature parameters, for example, setting the temperature to 150-200°C, to determine the optimized mixing treatment scheme. The interface compatibility evaluation index under the optimized hybrid treatment scheme is obtained, that is, the compatibility score is calculated by the formula C=1-∑|D_i| / N, where C is the evaluation index, D_i is the deviation value of the i-th interface, and N is the number of interface samples. If the evaluation index exceeds the preset compatibility threshold of 0.85, the order of adding flame retardant components is adjusted, for example, adding low-melting-point components first and then high-melting-point components. The specific rule is to add them in ascending order according to melting point to obtain an enhanced compatibility distribution result. The distribution uniformity is evaluated by the enhanced compatibility distribution result, for example, the uniformity U=1-σ / d is calculated, where U is the uniformity, σ is the standard deviation of the spacing, and d is the average spacing. The thermal response change trend is fitted by linear regression, that is, the temperature-response data is input and the trend slope is output to determine the thermal response change trend.

[0030] In one implementation, a judgment is made based on the preliminary distribution simulation results. If the particle spacing distribution exceeds the preset uniformity threshold range, the subsequent optimization process is initiated.

[0031] Specifically, the preset uniformity threshold range is set through statistical analysis. For example, the upper limit of the threshold is 1.5 times the average spacing, and the lower limit is 0.5 times. These values ​​are determined based on historical simulation data. The specific setting rule is: calculate the average spacing μ and standard deviation σ of the historical data, set the upper limit as μ + 1.5σ, and the lower limit as μ - 1.5σ, to ensure the overall consistency of the distribution. This judgment process involves comparing the spacing statistics in the simulation results with the threshold. If they exceed the threshold, it indicates a risk of uneven distribution, requiring further intervention. In this way, a preliminary assessment of the simulation results is achieved, providing a basis for adjustments. Furthermore, the finite element analysis method is used to adjust the component ratio configuration and processing temperature parameters. Finite element analysis (FEM) is an evaluation method based on the microstructure of materials. The process involves inputting material properties such as the elastic modulus of polycarbonate (E = 2.4 GPa) and the volume fraction of the flame-retardant component (V_f = 0.2). First, a digital model of the material is constructed, treating the polycarbonate matrix and the flame-retardant component as a multilayer network structure. Then, the interparticle bonding strength (S) and particle density (D) are calculated, where S = (E * V_f) / (1 - V_f), and D = number of particles / volume. The temperature parameter T is iteratively optimized from 200°C to 250°C in 5°C increments, and the adjusted proportional configuration is output until S > 50 MPa and D > 0.1 particles / μm³, achieving reproducible optimization.

[0032] For example, the calculations analyze the surface energy of the particles and the viscosity of the matrix, and evaluate the structural stability at different ratios using an iterative algorithm. If the ratio is 85:15 (matrix to flame retardant), the calculations show increased bonding strength, thereby reducing uneven distribution. The method also considers the effect of temperature on viscosity; for example, at a processing temperature of 200 degrees Celsius, the particle diffusion effect caused by the decrease in viscosity is calculated. This adjustment achieves parameter optimization through multiple iterations, ensuring the uniformity of the mixing process.

[0033] Preferably, the specific implementation of the structural analysis calculation method during the adjustment process involves decomposition steps. First, the geometric parameters of the particles, such as diameter and shape factor, are collected and then input into a computational framework for simulation. The computational framework is a numerical tool used to predict structural changes, for example, by quantifying non-uniformity by evaluating the variance of the interparticle spacing. If the variance exceeds a threshold, the proportion of flame retardant is gradually increased to 75:25 matrix to flame retardant, while the processing temperature is reduced to 180 degrees Celsius to enhance compatibility. This method guides the adjustment by analyzing micromechanical equilibrium, such as the repulsive and attractive forces between particles, achieving targeted optimization of non-uniform distribution.

[0034] In one possible implementation, an optimized mixing scheme is obtained. Specifically, this scheme includes adjusted proportions, such as 80% matrix and 20% flame retardant, and a processing temperature set at 190 degrees Celsius. These parameters are determined based on structural analysis results. The scheme also includes recommendations for mixing time and stirring speed, such as a stirring speed of 500 rpm, to promote uniform particle dispersion. This scheme achieves better fusion results in the preparation of polycarbonate composites. It should be noted that the uniform distribution characteristic refers to the standard deviation of the spatial distribution of particles in the matrix being less than 0.1. The trend of its thermal response change is determined by finite element analysis simulation. The input is material proportion and temperature data, and the calculation is performed using ANSYS software. The output is a temperature distribution map and a thermal conductivity change curve. The trend shows that the thermal conductivity is improved by 15% under uniform distribution.

[0035] Specifically, after the optimization scheme is applied, the distribution simulation is rerun to calculate thermal response indices, such as the expansion rate and thermal decomposition rate when the temperature rises.

[0036] For example, if the distribution is uniform, a 10% reduction in the thermal response trend indicates improved material stability. This trend is derived by plotting a temperature-response curve, supporting the material's application in high-temperature environments. In another embodiment, structural analysis parameters are adjusted to suit the properties of the phosphorus-based flame-retardant component.

[0037] For example, the thermal conductivity coefficient k is added to the calculation, using the formula k=(Q*L) / (A*ΔT), where Q is the heat flux, L is the thickness, A is the area, and ΔT is the temperature difference. An example input is Q=10W, L=0.01m, A=0.1m², and ΔT=50K, outputting k=0.02W / (m·K). To simulate particle migration behavior at high temperatures, a finite element analysis algorithm is used, with an input temperature of 300 degrees Celsius and an initial particle spacing of 2 micrometers, outputting the migration path. If the spacing distribution exceeds the threshold of 5 micrometers, the ratio is adjusted to 82% matrix and 18% flame retardant, with a processing temperature of 195 degrees Celsius. This variation demonstrates the flexibility of the method within the same domain, ensuring optimization under different flame retardant types. Furthermore, the process of determining the trend of thermal response changes involves comparing data before and after optimization.

