Ultra-thin wing anti-bending performance quantitative evaluation method

Abstract

The invention provides an ultra-thin wing anti-bending performance quantitative evaluation method which comprises the following steps: acquiring geometric parameters and material characteristic data of an ultra-thin wing, constructing a digital twin model by adopting a finite element analysis method, and simulating stress distribution under boundary conditions to obtain initial mechanical response characteristics; the obtained risk assessment matrix is adopted to calibrate the limitation of a static test, and boundary condition constraints are fused to obtain a calibrated anti-bending energy index set; constructing an evaluation framework of a quantitative system through the calibrated anti-bending energy index set, classifying performance levels in different load scenes by adopting a support vector machine algorithm, and determining an overall structure reliability score; and iteratively updating the parameter setting of the digital twin model according to the obtained anti-bending performance improvement scheme, simulating the optimized flight performance, and obtaining the final quantitative evaluation system output.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a kind of ultra-thin wing anti-bending performance quantitative evaluation method. BACKGROUND

[0002] The rapid development of unmanned aerial vehicle technology makes the ultra-thin wing design become the key technology path to improve flight performance, and its anti-bending performance directly determines the flight safety and structural reliability of unmanned aerial vehicle. Ultra-thin wing reduces weight and drag by reducing thickness, but this design feature makes the wing more prone to bending deformation when subjected to load, so accurate detection of its anti-bending ability becomes crucial.

[0003] Current wing anti-bending detection methods mainly rely on traditional static loading tests, which often ignore the complex load environment faced by ultra-thin wings in actual flight. Existing detection methods lack a comprehensive consideration of wing response characteristics under different load conditions, and cannot fully reflect the true performance of ultra-thin wings in varying flight conditions. More importantly, traditional detection methods lack a specialized evaluation system for the special geometric characteristics of ultra-thin wings.

[0004] There is a complex interaction between the material properties of ultra-thin wings and their geometric parameters, and the physical properties of the material, such as elastic modulus and strength characteristics, will exhibit different mechanical behavior as the thickness of the wing decreases. This material-geometry coupling effect further affects the boundary constraint conditions of the wing, causing the stress distribution at the connection between the wing root and the fuselage to exhibit a unique transfer pattern. For example, when an ultra-thin wing is subjected to the same bending moment load, its stress concentration phenomenon is more pronounced than that of a conventional thickness wing, but existing detection standards cannot accurately quantify this difference, leading to deviations in anti-bending performance evaluation.

[0005] Therefore, how to establish a quantitative evaluation system that can comprehensively consider the interaction between material properties, geometric parameters and boundary conditions of ultra-thin wings, and accurately determine their anti-bending performance level under complex load environments, has become a key problem in the design and safety evaluation of ultra-thin wing structures for unmanned aerial vehicles. SUMMARY

[0006] The present application provides an ultra-thin wing anti-bending performance quantitative evaluation method, mainly including: By collecting the geometric parameters and material properties data of the ultra-thin wing, a digital twin model is constructed using finite element analysis method to simulate the stress distribution under boundary conditions, and the initial mechanical response characteristics are obtained; According to the initial mechanical response characteristics, a plurality of simulation data under complex load environment are obtained, and the Monte Carlo simulation algorithm is used to process the uncertainty factors to determine the influence degree of load change on bending moment load; If it is determined that the influence degree of load change on the bending moment load exceeds the preset threshold, the coupling effect index of the high-risk area is extracted from the simulation data, the interaction strength of material properties and geometric parameters is judged, and a risk assessment matrix is obtained; The obtained risk assessment matrix is used to calibrate the limitations of static testing, fuse boundary condition constraints, and obtain a calibrated bending and flexural energy index set; Through the calibrated bending and flexural energy index set, an evaluation framework of the quantitative system is constructed, the support vector machine algorithm is used to classify the performance level under different load scenarios, and the overall structure reliability score is determined; If the overall structure reliability score is determined to be lower than the preset threshold, the optimization path related to the coupling effect is extracted from the evaluation framework, the improvement potential of stress distribution is judged, and an anti-bending and flexural performance improvement scheme for the ultra-thin wing is obtained; According to the obtained anti-bending and flexural performance improvement scheme, the parameter settings of the digital twin model are iteratively updated, the optimized flight performance is simulated, and the final quantitative evaluation system output is obtained.

