Composite material structure design decision method and system based on cross-scale consistency constraint

Abstract

The application discloses a composite material structure design decision method and system based on cross-scale consistency constraint, obtains a material scale performance vector and a structure scale response vector corresponding to a same group of material scale design parameters; inputs the two into a cross-scale consistency constraint function, and calculates a consistency measurement value for quantifying a physical compatibility degree; compares the measurement value with a preset threshold value; when the measurement value is greater than or equal to the threshold value, it is determined that the design scheme meets the consistency requirement and outputs a usable scheme; when the measurement value is less than the threshold value, it is determined that the design scheme does not meet the consistency requirement, and an optimization scheme is generated based on the measurement value as a new design parameter to repeatedly execute the above steps until a design scheme meeting the requirement is obtained or an iteration termination condition is reached. The application integrates multi-scale prediction results into a unified judgment framework through cross-scale consistency constraint, and constructs a closed-loop iteration mechanism, thereby solving the problem that prediction results are inconsistent and difficult to be directly used for engineering decision in the prior art.

Description

Technical Field

[0001] This invention relates to the field of composite material design, specifically to a composite material structure design decision-making method and system based on cross-scale consistency constraints. Background Technology

[0002] Composite materials are widely used in aerospace, high-end equipment, and advanced manufacturing due to their advantages such as high designability, specific strength, and specific stiffness. Engineering applications of composite materials typically involve multiple scale levels, including microscale fiber, matrix, and interface behavior, mesoscale ply structure characteristics, and macroscale overall structural response. Current engineering design processes usually involve establishing material performance models or structural analysis models at different scales to predict material properties or structural responses.

[0003] However, in practical engineering, models at different scales are often constructed independently based on different assumptions and equivalent methods. Even if the prediction accuracy is high at a single scale, inconsistencies may still occur when the prediction results are transferred across scales. For example, the equivalent material performance parameters obtained from microscopic or mesoscopic models may not produce a structural response consistent with their physical meaning in structural-scale analysis, leading to problems such as local satisfaction of design specifications but premature failure of the overall structure or insufficient safety margin.

[0004] Existing technologies primarily improve prediction results by increasing the accuracy of single-scale models or introducing more complex simulation methods. However, these methods do not fundamentally solve the problem of the lack of consistency constraints among multi-scale prediction results. When the cross-scale usability of prediction results cannot be determined, engineering design still relies on empirical judgment, making it difficult to form a traceable and verifiable basis for technical decisions. Therefore, there is an urgent need for a method and system that can introduce cross-scale consistency constraints in the structure-performance prediction process and form engineering decisions accordingly. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a composite material structure design decision-making method and system based on cross-scale consistency constraints, which solves the problems of inconsistent cross-scale results and the difficulty in directly using the prediction results for engineering decisions in the existing composite material structure-performance prediction process.

[0006] This invention is achieved through the following technical solution: In a first aspect, this application provides a composite material structure design decision-making method based on cross-scale consistency constraints, comprising the following steps: Obtain the material-scale performance vector and structural-scale response vector corresponding to the same set of material-scale design parameters; The material-scale performance vector and the structural-scale response vector are input into the cross-scale consistency constraint function to calculate a consistency metric value used to quantify the degree of physical compatibility between the two. The consistency metric value is compared with a preset threshold. When the consistency metric value is greater than or equal to a preset threshold, the current design scheme is determined to meet the consistency requirements, and the decision conclusion that the current design scheme is an available design scheme is output. When the consistency metric value is less than the preset threshold, it is determined that the current design scheme does not meet the consistency requirements. A design optimization scheme is generated based on the consistency metric value, and the design optimization scheme is used as a new material scale design parameter. The above steps are repeated until a design scheme that meets the consistency requirements is obtained or the preset iteration termination condition is reached.

[0007] Preferably, obtaining the material-scale property vector includes: Constructing a material-scale performance prediction model M m Taking material-scale design parameters as input, the output is the material-scale performance vector. The mathematical expression of the material-scale performance prediction model is as follows:

[0008] Where μ is the material-scale design parameter vector. P m This represents a vector of material-scale properties.

[0009] Preferably, obtaining the structural scale response vector includes: Constructing a structural-scale response prediction model M s Using the material-scale performance vector, structural boundary conditions, and external load parameters as input, the structural-scale response vector is output. R s The mathematical expression of the structural scale response prediction model is as follows:

[0010] in, R s B represents the structural scale response vector of the material, and L represents the structural boundary conditions.

[0011] Preferably, the cross-scale consistency constraint function includes at least one of the stiffness consistency function, energy consistency function, and failure consistency function.

[0012] Preferably, the stiffness consistency function is used to measure the consistency between the structural equivalent stiffness derived from the equivalent stiffness parameters in the material-scale performance vector and the structural stiffness index in the structural-scale response vector, and its expression is as follows:

[0013] in, K pred To predict stiffness, K s For structural stiffness performance indicators; The energy consistency function is used to measure the consistency between the structural strain energy predicted based on the material-scale performance vector and the structural strain energy calculated based on the structural-scale response vector. Its expression is as follows:

[0014] in, U pred and U actual These are the predicted strain energy vector and the actual strain energy vector obtained from finite element calculations, respectively.

[0015] The failure consistency function is used to measure the consistency between the material-scale failure index in the material-scale performance vector and the structural-scale failure index in the structural-scale response vector in terms of failure trend or failure region. The expression is as follows:

[0016] in, BE m For the scale failure index and BE s This is the structural scale failure index.