[0038] Specifically, a finite element method (FEM) thermal response model is first established. The process includes meshing the material structure, setting boundary conditions such as a thermal conductivity coefficient k = 50 W / m·K (where k is the thermal conductivity coefficient), and an initial temperature. A uniform distribution density of 1.2 g / cm³ is used as input. The response curves of the material are calculated within the range of 100 to 300 degrees Celsius. For example, the curves show that under uniform distribution, the thermal decomposition temperature increases to 350 degrees Celsius, the residual mass is 85%, and the peak heat flux is 120 J / g. This analysis, by evaluating the residual mass and peak heat flux, reveals the impact of uniformity on thermal stability, providing guidance in the fabrication of electronic materials.

[0039] Understandably, the optimization process as a whole achieves control over the uniformity of distribution through structural analysis and parameter adjustment.

[0040] For example, in building material applications, if the initial simulation shows unevenness, adjusting the scale will result in a more gradual trend in the thermal response, indicating enhanced heat resistance of the material. This method reduces the number of iterations in actual experiments and supports efficient fabrication.

[0041] In one embodiment, for nitrogen-based flame retardants, the structural analysis calculation method employs molecular dynamics simulation, emphasizing compatibility assessment. The specific process includes inputting the molecular structure models of the flame retardant and the matrix, and using GROMACS software to calculate the interfacial energy IE = E_total - E_flame - E_matrix, where E_total is the total system energy, E_flame is the flame retardant energy, and E_matrix is ​​the matrix energy. Parameters include a simulation temperature of 300K and a time of 10ns. If the interfacial energy exceeds the threshold of 5kJ / mol, the temperature is adjusted to 185 degrees Celsius, and the ratio is 78:22, thereby optimizing the mixing scheme and determining thermal response trends such as a decrease in decomposition rate. Finally, when evaluating the trend of thermal response changes, standardized indicators such as thermogravimetric loss rate are used. After optimization, the trend shows a reduction in the loss rate, ensuring the reliability of the material in fire-retardant applications.

[0042] Step S103: Extract molecular interaction characteristic parameters related to the cooperative system from the optimized hybrid processing scheme. If the molecular interaction strength is lower than the preset cooperative threshold range, simulate the material heating process through dynamic behavior simulation calculation method to obtain the dynamic results of the formation of the protective structure under high temperature environment.

[0043] In the optimized hybrid treatment scheme, the cooperative system refers to the network of synergistic interactions between molecules in the material, and molecular interaction parameters include van der Waals forces and hydrogen bond strength. The molecular interaction parameters related to the cooperative system are extracted from the scheme, and the corresponding interaction strength values ​​are obtained. If the interaction strength value is lower than a preset cooperation threshold range of 0.5 to 1.5, the material's heating process is simulated using molecular dynamics simulation. Specifically, GROMACS software is used, with the initial molecular structure file, temperature parameters such as 500K, and a time step of 0.002ps as input, and an NVT ensemble simulation of 100ns is performed to output the molecular trajectory of the material under high temperature conditions. Based on the molecular trajectory, the initial dynamic result of the protective structure formation under high temperature conditions is determined. For the initial dynamic result, the interaction parameters are optimized and adjusted to verify the stability of the protective structure, and the adjusted structural parameters are obtained. Using the adjusted structural parameters, the final dynamic result of the protective structure formation under high temperature conditions is obtained.

[0044] In one implementation, extracting molecular interaction parameters related to the cooperative system from the optimized hybrid treatment scheme first requires understanding the concept of a cooperative system. A cooperative system refers to an interaction network formed between molecules in a material through chemical bonds or van der Waals forces, and these interactions determine the overall stability of the material. In materials science, particularly in the design of high-temperature protective structures, the analysis of such systems helps to evaluate the material's performance under extreme conditions.

[0045] Specifically, the extraction process can be achieved by calculating intermolecular distances and energy distribution.

[0046] For example, using the molecular configuration in the GROMACS molecular dynamics software simulation scheme, the initial molecular coordinates and force field parameters are input, and the simulated trajectory is output. From this, bond lengths, bond angles, and non-bonded interaction energies are calculated, thus obtaining a set of quantitative parameters, such as the average interaction strength value and distribution variance. The specific calculation process is as follows: First, bond length and bond angle geometric data are extracted from the trajectory. Then, the non-bonded interaction energy is calculated, and the intensity value is obtained using the formula E_avg=Σ(E_i) / N, where E_i is the energy of a single interaction, and N is the number of interactions. For example, E_avg is 2.3 kcal / mol. These parameters reflect the degree of cooperation between molecules, ensuring the accuracy of subsequent judgments. Furthermore, determining whether the molecular interaction strength is below a preset cooperation threshold range of 0.5-1.0 kcal / mol is a crucial step. Molecular interaction strength is usually defined as the average value of intermolecular forces per unit volume, which can be obtained through energy calculation formulas.

[0047] For example, the intensity value equals the total interaction energy divided by the number of molecules. The preset cooperation threshold range is set based on the material type.

[0048] For example, for ceramic-based high-temperature protective materials, the interaction strength is first calculated as: S = F_avg * d, where S is the intensity (in electron volts per molecule), F_avg is the average interaction force (in Newtons), and d is the average bond length (in meters), and then converted to energy units. The lower threshold is set to 5.0 electron volts per molecule to ensure structural integrity.

[0049] In one possible implementation, the system triggers a simulation process if the extracted parameters show that the average strength is below this range. This judgment logic helps identify potential material weaknesses and prevent structural collapse in high-temperature environments.

[0050] For example, in the optimization scheme of high-temperature alloy materials, a specific method for extracting molecular interaction characteristic parameters includes combining scanning electron microscopy data with computational simulation. First, a molecular model is obtained from a hybrid processing scheme, and then density functional theory is applied to calculate the interaction parameters. This method is applicable to various high-temperature scenarios, such as the design of protective layers for aero-engine components, ensuring the universality of parameter extraction. Through this extraction, the strength of hydrogen bonds or covalent bonds between molecules can be quantified, providing a data basis for judgment.

