A method and system for predicting the properties of inorganic composite materials

Abstract

This invention relates to the field of computational materials science and technology, and discloses a method and system for predicting the performance of inorganic composite materials. The method includes: setting the spatial distribution of the composition of a target material and the preparation boundary conditions, and combining computer simulation of the virtual microstructure field of the target material; simulating phase evolution behavior in the virtual microstructure field based on the thermal diffusion constraints of the target material to obtain the phase evolution trajectory of the target material; analyzing the cooperative interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory, and locating the performance-sensitive distribution of the target material based on the cooperative interference; and establishing the symplectic geometric correlation between the corresponding local sensitive extreme points and the microstructure topological invariants of the target material based on the performance-sensitive distribution. This invention can solve the problem of insufficient accuracy in performance prediction calculations in the prior art, which cannot provide reliable data support for performance analysis.

Description

Technical Field

[0001] This invention relates to the field of computational materials science and technology, and in particular to a method and system for predicting the properties of inorganic composite materials. Background Technology

[0002] In the field of computational materials science, performance prediction of inorganic composite materials is a core aspect of materials research and development. Traditional prediction methods often treat parameters such as material composition and preparation conditions in isolation, without systematically integrating the spatial distribution of composition with preparation boundary conditions such as thermal cycling critical extremes and effective pressure boundaries. At the same time, existing phase evolution simulations do not fully couple thermal diffusion constraints and stress field effects, lack consideration of key factors such as topological thermal hysteresis memory and stress-phase transformation decoupling, and have insufficient accuracy in calculating phase evolution trajectories, thus failing to provide reliable data support for performance analysis.

[0003] Secondly, traditional methods often analyze grain boundary energy or stress field distribution separately, neglecting the synergistic interference effect between the two. This leads to fuzzy positioning of performance-sensitive distributions and an inability to accurately segment strain mismatch connected domains. Furthermore, existing technologies fail to establish a geometric relationship between local sensitive extrema and microstructural topological invariants, making it difficult to identify virtual defect initiation domains through topological features such as the Betti number. Moreover, they do not consider the influence of defect path connectivity and penetration weight, hindering the scientific construction of material failure probability distributions. This results in significant deviations between predicted results and actual service performance, failing to meet the accuracy and reliability requirements of engineering applications. Summary of the Invention

[0004] This invention provides a method and system for predicting the properties of inorganic composite materials, the main purpose of which is to address the problems raised in the background section above.

[0005] To achieve the above objectives, the present invention provides a method for predicting the properties of inorganic composite materials, comprising:

[0006] S1. Set the spatial distribution of the target material's composition and the preparation boundary conditions, and combine computer simulation with the virtual microstructure field of the target material;

[0007] S2. Based on the thermal diffusion constraint of the target material, the phase evolution behavior is simulated in the virtual microstructure field to obtain the phase evolution trajectory of the target material;

[0008] S3. Analyze the synergistic interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory, and locate the performance-sensitive distribution of the target material based on the synergistic interference.

[0009] S4. Based on the performance sensitivity distribution, establish the symplectic geometric correlation between the local sensitive extreme points and the microstructure topological invariants of the target material;

[0010] S5. Identify the virtual defect initiation domain of the target material by combining the symplectic geometric correlation;

[0011] S6. Based on the path connectivity of the virtual defect initiation domain, construct the material failure probability distribution of the target material.

[0012] Preferably, the setting of the spatial distribution of the target material's components and the preparation boundary conditions include:

[0013] Plan the elemental concentration range of the target material;

[0014] Determine the geometric configuration of the target material, and define the element concentration range and the spatial distribution of the composition of the geometric configuration;

[0015] Set the critical extreme value of thermal cycling for the preparation process corresponding to the target material;

[0016] By applying dynamic constraints to the spatial distribution of the components and the critical extreme value of the thermal cycle, a set of constraint conditions for the target material is obtained.

[0017] Verify the pressure boundary of the equipment operating parameters corresponding to the target material to obtain the effective pressure boundary range of the target material;

[0018] The effective pressure boundary range and the constraint conditions are integrated into the preparation boundary conditions of the target material.

[0019] Preferably, the step of combining computer simulation with the virtual microstructure field of the target material includes:

[0020] The mesoscopic topological configuration of the target material is constructed using the spatial distribution of the components and the preparation boundary conditions.

[0021] The mesoscopic topological configuration is subjected to thermal hysteresis phase transition rules to obtain the steady-state phase domain evolution rules of the mesoscopic topological configuration;

[0022] A virtual microstructure field of the target material is constructed based on the mesoscopic topological configuration and the steady-state phase domain evolution rules.

[0023] Preferably, the formula for calculating the phase evolution trajectory is:

[0024]

[0025] in: For the phase evolution trajectory, For the initial phase field distribution, For topological thermal hysteresis memory operators, For local curvature, The time interval is [0, The historical temperature gradient field within [the area] Stress field divergence potential gradient The stress-phase transition decoupling coefficient is... This refers to the thermal hysteresis relaxation time. For time, [0, The time parameter on ] These are the spatial coordinates.

[0026] Preferably, the analysis of the synergistic interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory includes:

[0027] Extract the grain boundary energy distribution gradient of the phase evolution trajectory;

[0028] The coupling gradient between grain boundary energy and spatial coordinates in the target material is determined based on the grain boundary energy distribution gradient.

[0029] The stress field divergence value in the phase evolution trajectory is quantitatively analyzed, and the spatial distribution of the stress field divergence value in the target material is marked.

[0030] Evaluate the cooperative interference of the coupling gradient and the spatial distribution.

[0031] Preferably, the step of locating the performance-sensitive distribution of the target material based on the cooperative interference amount includes:

[0032] Determine the deterioration distribution region of high interference in the aforementioned cooperative interference quantities;

[0033] Verify the compatibility of lattice strain in the deterioration distribution region, and segment the strain mismatch connected domain of the deterioration distribution region based on the verification results;

[0034] Based on a preset benchmark threshold, the performance sensitivity distribution in the strain mismatch connected domain is statistically analyzed.

[0035] Preferably, establishing the symplectic geometric correlation between the local sensitive extrema points and the microstructure topological invariants of the target material based on the performance-sensitive distribution includes:

[0036] Locate the sensitive extreme points in the performance-sensitive distribution and extract the topologically continuous regions in the virtual microstructure field;

[0037] Calculate the rank of the high-dimensional homology group of each of the topologically continuous regions to obtain the Betti number distribution spectrum;

[0038] The sensitive extreme points are mapped onto the Betti number distribution spectrum to obtain the Betti number features at the corresponding positions of the sensitive extreme points;

[0039] Establish the geometric correlation between the sensitive extreme points and the Betti number feature to obtain the symplectic geometric correlation of the target material.

[0040] Preferably, the step of identifying the virtual defect initiation domain of the target material by combining the symplectic geometric correlation includes:

[0041] Analyze the curvature overlimit distribution of the symplectic geometric correlation;

[0042] Verify the connectivity of dislocation loops in the curvature excess distribution to obtain the topologically connected domain of the dislocation loops in the curvature excess distribution;

[0043] By removing segments from the topologically connected domain of the dislocation loop that are blocked by the high-energy phase interface, the virtual defect initiation domain of the target material is obtained.

[0044] Preferably, constructing the material failure probability distribution of the target material based on the path connectivity of the virtual defect initiation domain includes:

[0045] Extract the vein-like skeleton of the virtual defect initiation domain;

[0046] The spatial coupling strength between the main path length and the bifurcation angle of the secondary path in the vein-like skeleton is quantitatively analyzed, and a penetration weight value is assigned based on the spatial coupling strength.

[0047] Based on the penetration weight value, the material failure probability distribution of the target material is located.

[0048] An inorganic composite material performance prediction system, the system comprising:

[0049] Computer simulation module: sets the spatial distribution of the target material's composition and the preparation boundary conditions, and combines computer simulation with the virtual microstructure field of the target material;

[0050] Evolution simulation module: Based on the thermal diffusion constraints of the target material, it simulates the phase evolution behavior in the virtual microstructure field to obtain the phase evolution trajectory of the target material;

[0051] Sensitive distribution module: Analyzes the synergistic interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory, and locates the performance sensitive distribution of the target material based on the synergistic interference.