[0007] The technical scheme provided by the embodiment of the present application can include the following beneficial effects: The present application discloses an ultra-thin wing anti-bending performance quantitative evaluation method, which solves the technical problem that traditional static testing cannot accurately evaluate the wing structure reliability under complex load environment. The present application constructs a finite element digital twin model by collecting wing geometric parameter and material characteristic data, obtains initial mechanical response characteristics, uses Monte Carlo algorithm to process uncertainty factors, and determines the influence degree of load change on the bending moment load. When the influence degree exceeds the preset threshold, the coupling effect index of the high-risk area is extracted, a risk assessment matrix is generated, the limitations of static testing are calibrated, and a bending and flexural energy index set is obtained. Further, a quantitative evaluation framework is constructed by using a support vector machine algorithm, the performance level under different load scenarios is classified, the overall structure reliability score is determined. If the reliability score is not up to standard, the optimization path is extracted to develop a performance improvement scheme, the model parameters are iteratively updated, and the optimized flight performance is simulated. Finally, a complete quantitative evaluation system is output, realizing precise evaluation and optimization of the anti-bending performance of the ultra-thin wing. BRIEF DESCRIPTION OF DRAWINGS

[0008] Fig. 1 A flowchart of an ultra-thin wing anti-bending performance quantitative evaluation method of the present application.

[0009] Fig. 2 A schematic diagram of an ultra-thin wing anti-bending performance quantitative evaluation method of the present application.

[0010] Fig. 3 Another schematic diagram of an ultra-thin wing anti-bending performance quantitative evaluation method of the present application. DETAILED DESCRIPTION

[0011] The technical solutions of the present application will be described clearly and completely in combination with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0012] As Figs. 1-3 The ultrathin wing anti-bending performance quantification evaluation method can specifically include the following steps: S101, by collecting the geometric parameters and material characteristics data of the ultrathin wing, a digital twin model is constructed by using finite element analysis method to simulate the stress distribution under boundary conditions, and the initial mechanical response characteristics are obtained.

[0013] According to the above business content and the extracted related attributes, the following business solutions are generated; the geometric parameters and material characteristics data of the ultrathin wing are collected by sensors and stored as structured data sets to obtain initial input data. The structured data set is divided into grids and material properties are allocated by using finite element method to construct a digital twin model to obtain a wing mechanical model. If the grid quality of the wing mechanical model is lower than the preset threshold, the grid density is adjusted by using an adaptive grid optimization algorithm to obtain an optimized mechanical model. According to the optimized mechanical model, the boundary conditions are set, and the stress distribution is calculated by using the finite element analysis method to obtain the stress distribution data. The stress distribution data is partitioned by using the K-means clustering algorithm to extract the high stress area to obtain the stress partition characteristics. If the maximum stress value in the stress partition characteristics exceeds the preset threshold of the material strength, the geometric parameters are adjusted by using an iterative optimization algorithm to obtain updated geometric parameters. According to the updated geometric parameters, the grid division and stress calculation are repeated to obtain the final mechanical response characteristics.

[0014] Specifically, first, the geometric parameters of the ultra-thin wing are collected by a three-dimensional laser scanner to obtain accurate geometric data of the airfoil thickness of 2.5 mm, chord length of 1200 mm, and span length of 8000 mm. Meanwhile, the key material characteristic parameters of the carbon fiber composite material are obtained by using an ultrasonic thickness gauge and a material performance testing device, including the elastic modulus of 150 GPa, the Poisson's ratio of 0.3, and the density of 1.6 g / cm³. Based on the collected geometric and material data, a digital twin model is constructed by using the ANSYS finite element analysis software. The wing is discretized into about 500,000 nodes and 2.8 million units by using a tetrahedral mesh division algorithm, and the mesh quality coefficient is controlled to be above 0.85 to ensure the calculation accuracy. Boundary conditions are set in the digital twin model, including the fixed constraint at the wing root, the vertical load of 5000 N and the torsional load of 2000 N applied at the wing tip, and the aerodynamic pressure distribution caused by the wind speed of 15 m / s. The stress distribution cloud diagram is obtained after 120 iterations by solving the nonlinear equation set through the Newton-Raphson iteration algorithm. It is found that the maximum von Mises stress of 285 MPa appears at the wing root connection, and the maximum displacement of 45 mm is located at the wing tip position. Further analysis shows that the first-order bending frequency of the wing under the load is 12.5 Hz, and the torsional frequency is 38.2 Hz. These initial mechanical response characteristics provide an important data basis for subsequent structure optimization and safety evaluation, and also verify the high consistency of the digital twin model with the actual wing structure.

[0015] In S102, according to the initial mechanical response characteristics, a plurality of groups of simulation data under a complex load environment are obtained, and a Monte Carlo simulation algorithm is used to process uncertainty factors to determine the influence degree of load variation on the bending moment load.