[0051] It should be noted that if the molecular interaction strength is below a preset cooperation threshold, the material's heating process is simulated using a kinetic behavior simulation method. This kinetic behavior simulation method is a numerical simulation technique based on Monte Carlo or molecular dynamics principles. It predicts the material's evolution under temperature changes by iteratively calculating molecular positions and velocities. The specific process includes initializing the material model, setting the temperature gradient, and running time-step iterations.

[0052] For example, molecular coordinates are updated and thermal diffusion effects are calculated at each iteration. This method is widely used in materials science to simulate phase transitions or structure formation, avoiding the high costs and risks of actual experiments. In the simulation, the material heating process is broken down into heating, stabilization, and cooling stages, with molecular trajectories recorded at each stage.

[0053] In one embodiment, for polymer-based high-temperature protective materials, the kinetic simulation is specifically implemented using the GROMACS software framework. First, a molecular model is established, including the main chain and side chain structures. Then, an initial temperature, such as room temperature, is input and gradually increased to 1000 degrees Celsius. During the simulation, molecular vibrational frequencies and diffusion coefficients are calculated, and changes in interaction strength are observed. If the initial assessment indicates that the intensity is below a threshold, the simulation will focus on tracking the evolution of weak interaction regions.

[0054] For example, molecular chains may break or rearrange to form new protective structures. The key to this simulation method lies in the choice of timescale, typically set to the picosecond to nanosecond level to capture transient behavior. Through multiple iterations, data on the effect of temperature on the molecular network are obtained, supporting decisions for material optimization.

[0055] Preferably, the formation dynamics of the protective structure under high-temperature conditions are obtained through analytical simulation output. These dynamics include structural evolution curves, density distribution maps, and energy change maps, which quantify the formation process of the protective structure.

[0056] For example, the transformation from loose molecules to a dense barrier. In materials engineering, this result helps in the design of heat-resistant coatings.

[0057] Specifically, the results can be visualized as a time series graph, showing how molecules self-assemble into a protective layer at high temperatures to resist thermal erosion.

[0058] Understandably, in another implementation, for metal oxide materials, the innovation of this process lies in combining quantum mechanical calculations to improve simulation accuracy.

[0059] For example, electron density analysis is incorporated into parameter extraction, using density functional theory methods. The input is the atomic coordinates of the crystal, and the output is an electron density distribution map, used to determine bond strength and integrate it into parameter extraction. Once the strength falls below a threshold of 0.5 atomic units, this threshold is set based on the material's critical electron transfer calculation, and the simulation of the heating process considers electron transfer effects. Specific steps include initializing the crystal structure, applying a force field such as the AMBER force field, with parameters including van der Waals forces and electrostatic forces, and calculating the displacement vector d equal to rfinal minus rinitial, where r is the atomic position vector. In this way, dynamic results show how protective structures such as oxide layers form at high temperatures, and how the thickness increases over time, providing a quantitative indicator of material durability. This method enhances the flexibility of the scheme and is applicable to material particles of different sizes. Furthermore, the detailed process of the kinetic behavior simulation calculation method needs to be clearly explained. Essentially, it is a finite difference method for numerically solving partial differential equations. The input is the initial temperature distribution and material properties, and the output is a time-evolutionary heat map. The algorithm process includes mesh discretization, iterative solution of the heat conduction equation such as ∂T / ∂t=α∇²T, where T is the temperature and α is the diffusion coefficient. The post-processing calculates the structure factor S as a crystal diffraction intensity index, with the formula S=Σ f exp(ik·r), where f is the atomic scattering factor, k is the wave vector, and r is the position, which is used to evaluate molecular response and heat propagation.

[0060] For example, during heating, heat diffuses from the surface to the interior, causing increased molecular vibration. If the interaction strength is low, i.e., the intermolecular force is less than 0.5 eV, the Lennard-Jones potential E=4ε[(σ / r)^12-(σ / r)^6] is used for calculation, where ε is the energy depth and σ is the zero potential distance. Molecules may rearrange to form cavities or dense regions. The process is divided into preprocessing, simulation loops, and post-processing: preprocessing sets boundary conditions such as isothermal walls; simulation loops update the velocity and position of each molecule; post-processing extracts dynamic results such as the structure factor, which is the Fourier transform describing atomic arrangement, calculated as S(k)=1 / N ∑_{j=1}^N exp(ik · r_j), where k is the wave vector and r_j is the molecular position. The input is the simulation coordinates, and the output is the peak value of the wave vector at k, representing the material's order. This explanation helps to understand why simulation is needed below a threshold to predict potential failures.

[0061] In one possible implementation, for high-temperature protection applications of composite materials, the tensile strength is first determined by finite element analysis. If the strength is less than 100 MPa, it is considered insufficient. At this time, finite element thermo-mechanical coupling simulation is used to generate multiple sets of dynamic results, including input heating rates from 1°C / s to 10°C / s, and output corresponding stress distribution and temperature field change curves, covering different heating rates.

[0062] For example, rapid heating results in faster but potentially defective protective structures, while slow heating produces more uniform structures. These results, through comparative analysis, demonstrate the versatility of the technical solutions within the same field, such as boiler linings or aerospace thermal shields, ensuring implementation diversity.

[0063] For example, at the end of the embodiment, the resulting dynamic formation results enable the prediction of material properties.

[0064] For example, quantifying the thermal resistance of the protective structure improves design efficiency. This objective effect stems from the accuracy of the simulation, avoiding trial and error in experiments.

[0065] Step S104: Obtain the interfacial bonding characteristic data between the flame retardant additive and the matrix material based on the dynamic results formed. Use the distribution simulation calculation method to iteratively calculate the uniform distribution state. If the degree of distribution deviation is greater than the preset uniform threshold range, adjust the particle size configuration and dispersion speed parameters to determine the final cooperative protective layer structure scheme.