[0052] Symplectic geometric correlation module: Based on the performance sensitivity distribution, establish symplectic geometric correlations between the local sensitive extreme points and the microstructure topological invariants of the target material;

[0053] Virtual Defect Module: Identifies the virtual defect initiation domain of the target material by combining the symplectic geometric correlation;

[0054] Performance prediction module: Constructs the material failure probability distribution of the target material based on the path connectivity of the virtual defect initiation domain.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] This invention overcomes the limitations of traditional methods that isolate material parameters by integrating the spatial distribution of the target material's composition and the preparation boundary conditions. It can construct more realistic mesoscopic topological configurations based on complete constraints, and derive steady-state phase domain evolution rules by combining thermal hysteresis phase transition rules, ultimately generating a high-fidelity virtual microstructure field that realistically reproduces the structural characteristics of the material at the microscopic level. On the other hand, in the simulation of phase evolution behavior, key parameters such as topological thermal hysteresis memory operators, stress-phase transition decoupling coefficients, and historical temperature gradient fields are introduced. Combined with thermal diffusion constraints, the phase evolution trajectory is derived, which can more accurately capture the dynamic process of material phase evolution and provide high-precision data support for the location of performance-sensitive regions.

[0057] This invention analyzes the synergistic interference between grain boundary energy distribution gradient and stress field divergence to identify high-interference deterioration regions, verify lattice strain compatibility, and segment strain mismatch connected domains. Compared to traditional methods that analyze grain boundary energy or stress field separately, this invention can more accurately pinpoint performance-sensitive regions. In failure probability prediction, it combines the Betti number distribution spectrum to establish a correlation between sensitive extreme points and microstructure topological invariants to accurately identify virtual defect initiation domains. Then, based on the spatial coupling strength of the vein-like skeleton of the defect initiation domain, it assigns penetration weights and finally constructs a failure probability distribution. This achieves both the accuracy of defect identification and the scientific nature of failure probability prediction. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating a method for predicting the properties of inorganic composite materials according to an embodiment of the present invention.

[0059] Figure 2 This is a functional block diagram of an inorganic composite material performance prediction system provided in an embodiment of the present invention;

[0060] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0061] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0062] This application provides a method for predicting the performance of inorganic composite materials. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for predicting the performance of inorganic composite materials can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0063] Reference Figure 1 The diagram shown is a flowchart illustrating a method for predicting the performance of inorganic composite materials according to an embodiment of the present invention. In this embodiment, the method for predicting the performance of inorganic composite materials includes:

[0064] S1. Set the spatial distribution of the target material's composition and the preparation boundary conditions, and combine this with computer simulation of the virtual microstructure field of the target material.

[0065] In this embodiment, the setting of the spatial distribution of the target material's components and the preparation boundary conditions include:

[0066] Plan the elemental concentration range of the target material;

[0067] Determine the geometric configuration of the target material, and define the element concentration range and the spatial distribution of the composition of the geometric configuration;

[0068] Set the critical extreme value of thermal cycling for the preparation process corresponding to the target material;

[0069] By applying dynamic constraints to the spatial distribution of the components and the critical extreme value of the thermal cycle, a set of constraint conditions for the target material is obtained.

[0070] Verify the pressure boundary of the equipment operating parameters corresponding to the target material to obtain the effective pressure boundary range of the target material;

[0071] The effective pressure boundary range and the constraint conditions are integrated into the preparation boundary conditions of the target material.

[0072] Specifically, the target material is an inorganic composite material, which may include ceramic matrix composites, glass matrix composites, inorganic non-metallic particle reinforced composites, etc. It is the core research object for performance prediction, and its performance is directly related to its elemental composition.

[0073] The element concentration range refers to the content range of various chemical elements that constitute the target inorganic composite material, such as Si, Al, O, C, Zr, etc. The concentration can be expressed as mass fraction, atomic fraction or mole fraction. It is a basic parameter for defining the material composition and directly determines the phase composition, microstructure and final properties of the material.

[0074] In detail, first determine the core application scenarios of the target material, such as high-temperature structural components, electronic packaging materials, wear-resistant coatings, etc., and then lock in the key performance indicators, such as room temperature / high temperature mechanical strength, thermal conductivity, dielectric constant, thermal shock resistance, etc., and use the performance requirements to deduce the general direction of element selection and concentration range.

[0075] For example, if the goal is a high-hardness ceramic matrix composite material, it is necessary to give priority to introducing elements such as Al, O, and Si, and ensure that the concentration ratio of related elements in the hard phase is within a reasonable range.

[0076] By consulting inorganic material phase diagram databases, relevant literature, and previous experimental data, the chemical compatibility between candidate elements was analyzed to avoid the formation of harmful phases between elements due to the planned concentration range, thus ensuring that the material maintains structural stability in the service environment.

[0077] Considering the subsequent preparation processes to be used for the target material, such as sintering, hot pressing, sol-gel, melt spinning, etc., the process has clear limitations on the element concentration. When using the sintering process, it is necessary to ensure that the element concentration range can meet the densification requirements of sintering, and to avoid excessive liquid phase and abnormal grain growth during the sintering process due to excessive concentration of a certain element, or insufficient sintering and excessive porosity due to excessive concentration of a certain element.

[0078] By collecting research and development cases and industrial production data of similar inorganic composite materials, and combining the laboratory's previous experience in preparing small batches of samples, the range of element concentrations was initially defined.

[0079] Based on the above analysis, the upper and lower limits of the concentration for each element are clearly defined. The upper limit must prevent the formation of harmful phases, process infeasibility, or performance degradation, while the lower limit must ensure that the corresponding function of the element can be effectively formed. This ultimately results in a clear and quantifiable list of element concentration ranges, providing fundamental data support for the subsequent definition of the spatial distribution of components.

[0080] Specifically, geometric configuration refers to the complete structural morphological characteristics of a material from macroscopic to microscopic levels, including macroscopic shape such as block, plate, fibrous, and thin film, and phase region structure at the microscopic scale. It is the core parameter reflecting the spatial structural characteristics of a material.

[0081] Spatial distribution of composition combines the range of element concentration with the geometric configuration to form a correspondence between element concentration and spatial location. In other words, it clarifies the different regions of each element in the geometric space of the material and is the key to describing the spatial heterogeneity of material composition.

[0082] In detail, the macroscopic geometry is determined based on the application scenario: the macroscopic shape and size of the target material are determined according to the actual application scenario of the target material.

[0083] The core performance indicators directly determine the microscopic geometry. If it is necessary to improve the mechanical strength and toughness of the material, the fiber-reinforced microstructure can be determined, and the arrangement, length, diameter and volume fraction of the fibers can be specified. If it is necessary to optimize the thermal conductivity, the particle-dispersed microstructure can be determined, and the diameter, distribution density and interface bonding morphology of the particle phase can be designed.

[0084] Considering the limitations of the preparation process on the geometric configuration, for example, for particle-reinforced composite materials prepared by sintering, the particle diameter must be larger than the critical size for grain growth during sintering, and the particle distribution density must match the sintering shrinkage rate to prevent the porosity from increasing due to particle aggregation.

[0085] Microscopic characterization techniques such as scanning electron microscopy, transmission electron microscopy, and X-ray diffraction are used to obtain microscopic structural images and data of similar materials or previous samples, and to calibrate the rationality of geometric parameters.

[0086] Clearly define the macroscopic dimensions and microscopic phase regions, such as granular phase, matrix phase, interface phase, geometric parameters of each phase region, and interface morphology, to provide a clear spatial framework for subsequently defining the spatial distribution of components.

[0087] Furthermore, based on the defined geometric configuration, the macroscopic-microscopic space of the material is divided into several distinct regions, and the boundaries of each region need to be precisely defined according to geometric parameters.

[0088] Specifically, the preparation process refers to the core process used in the actual production of the target material.

[0089] The critical extreme value of thermal cycling refers to the key temperature and time thresholds that the target material must strictly follow when undergoing a heating-holding-cooling thermal cycle in the corresponding preparation process.

[0090] Kinetic constraints refer to the physicochemical kinetic laws based on the preparation process of inorganic composite materials, such as element diffusion kinetics, phase transformation kinetics, and stress relaxation kinetics.