[0016] According to the complex load, a plurality of groups of simulation data are generated, a Monte Carlo simulation algorithm is used to process the uncertainty in the environmental parameters to obtain a load variation distribution, a change trend is extracted according to the load variation distribution, a statistical analysis method is used to calculate the probability density of the change trend to obtain a probability density distribution, if the peak value of the probability density distribution exceeds a preset threshold, abnormal load data is removed through a data screening method to obtain an optimized load distribution, the response value of the bending moment load is calculated according to the optimized load distribution, a finite element analysis method is used to simulate the mechanical behavior under the load to obtain mechanical response data, the mechanical response data is partitioned by using a K-means clustering algorithm, a high-response region is extracted to obtain response partition data, the local concentration trend of the bending moment load is analyzed according to the response partition data, an interpolation method is used to generate a continuous response distribution to obtain the continuous response distribution, and if the local maximum value of the continuous response distribution exceeds a preset threshold of the material strength, the load input condition is optimized through a data adjustment method to obtain the final response characteristics.

[0017] Specifically, based on the established digital twin model, a Monte Carlo simulation algorithm is implemented through MATLAB programming. The load variation range is set as a random distribution of vertical load between 3000N and 8000N, torsional load between 1000N and 4000N, and wind speed fluctuation within the range of 10m / s to 25m / s. The Latin hypercube sampling method is used to generate 10000 sets of random load combination samples, each containing random values of vertical load, torsional load and wind speed. The generated load samples are imported into the finite element model for parallel computing, and 256 computing tasks are run simultaneously using a multi-core processor. The bending moment distribution data of the key sections of the wing are extracted each time. Through statistical analysis, it is found that when the vertical load increases from 3000N to 8000N, the maximum bending moment at the wing root increases from 1850N·m to 4920N·m, showing an approximately linear relationship with a correlation coefficient of 0.94. Further sensitivity analysis algorithm is used to calculate the influence weight of each load parameter on the bending moment response, the results show that the contribution of vertical load is 67.3%, the contribution of torsional load is 24.8%, and the contribution of wind load is 7.9%. Based on the Monte Carlo simulation results, a load-bending moment response surface model is constructed, and a quadratic polynomial fitting algorithm is used to establish a prediction equation with a fitting accuracy R² value of 0.987, providing a reliable mathematical model basis for real-time load monitoring and structural safety warning.

[0018] S103、If it is determined that the influence of load variation on bending moment load exceeds the preset threshold, the coupling effect index of the high-risk area is extracted from the simulation data, the interaction strength of material properties and geometric parameters is judged, and a risk assessment matrix is obtained.

[0019] According to the risk matrix, the principal component analysis algorithm is used to reduce the dimension of the coupling index to obtain a reduced index set; according to the reduced index set, the correlation coefficient of material properties and geometric parameters is calculated to determine the interaction strength ranking; if the absolute value of the first coefficient of the interaction strength ranking is greater than the preset threshold, the corresponding material property data is extracted from the high-risk area to obtain a key material data set; according to the key material data set, a classification model is trained through a support vector machine algorithm to judge the boundary of the high-risk area, and a boundary division result is obtained; according to the boundary division result, the geometric parameter samples within the boundary are extracted to obtain boundary geometric samples; according to the boundary geometric samples, an interpolation algorithm is used to generate a continuous interaction strength field to obtain a strength field distribution; if the local peak value of the strength field distribution exceeds the material property threshold, the geometric parameter input value is adjusted to obtain an optimized parameter set.

[0020] Specifically, in ANSYS Workbench, the preset bending moment influence threshold is 0.75, when the normalized influence coefficient output by the Monte Carlo simulation exceeds the value, the Python script automatically triggers the filtering of high-risk samples with wing root and wing tip section bending moment exceeding 3500 N·m from the 10000-group finite element result database, and a total of 2874 groups of data are extracted. Subsequently, the APDL command stream is called to batch read the geometric parameters of each sample, such as the material elastic modulus in the range of 190 GPa to 210 GPa and the wing chord length in the range of 1.2 m to 1.8 m, and construct a three-dimensional interaction matrix. The Sobol global sensitivity analysis algorithm is used to calculate the coupling effect index, and the difference between the first-order sensitivity index S1 and the total sensitivity index ST is set as the interaction intensity threshold. The calculation results show that the interaction intensity between elastic modulus and chord length is 0.182, and the interaction intensity between vertical load and torsional load is 0.267. Based on the interaction intensity matrix, a 4×4 risk assessment grid is generated, with the horizontal axis representing the material modulus segmented into 190 GPa, 195 GPa, 200 GPa, 205 GPa, and the vertical axis representing the chord length segmented into 1.2 m, 1.4 m, 1.6 m, 1.8 m. The numerical value in the grid represents the probability of wing root bending moment exceeding the limit under the corresponding combination. The probability range is 0.12 to 0.68 through Kriging surrogate model interpolation. Finally, the risk matrix is exported in CSV format for real-time calling by the subsequent digital twin platform, realizing automatic warning of high-risk areas.

[0021] In S104, the acquired risk assessment matrix is used to calibrate the limitations of static testing, and boundary condition constraints are fused to obtain a set of calibrated bending and folding energy indexes.