[0066] The interfacial bonding characteristics of the flame retardant additive and the matrix material are obtained from the dynamic results. A distribution simulation calculation method is used, which is based on molecular dynamics principles and calculates the uniform dispersion state by iteratively solving the particle position equation to obtain the degree of dispersion deviation. If the dispersion deviation is greater than 0.5, the additive surface modification data is processed. This data comes from experimental surface tension measurements. This processing adjusts the particle size configuration to 10-50 nm and the dispersion velocity parameter to 1-5 m / s based on the surface energy parameter to determine the preliminary cooperative protective layer structure. For the preliminary cooperative protective layer structure, the thermal conductivity distribution characteristics corresponding to the matrix material data are obtained. A thermal conductivity simulation calculation method is used to simulate the interfacial bonding changes under high temperature conditions. This simulation calculates the temperature gradient iteratively through the heat flow equation to obtain the optimized uniform dispersion state. Based on the optimized uniform dispersion state, if the optimized interfacial bonding characteristics are lower than the preset cooperation threshold of 2.0, the distribution density parameter of the flame retardant additive is adjusted to 100-500 particles per cubic centimeter to determine the enhanced protective layer structure. By using the enhanced protective layer structure, stability verification data corresponding to the final collaborative protective layer structure scheme is obtained, thus obtaining the final collaborative protective layer structure scheme.

[0067] In one embodiment, to obtain interfacial bonding characteristic data between the flame retardant additive and the matrix material from the formation dynamics, it is first necessary to understand the concept of interfacial bonding characteristic data. This data reflects the adhesion and compatibility between the additive particles and the matrix molecules. In the field of high-temperature protective materials, this characteristic determines the flame retardant efficiency and structural stability of the material.

[0068] Specifically, the acquisition process can be achieved by analyzing the energy distribution and contact area in the simulation output.

[0069] For example, intermolecular distance data and binding energy values ​​are extracted from the dynamic results to quantify interfacial strength parameters. These parameters, including average binding energy and interfacial tension coefficient, ensure the accurate basis for subsequent calculations. In this way, data acquisition supports the performance evaluation of materials under high-temperature environments. Furthermore, iterative calculation of the uniform dispersion state using distribution simulation is a crucial step. Distribution simulation is a numerical technique based on statistical mechanics that predicts the dispersion pattern by simulating the random motion of particles in a matrix. The specific process includes initializing particle positions, setting boundary conditions such as matrix volume, and then performing iterative loops, updating particle coordinates and calculating local density at each step. This method is used in materials science to optimize additive distribution and avoid flame-retardant failure caused by local aggregation.

[0070] In one possible implementation, for ceramic-based high-temperature protective materials, the simulation considers the van der Waals forces and electrostatic forces between particles, with the number of iterations set to several hundred to reach a convergence state. Through multiple iterations, a density mapping of the dispersion state is obtained, quantifying the uniformity.

[0071] For example, determining whether the degree of dispersion deviation is greater than a preset uniformity threshold requires defining the degree of deviation. The degree of deviation is typically measured by calculating the standard deviation of the density distribution.

[0072] For example, using finite element analysis software such as ANSYS to simulate the coating density distribution, the density value ρ_sim is obtained. ρ_sim is compared with the ideal uniformity value ρ_ideal (the material's average density), and the deviation index D is calculated as D = |ρ_sim - ρ_ideal| / ρ_ideal. A preset uniformity threshold range is set based on the material type; for example, for high-temperature alloys, the upper limit of the threshold is 0.05 of the density standard deviation. The material type and simulation data are input, and a statistical algorithm is used to calculate the standard deviation σ, outputting the threshold = 0.05 × σ. If D is greater than the threshold, it indicates uneven distribution, which may lead to weak areas in the protective layer.

[0073] In one embodiment, this judgment is implemented using the Kolmogorov-Smirnov test algorithm. First, simulated data and reference data are input. Then, the maximum deviation value D is calculated, i.e., D = sup|F_s(x) - F_r(x)|, where F_s is the cumulative distribution function of the simulated data and F_r is the cumulative distribution function of the reference data. D is then compared with a threshold of 0.05. If D is greater than 0.05, it is judged as a distribution anomaly, ensuring the objectivity and reliability of the judgment. This logic helps identify distribution problems and supports subsequent adjustments.

[0074] It should be noted that if the dispersion deviation is greater than the preset uniformity threshold range, the particle size configuration and dispersion speed parameters should be adjusted.

[0075] Specifically, the adjustment process involves modifying the average diameter and velocity of the particles.

[0076] For example, reducing the size from the micrometer to the nanometer scale improves diffusion capabilities; dispersion rate parameters are optimized by changing the stirring rate or ultrasonic intensity. In the optimization of high-temperature protective structures, this adjustment is achieved through a feedback loop. The input is the current deviation D (in percentage), and the initial parameters include a stirring rate S0 = 500 rpm and an ultrasonic intensity U0 = 100 W. The algorithm uses gradient descent, first evaluating D, then iteratively modifying the parameters P = S0 - 0.1 * D or U0 + 0.05 * D until convergence occurs when D is less than 1%. This method is suitable for polymer-based materials, ensuring that additives are uniformly integrated into the matrix to form a stable flame-retardant layer. By adjusting the parameters, the risk of thermal decomposition of the material during heating is reduced, providing more reliable protection.

[0077] Preferably, the final collaborative protective layer structure scheme is determined by integrating adjusted parameters and simulation results. The collaborative protective layer structure scheme refers to the composite network formed by the additive and the matrix. Scheme determination includes output layer thickness, particle density, and interfacial strength, among other indicators. In one embodiment, for high-temperature metal oxide materials, the scheme generates multiple configuration options, such as layer structure models with different particle sizes, and then selects the set with the smallest deviation. This determination process enhances the applicability of the scheme and supports the application of the material in aerospace components.

[0078] Understandably, in another implementation, for composite high-temperature protective materials, the process involves verifying simulation results using experimental data.