[0091] The constraint set is a complete and coordinated set of constraints that integrates various dynamic constraints according to the composition-temperature-time-dynamic dimensions. It is the core foundation for subsequent integration and preparation of boundary conditions, ensuring that the various constraints are conflict-free and feasible.

[0092] Equipment operating parameters refer to the pressure-related operating parameters in static presses such as hot press furnaces and pressure sintering furnaces used in the preparation of target materials.

[0093] The pressure boundary refers to the theoretically achievable pressure range of the preparation equipment. It represents the technical limit of the equipment itself and does not take into account the actual needs of material preparation.

[0094] The effective pressure boundary range refers to the range of pressure parameters that, after verification, meet both the equipment's operational capabilities and the requirements for preparing the target material. It is the core input of the pressure dimension in the preparation boundary conditions, ensuring that the pressure parameters are highly compatible with the material properties and the process.

[0095] Preparation boundary conditions refer to all the boundary constraints that the target material must follow during the preparation process. They are the core inputs for subsequent computer simulations of virtual microstructure fields and phase evolution behavior simulations. They integrate multi-dimensional constraints such as composition, temperature, pressure, and kinetics to ensure that the preparation process closely matches the simulation process and improve the accuracy of performance prediction.

[0096] In this embodiment, the step of combining computer simulation with the virtual microstructure field of the target material includes:

[0097] The mesoscopic topological configuration of the target material is constructed using the spatial distribution of the components and the preparation boundary conditions.

[0098] The mesoscopic topological configuration is subjected to thermal hysteresis phase transition rules to obtain the steady-state phase domain evolution rules of the mesoscopic topological configuration;

[0099] A virtual microstructure field of the target material is constructed based on the mesoscopic topological configuration and the steady-state phase domain evolution rules.

[0100] Specifically, mesoscopic topology is a description of structural features between the microscopic atomic scale and the macroscopic scale. It includes the phase region arrangement of the target material, the connection relationship between phase regions, and the topological parameters of the phase regions. It is the topological framework of the material's mesoscopic structure.

[0101] First, based on the spatial distribution of components, the mesoscopic space of the target material is divided into phase regions corresponding to the components; at the same time, according to the component concentration boundaries, the topological regions of the matrix phase and the interface transition phase are divided, and the initial spatial range of each phase region is clarified.

[0102] Specifically, the thermal hysteresis phase transition rule is the rule corresponding to the hysteresis characteristic of phase transition behavior as temperature changes. It includes the phase transition triggering temperature range, phase transition triggering conditions, phase transition rate constraints, etc., and is the core rule describing the phase transition hysteresis characteristic.

[0103] The steady-state domain evolution rule is the change law of the morphology, size and distribution of domains in the observation topology when they reach a steady state during the thermal hysteresis phase transition. It includes the growth direction, merging conditions and steady-state size range of the domains, and is the basis for the evolution of domains to maintain structural stability.

[0104] In detail, within the constructed mesoscopic topological configuration, phase regions exhibiting thermally hysteretic phase transition behavior are identified, and their topological locations, topological dimensions, and topological connections with other phase regions are clarified.

[0105] By referring to the critical extreme value of thermal cycling of the preparation boundary conditions, the thermal hysteresis interval of the phase transition region is determined. This temperature interval is the thermal hysteresis interval and serves as the temperature basis for the phase transition rule.

[0106] By combining the phase region constraints of mesoscopic topology, the triggering conditions for phase transition are derived. If the interface topology between the phase region and the matrix phase is tightly bound, the triggering temperature for cooling phase transition is reduced to 900℃. These conditions are the triggering rules for thermal hysteresis phase transition.

[0107] Based on the topological connectivity of phase regions, the constraints for phase transitions are determined, and this relationship is the rule for thermal hysteresis phase transitions.

[0108] Specifically, based on the thermal hysteresis phase transition rule, the data on the changes in topology, shape, and distribution of the phase transition region during thermal cycling are simulated.

[0109] Based on the performance requirements of the target material, the conditions for steady-state phase domains should be defined; at the same time, the structural constraints of the mesoscopic topology must be met.

[0110] By integrating the phase domain change trend and steady-state constraints, the evolution rules are obtained; the growth direction must be perpendicular to the stress concentration direction of the mesoscopic topology to avoid the superposition of phase transformation stress.

[0111] By comparing the preparation boundary conditions, check whether the phase domains can complete the steady-state evolution within the specified time; finally, a complete document of steady-state phase domain evolution rules is formed.

[0112] Specifically, the virtual microstructure field is a digital structure field constructed through computer simulation. It includes the mesoscopic topology, steady-state morphology of phase domains, spatial distribution of composition, and interface topological characteristics of the target material. It serves as a digital carrier for subsequent performance prediction and must be highly consistent with the microstructure of the actual material.

[0113] In detail, the structured description of the mesoscopic topological configuration is imported into the computer simulation environment to build the structural skeleton of the virtual space.

[0114] Based on the steady-state phase domain evolution rules, phase domain morphology is filled in the phase transition region of the mesoscopic topology, while the phase domains are closely matched with the topological boundaries of other phase regions, with no gaps.

[0115] The element concentration-spatial position relationship of the spatial distribution of components is superimposed onto the phase region of the virtual structure field, so that the virtual structure field contains both structural and compositional information.

[0116] By combining the interface constraints of the preparation boundary conditions, the interface features between phase regions are supplemented, and the element diffusion traces in the interface region are marked to match the concentration gradient of the spatial distribution of components.

[0117] The characteristics of the virtual microstructure field are compared with the actual characterization data of the target material, and the parameters are adjusted to make the consistency between the two ≥90%. At the same time, it is checked whether the structure field meets all the preparation boundary condition constraints, and finally a virtual microstructure field consistent with the actual material is formed, which serves as the digital basis for subsequent performance prediction.

[0118] S2. Based on the thermal diffusion constraint of the target material, the phase evolution behavior is simulated in the virtual microstructure field to obtain the phase evolution trajectory of the target material.

[0119] In this embodiment, the formula for calculating the phase evolution trajectory is:

[0120]

[0121] in: For the phase evolution trajectory, For the initial phase field distribution, For topological thermal hysteresis memory operators, For local curvature, The time interval is [0, The historical temperature gradient field within [the area] Stress field divergence potential gradient The stress-phase transition decoupling coefficient is... This refers to the thermal hysteresis relaxation time. For time, [0, The time parameter on ] These are the spatial coordinates.

[0122] In detail, In the performance prediction of inorganic composite materials, it is used to describe the target inorganic composite material at a specific spatial location. , specific time Below, the trajectory of phase change over time.

[0123] It refers to the initial phase distribution state of the target material at various spatial locations x at the starting moment of the performance prediction of inorganic composite materials. It is the starting benchmark of the phase evolution process and directly determines the initial trend of phase evolution.

[0124] It is a key parameter in the performance prediction of inorganic composite materials, which integrates local curvature and historical temperature gradient field to reflect the memory effect of material thermal hysteresis characteristics on phase evolution.

[0125] It describes the spatial position of the target inorganic composite material. The physical quantity related to the degree of local bending of the microstructure at a certain location directly reflects the geometric morphological characteristics of the microstructure at that location.

[0126] This refers to the time from the initial moment of performance prediction to the intermediate moment. The cumulative record of temperature change gradients at various locations in space for the target inorganic composite material fully presents the potential impact of the material's temperature change history on phase evolution during this period.

[0127] It is used to characterize the degree of independence between stress and phase transformation processes in inorganic composite materials. Its core function is to control the interference intensity of the stress field on phase transformation in phase evolution trajectory calculation, so that the phase evolution law is more consistent with the physical process in actual service of the material.

[0128] It describes the spatial variation rate of the stress field divergence potential of the target inorganic composite material at its spatial location and time. It intuitively reflects the distribution and variation characteristics of the stress field at that location and is a key parameter for quantifying the influence of stress on phase evolution.

[0129] It is the total phase evolution time set in the process of predicting the performance of inorganic composite materials. It is the upper limit of the time for calculating the phase evolution trajectory and defines the time range of phase change.

[0130] It is the intermediate time point traversed during the integration process, used to cover the entire time interval from the initial prediction time to the total duration, and to realize the cumulative calculation of the factors affecting the phase evolution at different times.