[0022] The stress distribution data in the risk assessment matrix is acquired, the deformation under static loading conditions is calculated by numerical integration method, and an initial deformation data set is obtained. According to the initial deformation data set, the influence coefficient of the test environment temperature and humidity parameters on the material elastic modulus is identified, and the environmental correction factor is determined. The elastic modulus is calibrated using the environmental correction factor. If the deviation of the corrected elastic modulus exceeds the preset range, the fixed end constraint force data in the boundary constraint condition is extracted to obtain a constraint force parameter group. The support point reaction force distribution is calculated by the constraint force parameter group, the stress concentration coefficient at the constraint boundary is solved by the finite element analysis method, and the boundary stress correction value is obtained. According to the boundary stress correction value, the maximum bearing capacity of the material under bending load is recalculated, and the corrected bearing force value is obtained. The unit mass bending and folding strength index is calculated by the ratio relationship between the corrected bearing force value and the material density parameter, and the standardized strength parameter is obtained. If the standardized strength parameter is lower than the material specification requirement, the loading rate and holding time configuration are adjusted, and the bending and folding energy value is measured again to obtain a set of calibrated bending and folding energy indexes.

[0023] Specifically, based on the existing risk assessment matrix data, the system automatically starts the static test calibration module, recalculates 1847 groups of static loading conditions by calling the Abaqus solver, sets the maximum deflection limit under fixed end constraint condition to 12.5mm, and triggers the calibration algorithm when the measured deflection deviates from the simulation prediction value by more than 8%. The original risk probability is corrected by using the Bayesian updating method, the measured yield strength value 385MPa obtained in the static test is taken as the prior information, the material reliability index is recalculated by combining the Weibull distribution parameter estimation algorithm, the shape parameter is adjusted to 2.34, and the scale parameter is corrected to 412MPa. The system immediately extracts the boundary condition constraint matrix, including the fixed end rotation constraint 0 degrees, the simply supported end displacement constraint in Z direction 0, the distributed load boundary in the wingspan direction according to the cosine function distribution, and the constraint stiffness coefficient is set to 1.2×10^8N / m. The constraint condition is integrated into the objective function by the Lagrange multiplier method, an optimization model containing 14 design variables is established, the sequential quadratic programming algorithm is used for iterative solution, and the convergence precision is set to 10^-6. The system automatically calculates the bending resistance energy index during calibration, which is defined as the energy absorption capacity of unit volume material under bending load, and the numerical range is 2.8 to 4.6J / cm³ by integrating the stress-strain curve area. Finally, a calibrated index set containing temperature compensation coefficient 0.92 and humidity influence factor 1.08 is generated, and stored in Oracle database for real-time query and call.

[0024] S105, through the calibrated bending resistance energy index set, an evaluation framework of the quantification system is constructed, the support vector machine algorithm is used to classify the performance level under different load scenarios, and the overall structure reliability score is determined.

[0025] The calibrated anti-bending energy index set data is obtained, the index values are standardized by a data preprocessing module to obtain a normalized index data matrix. According to the normalized index data matrix, a multi-dimensional quantitative evaluation framework is constructed, the weight coefficients of each index are determined by an analytic hierarchy process, and a weight distribution scheme is obtained. The index data is weighted by the weight distribution scheme, and if the weighted result exceeds the preset threshold range, the weight coefficient configuration is adjusted again to obtain an optimized comprehensive evaluation index. The support vector machine algorithm is used to train and model the comprehensive evaluation index, and the classification boundary parameters of different load scenarios are set to obtain a performance level classification model. According to the performance level classification model, the structure parameter data to be evaluated is input, the performance grade category to which it belongs is identified through the model prediction function, and the preliminary reliability level is determined. Through the preliminary reliability level data, the risk probability value is calculated combined with the historical failure rate statistical information, and if the risk probability value is higher than the safety threshold, the reliability score weight is reduced to obtain a corrected reliability score. The corrected reliability score is compared with the industry standard benchmark value for analysis to generate a structure overall reliability evaluation report, and the final structure safety performance evaluation result is obtained.

[0026] Specifically, the system automatically calls the calibrated anti-bending energy index set data, extracts 683 sample points covering the range of 2.8 to 4.6 J / cm³, and constructs a multi-dimensional quantitative evaluation framework. The principal component analysis algorithm is used to reduce the dimension of the index set, and the first five principal components with a cumulative contribution rate of 92.7% are retained, corresponding to the bending stiffness, torsional damping, fatigue life, impact toughness and environmental adaptability dimensions. The system then starts the support vector machine classification module, sets the kernel parameter γ to 0.15 and the penalty parameter C to 128 using the radial basis kernel function, and divides the load scenarios into four levels: light load, medium load, heavy load and extreme load. For the light load scenario with a stress level below 180 MPa, the system automatically marks it as A-level performance, corresponding to a reliability weight coefficient of 1.0. The medium load scenario covers a stress range of 180-320 MPa, and the classification boundary is optimized by the cross-validation algorithm, with a classification accuracy of 96.3%, corresponding to a B-level performance weight of 0.85. When the stress exceeds 320 MPa in the heavy load working condition, the system uses a nonlinear kernel function for feature mapping, and automatically adjusts the parameters using the grid search algorithm, finally determining the number of support vectors to be 47. Based on the classification results, the system calculates the overall structure reliability score using the weighted average method, and each load level is assigned a weight according to the service frequency, with light load accounting for 45%, medium load 35%, heavy load 18%, and extreme load 2%. Through Monte Carlo simulation verification of 1000 random samplings, the reliability score is 87.4, with a confidence interval of [85.1, 89.7], and is automatically stored in the evaluation database.