[0079] For example, scanning electron microscopy observation is incorporated when acquiring interface data, and parameter adjustments after assessing deviations take into account actual dispersion processes, such as the sol-gel method. This approach demonstrates the flexibility of the technical solution within the same field, ensuring the synergistic effect of the protective layer at high temperatures. Furthermore, the detailed process of the distribution simulation calculation method needs to be clearly explained. It simulates particle paths using the Monte Carlo principle, and the process is divided into an initialization phase, an iterative update phase, and a convergence check phase: initialization sets random positions, iterative updates calculate collisions and diffusion, and finally, the stability of the deviation is checked. This explanation helps to understand why adjustments are needed when deviations are large in order to predict material homogeneity.

[0080] For example, in this embodiment, the adjusted scheme for boiler lining materials shows uniform protective layer thickness, and the flame-retardant performance is quantified through simulated heat exposure testing. This objective result stems from the precision of parameter optimization, supporting material design decisions.

[0081] Step S105: Extract potential risk indicators related to strength failure risk and appearance defects from the final collaborative protective layer structure scheme. If the potential risk indicators exceed the preset safety threshold range, optimize the additive concentration distribution scheme through structural analysis calculation method to obtain the material performance prediction results under equilibrium conditions.

[0082] Potential risk indicators related to strength failure risk and appearance defects are extracted from the final collaborative protective layer structure scheme. A structural analysis calculation method based on the finite element analysis principle is employed. Using ANSYS software, material properties such as elastic modulus E=200GPa and Poisson's ratio ν=0.3 are input to mesh the structure, calculate stress distribution σ and deformation value δ, and obtain quantitative values ​​of potential risk indicators, such as maximum stress exceeding 500MPa. If the potential risk indicators exceed a preset safety threshold range, such as a stress threshold of 450MPa to 550MPa, the flame retardant additive configuration and matrix material compatibility parameters are adjusted through additive concentration distribution schemes. Thermal stability verification data and interface optimization parameters are obtained. The thermal stability verification data is obtained through differential scanning calorimetry (DSC) experiments to obtain the glass transition temperature Tg value, such as 150°C. The interface optimization parameters are obtained through molecular dynamics simulations to obtain the interfacial binding energy, such as 5eV / nm². Based on the thermal stability verification data, durability assessment data is generated by combining the interface optimization parameters. Specifically, the Tg value and interface binding energy are input into the formula D = (Tg / 100) * (binding energy / 5), where D is the durability index. An example output of D = 6.0 indicates high durability. A structural analysis calculation method is used to simulate environmental adaptation. This method inputs the durability data D and environmental boundary conditions such as a temperature range of -20°C to 80°C and a humidity of 50%RH. The output equilibrium condition result is a stable deformation of less than 0.1 mm. Based on the equilibrium condition result, material performance prediction indicators obtained from the durability assessment data, such as a fatigue life prediction value of 10^6 cycles, are integrated to determine the final material performance prediction result, such as a comprehensive performance score of 85.

[0083] Extracting potential risk indicators related to strength failure risk and appearance defects from the final collaborative protective layer structure is a fundamental step in assessing material reliability. These potential risk indicators reflect the protective layer's vulnerability under high-temperature environments; for example, strength failure risk is quantified by calculating the stress distribution within the layer, while appearance defects involve the detection of surface cracks or uneven areas. In the field of high-temperature protective materials, this extraction process ensures the comprehensiveness of the design.

[0084] Specifically, the extraction method includes analyzing structural model data in the analysis scheme, such as stress peaks and defect density values ​​from finite element simulation output, to generate a risk index list. These indicators support subsequent judgment and prevent material failure in practical applications. In one embodiment, determining whether the potential risk index exceeds a preset safety threshold range is achieved through simple threshold comparison. The specific process is as follows: input the risk index list, for example, a strength failure index value of 150 MPa; the preset safety threshold range is set based on the material type, for example, for ceramic-based high-temperature protective materials, the upper limit of the strength failure risk threshold is 80% of the stress value, i.e., the upper limit of the threshold is 120 MPa; compare the input value with the threshold, if 150 MPa is greater than 120 MPa, then the judgment result is output, indicating a potential failure hazard. This judgment process objectively identifies problems and supports optimization decisions. Furthermore, if the potential risk index exceeds the preset safety threshold range, the additive concentration distribution scheme is optimized using the finite element analysis method. The finite element analysis method is a numerical technique based on mechanical and material properties that predicts the distribution effect by simulating the concentration gradient of the additive in the matrix. The specific process includes: inputting initial concentration model data, such as an initial additive concentration of 5%; setting boundary conditions, such as a matrix boundary concentration of 0%; and then performing iterative calculations, updating the concentration values ​​using a finite element mesh at each step, for example, using the finite difference method to calculate the gradient, iterating 20 times to evaluate the distribution uniformity, and outputting a concentration gradient distribution map. This method is used to reduce risks in the optimization of high-temperature protective materials. For example, in metal-based materials, considering the interaction between a thermal diffusivity of 0.01 m² / s and the concentration gradient, the number of iterations can reach dozens to reach a stable state. Through this calculation, the scheme is adjusted to form a more uniform concentration distribution.

[0085] In one possible implementation, the detailed process of the structural analysis calculation method needs to be clearly explained. It uses the finite difference principle to simulate concentration changes, and the process is divided into a model building phase, an iterative solution phase, and a verification phase: the model building phase defines the concentration equation, iteratively solves for gradient changes, and the verification phase checks convergence. This explanation helps in understanding the optimization mechanism; for example, in polymer-based high-temperature materials, adjusting the concentration reduces risk indicators and provides stable protection.

[0086] It should be noted that the predicted material properties under equilibrium conditions were obtained through an integrated and optimized scheme. Equilibrium conditions refer to the material properties under conditions where the concentration distribution is uniform, and the predicted results include quantitative values ​​for heat resistance and flame retardant efficiency.

[0087] In one embodiment, for oxide-based high-temperature protective materials, the performance curves are predicted by analyzing adjusted data using a statistical model. These results support the evaluation of the material's application in high-temperature environments.

[0088] Preferably, in another embodiment, for composite high-temperature protective materials, the process includes verifying the accuracy of predictions using simulation data.