[0017] Preferably, comparing the consistency metric with a preset threshold includes: When the consistency metric is a scalar, the scalar value is directly compared with a preset threshold. When the consistency metric is a vector, each component is compared with its corresponding preset threshold. If all components are greater than or equal to the corresponding threshold, the consistency requirement is satisfied. If any component is less than the corresponding threshold, the consistency requirement is not satisfied, and the component with the largest deviation is output as the main inconsistency item.

[0018] Preferably, the generation of design optimization schemes based on the consistency metric value includes at least one of the following methods: Based on sensitivity analysis, the material-scale design parameters that have the greatest impact on the consistency metric value are identified, and design optimization schemes are generated based on the identification results. The gradient optimization algorithm is used to find the direction of adjustment of material-scale design parameters that improves the consistency metric value, and a design optimization scheme is generated based on the adjustment direction. Based on a predefined expert rule base, the parameter correction rule corresponding to the current consistency metric is matched, and a design optimization scheme is generated according to the correction rule.

[0019] Preferably, the preset iteration termination conditions include reaching the maximum number of iterations, the change in the consistency metric value between two adjacent iterations being less than the convergence tolerance, or the iteration time exceeding a preset duration. When the preset iteration termination condition is reached and a design scheme that meets the consistency requirements is still not obtained, a design unusable flag is output, indicating the main physical quantity items and corresponding material scale design parameters that cause the consistency to be unsatisfactory.

[0020] Secondly, this application provides a composite material structure design decision system based on cross-scale consistency constraints, including: The data acquisition module is used to acquire the material-scale performance vector and structural-scale response vector corresponding to the same set of material-scale design parameters; The consistency calculation module is used to input the material-scale performance vector and the structural-scale response vector into the cross-scale consistency constraint function to calculate a consistency metric value used to quantify the degree of physical compatibility between the two. A threshold comparison module is used to compare the consistency metric value with a preset threshold. The decision output module is used to determine that the current design scheme meets the consistency requirements when the consistency metric value is greater than or equal to a preset threshold, and output the current design scheme as a decision conclusion that the current design scheme is an available design scheme. When the consistency metric value is less than the preset threshold, it is determined that the current design scheme does not meet the consistency requirements. A design optimization scheme is generated based on the consistency metric value, and the design optimization scheme is used as a new material scale design parameter. The data acquisition module, consistency calculation module, threshold comparison module and decision output module are repeatedly started until a design scheme that meets the consistency requirements is obtained or the preset iteration termination condition is reached.

[0021] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the composite material structure design decision method based on cross-scale consistency constraints as described above.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects: This application provides a composite material structure design decision-making method based on cross-scale consistency constraints. Based on multi-scale modeling, it correlates the material-scale properties and structural-scale responses corresponding to the same set of material design parameters. A consistency constraint function transforms the physical compatibility between the two into a calculable numerical index, which is then compared with a preset threshold to objectively determine the usability of the design scheme. When consistency is satisfied, a usable scheme is directly output; when consistency is not satisfied, an optimized scheme is generated based on the metric and iterated until the requirements are met or the termination condition is met. This method transforms the traditional experience-dependent multi-scale verification problem into a quantifiable and comparable numerical judgment, providing an objective basis for engineering decisions. Secondly, the closed-loop iterative mechanism enables the design process to have adaptive correction capabilities, significantly reducing reliance on human experience. Finally, it ensures that material properties and structural responses originate from the same design source, making the design results traceable and verifiable. This fundamentally solves the technical problem of inconsistent prediction results in multi-scale composite material design, making them difficult to directly apply to engineering decisions.

[0023] This application also proposes a composite material structure design decision system based on cross-scale consistency constraints, an electronic device, and a computer storage medium, which possess all the advantages of the aforementioned composite material structure design decision method based on cross-scale consistency constraints. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the composite material structure design decision method based on cross-scale consistency constraints of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0028] A composite material structure design decision-making method based on cross-scale consistency constraints includes the following steps: Step 1: Obtain the material-scale performance vector and structural-scale response vector corresponding to the same set of material-scale design parameters; By acquiring the material-scale performance vectors and structural-scale response vectors corresponding to the same set of material-scale design parameters, the performance correlation of composite materials at the material and structural levels is achieved. The principle is based on multi-scale modeling, which unifies the microscopic compositional characteristics of the material (such as fiber volume fraction and layup angle) with the macroscopic response of the structure under external loads (such as displacement, stress, and stiffness). This provides physically meaningful and traceable paired data for subsequent consistency assessments, ensuring that material properties and structural responses originate from the same design source and avoiding judgment biases caused by inconsistent data sources.

[0029] Step 2: Input the material-scale performance vector and the structural-scale response vector into the cross-scale consistency constraint function to calculate the consistency metric used to quantify the degree of physical compatibility between the two. This method maps material-scale performance vectors to structural-scale response vectors into a quantifiable consistency metric. The principle is to quantitatively assess the applicability of equivalent material performance parameters in structural analysis using mathematical expressions of physical compatibility (such as stiffness consistency, energy consistency, or failure consistency functions), reflecting the inherent coordination between the prediction results of the two scales. This transforms the originally isolated, qualitative cross-scale verification problem into a calculable and comparable numerical indicator, providing an objective basis for engineering decisions and solving the technical difficulties of traditional methods that rely on empirical judgment and lack quantitative standards.

[0030] Step 3: Compare the consistency metric value with a preset threshold; When the consistency metric value is greater than or equal to a preset threshold, the current design scheme is determined to meet the consistency requirements and can be used for engineering decision-making. When the consistency metric value is less than the preset threshold, it is determined that the current design scheme does not meet the consistency requirements. A design optimization scheme is generated based on the consistency metric value, and the design optimization scheme is used as a new material scale design parameter. The above steps are repeated until a design scheme that meets the consistency requirements is obtained or the preset iteration termination condition is reached.