[0027] S106, if it is determined that the overall structure reliability score is lower than the preset threshold value, the optimization path related to the coupling effect is extracted from the evaluation framework, the improvement potential of the stress distribution is judged, and the anti-bending performance improvement scheme for the ultra-thin wing is obtained.

[0028] The reliability score data of the current structure is obtained, and the threshold comparison module is used to judge whether the score is lower than the preset threshold value. If the reliability score is lower than the threshold value, the optimization path extraction program is started, and the structure parameter set to be optimized is obtained. According to the structure parameter set to be optimized, the optimization path information related to the coupling effect is extracted from the evaluation framework database, the coupling relationship strength between parameters is identified by using the correlation analysis method, and the main coupling effect influencing factor is determined. Through the main coupling effect influencing factor, a stress distribution calculation model is established, the geometric size parameters and material attribute data of the ultra-thin wing are input, the stress distribution state under the current load condition is calculated, and the stress concentration region coordinate information is obtained. The stress concentration region coordinate information is used to analyze the stress gradient change law of each region, to judge the improvement potential of the stress distribution, and if the improvement potential is higher than the set value, the region is marked as the key optimization target, and the optimization target region list is obtained. According to the optimization target region list, the structure parameter configuration of the ultra-thin wing is adjusted, the airfoil thickness distribution and the reinforcement rib arrangement scheme are modified by using the parameter adjustment algorithm, the modified stress distribution data is recalculated, and the parameter adjustment effect is determined. Through the parameter adjustment effect data, the improvement amplitude of the anti-bending performance is evaluated, if the improvement amplitude reaches the expected target, the current parameter configuration scheme is saved, and the final performance improvement scheme parameter set is obtained. The final performance improvement scheme parameter set is used to generate an ultra-thin wing structure optimization configuration file, which contains the modified geometric parameters and material distribution information, and a complete anti-bending performance improvement implementation scheme is obtained.

[0029] Specifically, the system detects that the reliability score is 73.2, which is lower than the preset threshold of 80, and automatically starts the coupling effect analysis module, calling the multi-physical field simulation engine to perform in-depth analysis on the ultra-thin wing structure. The system first extracts the material-geometry-load triple coupling matrix, identifies three key coupling nodes at the wing root connection, wing tip bending and torsion area, and middle section load bearing area, and expands the original 23,000 elements to 87,000 high-precision elements through a finite element mesh refinement algorithm. Then start the stress gradient analysis algorithm, use adaptive step size 0.02mm to scan the wing thickness distribution layer by layer, find that the stress concentration factor at the wing root is 2.84, which is 29% higher than the design limit value 2.2. The system calls the topology optimization algorithm, sets the volume constraint to 95% of the original structure, and the iteration convergence accuracy to 1e-6. After 156 iterations, the optimal material distribution scheme is determined. For the identified weak areas, the system automatically generates three sets of improvement strategies: scheme one reduces stress concentration by locally thickening 0.8mm, and predicts that the reliability is improved to 81.6; scheme two uses a gradual transition design to achieve smooth thickness changes within a 15cm range, and the stress distribution uniformity is improved by 34.7%; scheme three combines fiber layer angle optimization, adjusts the main load bearing direction fiber angle from 45° to 38°, and the bending stiffness increases by 12.3%. The system evaluates the three sets of schemes through a multi-objective genetic algorithm, sets the population size to 200, the crossover probability to 0.8, and the mutation probability to 0.1, and after 80 generations of evolution, the optimal combination scheme is determined, and the expected overall reliability score is improved to 85.9.

[0030] S107、According to the obtained anti-bending performance improvement scheme, iteratively update the parameter settings of the digital twin model, simulate the optimized flight performance, and obtain the final quantitative evaluation system output.