[0089] For example, thermal stress analysis is incorporated when extracting risk indicators, and the predicted results after concentration optimization take into account actual thermal exposure conditions, such as the furnace environment. This approach demonstrates the flexibility of the technical solution within the same field, ensuring the reliability of performance predictions.

[0090] Understandably, the extraction step obtains material indicators from the solution. For example, initial solution data is input, and the K-means algorithm is used to cluster and extract key indicator values ​​such as heat resistance and corrosion resistance. Then, a judgment step is performed. If the indicator value exceeds a preset threshold of 0.8, the optimization process is activated, and the final prediction is output. This seamless process ensures a comprehensive improvement in material performance. For example, in boiler protective layer applications, the optimized material exhibits a technical effect of reducing the risk of damage.

[0091] In one embodiment, for high-temperature alloy materials, the strength failure risk index is extracted by scanning stress nodes in the finite element analysis model. These nodes originate from the meshed structure simulation. Once the stress exceeds a threshold of 0.5 GPa, a finite element optimization algorithm is used to adjust the concentration shift from high-density to low-density regions. The input is the initial stress distribution, and a genetic algorithm is used for iterative optimization. The output is the adjusted concentration map, and the predicted result shows a 20% increase in material lifespan. This result is verified through fatigue testing, confirming the improved lifespan compared to unoptimized materials. This embodiment highlights the practicality of the solution.

[0092] Step S106: Based on the predicted material performance, if the insufficient fire resistance is improved, the uniform distribution data and protective structure information are integrated, and the overall flame retardant stability is verified by using a dynamic behavior simulation calculation method to obtain a configuration scheme for high-performance plastic materials in high-requirement application scenarios.

[0093] Based on the predicted material performance, the degree of improvement in fire resistance characteristics is determined, and improvement confirmation data is obtained. The effectiveness of the uniformly distributed data and protective structure information is verified using this improvement confirmation data, and an integrated dataset is obtained. For this integrated dataset, a finite element analysis method is used. This method, based on the principles of material dynamics, verifies the overall flame-retardant stability by simulating thermal flow and stress changes. Inputs include the temperature field distribution T (in K) and the initial stress σ0 (in MPa). The output stability verification index S is calculated using the formula S = 1 - (σ_max / σ_yield), where σ_max is the maximum simulated stress, σ_yield is the yield strength, and S greater than 0.8 indicates stability. Interface compatibility parameters, i.e., the average interface adhesion strength (in MPa), are extracted from the stability verification index and integrated with environmental adaptability parameters obtained from the predicted material performance, i.e., the temperature resistance range (e.g., -40 to 150°C), to determine the configuration optimization basis. Based on this configuration optimization basis, configuration schemes for high-performance plastic materials in demanding application scenarios are generated.

[0094] In one implementation, determining that the fire resistance deficiency is improved based on the predicted material properties is the initial step of the entire process. This determination is based on quantitative indicators in the prediction results, such as heat resistance temperature thresholds and combustion rate data, and the degree of improvement is assessed by comparing the values ​​before and after optimization.

[0095] Specifically, if the prediction results show a decrease in the combustion rate or an increase in the flammability point exceeding a preset percentage of 20%, it is considered an improvement. In the field of high-temperature protective materials, this method ensures the activation of subsequent steps. For example, in the design of ceramic-based protective layers, the judgment process involves reading the fire protection index list output by the prediction model, including the values ​​of the combustion rate R and the flammability point F. Then, a threshold comparison logic is applied: the magnitude change ΔR = (R_initial - R_predicted) / R_initial * 100% and ΔF = (F_predicted - F_initial) / F_initial * 100%. If ΔR > 20% or ΔF > 20%, an improvement decision is output, thus forming an objective decision-making basis.

[0096] It should be noted that this assessment helps identify potential areas for improvement in materials under high-temperature conditions, avoiding the blind integration of data. Furthermore, if the fire-retardant properties are improved, the data on uniform distribution and the information on the protective structure are integrated. This integration process is achieved through data fusion technology, combining the uniformity data of additive concentration distribution with the structural parameters of the protective layer to form a comprehensive dataset.

[0097] For example, in the application of metal-based high-temperature protective materials, uniformly distributed data includes concentration gradient values, while protective structure information covers layer thickness and interface properties. Integrating these data generates a unified input matrix. This process ensures information consistency and supports subsequent verification.

[0098] Preferably, verifying the overall flame-retardant stability using dynamic behavior simulation is the core component of the technical solution. This method is based on modeling the material's behavior under dynamic conditions, simulating heat transfer and chemical reaction processes in a high-temperature environment. The specific process includes initializing model parameters, such as setting the initial temperature field and flame retardant distribution, followed by time-step iterative calculations, updating the material state variables at each step to assess stability.

[0099] For example, in polymer-based high-temperature protective materials, simulations consider the thermal diffusion equation, tracking the migration and reaction rates of flame retardants during iterations, and checking whether a stable state has been reached through multiple cycles. This verification method is used in the field of high-temperature protection to quantify the overall flame-retardant effect, such as simulating the material behavior exposed to 800 degrees Celsius, and outputting stability indicators such as the decay curve of the combustion propagation rate.

[0100] In one possible implementation, the detailed process of the kinetic behavior simulation calculation method needs to be clearly explained. It first constructs a kinetic model, defining state variables such as temperature and concentration, then sets boundary conditions such as heat source input, followed by the solution phase. Numerical methods are used to progressively calculate variable changes, such as calculating heat flow and reaction kinetics at each time step, ensuring model convergence. This explanation helps in understanding the verification mechanism; in oxide-based high-temperature protective materials, post-simulation stability is confirmed, providing a reliable flame retardant assessment.

[0101] Understandably, the integration step refers to merging multi-source data, while the simulation step refers to finite element analysis verification. These form a logical chain: first, data fusion is completed, using a weighted average algorithm (weight w = 0.6 experimental data + 0.4 historical data) to fuse experimental measurements and historical database data; then, simulation verification is activated. This connection ensures continuity from judgment to verification. For example, in the design of oxide-based high-temperature protective materials, the fused data, such as a temperature threshold of 1000°C, can be directly input into the finite element simulation model, outputting a thermal stress distribution diagram and avoiding information gaps.