[0031] By comparing metric values ​​with engineering tolerance ranges or historical data statistical thresholds, the system automatically identifies two states: "consistency satisfied" and "consistency not satisfied." When consistency is satisfied, it outputs a conclusion that the current design scheme is usable, directly serving the engineering design. When consistency is not satisfied, it generates an optimized scheme based on the metric values ​​and triggers an iterative loop until the consistency requirements are met or the termination condition is met. This constructs a closed-loop decision-making mechanism, enabling the design process to have adaptive correction capabilities. This not only improves the reliability and traceability of design results but also significantly reduces reliance on human experience, achieving intelligent closed-loop control for composite material structure design.

[0032] In some embodiments, obtaining the material-scale property vector includes: A material-scale performance prediction model is constructed, taking material-scale design parameters as input and outputting a material-scale performance vector. The mathematical expression of the material-scale performance prediction model is as follows:

[0033] Where μ is a material-scale design parameter vector, which includes at least one or more of the following: fiber volume fraction, layup angle, layup thickness, and interface performance parameters.

[0034] The material-scale property vector includes at least one of the following: equivalent Young's modulus, equivalent shear modulus, equivalent Poisson's ratio, material-scale failure index, and equivalent thermal parameter.

[0035] Furthermore, the material-scale performance prediction model is constructed based on an analytical model, a numerical simulation model of a representative volume element, or a data-driven model.

[0036] Taking the design of carbon fiber / epoxy resin composite material for the wing skin of a certain type of UAV as an example, the material-scale design parameter vector μ is set as follows: fiber volume fraction 58%, ply angle [0° / 45° / 90° / -45°]s symmetrical ply, single layer thickness 0.125mm, and interfacial shear strength 52MPa. Based on these parameters, a material-scale performance prediction model is constructed using the Halpin-Tsai analytical model, and the equivalent engineering constants of the composite material are calculated using micromechanical theoretical formulas.

[0037] The calculation process is as follows: The longitudinal equivalent Young's modulus is obtained as 136.7 GPa by weighted averaging the fiber modulus (230 GPa) and matrix modulus (3.2 GPa) according to the mixing law; the transverse modulus and in-plane shear modulus are calculated using the Halpin-Tsai semi-empirical formula, with geometric factors introduced to correct for the influence of fiber shape and arrangement, yielding values ​​of 9.5 GPa and 4.6 GPa respectively; the equivalent Poisson's ratio is calculated to be 0.27 using the mixing law; the material-scale failure index is calculated to be 0.30 under unit stress based on the Tsai-Wu failure criterion; the equivalent thermal conductivity is calculated as 29.1 W / (m·K) longitudinally and 0.48 W / (m·K) transversely, based on the fiber thermal conductivity (50 W / (m·K)) and matrix thermal conductivity (0.2 W / (m·K)) using a series-parallel model. Thus, the material-scale performance vector P is obtained. m =[136.7GPa, 9.5GPa, 4.6GPa, 0.27, 0.30, 29.1W / (m·K),0.48W / (m·K)].

[0038] Based on the fundamental theoretical framework of micromechanics of composite materials, composite materials, as multiphase materials, have macroscopic equivalent properties that depend on the properties, volume fractions, and geometric distributions of each constituent phase. Material-scale property prediction models establish constitutive relationships between microstructural parameters and macroscopic equivalent properties, achieving a cross-scale mapping from microstructure to macroscopic performance. The longitudinal modulus is primarily controlled by the fibers; therefore, a mixing law model based on the iso-strain assumption is adopted, assuming that the fibers and matrix experience the same strain in the stress direction, consistent with the stress characteristics of continuous fiber-reinforced composites. The transverse modulus and shear modulus are significantly influenced by matrix properties and interfacial bonding states; simply using the mixing law would produce large deviations. Therefore, the Halpin-Tsai semi-empirical formula is introduced, using geometric factors to correct for the influence of fiber shape and arrangement, more accurately reflecting the anisotropic characteristics of composite materials. The failure index is calculated based on strength criterion theory. The Tsai-Wu criterion, as a tensor polynomial criterion, comprehensively considers the contribution of each stress component to failure under multiaxial stress states, comparing the current stress state with strength parameters to quantitatively assess the material's failure risk. The calculation of equivalent thermal parameters is based on the theory of heat conduction, taking into account the difference in thermal conductivity between the fiber and the matrix and the heat transfer path, and describing the thermal conduction behavior of the composite material in different directions through a series and parallel model.

[0039] This example first establishes a mapping relationship between material-scale design parameters and material-scale performance vectors, transforming the originally discrete and qualitative material composition information into continuous and quantitative engineering constants. This provides input data with clear physical meaning for subsequent structural-scale analysis and cross-scale consistency determination. Second, the material-scale performance vectors are calculated entirely based on the same set of design parameters, forming paired data with the subsequent structural-scale response vectors, ensuring that cross-scale comparisons are based on traceability and verifiability, avoiding judgment biases caused by inconsistent data sources. Third, the output material-scale performance vectors include not only mechanical performance parameters but also thermal performance parameters, providing complete data support for subsequent force-thermal coupling analysis or structural response prediction under multiphysics conditions.

[0040] In some embodiments, obtaining the structural scale response vector includes: Constructing a structural-scale response prediction model M s Taking the material-scale performance vector, structural boundary conditions, and external load parameters as inputs, the structural-scale response vector is output. The mathematical expression of the structural-scale response prediction model is as follows:

[0041] Where B represents the structural boundary conditions, and L represents the external loads or operating parameters.

[0042] The structural scale response vector includes at least one of the following: structural displacement response, structural stress response, structural strain response, structural stiffness index, structural stability index, structural scale failure index, and structural dynamic performance index.