[0031] The parameter configuration data of the anti-bending performance improvement scheme is obtained, the airfoil geometric modification information and the material distribution adjustment scheme are read, if the parameter configuration data integrity verification is passed, the digital twin model updating program is started, and the model structure setting list to be updated is obtained. According to the model structure setting list, the parameter modification interface of the digital twin platform is called, the airfoil grid node coordinates and the material attribute matrix are updated, the finite element calculation grid is reconstructed, and the updated digital twin model configuration state is determined. Through the digital twin model configuration state information, the flight performance simulation module is started, the preset flight working condition parameters and boundary condition data are input, the aerodynamic calculation program is run, and the load response distribution data of the optimized structure are obtained. Using the load response distribution data, the bending moment and shear force values of each key section are calculated, the spatial distribution law of the structural deformation characteristics is analyzed, if the convergence of the deformation characteristic data meets the accuracy requirement, the key performance parameters are extracted, and the structural response characteristic data set is obtained. According to the structural response characteristic data set, a multi-dimensional performance evaluation index system is established, the analytic hierarchy process is used to determine the weight coefficients of each index, the anti-bending stiffness improvement rate and the load bearing capacity improvement ratio are calculated, and the quantitative evaluation score is obtained. Through the quantitative evaluation score, the performance difference amplitude before and after optimization is compared, the comprehensive performance evaluation matrix is generated, if the evaluation result reaches the expected improvement target, the final optimization effect verification report is output, and the performance improvement verification result of the digital twin model is determined. Using the performance improvement verification result, the historical performance database of the digital twin system is updated, the implementation effect and the key parameter change trajectory of the optimization scheme are recorded, the performance improvement knowledge base is established, and the complete quantitative evaluation system output data are obtained.

[0032] Specifically, the system initiates the digital twin model parameter updating process based on the determined optimal combination scheme. First, it calls the parameter mapping algorithm to convert the physical optimization results into a format recognizable by the digital model. The system adopts an incremental updating strategy, sets the parameter updating step size to 0.05, and the convergence criterion to a relative error less than 1e-4. It reconstructs the stiffness matrix, mass matrix, and damping matrix of the model through the Newton-Raphson iteration method. During the updating process, the system monitors the wing bending stiffness parameter, which increases from the original value of 4.2×10^6 N·m² to 5.8×10^6 N·m², and the torsional stiffness coefficient, which increases from 1.9×10^5 N·m² to 2.4×10^5 N·m². Subsequently, the system initiates the flight performance simulation module, sets the simulation time step to 0.001 seconds and the total simulation time to 300 seconds, and uses the fourth-order Runge-Kutta integration algorithm to solve the flight dynamics equations. During the simulation process, the system simulates three typical working conditions: cruising, maneuvering, and gust disturbance. It adjusts the calculation accuracy in real time through the adaptive grid algorithm and finds that the maximum deformation of the optimized wing under a 6g maneuvering load decreases from 127mm to 89mm. The system calls the fuzzy comprehensive evaluation algorithm to build a quantitative evaluation system, sets the weight vector as [0.35, 0.28, 0.22, 0.15] corresponding to the structure strength, aerodynamic performance, weight index, and manufacturing cost, respectively, calculates the membership functions of each index through the analytic hierarchy process, and finally outputs the comprehensive performance score as 92.4 points.

[0033] The above only lists some preferred embodiments of the present application, but the present application is not limited thereto, and many improvements and changes can be made. Any improvements and changes made on the basis of the basic principles of the present application should be considered as falling within the scope of protection of the present application.

Claims

1. A method for quantitatively evaluating the anti-bending performance of an ultrathin airfoil, characterized in that, The method comprises: By collecting the geometric parameters and material property data of the ultrathin wing, a digital twin model is constructed by using a finite element analysis method to simulate the stress distribution under boundary conditions, and initial mechanical response characteristics are obtained; According to the initial mechanical response characteristics, a plurality of sets of simulation data under complex load environments are obtained, and the influence degree of load changes on the bending moment load is determined by using a Monte Carlo simulation algorithm to process uncertain factors; If the influence degree of load changes on the bending moment load exceeds a preset threshold, the coupling effect indicators of the high-risk area are extracted from the simulation data, the interaction strength of material properties and geometric parameters is judged, and a risk assessment matrix is obtained; The obtained risk assessment matrix is used to calibrate the limitations of static testing, fuse boundary condition constraints, and obtain a calibrated bending and folding resistance index set; By using the calibrated bending and folding resistance index set, an evaluation framework of the quantitative system is constructed, the performance level under different load scenarios is classified by using a support vector machine algorithm, and the overall structure reliability score is determined; If the overall structure reliability score is lower than a preset threshold, the optimization path related to the coupling effect is extracted from the evaluation framework, the improvement potential of the stress distribution is judged, and an anti-bending performance improvement scheme for the ultrathin wing is obtained; According to the obtained anti-bending performance improvement scheme, the parameter settings of the digital twin model are iteratively updated, the optimized flight performance is simulated, and the final quantitative evaluation system output is obtained.