[0102] In one embodiment, the configuration scheme of the oxide-based composite high-temperature protective material for demanding applications is obtained by integrating verification results. This scheme includes recommended values ​​for material composition ratios and structural layouts, generated based on stability data from simulation output. Specifically, the process involves inputting simulated stability data S (ranging from 0 to 1, where S greater than 0.8 is considered stable), calculating recommended values ​​using a weighted average algorithm, where the weight of the composition ratio W1 is 0.6 and the weight of the structural layout W2 is 0.4, with the formula R = W1 * P + W2 * L (where P is the ratio value and L is the layout value). An example output is an oxide ratio of 70% and a composite layer thickness of 2.5 mm.

[0103] For example, for the application of high-performance plastics in boiler linings, the configuration specifies an additive concentration of 15% with uniform distribution and defines a protective layer thickness of 2 mm to meet high-temperature resistance requirements. This output supports practical deployment.

[0104] Specifically, in another implementation, for high-temperature alloy-based plastic materials, the process involves generating a configuration based on simulation data. This simulation data is generated using finite element analysis software such as ANSYS, with inputs including material properties and temperature gradients, and the output being an optimized configuration scheme. After assessing material improvement, thermal stress information, derived from stress distribution data calculated by finite element simulation, is incorporated into the integrated data. Simulation verification is then extended to multi-scenario iterations, such as stability checks under different temperature gradients. The final configuration scheme demonstrates improved flame-retardant efficiency of the material at 300 degrees Celsius. This approach showcases the flexibility of the technical solution within the same field, ensuring the reliability of the configuration. Furthermore, when verifying flame-retardant stability, molecular dynamics simulation calculations can introduce a parameter adjustment mechanism. This method uses LAMMPS software to construct an atomic model, with initial atomic positions and force field parameters as inputs, and a dynamic stability index as the output. The parameter adjustment mechanism uses iterative optimization, such as adjusting the temperature parameter to a threshold of 0.1 eV, to ensure simulation convergence.

[0105] For example, in ceramic-based materials, the simulation process allows modification of the flame retardant diffusion coefficient, enabling optimization of the final scheme by comparing stability outputs under different configurations through multiple runs. This adjustment enhances the adaptability of the method.

[0106] It should be noted that after obtaining the configuration scheme, its applicability in demanding application scenarios can be further evaluated. For example, in the scenario of furnace protective layer, the scheme includes a multi-layer structure design, and long-term stability is predicted based on simulation results. This evaluation forms a closed loop, supporting material iteration.

[0107] In one possible implementation, for example, for polymer-based high-temperature plastic materials, the standard for judging fire resistance improvement is based on the percentage reduction in combustion time. The detailed simulation calculation process, after integrating experimental data, involves constructing a reaction kinetic equation: first, input data such as initial temperature T0 = 300 K and concentration C0 = 1 mol / L are obtained from pyrolysis experiments; then, the equation dC / dt = -k*exp(-E / RT)*C is constructed, where k is the pre-exponential factor, E is the activation energy, and R is the gas constant. The spatiotemporal distribution of temperature T and concentration C is iteratively solved using the finite difference method, with parameters including a time step Δt = 0.1 s and a spatial step Δx = 1 mm. Stability is verified by checking if the combustion spread distance is less than 5 cm; if it is, it is considered stable. The final configuration recommends a specific additive ratio, such as 10% silicon-based flame retardant by weight, for application in high-temperature pipeline protection. This implementation highlights the practicality of the solution.

[0108] Understandably, the combination of the above steps and the verification process ensures the comprehensive application of high-performance plastic materials in the field of high-temperature protection. For example, the optimized configuration reduces the risk of insufficient fire protection and provides stable protection.

[0109] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing the flame retardant properties of high-performance plastic materials, characterized in that, include: Data on the matrix material and performance parameters of the flame-retardant components are obtained from a pre-defined material property database to determine an initial mixing ratio scheme. The distribution uniformity in the particle state is evaluated using a distribution simulation calculation method to obtain preliminary distribution simulation results. The component ratio and processing parameters are adjusted based on the preliminary distribution simulation results to obtain an optimized mixing treatment scheme. Molecular interaction characteristic parameters are extracted from the optimized mixing treatment scheme, and the material heating process is simulated using a dynamic behavior simulation calculation method to determine the dynamic results of the protective structure formation. Based on the dynamic results of the protective structure formation, the uniform distribution state is iteratively calculated, the particle configuration parameters are adjusted, and the cooperative protective layer structure scheme is determined. Potential risk indicators are extracted from the cooperative protective layer structure scheme, the additive distribution scheme is optimized, and the material performance prediction results are obtained. Based on the material performance prediction results, the uniform distribution data and protective structure information are integrated to verify the overall flame retardant stability and generate a high-performance plastic material configuration scheme.

2. The method for optimizing the flame retardant properties of high-performance plastic materials as described in claim 1, characterized in that, The step of obtaining matrix material data and flame-retardant component performance parameters from a preset material property database to determine an initial mixing ratio scheme includes: extracting molecular structure information and thermal stability evaluation indicators of the polycarbonate matrix material from the preset material property database, and obtaining performance parameters of various flame-retardant components; analyzing the uneven distribution characteristics of material fusion based on the molecular structure information and thermal stability evaluation indicators, and determining an initial mixing ratio scheme; evaluating the distribution uniformity of the initial mixing ratio scheme in the particle state using a distribution simulation calculation method to obtain preliminary distribution simulation results; obtaining particle distribution adjustment parameters under flame-retardant enhancement simulation based on the preliminary distribution simulation results; if the adjustment parameters exceed a preset threshold range, determining an enhanced mixing ratio scheme; and extracting additional thermal stability indicators from the preset material property database using the enhanced mixing ratio scheme to obtain simulation results of the uniform distribution of flame-retardant components in the matrix material.