[0043] Furthermore, the structural scale response prediction model is constructed based on the finite element analysis model, the isogeometric analysis model, or the equivalent engineering beam / slab theoretical model.

[0044] For example, taking the structural analysis of a carbon fiber / epoxy resin composite laminate used for the wing skin of a certain type of UAV as an example, based on the material-scale property vector obtained in the aforementioned embodiments... P m =[136.7GPa, 9.5GPa, 4.6GPa, 0.27, 0.30, 29.1W / (m·K), 0.48W / (m·K)], which are used as input parameters for the structural scale response prediction model. The structural boundary condition B is set as a fixed support at the wing root, meaning that all nodal degrees of freedom at the root are constrained; the external load parameter L is set as an aerodynamic load of 2500Pa on the upper surface of the wing, while considering a concentrated force of 500N applied to the wingtip to simulate the wingtip load.

[0045] A structural scale response prediction model was constructed based on the finite element analysis model. M s A laminated shell model was created in ANSYS Workbench, meshed using Shell181 elements with an element size of 10 mm, generating a total of 15680 elements. Material properties were assigned layer by layer according to the layup sequence [0° / 45° / 90° / -45°]s, with each layer having a thickness of 0.125 mm and a total thickness of 1 mm. Static analysis revealed a maximum structural displacement response of 12.3 mm, occurring at the wingtip; a maximum structural stress response of 187 MPa, occurring in the transition region at the wing root; and a maximum structural strain response of 0.0021. The structural stiffness index K... s The result was calculated by applying a unit load of 1.85 × 10⁻⁶. 6 N / mm; Structural stability index λ cr The eigenvalue buckling analysis yielded a value of 2.35, indicating that the structure buckled under 2.35 times the current load; the structural scale failure index FI... s The stress state of each element was calculated based on the Tsai-Wu criterion, and the maximum value of 0.28 appeared in the root region of the wing; the structural dynamic performance index f1 (first natural frequency) was obtained as 24.6 Hz through modal analysis.

[0046] This yields the structural scale response vector. R s =[12.3mm, 187MPa, 0.0021, 1.85×10 6 N / mm, 2.35, 0.28, 24.6Hz.

[0047] The core idea of ​​the structural scale response prediction model is to discretize the continuous structure into a finite number of elements, describe the displacement field in each element through shape function interpolation, establish equilibrium equations based on the principle of minimum potential energy or the principle of virtual work, and finally solve for the structural response quantities such as displacement, stress, and strain.

[0048] The technical advantages of the structural-scale response prediction model in this embodiment are reflected in the following aspects: First, by using the material-scale performance vector as the input parameter for structural analysis, the design information at the material level can be accurately transmitted to the structural level, ensuring the integrity of the data chain for multi-scale analysis. Second, it provides comprehensive structural response information. The output structural-scale response vector simultaneously includes static response (displacement, stress, strain, stiffness), stability response (buckling factor), failure assessment (failure index), and dynamic response (natural frequency), covering the main performance indicators of concern in engineering design, and providing multi-dimensional comparison data for subsequent cross-scale consistency determination. Third, it ensures the reliability of the analysis results. As a mature numerical analysis method, the finite element method can handle complex geometries, boundary conditions, and load conditions. With reasonable element sizes, the calculation accuracy can meet engineering requirements. Fourth, it establishes a correspondence with the material-scale prediction. The structural-scale response vector is calculated entirely based on the material-scale performance vector of the aforementioned embodiment, forming a homologous data pair with the material-scale performance vector, ensuring a clear physical correspondence for subsequent cross-scale consistency determination and avoiding judgment bias caused by different data sources.

[0049] Furthermore, obtaining the material-scale performance vector and structural-scale response vector corresponding to the same set of material-scale design parameters includes: For the same set of material-scale design parameters μ, material-scale prediction and structural-scale prediction are performed sequentially to obtain one-to-one material-scale performance vectors and structural-scale response vectors, forming a joint prediction data pair.

[0050] The data pairs are used as inputs to the cross-scale consistency constraint function. This function compares the differences between material-scale and structural-scale predictions, and this comparison is only physically meaningful if the data pairs share the same design origin. The multi-scale characteristics of composite materials determine an inherent physical relationship between equivalent material properties and structural responses. Material-scale properties serve as the input basis for structural-scale analysis, while the structural-scale response is the external manifestation of material-scale properties under specific boundaries and loads. Cross-scale consistency determination is only physically meaningful and truly reflects whether deviations exist in the transfer process from material to structure, provided that the data are consistent—that is, the material-scale property vector and the structural-scale response vector originate from the same set of material-scale design parameters. If different design parameters are used to obtain material properties and structural responses separately, there is no physical correspondence between them. Consistency determination based on this lacks accurate identification of design flaws and fails to provide a traceable basis for subsequent optimization.

[0051] In some embodiments, the stiffness consistency function is used to measure the consistency between the structural equivalent stiffness derived from the equivalent stiffness parameters in the material-scale performance vector and the structural stiffness index in the structural-scale response vector.

[0052] The energy consistency function is used to measure the consistency between the structural strain energy predicted based on the material-scale performance vector and the structural strain energy calculated based on the structural-scale response vector.

[0053] The failure consistency function is used to measure the consistency between the material-scale failure index in the material-scale performance vector and the structural-scale failure index in the structural-scale response vector in terms of failure trend or failure region.

[0054] The cross-scale consistency constraint function includes at least one of the stiffness consistency function, energy consistency function, and failure consistency function.

[0055] The consistency metric is a scalar or a vector. When it is a vector, the comprehensive evaluation index is obtained by weighted summation or minimum value of each component.