[0131] It is a spatial location marker for locating the internal microstructure of the target inorganic composite material, ensuring that all location-related parameters can be accurately mapped to specific micro-regions of the material.

[0132] In detail, the topological thermal hysteresis memory operator, through its interaction with... The combination of these elements enables a deep integration of the material's microstructure geometry with its temperature change history.

[0133] It provides information on the degree of curvature of the microstructure at this location, which determines the geometric basis of phase evolution. The more significant the curvature of the microstructure, the stronger the constraint or driving effect on phase change. This provides the time from the start of the forecast to the end of the forecast. The accumulated history of temperature gradients determines the temperature memory basis of phase evolution. Temperature gradient changes at different times will be remembered by the material and continue to affect phase transitions.

[0134] and The combination of these two factors is as follows: the former is the core driving factor of phase evolution, integrating the key influences of microscopic geometry and temperature history; the latter is the moderating factor of stress field on phase evolution, through... and The synergistic effect enables appropriate correction of phase evolution.

[0135] As a decoupling coefficient, the degree of coupling between stress and phase transformation can be adjusted according to material properties. The larger the stress field, the weaker its interference with phase evolution, and the more the phase evolution is dominated by temperature history and micro-geometry.

[0136] The magnitude of the change in the divergence potential of the stress field was quantified. The negative exponential form indicates that as the magnitude of the change in the divergence potential gradient of the stress field increases, the value of the adjustment factor will decrease. That is, the more drastic the change in the stress field, the stronger its weakening effect on the phase evolution driving factor, thereby avoiding excessive interference of stress factors with the core law of phase evolution, and ensuring that while considering the influence of stress, the phase evolution trend dominated by temperature history and micro-geometry does not deviate.

[0137] Furthermore, It serves as the starting point for phase evolution, clearly defining the initial phase state of the material at each location when the prediction begins, and is the starting point for the phase evolution trajectory.

[0138] Specifically, The formula for calculating the topological thermal hysteresis memory operator is:

[0139]

[0140] in: For topological thermal hysteresis memory operators, For local curvature, for Time and location Historical temperature gradient field, [0, The time parameter on ] This refers to the thermal hysteresis relaxation time. Spatial location coordinates, This refers to the thermal hysteresis relaxation time.

[0141] In detail, It is a core parameter in the performance prediction of inorganic composite materials, integrating the geometric characteristics of microstructure, thermal hysteresis relaxation characteristics and historical temperature gradient field, and is used to quantify the driving strength of material thermal hysteresis memory on phase evolution.

[0142] The decay rate of the thermal hysteresis effect in inorganic composite materials is characterized by the duration of the material's memory of past temperature changes. The smaller the value, the faster the thermal hysteresis memory decays, and the more significant the impact of recent temperature changes.

[0143] It refers to the temperature change gradient of the target inorganic composite material at a historical time point and spatial location. It is the basic unit that constitutes the historical temperature gradient field and directly reflects the intensity of temperature change at that time and location.

[0144] The memory decay law used to characterize the historical temperature gradient field, wherein yes and The time difference, combined with the thermal hysteresis relaxation time, enables the weighting of the contribution of temperature gradients at different times.

[0145] The spatial position of inorganic composite materials was quantified. The geometric bending strength of the microstructure is related to the degree of bending; the more pronounced the bending, the better. The larger the value, the stronger its contribution to the basic strength of thermal hysteresis memory;

[0146] This determines the decay rate of the material's thermal hysteresis memory. The smaller the value, the faster the memory decays; these two factors constitute the fundamental strength factor of the topological thermal hysteresis memory operator.

[0147] By directly linking the microstructure geometric bending strength with the thermal hysteresis memory decay rate, the greater the bending strength and the slower the decay rate, the higher the basic strength of the thermal hysteresis memory and the stronger its driving effect on phase evolution. This combination achieves a deep fit between microstructure geometric features and thermal hysteresis memory decay characteristics, allowing the strength of the memory operator to accurately match the microstructure and thermal hysteresis characteristics at different locations of the material.

[0148] More specifically, This combination allows the temperature gradient intensity at each historical moment to participate in the construction of thermal hysteresis memory according to the memory decay law of strong recent and weak distant, which not only fully preserves the influence of temperature history, but also conforms to the physical law of the natural decay of thermal hysteresis memory of inorganic composite materials over time.

[0149] The formula as a whole achieves a comprehensive integration of microstructural geometry, thermal hysteresis decay characteristics, and temperature history memory. This combination means that the topological thermal hysteresis memory operator not only depends on the geometric bending strength and thermal hysteresis decay rate of the material's microstructure, but also deeply integrates the memory contribution of temperature gradients at different times.

[0150] in: The formula for calculating local curvature is:

[0151]

[0152] in: For local curvature, For position The minimum radius of curvature at that point. For position The microstructure topological invariants at the location.

[0153] In detail, The radius corresponding to the minimum curvature of the bent portion of the microstructure at spatial location x in inorganic composite materials directly reflects the sharpness of the structural bend at that location.

[0154] Characterizing the spatial position of inorganic composite materials The topological features in a microstructure that do not change with geometric deformation are the core parameters reflecting the stability of the topological properties of the microstructure at that location.

[0155] More specifically, by comprehensively integrating the geometric bending characteristics and topological features of the microstructure of inorganic composite materials, a multi-dimensional and precise characterization of local curvature was achieved. By focusing on the degree of geometric bending of the microstructure, the sharpness of the bending at that location is directly quantified by the reciprocal of the minimum radius of curvature.

[0156] By focusing on the intensity of topological feature changes in microstructures: the spatial variation of topological features at a given location is quantified using the norm of topological invariants.

[0157] This additive combination of geometric bending contribution and topological change contribution implies that inorganic composite materials are positioned... The local curvature at a point is not determined by a single factor, but is a synergistic result of the degree of geometric curvature and the intensity of changes in topological features.

[0158] S3. Analyze the synergistic interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory, and locate the performance-sensitive distribution of the target material based on the synergistic interference.

[0159] In this embodiment, the analysis of the cooperative interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory includes:

[0160] Extract the grain boundary energy distribution gradient of the phase evolution trajectory;

[0161] The coupling gradient between grain boundary energy and spatial coordinates in the target material is determined based on the grain boundary energy distribution gradient.

[0162] The stress field divergence value in the phase evolution trajectory is quantitatively analyzed, and the spatial distribution of the stress field divergence value in the target material is marked.

[0163] Evaluate the cooperative interference of the coupling gradient and the spatial distribution.

[0164] Specifically, in the grain boundary energy distribution gradient, grain boundary energy is the energy state at the boundaries of different grains in inorganic composite materials; the distribution gradient refers to the degree of variation of this grain boundary energy at different locations in the material space, which is reflected in the trend of the difference in grain boundary energy between adjacent spatial locations. It is a key parameter reflecting the spatial heterogeneity of grain boundary energy and is directly related to the stability of the material's microstructure.

[0165] In detail, the phase evolution trajectory full-time data of the target material is retrieved, and each node is associated with the microstructure information at the corresponding time.

[0166] For each time node of the phase evolution trajectory, all grain boundary regions are identified and marked one by one in the virtual microstructure field, while the spatial range of each grain boundary region is defined.

[0167] By combining the energy parameters of the virtual structural field, the grain boundary energy value of each grain boundary region is read. First, the reference energy value is matched according to the grain boundary type, and then the element concentration gradient correction value of the interface region is combined to obtain the actual grain boundary energy of each grain boundary region.

[0168] The grain boundary energy values ​​of each grain boundary region are mapped to the three-dimensional spatial coordinates of the virtual microstructure field, forming a dataset with a one-to-one correspondence between spatial coordinates and grain boundary energy.

[0169] Spatial neighborhood analysis was performed on the dataset. Adjacent coordinate points with a spacing of 0.1 μm were selected, and the grain boundary energy difference between the two points was calculated and divided by the spatial distance to obtain the grain boundary energy change rate in that direction. All neighborhood pairs were traversed, and the change rates in different directions were integrated to finally obtain the grain boundary energy distribution gradient under the phase evolution trajectory.

[0170] Specifically, the coupling gradient refers to the degree of correlation between the grain boundary energy change and the material spatial coordinate change. It is reflected in the synergistic relationship between the grain boundary energy and the x, y, z coordinate changes. It is a parameter that binds energy characteristics with spatial position depth and reflects the coupling law between energy and geometric position.