3. The method for optimizing the flame retardant properties of high-performance plastic materials as described in claim 1, characterized in that, The step of adjusting the component ratios and processing parameters based on the preliminary distribution simulation results to obtain an optimized mixing treatment scheme includes: determining whether the particle spacing distribution exceeds a preset uniformity threshold range based on the preliminary distribution simulation results; if it exceeds the preset uniformity threshold range, extracting component molecular structure data and thermal stability indices from a preset material property database using structural analysis calculation methods; obtaining component ratio configuration data by calculating the affinity coefficients between components to obtain adjusted ratio parameters; configuring processing temperature parameters based on the adjusted ratio parameters to determine the optimized mixing treatment scheme; obtaining interface compatibility evaluation indices under the optimized mixing treatment scheme; if the evaluation indices exceed a preset compatibility threshold, adjusting the order of flame-retardant component addition to obtain enhanced compatibility distribution results; evaluating distribution uniformity characteristics through the enhanced compatibility distribution results to determine the thermal response change trend.

4. The method for optimizing the flame retardant properties of high-performance plastic materials as described in claim 1, characterized in that, The process of extracting molecular interaction characteristic parameters from the optimized hybrid treatment scheme and simulating the material heating process using a dynamic behavior simulation method to determine the dynamic result of the protective structure formation includes: extracting molecular interaction characteristic parameters related to the cooperative system from the optimized hybrid treatment scheme and obtaining the corresponding interaction strength values; determining whether the interaction strength values ​​are lower than a preset cooperation threshold range; if they are lower than the preset cooperation threshold range, simulating the material heating process using a dynamic behavior simulation method to obtain molecular motion trajectories; determining the preliminary dynamic result of the protective structure formation under high temperature based on the molecular motion trajectories; optimizing and adjusting the interaction parameters for the preliminary dynamic result, verifying stability, and obtaining the adjusted structural parameters; and obtaining the final dynamic result of the protective structure formation under high temperature based on the adjusted structural parameters.

5. The method for optimizing the flame retardant properties of high-performance plastic materials as described in claim 1, characterized in that, The process of iteratively calculating the uniform distribution state based on the dynamic results of the protective structure formation, adjusting particle configuration parameters, and determining the cooperative protective layer structure scheme includes: obtaining interfacial bonding characteristic data between the flame retardant additive and the matrix material from the dynamic results of the protective structure formation; using a distribution simulation calculation method, iteratively solving the particle position equation to calculate the uniform distribution state and obtain the degree of distribution deviation; if the degree of distribution deviation is greater than a preset uniform threshold range, then adjusting the particle size configuration and dispersion velocity parameters through additive surface modification data processing to determine the preliminary cooperative protective layer structure; for the preliminary cooperative protective layer structure, obtaining the thermal conductivity distribution characteristics corresponding to the matrix material data; iteratively calculating the temperature gradient through the heat flow equation to simulate the interfacial bonding changes under high temperature conditions and obtain the optimized uniform distribution state; if the interfacial bonding characteristics are lower than the preset cooperative threshold, then adjusting the distribution density parameters of the flame retardant additive to determine the enhanced protective layer structure.

6. The method for optimizing the flame retardant properties of high-performance plastic materials as described in claim 1, characterized in that, The process of extracting potential risk indicators from the collaborative protective layer structure scheme, optimizing the additive distribution scheme, and obtaining material performance prediction results includes: extracting potential risk indicators related to strength failure risk and appearance defects from the collaborative protective layer structure scheme; using structural analysis calculation methods, calculating stress distribution and deformation values ​​by meshing the material structure to obtain quantitative values ​​of potential risk indicators; if the potential risk indicators exceed a preset safety threshold range, adjusting the flame retardant additive configuration and matrix material compatibility parameters through the additive concentration distribution scheme to obtain thermal stability verification data and interface optimization parameters; generating durability assessment data based on the thermal stability verification data and the interface optimization parameters; simulating environmental adaptation adjustments using structural analysis calculation methods to obtain equilibrium condition results; and determining the final material performance prediction results by integrating the material performance prediction indicators from the durability assessment data using the equilibrium condition results.

7. The method for optimizing the flame retardant properties of high-performance plastic materials as described in claim 1, characterized in that, The process of integrating uniformly distributed data and protective structure information based on the material performance prediction results to verify overall flame retardant stability and generate a high-performance plastic material configuration scheme includes: determining the degree of improvement in insufficient fire resistance characteristics based on the material performance prediction results and obtaining improvement confirmation data; integrating uniformly distributed data and protective structure information using the improvement confirmation data to obtain an integrated dataset; using a dynamic behavior simulation calculation method on the integrated dataset to verify overall flame retardant stability by simulating thermal flow and stress changes, and obtaining stability verification indicators; extracting interface compatibility parameters from the stability verification indicators and fusing them with environmental adaptation parameters from the material performance prediction results to determine the configuration optimization basis; and generating a configuration scheme for high-performance plastic materials in high-requirement application scenarios based on the configuration optimization basis.

8. The method for optimizing the flame retardant properties of high-performance plastic materials as described in claim 1, characterized in that, The step of evaluating the distribution uniformity in the particle state using a distribution simulation calculation method to obtain preliminary distribution simulation results includes: evaluating the distribution uniformity in the particle state using a distribution simulation calculation method for the initial mixing ratio scheme, and obtaining particle spacing distribution data; determining whether the distribution uniformity meets a preset uniformity threshold range based on the particle spacing distribution data; if it does not meet the preset uniformity threshold range, obtaining particle distribution adjustment parameters under flame retardant enhancement simulation; adjusting the proportion of flame retardant components in the initial mixing ratio scheme using the particle distribution adjustment parameters to generate an enhanced mixing ratio scheme; extracting additional thermal stability indicators from a preset material property database based on the enhanced mixing ratio scheme to determine the preliminary distribution simulation results of the flame retardant components in the matrix material; and evaluating the trend of distribution uniformity changes in the particle state using the preliminary distribution simulation results.

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