[0056] The mathematical expression of the stiffness uniformity function is as follows:

[0057] The cross-scale consistency constraint function can be constructed in the following ways: by constructing an analytical expression based on physical mechanisms, by constructing an empirical formula based on historical data fitting, or by constructing a data-driven model based on machine learning algorithms.

[0058] For example, taking the design of a composite material laminate for the wing of a certain type of UAV as an example, based on the material-scale performance vector obtained in the aforementioned embodiments... P m =[136.7GPa, 9.5GPa, 4.6GPa, 0.27, 0.30] and structural scale response vector R s =[1.85×10 6 [N / mm, 0.28], three cross-scale consistency constraint functions were constructed for measurement.

[0059] The calculation process of the stiffness uniformity function is as follows: It is derived from the equivalent Young's modulus E in the material-scale property vector. eff =136.7 GPa and equivalent shear modulus G eff =4.6GPa. Combining the wing structure's geometric parameters (wingspan 3200mm, average chord length 450mm), the wing bending stiffness was calculated using classical laminate theory, yielding a predicted stiffness of 1.72×10⁻⁶ GPa. 6 N / mm. Structural stiffness index K in the structural dimensional response vector. s=1.85×10 6 N / mm. Substituting the stiffness consistency function = 0.93 indicates that the stiffness predicted by the material scale has a high degree of consistency with the stiffness calculated by the structural scale.

[0060] The essence of cross-scale consistency constraint functions is to establish a physical compatibility measure between material-scale prediction results and structural-scale prediction results. Among them, stiffness consistency is based on the principle of energy equivalence. By comparing the structural stiffness derived from the material equivalent stiffness with the stiffness obtained from direct structural analysis, it reflects the applicability of the material constitutive relation at the structural scale. Energy consistency is based on the principle of energy conservation. By comparing the strain energy predicted based on material properties with the actual strain energy calculated by structural analysis, it reflects the accuracy of material performance parameters at the energy level. Failure consistency is based on the scale correlation of failure mechanisms. By comparing the spatial distribution correspondence between the material-scale failure index and the structural-scale failure index, it reflects the coordination of failure criteria at different scales.

[0061] By using three consistency functions with different physical meanings, a multi-dimensional evaluation of cross-scale prediction results is achieved from three dimensions: stiffness, energy, and failure, avoiding the one-sidedness of a single indicator. The abstract concept of consistency is transformed into a numerical indicator through specific mathematical expressions, providing an objective basis for subsequent threshold judgment.

[0062] In some embodiments, comparing the consistency metric with a preset threshold includes: When the consistency metric is a scalar, the scalar value is directly compared with a preset threshold; if the scalar value is greater than or equal to the preset threshold, the consistency requirement is determined to be met; if the scalar value is less than the preset threshold, the consistency requirement is determined to be unmet.

[0063] The scalar form of the consistency metric condenses the degree of physical compatibility across scales into a single value, which is suitable for quickly assessing the overall consistency of design schemes.

[0064] For example, when a weighted summation method is used to combine the three consistency indicators of stiffness, energy, and failure into a single comprehensive evaluation indicator, a judgment can be made directly by comparing this indicator with a threshold.

[0065] Additionally, the consistency metric can be compared with a preset threshold, including: When the consistency metric is a vector, each component is compared with its corresponding preset threshold. If all components are greater than or equal to the corresponding threshold, the consistency requirement is satisfied. If any component is less than the corresponding threshold, the consistency requirement is not satisfied.

[0066] The vector-based consistency metric preserves independent information for different physical dimensions such as stiffness, energy, and failure. The threshold for each component can be set separately based on the engineering importance and allowable deviation of different performance indicators. For example, the stiffness consistency threshold can be set to 0.9, the energy consistency threshold to 0.85, and the failure consistency threshold to 0.95, reflecting the consistency requirements of different physical quantities. The advantage of this approach is its ability to identify which specific physical dimension exhibits inconsistency, providing more precise guidance for subsequent optimization.

[0067] When multiple components are less than their corresponding thresholds, they are sorted according to the degree of deviation between each component and its corresponding threshold, and the component with the largest deviation is output as the main inconsistency item.

[0068] In situations involving inconsistencies across multiple dimensions, engineering design must prioritize addressing the most critical issues. By calculating the differences or relative deviations between each component and a threshold, the dimension with the largest deviation can be identified, allowing the optimization process to focus on the most critical points of conflict and improving iterative efficiency. For example, when stiffness consistency deviates from the threshold by 0.08, energy consistency by 0.03, and failure consistency by 0.05, the stiffness consistency problem should be addressed first.

[0069] Example 1 To avoid the limitations of existing joint prediction or consistency analysis methods that remain only at the conceptual level, this invention provides a structured description of the technical solution from four levels: model construction, function mapping, consistency criterion calculation, and decision output.

[0070] The composite material structure design decision method based on cross-scale consistency constraints of the present invention includes the following technical steps: Step S1: Construct a material-scale property prediction model M m Taking material design parameters as input, the output is a material-scale property vector describing the equivalent constitutive and failure characteristics of composite materials. P m .

[0071] Specifically, the first step is to construct a material-scale property prediction model. M m The core function of this model is to establish the mapping relationship between the microscopic composition parameters of composite materials and their macroscopic equivalent properties, which is expressed mathematically as follows:

[0072] Where μ represents a material-scale design parameter vector, which includes at least one or more of the following: fiber volume fraction, layup angle, layup thickness, and interfacial performance parameters; P mThis represents a material-scale property vector, including the equivalent Young's modulus E. eff Equivalent shear modulus G eff Equivalent Poisson ratio ν eff Material-scale failure index FI m and equivalent thermal parameter k eff At least one of them.