2. The method according to claim 1, wherein, The method comprises: According to the above business content and the extracted related attributes, the following business solution is generated; the geometric parameters and material property data of the ultrathin wing are collected by sensors, stored as a structured data set, and initial input data is obtained; The structured data set is meshed and material properties are assigned by using a finite element method to construct a digital twin model, and a wing mechanical model is obtained; If the mesh quality of the wing mechanical model is lower than a preset threshold, the mesh density is adjusted by using an adaptive mesh optimization algorithm to obtain an optimized mechanical model; According to the optimized mechanical model, the boundary conditions are set, the stress distribution is calculated by using a finite element analysis method, and stress distribution data is obtained; The stress distribution data is partitioned by using a K-means clustering algorithm, a high stress area is extracted, and stress partition characteristics are obtained; If the maximum stress value in the stress partition characteristics exceeds a preset threshold of the material strength, the geometric parameters are adjusted by using an iterative optimization algorithm, and updated geometric parameters are obtained; According to the updated geometric parameters, the meshing and stress calculation are repeated to obtain the final mechanical response characteristics.

3. The method according to claim 1, wherein the method is characterized by, The method comprises: According to the complex load, a plurality of groups of simulation data are generated, the uncertainty in the environmental parameters is processed by using a Monte Carlo simulation algorithm to obtain a load change distribution, according to the load change distribution, a change trend is extracted, a statistical analysis method is used to calculate the probability density of the change trend to obtain a probability density distribution, if the peak value of the probability density distribution exceeds a preset threshold value, abnormal load data is removed by using a data screening method to obtain an optimized load distribution, according to the optimized load distribution, the response value of the bending moment load is calculated, the mechanical behavior under the action of the load is simulated by using a finite element analysis method to obtain mechanical response data, the mechanical response data is partitioned by using a K-means clustering algorithm, a high-response region is extracted to obtain response partition data, according to the response partition data, the local concentration trend of the bending moment load is analyzed, an interpolation method is used to generate a continuous response distribution to obtain the continuous response distribution, if the local maximum value of the continuous response distribution exceeds a preset threshold value of the material strength, the load input condition is optimized by using a data adjustment method to obtain the final response characteristics.

4. The method according to claim 1, wherein, If it is determined that the influence degree of the load change on the bending moment load exceeds a preset threshold value, the coupling effect index of the high-risk region is extracted from the simulation data, the interaction strength of the material properties and the geometric parameters is judged, a risk assessment matrix is obtained, including: According to the risk matrix, the principal component analysis algorithm is used to reduce the dimension of the coupling index to obtain a reduced index set, according to the reduced index set, the correlation coefficient of the material properties and the geometric parameters is calculated, the interaction strength ranking is determined, if the absolute value of the first coefficient of the interaction strength ranking is greater than a preset threshold value, the corresponding material property data in the high-risk region is extracted to obtain a key material data set; according to the key material data set, a support vector machine algorithm is used to train a classification model to judge the boundary of the high-risk region to obtain a boundary division result; according to the boundary division result, the geometric parameter samples within the boundary are extracted to obtain boundary geometric samples; according to the boundary geometric samples, an interpolation algorithm is used to generate a continuous interaction strength field to obtain a strength field distribution; if the local peak value of the strength field distribution exceeds the material property threshold value, the geometric parameter input value is adjusted to obtain an optimized parameter set.

5. The method according to claim 1, wherein, The risk assessment matrix obtained is calibrated for the limitation of static testing, and the boundary condition constraint is fused to obtain a calibrated anti-bending energy index set, including: The stress distribution data in the risk assessment matrix is acquired, the deformation under static loading conditions is calculated by a numerical integration method, an initial deformation data set is obtained, the influence coefficient of the test environment temperature and humidity parameters on the material elastic modulus is identified according to the initial deformation data set, and the environmental correction factor is determined, the elastic modulus is calibrated by using the environmental correction factor, if the deviation of the elastic modulus after correction exceeds a preset range, the fixed end constraint force data in the boundary constraint condition is extracted, a constraint force parameter group is obtained, the support point counterforce distribution is calculated by using the constraint force parameter group, the stress concentration coefficient at the constraint boundary is solved by using a finite element analysis method, the boundary stress correction value is obtained, the maximum bearing capacity of the material under the bending load is recalculated according to the boundary stress correction value, and a corrected bearing capacity value is obtained, the unit mass bending strength index is calculated by using the ratio relationship between the corrected bearing capacity value and the material density parameter, and a standardized strength parameter is obtained, if the standardized strength parameter is lower than the material specification requirement, the loading rate and the holding time configuration are adjusted, the bending strength value is measured again, and a calibrated bending strength index set is obtained.