[0171] In detail, the gradient data of grain boundary energy distribution is mapped onto the three-dimensional coordinate system of the virtual microstructure field, and the coordinate direction and coordinate interval corresponding to each gradient value are clearly defined.

[0172] For each coordinate direction, select continuous coordinate points, count the grain boundary energy value at each point, analyze the trend of grain boundary energy change with coordinates, and record the change ratio of that interval.

[0173] By comparing the grain boundary energy-coordinate variation relationships in the x, y, and z directions, the differences in coupling strength in each direction can be clarified.

[0174] By combining the coupling relationships in each direction, the variation of grain boundary energy with coordinates is quantified as a coupling gradient, thereby clarifying the coupling gradient between grain boundary energy and spatial coordinates.

[0175] Specifically, in stress field divergence, stress field is the field formed by stress states at different locations within the material; divergence value is a parameter reflecting the degree of divergence / convergence of stress field at a certain spatial point, embodying the local variation characteristics of stress field, and is directly related to the risk of material cracking and deformation.

[0176] Spatial distribution refers to the distribution of stress field divergence values ​​at different spatial locations within a material, reflecting the spatial heterogeneity of divergence values.

[0177] In detail, stress field data for each time node of the phase evolution trajectory are retrieved from the database, including the stress type, magnitude, and direction at each location.

[0178] For the time-series nodes corresponding to the steady-state phase structure, the spatial region covered by the stress field is clearly defined in the virtual microstructure field, excluding the blank region outside the material.

[0179] For each spatial point covered by the stress field, the divergence value is calculated based on its stress state. Tensile stress divergence corresponds to a positive value, and compressive stress convergence corresponds to a negative value, thus completing the divergence quantification of all points.

[0180] Divide the divergence values ​​into numerical intervals and clarify the stress concentration characteristics corresponding to each interval.

[0181] Furthermore, the divergence value of each spatial point is mapped to the three-dimensional coordinates of the virtual structure field to form a spatial coordinate-divergence value dataset.

[0182] By setting the marking method according to the divergence value interval, the divergence value marking information is marked one by one at the corresponding coordinate position in the virtual structure field, forming a complete spatial distribution marking result.

[0183] Specifically, the synergistic interference quantity refers to the degree of interaction and influence between the coupling gradient and the stress field divergence distribution in the process of affecting material properties. It is the core indicator for measuring the synergistic effect between the two.

[0184] In detail, the coupled gradient data and the stress field divergence distribution data are spatially matched to form a correlated dataset of spatial coordinates-coupled gradient-divergence values.

[0185] For each region, the interaction between the two is analyzed; if a low-coupling gradient is superimposed, the negative impact is reduced.

[0186] The percentage of regions with different effects was statistically analyzed. For example, 30% of the regions were enhanced by high coupling and high dispersion, while 20% of the regions were weakened by low coupling and high dispersion.

[0187] By combining local effects with global proportions, the degree of synergy is quantified into interference quantity, thus completing the assessment of synergistic interference quantity.

[0188] In this embodiment, locating the performance-sensitive distribution of the target material based on the cooperative interference amount includes:

[0189] Determine the deterioration distribution region of high interference in the aforementioned cooperative interference quantities;

[0190] Verify the compatibility of lattice strain in the deterioration distribution region, and segment the strain mismatch connected domain of the deterioration distribution region based on the verification results;

[0191] Based on a preset benchmark threshold, the performance sensitivity distribution in the strain mismatch connected domain is statistically analyzed.

[0192] Specifically, high interference refers to segments within the numerical range of cooperative interference where the intensity of the interaction is at a relatively high level. These segments typically correspond to superimposed regions of abrupt changes in grain boundary energy gradients and highly concentrated stress fields. Such regions have a more significant negative impact on the performance of inorganic composite materials.

[0193] The deterioration distribution region is the spatial region in the target inorganic composite material where the performance tends to deteriorate due to high synergistic interference, and it is a region with a high risk of potential performance failure of the material.

[0194] In detail, the cooperative interference data that has been evaluated is retrieved from the digital database of performance prediction. This data is bound to the three-dimensional spatial coordinates of the virtual microstructure field of the target material, including the cooperative interference intensity level and corresponding structural features of each coordinate region.

[0195] Based on the application scenarios of the target inorganic composite material, the numerical range of the cooperative interference quantity corresponding to high interference is determined.

[0196] In the virtual microstructure field, spatial coordinate points with cooperative interference quantities in the high interference threshold range are selected one by one, and the three-dimensional boundaries of these points are marked.

[0197] For the marked high interference spatial region, the structural data of the region in the phase evolution trajectory are retrieved to verify whether the region has the conditions for performance degradation. For example, if the region has both a sudden change in grain boundary energy gradient and a stress field concentration factor ≥1.5, it is identified as a potential region for performance degradation.

[0198] All spatial regions that meet the structural basis of high interference and performance degradation are merged, their complete three-dimensional spatial range and boundary characteristics are clarified, and finally a set of spatial coordinates of the deterioration distribution area is formed to complete the judgment action.

[0199] Specifically, lattice strain refers to the degree of deformation of the internal lattice structure of a target inorganic composite material due to external forces, temperature changes, and other factors. It is a key parameter reflecting the stability of the lattice structure and is directly related to the mechanical properties of the material.

[0200] Coordination refers to the degree of matching in magnitude and direction of lattice strain at different locations within a deterioration distribution area. If the strain magnitudes are similar and the directions are consistent, the coordination is high; otherwise, the coordination is low. Strain concentration is likely to occur in areas with low coordination.

[0201] The strain mismatch connectivity region is a region in the deterioration distribution area where the lattice strain compatibility is low, the strain state is significantly different, and they are spatially interconnected. It is one of the core regions that are sensitive to material properties.

[0202] In detail, lattice strain data, including the magnitude and direction of strain, are extracted from the virtual microstructure field database for each spatial coordinate point within the deterioration distribution region.

[0203] The deterioration distribution area was divided into several local sub-regions with a spatial interval of 0.5 μm. Each sub-region contained lattice strain data at at least 5 spatial coordinate points to ensure that the sub-regions were statistically representative.

[0204] For each local sub-region, the strain characteristics of all coordinate points within it are statistically analyzed. If the maximum difference in strain magnitude within the sub-region is ≤0.02% and the directions are consistent, the lattice strain compatibility of the sub-region is determined to be high. If the difference in magnitude is >0.02%, or if there is strain in both tensile and compressive directions, the compatibility is determined to be low.

[0205] The coordination of all local sub-regions within the deterioration distribution area is determined, the coordination result of each sub-region is recorded, and the location of the sub-region with low coordination is marked in the virtual microstructure field to complete the verification action.

[0206] Furthermore, in the virtual microstructure field, local sub-regions with low coordination are classified according to their spatial location. If the spatial distance between two sub-regions with low coordination is ≤0.2μm, they are merged into a regional cluster.

[0207] For each regional cluster, determine its maximum and minimum coordinates in three-dimensional space, and delineate the complete spatial boundary of the cluster.

[0208] If there are multiple non-adjacent regional clusters, each cluster is divided into an independent strain mismatch connected domain; if there are spatial barriers within a cluster, the cluster is further split into multiple connected domains, and finally the strain mismatch connected domain segmentation of the deterioration distribution area is completed.

[0209] Specifically, the preset benchmark threshold is a threshold standard set in advance based on the application scenario of the target material, including lattice strain threshold and performance degradation threshold, which serves as the basis for determining the performance sensitivity.

[0210] Performance-sensitive distribution refers to the spatial distribution of regions in a target inorganic composite material whose properties respond significantly to factors such as structural changes and external loads. It is one of the key outputs for performance prediction.

[0211] In detail, a baseline threshold matching the target material is retrieved from the parameter configuration library for performance prediction. For example, for ceramic matrix composites, a threshold range is preset where lattice strain ≥0.1% is highly sensitive, 0.05% to 0.1% is moderately sensitive, and <0.05% is slightly sensitive.

[0212] For each strain mismatch connected domain, the lattice strain data of all spatial coordinate points within it are read one by one, and the corresponding performance degradation trend data of that point is correlated.