[0073] The material-scale performance prediction model can be constructed based on the micromechanics theory of composite materials, using analytical models (such as the Halpin-Tsai model and the Mori-Tanaka model), numerical simulation of representative volume elements (RVE), or data-driven models. This invention does not limit this.

[0074] The physical quantities of the above-mentioned material-scale performance indicators are explained below: Material-scale performance indices are used to characterize the constitutive and failure properties of composite materials in an equivalent sense, and their prediction results serve as input parameters for structural-scale analysis. These material-scale performance indices include, but are not limited to, the following: 1) Equivalent Young's modulus E eff : Represents the equivalent tensile stiffness of a composite material in a given principal direction or equivalent direction. It is used to characterize the material's ability to resist axial deformation. Its physical meaning is the equivalent normal stress generated under unit strain conditions.

[0075] (2) Equivalent shear modulus G eff : Represents the equivalent shear stiffness of a composite material in the in-plane or out-of-plane direction, used to characterize the material's ability to resist shear deformation.

[0076] (3) Equivalent Poisson's ratio ν eff : Represents the ratio between transverse strain and longitudinal strain of a composite material under equivalent conditions, used to describe the transverse deformation characteristics of the material.

[0077] (4) Equivalent strength parameter S eff : Represents the ultimate bearing strength parameter of a composite material in an equivalent sense, including the equivalent tensile strength S T Equivalent compressive strength S C and equivalent shear strength S S It is used to evaluate the failure threshold of materials under different stress modes.

[0078] (5) Material-scale failure index FI m : Represents the material-scale failure index calculated based on composite material failure criteria. Its value is used to determine whether the material has failed under equivalent conditions. When FI m A value of ≥1 indicates that the material has reached or exceeded its failure state.

[0079] (6) Equivalent thermal parameter keff : Represents the equivalent thermal conductivity of a composite material, used to characterize the heat transfer capacity of a material during steady-state or transient heat conduction processes.

[0080] Step S2: Construct a structural scale response prediction model, taking the material scale performance vector and working conditions as input, and output the structural scale response results of the composite material structure under specific boundaries and loads.

[0081] Based on step S1, a structural scale response prediction model is constructed. M s The core function of this model is to predict the structural behavior of materials under specific geometries, boundary conditions, and external loads, taking the equivalent properties of the materials as input. Its mathematical expression is as follows:

[0082] in, R s This represents the structural-scale response vector of the material, including the structural displacement response U. s Structural stress response σ s Structural strain response ε s Structural stiffness index K s Structural stability index λ cr Structural Scale Failure Index FI s and structural dynamic performance index f n At least one of them; B represents the structural boundary conditions; L represents the external loads or operating parameters.

[0083] The structural scale response prediction model can be constructed based on finite element analysis, isogeometric analysis, or equivalent engineering beam / plate theory, and this invention does not limit it in this regard.

[0084] The physical quantities of the above-mentioned structural scale performance indicators are explained below: Structural-scale performance indices are used to characterize the response characteristics of composite material structures under actual loads, boundary conditions, and operating conditions. Their prediction results are used for cross-scale consistency assessment and engineering decision output. These structural-scale performance indices include, but are not limited to, the following: 1) Structural displacement response U s : Represents the overall or local displacement vector generated by a composite material structure under external load, used to characterize the deformation characteristics of the structure.

[0085] (2) Structural stress response σ s : Represents the stress distribution state inside a composite material structure, which can be equivalent stress or component stress, and is used to evaluate the structural strength and safety.

[0086] (3) Structural strain response εs : Indicates the strain distribution of a composite material structure under load, used to reflect the degree of deformation of the structure locally or as a whole.

[0087] (4) Structural stiffness performance index K s : Represents the equivalent stiffness parameter of a composite material structure under a given load condition, used to characterize the overall ability of the structure to resist deformation.

[0088] (5) Structural stability index λ cr : Represents the critical buckling load factor of a composite material structure. Its value is used to determine the safety margin of the structure's stability under compressive or combined load conditions.

[0089] (6) Structural dynamic performance index f n : Represents the nth natural frequency of the composite material structure, used to characterize the dynamic properties of the structure and avoid the risk of resonance.

[0090] (7) Structural-scale failure index FI s : This refers to the failure index calculated based on the stress or strain field at the structural scale, which is used to determine whether the structure has failed or is approaching failure under the current operating conditions.

[0091] Step S3: Generate a joint prediction dataset, which associates the material-scale performance and structural-scale response corresponding to the same set of material design parameters to form data pairs for consistency determination.

[0092] Based on the model constructed in steps S1 and S2, for the same set of given material-scale design parameters μ, material-scale prediction and structural-scale prediction are performed sequentially to obtain a one-to-one corresponding material-scale performance vector. P m and structural scale response vector R s This generates a structure-performance joint prediction data pair ( P m , R s This data forms the basis for subsequent cross-scale consistency determinations.

[0093] Step S4: Construct cross-scale consistency constraint functions and calculate consistency metrics. Through quantitative mathematical expressions, evaluate the physical compatibility between material-scale prediction results and structural-scale prediction results.

[0094] The cross-scale consistency constraint function C is constructed, the core of which is to map the joint prediction data pairs generated in step S3 into one or more quantifiable scalar or vector indices to evaluate the physical compatibility between prediction results at different scales. Its general form is expressed as:

[0095] Each consistent component function Ci includes at least one of the following, and may take the following exemplary mathematical form: (1) Stiffness uniformity function C K It is used to measure the structural equivalent stiffness and structural scale predicted stiffness index derived from the material-scale equivalent stiffness parameters Eeff and Geff. K s Consistency between them, in an example form:

[0096] in, K pred For the reason E eff , G eff The predicted stiffness is obtained by calculating the structural geometric parameters using classical laminate theory or simplified formulas.