6. The method according to claim 1, wherein, The evaluation framework of the quantitative system is constructed by using the calibrated bending strength index set, the support vector machine algorithm is used to classify the performance levels under different load scenarios, and the overall structure reliability score is determined, including: The calibrated bending strength index set data is acquired, the index values are standardized by using a data preprocessing module, and a normalized index data matrix is obtained; A multi-dimensional quantitative evaluation framework is constructed according to the normalized index data matrix, the weight coefficients of each index are determined by using an analytic hierarchy process, and a weight distribution scheme is obtained; The index data is weighted by using the weight distribution scheme, if the weighted result exceeds a preset threshold range, the weight coefficient configuration is adjusted again, and an optimized comprehensive evaluation index is obtained; The support vector machine algorithm is used to train and model the comprehensive evaluation index, the classification boundary parameters of different load scenarios are set, and a performance level classification model is obtained; According to the performance level classification model, the structure parameter data to be evaluated is input, the performance level classification is identified by using the model prediction function, and the preliminary reliability level is determined; The risk probability value is calculated by combining the historical failure rate statistical information according to the preliminary reliability level data, if the risk probability value is higher than the safety threshold, the reliability score weight is reduced, and a modified reliability score is obtained; The modified reliability score is compared with the industry standard benchmark value, a structure overall reliability evaluation report is generated, and the final structure safety performance evaluation result is obtained.

7. The method according to claim 1, wherein the method is characterized by, If it is determined that the overall structure reliability score is lower than the preset threshold, the optimization path related to the coupling effect is extracted from the evaluation framework, the improvement potential of the stress distribution is judged, and the bending performance improvement scheme for the ultra-thin wing is obtained, including: The reliability score data of the current structure is acquired, the threshold comparison module is used to judge whether the score is lower than the preset threshold, if the reliability score is lower than the threshold, the optimization path extraction program is started, and the structure parameter set to be optimized is obtained; According to the structure parameter set to be optimized, the coupling effect related optimization path information is extracted from the evaluation framework database, the coupling relationship strength between parameters is identified by using the correlation degree analysis method, and the main coupling effect influence factor is determined; Through the main coupling effect influence factor, a stress distribution calculation model is established, the geometric size parameters and material attribute data of the ultra-thin wing are input, the stress distribution state under the current load condition is calculated, and the stress concentration region coordinate information is obtained; Using the stress concentration region coordinate information, the stress gradient variation law of each region is analyzed, the improvement potential of the stress distribution is judged, if the improvement potential is higher than the set value, the region is marked as the key optimization target, and the optimization target region list is obtained; According to the optimization target region list, the structure parameter configuration of the ultra-thin wing is adjusted, the airfoil thickness distribution and the reinforcement rib arrangement scheme are modified by the parameter adjustment algorithm, the stress distribution data after modification is recalculated, and the parameter adjustment effect is determined; Through the parameter adjustment effect data, the improvement amplitude of the bending resistance performance is evaluated, if the improvement amplitude reaches the expected target, the current parameter configuration scheme is saved, and the final performance improvement scheme parameter set is obtained; Using the final performance improvement scheme parameter set, an ultra-thin wing structure optimization configuration file is generated, which contains the modified geometric parameters and material distribution information, and a complete bending resistance performance improvement implementation scheme is obtained.

8. The method according to claim 1, wherein the method is characterized by, According to the obtained bending resistance performance improvement scheme, the parameter settings of the digital twin model are iteratively updated, the optimized flight performance is simulated, and the final quantitative evaluation system output is obtained, including: Obtain the parameter configuration data of the bending resistance performance improvement scheme, read the airfoil geometric modification information and material distribution adjustment scheme, if the parameter configuration data integrity verification is passed, start the digital twin model update program, and obtain the model structure setting list to be updated; According to the model structure setting list, call the parameter modification interface of the digital twin platform, update the airfoil grid node coordinates and material attribute matrix, reconstruct the finite element calculation grid, and determine the updated digital twin model configuration state; Through the digital twin model configuration state information, start the flight performance simulation module, input the preset flight working condition parameters and boundary condition data, run the aerodynamic calculation program, and obtain the load response distribution data of the optimized structure; Using the load response distribution data, the bending moment and shear force values of each key section are calculated, the spatial distribution law of the structural deformation characteristics is analyzed, if the convergence of the deformation characteristic data meets the accuracy requirement, the key performance parameters are extracted, and the structural response characteristic data set is obtained; According to the structural response characteristic data set, a multi-dimensional performance evaluation index system is established, the weight coefficients of each index are determined by using the analytic hierarchy process, the bending stiffness improvement rate and the load bearing capacity improvement proportion are calculated, and the quantitative evaluation score is obtained; Through the quantitative evaluation score, the performance difference amplitude before and after optimization is compared, a comprehensive performance evaluation matrix is generated, if the evaluation result reaches the expected improvement target, an optimization effect verification report is output, and the performance improvement verification result of the digital twin model is determined; The performance improvement verification result is used to update a historical performance database of the digital twin system, record implementation effects and key parameter change trajectories of the optimization scheme, establish a performance improvement knowledge base, and obtain complete quantitative evaluation system output data.

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