[0213] The lattice strain data at each coordinate point is compared with the baseline threshold. If the strain is ≥0.1%, it is determined to be a highly sensitive area; if the strain is between 0.05% and 0.1%, it is determined to be a moderately sensitive area; if the strain is <0.05%, it is determined to be a slightly sensitive area.

[0214] For each sensitivity level, the three-dimensional spatial range of the corresponding region is statistically analyzed, and its proportion to the strain mismatch connected domain is calculated.

[0215] Regions with different levels of sensitivity are integrated according to their spatial location to form a complete performance sensitivity distribution description document, thus completing the statistical process.

[0216] S4. Based on the performance-sensitive distribution, establish the symplectic geometric correlation between the local sensitive extreme points and the microstructure topological invariants of the target material.

[0217] In this embodiment, establishing the symplectic geometric correlation between the local sensitive extrema points and the microstructure topological invariants of the target material based on the performance-sensitive distribution includes:

[0218] Locate the sensitive extreme points in the performance-sensitive distribution and extract the topologically continuous regions in the virtual microstructure field;

[0219] Calculate the rank of the high-dimensional homology group of each of the topologically continuous regions to obtain the Betti number distribution spectrum;

[0220] The sensitive extreme points are mapped onto the Betti number distribution spectrum to obtain the Betti number features at the corresponding positions of the sensitive extreme points;

[0221] Establish the geometric correlation between the sensitive extreme points and the Betti number feature to obtain the symplectic geometric correlation of the target material.

[0222] Specifically, the sensitive extreme point is the spatial coordinate point in the performance-sensitive distribution where the performance response to internal / external factors reaches the most severe level. It is the local location with the highest risk of performance degradation of the target material and is the core object for establishing subsequent topological associations.

[0223] A topologically continuous region is a local spatial region in a virtual microstructure field where the structural topology remains uninterrupted, and its topological characteristics are directly related to the material's performance response.

[0224] In detail, the performance sensitivity distribution data from the previous statistical analysis was retrieved from the digital database. This data includes the three-dimensional coordinate range, performance response degree, and lattice strain state of each sensitive region, and has been bound to the coordinates of the virtual microstructure field.

[0225] Based on the application scenario of the target material, the threshold of extreme value response is defined. For example, the strength reduction ratio ≥30% and the toughness loss coefficient ≥0.4 are used as the criteria for judging sensitive extreme value points.

[0226] Traverse all spatial coordinate points of the performance-sensitive distribution and check the degree of performance response for each point: if the strength reduction ratio of a certain coordinate point is 35%, the toughness loss coefficient is 0.45, and the lattice strain is ≥0.1%, then mark it as a candidate sensitive extreme point.

[0227] Centered on the candidate point, analyze the performance response within a 0.2 μm radius around it: if the performance response of the candidate point is significantly higher than that of the surrounding points, it is confirmed as a sensitive extreme point; if there are adjacent candidate points, they are merged into an extreme region and the center coordinates are taken as the representative point.

[0228] The precise coordinates of each sensitive extreme point are recorded in the virtual microstructure field, and its performance response and lattice strain state are correlated to complete the localization.

[0229] Furthermore, the extreme points are correlated with topological features: with the coordinates of the sensitive extreme points as the center, the topological information within a radius of 1 μm in the virtual microstructure field is retrieved, including phase region type, grain boundary distribution, pore morphology, etc.

[0230] Analyze the topological information to determine the spatial boundary of the continuous topological elements: for example, the sensitive extreme point is located at the SiC grain boundary, which extends to 4.5 μm in the positive x-axis direction and to 2.0 μm in the negative x-axis direction, and is connected to the grain boundaries of 3 SiC particles. Then, the grain boundary and the connected particle phase region are defined as the initial topological region.

[0231] Check whether the topological elements in the initial region are interrupted: if there are no grain boundary fractures and no discrete phase distribution, it is confirmed as a topologically continuous region; if there are grain boundary fractures, divide the region into complete and continuous sub-regions with the fracture points as boundaries.

[0232] The proportion of phase regions, grain boundary types, and number of connected branches within the recorded region provide a basis for subsequent calculation of the rank of the high-dimensional homology group, thus completing the extraction.

[0233] Specifically, the rank of a high-dimensional homology group is a numerical value that describes the topological features of a topologically continuous region in different dimensions, such as the number of connected components corresponding to 0 dimensions and the number of holes corresponding to 1 dimensions.

[0234] The Betti number distribution spectrum is a map formed by classifying and organizing the ranks of high-dimensional homology groups of various topological continuous regions according to their dimensions, which intuitively reflects the differences in high-dimensional characteristics of different topological regions.

[0235] In detail, combining the three-dimensional properties of the virtual microstructure field, the analysis dimensions are clearly defined as 0-dimensional, 1-dimensional, and 2-dimensional.

[0236] Identify topological features across various dimensions: For each topologically continuous region, perform the following statistical analyses:

[0237] 0-dimensional feature: The number of mutually independent connected branches within a region. For example, the number of 0-dimensional features is 2 for two unconnected SiC particle clusters.

[0238] 1D feature: The number of pores in a region, such as the porous region enclosed by the matrix phase. If the number is 3, then the number of 1D features is 3.

[0239] 2D feature: The number of closed surfaces within the region, such as the number of spherical particle surfaces wrapped by the matrix phase. If the number is 5, then the number of 2D features is 5.

[0240] The topological feature number of each dimension can be directly used as the homology group rank of the corresponding dimension. For example, if the number of features in a certain region is 2 in 0-dimensional, 3 in 1-dimensional, and 5 in 2-dimensional, then its rank in 0-dimensional is 2, 3 in 1-dimensional, and 5 in 2-dimensional.

[0241] Using dimension as the horizontal axis and homology group rank as the vertical axis, the rank of all topological regions is marked at the corresponding dimensional position. At the same time, the region coordinates are associated to form a spectrum containing 0 / 1 / 2-dimensional rank distribution, thus completing the construction of the Betti number distribution spectrum.

[0242] Specifically, the Betti number is the rank of the high-dimensional homology group in each dimension corresponding to the topological region where the sensitive extremum point is located, and it is the core parameter reflecting the topological characteristics around the extremum point.

[0243] In detail, retrieve the coordinates of the sensitive extreme points, match them with the topologically continuous regions in which they are located, and bind the two together.

[0244] Find the rank label of region T1 in the Betty number distribution spectrum and extract its 0-dimensional rank 2, 1-dimensional rank 3, and 2-dimensional rank 5.

[0245] The rank of region T1 is used as the Betti number feature of the extreme point, and the performance responsiveness of the extreme point is correlated to ensure the integrity of the correlation between the feature and the extreme point.

[0246] If there are multiple extreme points within a topological region, confirm that their Betti number characteristics are consistent; if an extreme point spans multiple regions, split the extreme points and match the characteristics of the corresponding regions to complete the mapping.

[0247] Specifically, symplectic geometric correlation is the spatial geometric correspondence between the performance-sensitive characteristics of sensitive extrema and the corresponding Betti number characteristics, and it is the core rule for relating the topology of materials to performance-sensitive regions.

[0248] In detail, the coordinates of sensitive extreme points, performance-sensitive data, and Betty number features are mapped one-to-one to form a related dataset.

[0249] Traverse the dataset to identify the performance differences corresponding to feature differences—for example, when the Betty number has a 1-dimensional rank ≥ 3, the intensity decrease at extreme points is generally ≥ 30%; when the 0-dimensional rank ≤ 1, the intensity decrease is < 20%.

[0250] Rules are formulated based on corresponding patterns. For example, in a 3D virtual field, if the Betty number of the region where the extreme point is located satisfies "0-dimensional rank = 2, 1-dimensional rank ≥ 3", then the intensity of that point decreases by ≥ 30%, and it is a highly sensitive point. At the same time, the spatial range in which the rules apply is clearly defined.

[0251] By integrating association rules according to performance sensitivity levels, a symplectic geometric association set covering all sensitive extreme points is formed. This set includes the Betti number range, spatial range, and performance responsiveness corresponding to each level, thus completing the establishment of the symplectic geometric association.

[0252] S5. Identify the virtual defect initiation domain of the target material by combining the symplectic geometric correlation.