[0097] (2) Energy uniformity function C E This is used to measure the consistency between the structural strain energy predicted based on material-scale performance parameters and the actual strain energy calculated based on the structural-scale response. An example is shown below:

[0098] in, U pred and U actual These are the predicted strain energy vector and the actual strain energy vector obtained from finite element calculations, respectively.

[0099] (3) Failure Consistency Function C F Used to measure the material-scale failure index BE m With structural scale failure index BE s Consistency in failure trends or failure regions, exemplified by:

[0100] The summation covers the key or all regions of the structure, where N is the total number of regions.

[0101] Each consensus function C i The output is a scalar or vector [0,1] or [0,∞), whose value is used to characterize the degree to which the corresponding consistency condition is satisfied.

[0102] Step S5: Construct a consistency judgment and engineering decision model, evaluate the consistency measurement results according to preset thresholds, and drive different engineering decision conclusions or design modification suggestions based on the evaluation results.

[0103] Construct a consistency determination model D Engineering decision-making models Q It is used to make logical judgments on the calculation results of step S4 and output the final engineering decision.

[0104] First, based on the aforementioned cross-scale consistency constraint function C Construct a consistency determination model D This is used to determine the consistency of joint prediction results, and its expression is as follows: =Consistency determination result Among them, when each consensus function C i ( P m ,R s The output values ​​of all values ​​are greater than or equal to the corresponding preset threshold. T i If the condition is met, the result is "consistency satisfied"; otherwise, the result is "consistency not satisfied".

[0105] This threshold can be set according to the accuracy requirements of the specific engineering problem, the statistical results of historical design data, or the allowable error range, and is usually between 0.8 and 0.95.

[0106] Secondly, construct an engineering decision-making model. Q The engineering decision model Q Receiver decision model D The output, based on the consistency determination result, outputs the engineering decision, which is expressed in the following form:

[0107] Scenario 1 (Consistency Satisfaction): When the consistency determination result is "consistency satisfaction", the engineering decision model... Q Output the current material-structure design scheme and make decision-making conclusions that can be used in engineering design. Key performance parameters and consistency scores can also be attached as design basis.

[0108] Scenario 2 (Consistency Not Satisfied): When the consistency determination result is "consistency not satisfied", the engineering decision model... QInitiate a parameter correction mechanism, which can output specific parameter correction suggestions (such as "it is recommended to adjust the fiber volume fraction to the range of 55%-65%" or "optimize the layup sequence to reduce stiffness prediction deviation) based on sensitivity analysis (identifying the parameters that have the greatest impact on consistency), gradient optimization algorithm (seeking the direction of parameter adjustment to improve consistency) or predefined expert rule base.

[0109] If the analysis finds that the consistency requirements cannot be met by modification within the current design space, the "Design Unavailable" flag will be output, and the main physical quantities causing the inconsistency will be clearly indicated.

[0110] This invention aims to achieve joint prediction of material equivalent properties and structural response within a multi-scale analysis framework for composite materials. By constructing a cross-scale consistency criterion, it quantitatively determines the physical compatibility between prediction results at different scales, and on this basis, forms a decision-making mechanism oriented towards engineering applications. This provides reliable and traceable technical basis for composite material structure design, thereby improving the credibility of prediction results and reducing design risks caused by inconsistent scale assumptions.

[0111] Example 2 A composite material structure design decision system based on cross-scale consistency constraints, comprising: The data acquisition module is used to acquire the material-scale performance vector and structural-scale response vector corresponding to the same set of material-scale design parameters; The consistency calculation module is used to input the material-scale performance vector and the structural-scale response vector into the cross-scale consistency constraint function to calculate a consistency metric value used to quantify the degree of physical compatibility between the two. A threshold comparison module is used to compare the consistency metric value with a preset threshold. The decision output module is used to determine that the current design scheme meets the consistency requirements when the consistency metric value is greater than or equal to a preset threshold, and output the current design scheme as a decision conclusion that the current design scheme is an available design scheme. When the consistency metric value is less than the preset threshold, it is determined that the current design scheme does not meet the consistency requirements. A design optimization scheme is generated based on the consistency metric value, and the design optimization scheme is used as a new material scale design parameter. The data acquisition module, consistency calculation module, threshold comparison module and decision output module are repeatedly started until a design scheme that meets the consistency requirements is obtained or the preset iteration termination condition is reached.

[0112] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0113] Furthermore, in the various embodiments of the present invention, the modules can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0114] An electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the composite material structure design decision method based on cross-scale consistency constraints as described in any of the above embodiments.

[0115] Another electronic device provided in this application embodiment may further include: an input port connected to a processor for transmitting multimodal data collected by an external acquisition device to the processor; a display unit connected to the processor for displaying the processor's processing results to the outside world; and a communication module connected to the processor for enabling communication between the electronic device and the outside world. The display unit may be a display panel, a laser scanning display, etc.; the communication method adopted by the communication module includes, but is not limited to, Mobile High Definition Link (HML), Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), and wireless connection (including Wi-Fi, Bluetooth, Bluetooth Low Energy, and IEEE 802.11s-based communication technology).

[0116] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the composite material structure design decision method based on cross-scale consistency constraints as described in any of the above embodiments.