[0253] In this embodiment, the step of identifying the virtual defect initiation domain of the target material by combining the symplectic geometric correlation includes:

[0254] Analyze the curvature overlimit distribution of the symplectic geometric correlation;

[0255] Verify the connectivity of dislocation loops in the curvature excess distribution to obtain the topologically connected domain of the dislocation loops in the curvature excess distribution;

[0256] By removing segments from the topologically connected domain of the dislocation loop that are blocked by the high-energy phase interface, the virtual defect initiation domain of the target material is obtained.

[0257] Specifically, curvature excess distribution is the spatial distribution set of regions in the virtual microstructure field of the target material where curvature values ​​of these topologies exceed a preset safety limit, where curvature is a parameter describing the degree of bending of the topological structure in a symplectic geometric relationship; excess distribution refers to the spatial distribution set of these regions in the virtual microstructure field of the target material where the curvature values ​​of these topologies exceed a preset safety limit.

[0258] In detail, the previously established symplectic geometric correlation data is retrieved from the digital database of performance prediction. This data is bound to the three-dimensional coordinates of the virtual microstructure field of the target material and includes the topological position, Betti number characteristics, initial curvature value of the topology, and associated performance sensitivity of each sensitive extreme point.

[0259] Based on the preparation process and service scenarios of the target inorganic composite material, and referring to the experimental data on the topological stability of similar materials, a safety limit for curvature is preset.

[0260] Traverse all topological structures in the symplectic geometric correlation data and check their curvature values ​​one by one.

[0261] All marked out-of-limit topologies are classified according to their spatial location, and the overlapping coordinate regions of adjacent out-of-limit structures are merged. The three-dimensional boundary, topology type, and associated sensitive extreme point information of each out-of-limit region are clearly defined.

[0262] In the virtual microstructure field, the curvature value, topology type, and associated performance sensitivity of each out-of-limit region are labeled to complete the analysis of curvature out-of-limit distribution.

[0263] Specifically, in the continuity of dislocation loops, the dislocation loops are the dislocation loops themselves; continuity refers to the degree of spatial connectivity of these dislocation loops within the curvature over-limit region, that is, whether the dislocation loops are distributed continuously and without interruption, which is a key indicator for judging the risk of defect expansion.

[0264] A dislocation loop topologically connected region is a local spatial region in a curvature-over-limit distribution where the dislocation loop remains continuous and uninterrupted.

[0265] In detail, retrieve dislocation loop data for curvature excess distribution: extract dislocation loop information corresponding to the curvature excess region from the digital database, including the initial position, shape, length of the dislocation loop, and its relationship with the surrounding topological structure.

[0266] In the virtual microstructure field, the endpoint positions of each dislocation loop are marked one by one: if the two endpoints of a dislocation loop coincide with the endpoints of the adjacent dislocation loops, it is marked as initially connected; if the endpoints have no matching objects, it is marked as initially isolated.

[0267] Starting from the initially connected dislocation loop, the extension direction of the dislocation loop is tracked along the three-dimensional spatial trajectory of the virtual microstructure field: if the path continuously covers a spatial range of ≥5μm without interruption, the dislocation loop corresponding to the path is determined to be continuous; if the path is interrupted, the interruption point is marked and the tracking is terminated.

[0268] Furthermore, for dislocation loop paths with connectivity, the maximum and minimum coordinates in three-dimensional space are determined, and the continuous dislocation loop regions within this range are defined as dislocation loop topological connected domains.

[0269] The dislocation loop density and associated curvature excess region characteristics of each connected region are statistically analyzed to obtain the topological connected regions of dislocation loops.

[0270] Specifically, in inorganic composite materials, a high-energy phase interface refers to a region with a high interfacial energy between phase regions.

[0271] Virtual defect initiation domain: This is the spatial region in the virtual microstructure field of the target material where dislocation loops are prone to aggregate, defects are easily generated, and can continue to expand. It is a defect risk region that needs to be focused on in the performance prediction of inorganic composite materials.

[0272] In detail, high-energy phase interfaces in connected domains are identified: from the virtual microstructure field database, the phase region interface information corresponding to the dislocation loop topological connected domain is retrieved, high-energy phase interfaces are filtered out by interface energy values, and their three-dimensional coordinate range is marked in the connected domain.

[0273] Along the extension path of the topological connected domain of the dislocation loop, check the positional relationship between the dislocation loop and the high-energy phase interface one by one: if a segment of the dislocation loop is completely surrounded by the high-energy phase interface, it is determined that the segment is blocked and cannot continue to expand.

[0274] Record the three-dimensional coordinates and lengths of all blocked dislocation loop segments, delete the information corresponding to these segments in the connected component data, and retain the unblocked continuous dislocation loop regions.

[0275] In detail, after removing the obstructing segments, the remaining continuous regions of dislocation loops are merged according to their spatial location to clarify their complete three-dimensional spatial range.

[0276] Record the dislocation loop density and associated performance sensitivity in this region; this region is the virtual defect initiation domain of the target material.

[0277] S6. Based on the path connectivity of the virtual defect initiation domain, construct the material failure probability distribution of the target material.

[0278] In this embodiment, constructing the material failure probability distribution of the target material based on the path connectivity of the virtual defect initiation domain includes:

[0279] Extract the vein-like skeleton of the virtual defect initiation domain;

[0280] The spatial coupling strength between the main path length and the bifurcation angle of the secondary path in the vein-like skeleton is quantitatively analyzed, and a penetration weight value is assigned based on the spatial coupling strength.

[0281] Based on the penetration weight value, the material failure probability distribution of the target material is located.

[0282] Specifically, the vein-like skeleton is the main continuous structure of the defect propagation path within the virtual defect initiation domain. It is the core topological structure that reflects the trend of defect penetration and propagation and is directly related to the path characteristics of material failure.

[0283] The main path length is the spatial extension length of the main path in the vein-like skeleton. It is a core parameter that reflects the main expansion range of the defect. The longer the length, the wider the basic range of defect penetration.

[0284] The secondary path bifurcation angle is the angle between the secondary path and the main path in the vein-like skeleton. It is a parameter that reflects the diversity of defect propagation directions. The closer the angle is to the reasonable range, the easier it is for the defect to penetrate in multiple directions.

[0285] Spatial coupling strength is the degree of correlation between the length of the main path and the bifurcation angle of the secondary path. For example, the longer the main path and the closer the bifurcation angle is to 50°, the closer the correlation between the two is, the higher the coupling strength is, and the stronger the corresponding defect penetration ability is.

[0286] The penetration weight value is a numerical value that is assigned based on the spatial coupling strength and reflects the ability of defects to penetrate into the vein-like skeleton. The higher the weight value, the easier it is for defects to penetrate along the skeleton and cause material failure.

[0287] In detail, based on the correlation of failure risk of the combination, the spatial coupling strength is divided into three levels: high, medium, and low.

[0288] High strength: corresponding to a long main path + excellent bifurcation angle combination: strong defect penetration ability;

[0289] Medium strength: corresponding to the combination of medium main path and medium bifurcation angle: medium defect penetration ability;

[0290] Low strength: corresponds to a combination of short main path and poor bifurcation angle: weak defect penetration ability.

[0291] In the virtual microstructure field, the spatial coupling strength level is labeled for each connected node of the vein-like skeleton to complete the quantitative analysis.

[0292] Specifically, the material failure probability distribution is the spatial distribution set of the probability of failure occurring in different spatial regions of the target material, which intuitively reflects the difference in failure risk in different regions of the material.

[0293] Based on the failure test data of inorganic composite materials, the penetration weight value is mapped to a failure probability interval:

[0294] Weight values ​​of 0.8-1.0 correspond to a failure probability of 80% to 100% (extremely high risk zone).

[0295] Weight values ​​of 0.5-0.8 correspond to a failure probability of 50% to 80% (high-risk area);

[0296] Weight values ​​of 0.2-0.5 correspond to a failure probability of 20% to 50% (medium risk zone).

[0297] Weight value < 0.2: corresponding failure probability < 20% (low risk zone).

[0298] Integrate the spatial distribution of failure probabilities: merge regions with the same failure probability interval according to their spatial location, clarify the three-dimensional range of each probability interval and its proportion of the total volume of the target material, such as the extremely high risk area accounting for 5% of the total volume, and associate it with the corresponding vein-like skeleton features.

[0299] like Figure 2 The diagram shown is a functional block diagram of an inorganic composite material performance prediction system provided in an embodiment of the present invention.

[0300] The inorganic composite material performance prediction system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the inorganic composite material performance prediction system 100 may include a computer simulation module 101, a derivation simulation module 102, a sensitive distribution module 103, a symplectic geometric correlation module 104, a virtual defect module 105, and a performance prediction module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, and are stored in the memory of the electronic device.

[0301] In this embodiment, the functions of each module / unit are as follows:

[0302] Computer simulation module 101: sets the spatial distribution of the target material's composition and the preparation boundary conditions, and combines computer simulation of the virtual microstructure field of the target material;

[0303] Evolution simulation module 102: Based on the thermal diffusion constraints of the target material, it simulates phase evolution behavior in the virtual microstructure field to obtain the phase evolution trajectory of the target material;

[0304] Sensitive distribution module 103: Analyzes the synergistic interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory, and locates the performance sensitive distribution of the target material based on the synergistic interference.

[0305] Symplectic geometric correlation module 104: Based on the performance sensitivity distribution, establish symplectic geometric correlations between the local sensitive extreme points and the microstructure topological invariants of the target material;

[0306] Virtual defect module 105: Identifies the virtual defect initiation domain of the target material by combining the symplectic geometric correlation;

[0307] Performance prediction module 106: Constructs the material failure probability distribution of the target material based on the path connectivity of the virtual defect initiation domain.

[0308] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0309] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0310] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0311] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0312] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0313] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method of predicting the performance of an inorganic composite material, characterized by, The method comprises: S1, setting the composition spatial distribution of the target material and the preparation boundary condition, and combining to simulate the virtual microstructure field of the target material by computer; S2, simulating the phase evolution behavior in the virtual microstructure field based on the thermal diffusion constraint of the target material, to obtain the phase evolution trajectory of the target material; S3, analyzing the synergistic interference amount of the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory, and positioning the performance sensitive distribution of the target material according to the synergistic interference amount; S4, establishing the symplectic geometry correlation of the corresponding local sensitive extreme point and the microstructure topological invariant of the target material according to the performance sensitive distribution, including: locating the sensitive extreme point in the performance sensitive distribution, and extracting the topologically continuous region in the virtual microstructure field; calculating the high-dimensional homology group rank number of each topologically continuous region to obtain the Betti number distribution spectrum; mapping the sensitive extreme point to the Betti number distribution spectrum to obtain the Betti number characteristics of the corresponding position of the sensitive extreme point; establishing the geometric correlation between the sensitive extreme point and the Betti number characteristics to obtain the symplectic geometry correlation of the target material; S5, combining the symplectic geometry correlation to identify the virtual defect nucleation domain of the target material, including: analyzing the curvature over-limit distribution of the symplectic geometry correlation; verifying the dislocation loop topological connected domain of the curvature over-limit distribution; eliminating the fragments blocked by high-energy phase interface in the dislocation loop topological connected domain to obtain the virtual defect nucleation domain of the target material; S6, constructing the material failure probability distribution of the target material according to the path connectivity of the virtual defect nucleation domain, including: extracting the vein skeleton of the virtual defect nucleation domain; quantitative analysis of the spatial coupling strength of the main path length and the secondary path branch angle in the vein skeleton, and assigning the penetration weight value according to the spatial coupling strength; locating the material failure probability distribution of the target material according to the penetration weight value.

2. A method of predicting the performance of an inorganic composite material as claimed in claim 1, wherein, The setting of the composition spatial distribution of the target material and the preparation boundary condition comprises: planning the element concentration range of the target material; determining the geometric configuration of the target material, defining the composition spatial distribution of the element concentration range and the geometric configuration; setting the thermal cycle critical extreme value of the corresponding preparation process of the target material; performing dynamics constraint on the composition spatial distribution and the thermal cycle critical extreme value to obtain the constraint condition group of the target material; verifying the pressure boundary of the corresponding equipment operation parameter of the target material to obtain the effective pressure boundary range of the target material; integrating the effective pressure boundary range and the constraint condition group into the preparation boundary condition of the target material.

3. The method for predicting the properties of inorganic composite materials as described in claim 1, characterized in that, The combination of computer simulation of the virtual microstructure field of the target material comprises: constructing the mesoscopic topological configuration of the target material through the composition spatial distribution and the preparation boundary condition; deriving the thermal hysteresis phase change rule of the mesoscopic topological configuration to obtain the steady-state phase domain evolution rule of the mesoscopic topological configuration; constructing the virtual microstructure field of the target material based on the mesoscopic topological configuration and the steady-state phase domain evolution rule.

4. The method of claim 1, wherein the inorganic composite material is a cement-based material. The formula for calculating the phase evolution trajectory is: wherein: is the phase evolution trajectory, is the initial phase field distribution, is the topological hysteresis memory operator, is the local curvature, is the historical temperature gradient field over the time interval [0, ], is the stress field divergence potential gradient, is the stress-phase decoupling coefficient, is the thermal hysteresis relaxation time, is the time, is the time parameter over [0, ], is the spatial position coordinate.

5. A method of predicting the performance of an inorganic composite material as claimed in claim 4, wherein, The analysis of the synergistic interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory comprises: Extracting the grain boundary energy distribution gradient of the phase evolution trajectory; Determining the coupling gradient of the grain boundary energy and the spatial coordinates in the target material based on the grain boundary energy distribution gradient; Quantitative analysis of the stress field divergence value in the phase evolution trajectory, and marking the spatial distribution of the stress field divergence value in the target material; Evaluating the synergistic interference of the coupling gradient and the spatial distribution.

6. A method of predicting the performance of an inorganic composite material as claimed in claim 5, wherein, According to the synergistic interference, the performance sensitive distribution of the target material is located, comprising: Determining the degradation distribution area with high interference in the synergistic interference; Verifying the coordination of lattice strain in the degradation distribution area, and segmenting the strain mismatch connected domain of the degradation distribution area through the verification result; Based on the preset reference threshold, the performance sensitive distribution in the strain mismatch connected domain is counted.

7. An inorganic composite material performance prediction system for implementing the inorganic composite material performance prediction method according to any one of claims 1 to 6, characterized by The system comprises: A computer simulation module: setting the composition spatial distribution and preparation boundary conditions of the target material, and combining the computer simulation to simulate the virtual microstructure field of the target material; A derivative simulation module: based on the thermal diffusion constraint of the target material, simulating the phase evolution behavior in the virtual microstructure field to obtain the phase evolution trajectory of the target material; A sensitive distribution module: analyzing the synergistic interference between the grain boundary energy distribution gradient and the stress field divergence in the phase evolution trajectory, and locating the performance sensitive distribution of the target material according to the synergistic interference; A symplectic geometry correlation module: according to the performance sensitive distribution, establishing the symplectic geometry correlation between the corresponding local sensitive extreme points and the microstructure topological invariants of the target material, comprising: Locating the sensitive extreme points in the performance sensitive distribution, and extracting the topologically continuous region in the virtual microstructure field; Calculating the high-dimensional homology group rank number of each topologically continuous region to obtain the Betti number distribution spectrum; Mapping the sensitive extreme points to the Betti number distribution spectrum to obtain the Betti number characteristics of the corresponding position of the sensitive extreme points; Establishing the geometric correlation between the sensitive extreme points and the Betti number characteristics to obtain the symplectic geometry correlation of the target material; A virtual defect module: combining the symplectic geometry correlation to identify the virtual defect nucleation domain of the target material, comprising: Analyzing the curvature overlimit distribution of the symplectic geometry correlation; Verifying the dislocation loop topological connected domain of the curvature overlimit distribution; Eliminating the fragments blocked by high-energy phase interfaces in the dislocation loop topological connected domain to obtain the virtual defect nucleation domain of the target material; A performance prediction module: according to the path connectivity of the virtual defect nucleation domain, constructing the material failure probability distribution of the target material, comprising: Extracting the vein skeleton of the virtual defect nucleation domain; Quantitative analysis of the spatial coupling strength of the main path length and the secondary path branch angle in the vein skeleton, and distributing the penetration weight value according to the spatial coupling strength; According to the penetration weight value, the material failure probability distribution of the target material is located.

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