[0117] For descriptions of relevant parts of the composite material structure design decision system, electronic device, and computer-readable storage medium based on cross-scale consistency constraints provided in this application's embodiments, please refer to the detailed descriptions of the corresponding parts in the composite material structure design decision method based on cross-scale consistency constraints provided in this application's embodiments; they will not be repeated here. Furthermore, parts of the technical solutions provided in this application that are consistent with the implementation principles of corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0118] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A composite material structure design decision method based on cross-scale consistency constraints, characterized in that, Includes the following steps: Obtain the material-scale performance vector and structural-scale response vector corresponding to the same set of material-scale design parameters; The material-scale performance vector and the structural-scale response vector are input into the cross-scale consistency constraint function to calculate a consistency metric value used to quantify the degree of physical compatibility between the two. The consistency metric value is compared with a preset threshold. When the consistency metric value is greater than or equal to a preset threshold, the current design scheme is determined to meet the consistency requirements, and the decision conclusion that the current design scheme is an available design scheme is output. When the consistency metric value is less than the preset threshold, it is determined that the current design scheme does not meet the consistency requirements. A design optimization scheme is generated based on the consistency metric value, and the design optimization scheme is used as a new material scale design parameter. The above steps are repeated until a design scheme that meets the consistency requirements is obtained or the preset iteration termination condition is reached.

2. The composite material structure design decision method based on cross-scale consistency constraints according to claim 1, characterized in that, Obtaining the material-scale property vector includes: Constructing a material-scale performance prediction model M m Taking material-scale design parameters as input, the output is the material-scale performance vector. The mathematical expression of the material-scale performance prediction model is as follows: Where μ is the material-scale design parameter vector. P m This represents a vector of material-scale properties.

3. The composite material structure design decision method based on cross-scale consistency constraints according to claim 1, characterized in that, Obtaining the structural scale response vector includes: Constructing a structural-scale response prediction model M s Using the material-scale performance vector, structural boundary conditions, and external load parameters as input, the structural-scale response vector is output. R s The mathematical expression of the structural scale response prediction model is as follows: in, R s B represents the structural scale response vector of the material, and L represents the structural boundary conditions.

4. The composite material structure design decision method based on cross-scale consistency constraints according to claim 1, characterized in that, The cross-scale consistency constraint function includes at least one of the stiffness consistency function, energy consistency function, and failure consistency function.

5. The composite material structure design decision method based on cross-scale consistency constraints according to claim 4, characterized in that, The stiffness consistency function is used to measure the consistency between the structural equivalent stiffness derived from the equivalent stiffness parameters in the material-scale performance vector and the structural stiffness index in the structural-scale response vector. The expression is as follows: in, K pred To predict stiffness, K s For structural stiffness performance indicators; The energy consistency function is used to measure the consistency between the structural strain energy predicted based on the material-scale performance vector and the structural strain energy calculated based on the structural-scale response vector. Its expression is as follows: in, U pred and U actual These are the predicted strain energy vector and the actual strain energy vector obtained from finite element calculations, respectively. The failure consistency function is used to measure the consistency between the material-scale failure index in the material-scale performance vector and the structural-scale failure index in the structural-scale response vector in terms of failure trend or failure region. The expression is as follows: in, FI m For the scale failure index and FI s This is the structural scale failure index.

6. The composite material structure design decision method based on cross-scale consistency constraints according to claim 1, characterized in that, The consistency metric is compared with a preset threshold, including: When the consistency metric is a scalar, the scalar value is directly compared with a preset threshold. When the consistency metric is a vector, each component is compared with its corresponding preset threshold. If all components are greater than or equal to the corresponding threshold, the consistency requirement is satisfied. If any component is less than the corresponding threshold, the consistency requirement is not satisfied, and the component with the largest deviation is output as the main inconsistency item.

7. The composite material structure design decision method based on cross-scale consistency constraints according to claim 1, characterized in that, The generation of design optimization schemes based on the consistency metric value includes at least one of the following methods: Based on sensitivity analysis, the material-scale design parameters that have the greatest impact on the consistency metric value are identified, and design optimization schemes are generated based on the identification results. The gradient optimization algorithm is used to find the direction of adjustment of material-scale design parameters that improves the consistency metric value, and a design optimization scheme is generated based on the adjustment direction. Based on a predefined expert rule base, the parameter correction rule corresponding to the current consistency metric is matched, and a design optimization scheme is generated according to the correction rule.

8. The composite material structure design decision method based on cross-scale consistency constraints according to claim 1, characterized in that, The preset iteration termination conditions include reaching the maximum number of iterations, the change in the consistency metric value between two adjacent iterations being less than the convergence tolerance, or the iteration time exceeding the preset duration. When the preset iteration termination condition is reached and a design scheme that meets the consistency requirements is still not obtained, a design unusable flag is output, indicating the main physical quantity items and corresponding material scale design parameters that cause the consistency to be unsatisfactory.

9. A composite material structure design decision system based on cross-scale consistency constraints, characterized in that, include: The data acquisition module is used to acquire the material-scale performance vector and structural-scale response vector corresponding to the same set of material-scale design parameters; The consistency calculation module is used to input the material-scale performance vector and the structural-scale response vector into the cross-scale consistency constraint function to calculate a consistency metric value used to quantify the degree of physical compatibility between the two. A threshold comparison module is used to compare the consistency metric value with a preset threshold. The decision output module is used to determine that the current design scheme meets the consistency requirements when the consistency metric value is greater than or equal to a preset threshold, and output the current design scheme as a decision conclusion that the current design scheme is an available design scheme. When the consistency metric value is less than the preset threshold, it is determined that the current design scheme does not meet the consistency requirements. A design optimization scheme is generated based on the consistency metric value, and the design optimization scheme is used as a new material scale design parameter. The data acquisition module, consistency calculation module, threshold comparison module and decision output module are repeatedly started until a design scheme that meets the consistency requirements is obtained or the preset iteration termination condition is reached.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the composite material structure design decision method based on cross-scale consistency constraints as described in any one of claims 1 to 8.

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