Airplane model wing rapid correction method considering random uncertainty

Abstract

The invention provides an airplane model wing rapid correction method considering random uncertainty, belongs to the technical field of aerospace, and solves the problems that a traditional random model correction method shows relatively high calculation complexity in model correction; in order to solve the technical problems in the prior art that an airplane model is simulated through a finite element technology and a large number of random samples containing wing structure parameters are generated, and errors are possibly caused between the model and an actual structure when multi-source uncertainty is processed; a CNN is adopted to construct a mapping relation between wing dynamic response and parameters, and the model is used for parameter correction. Designing and training a CNN model, and predicting a mean value and a variance of a wing size by taking five groups of wing inherent frequencies as input; a data generation and processing pipeline is established in ANSYS and Python environments, and the process is automated to generate samples. And training a CNN (Convolutional Neural Network) by using the generated data, and carrying out parameter inversion so as to quickly correct the uncertainty in the model.

Description

Technical Field

[0001] The invention relates to a method for quickly correcting an aircraft model wing taking random uncertainty into consideration, and belongs to the technical field of aerospace. Background Art

[0002] In the field of aerospace, in order to improve efficiency and reduce costs, finite element technology is often used to simulate aircraft structures. Finite element technology is a numerical analysis method used to predict the response behavior of complex structures. By dividing the structure into small finite element units, engineers can accurately simulate the response of each part of the structure under different conditions. This technology has become a basic tool for studying the performance of aircraft structures. At the same time, the rise of deep learning technology has brought new changes to various fields. Deep learning can effectively capture complex nonlinear relationships by constructing multi-layer neural networks, and has achieved remarkable results in pattern recognition, image processing, etc. Combining deep learning with traditional engineering technology has become a hot topic of research.

[0003] The existing technology has the following defects: 1. The traditional random model correction method relies on the construction of optimization algorithms, which usually consumes a lot of computing resources and time. This method exhibits high computational complexity in model correction, making real-time or fast correction difficult to achieve. 2. The existing method may cause errors between the model and the actual structure when dealing with multi-source uncertainties, which limits its applicability and accuracy in dynamic environments. Summary of the invention

[0004] The present invention aims to solve the technical problems that the traditional random model correction method has high computational complexity in model correction and may cause errors between the model and the actual structure when dealing with multi-source uncertainty, and then proposes a method for rapid correction of aircraft model wings considering random uncertainty, which specifically includes:

[0005] Step 1: Perform finite element modeling on the aircraft wing model, perform dynamic simulation on the finite element model of the aircraft wing, and complete the parameterization setting of the aircraft wing model, wherein the geometric dimensions of the aircraft wing, wing length a and wing width b, are selected as uncertain parameters in the parameterization setting;

[0006] Step 2: Input the parameters of the wing model to be corrected into the finite element model for simulation calculation to obtain the corresponding structural characteristic quantities;

[0007] Step 3: In the Python and Nastran environments, generate multiple random samples of wing structural parameters, calculate the natural frequencies in batches, and complete the construction of the training data set;

[0008] Step 4: Construct a convolutional neural network to learn the mapping relationship between wing dynamic response and parameters;

[0009] Step 5: Use the training data set to train the convolutional neural network, input the parameters of the wing model to be corrected into the trained convolutional neural network for parameter inversion, and obtain the model parameters generated by the inversion;

[0010] Step 6: Input the model parameters generated by inversion into the finite element model to calculate the corrected structural characteristic quantities;

[0011] Step 7: Calculate the error between the corrected structural feature and the actual structural feature. If the error is greater than the preset value, process the random sample and the convolutional neural network until the error between the corrected structural feature and the actual structural feature is less than the preset value, thus completing the rapid correction of the aircraft model wing.

[0012] Preferably, the step of establishing a finite element model of the aircraft wing in step 1 comprises:

[0013] Step 1.1.1: Select quadrilateral elements as mesh elements, use the mesh generator to perform global mesh division on the wing model, and ensure that the mesh is evenly distributed after division;

[0014] Step 1.1.2: Dense meshing is performed in the stress concentration area of ​​the wing model, and the mesh quality is tested using finite element software to complete the meshing of the wing model;

[0015] Step 1.1.3: Select aluminum as the material property of the wing model, set the elastic modulus, Poisson's ratio and density of the aluminum, and complete the establishment of the finite element model.

[0016] Preferably, the step of performing dynamic simulation on the finite element model of the aircraft wing in step 1 comprises:

[0017] Step 1.2.1: Perform finite element analysis using Nastrn software;

[0018] Step 1.2.2: Read the finite element analysis results and obtain the first six natural frequencies of the wing;

[0019] Step 1.2.3: Perform static analysis on the finite element model of the aircraft wing to evaluate the strength and displacement of the wing under all loads and complete the parameterization of the aircraft wing model.

[0020] Preferably, step 1.2.1 specifically includes:

[0021] Step 1.2.1.1: Check the integrity of the wing model and the correctness of the finite element model parameter settings;

[0022] Step 1.2.1.2: Import the set finite element model, set the calculation options and solution parameters;

[0023] Step 1.2.1.3: Start the solver to solve the problem and monitor the calculation status and convergence in real time during the finite element analysis process.

[0024] Preferably, step 3 specifically includes:

[0025] Step 3.1: Set the standard deviation of the two sets of random variables a and b and the generation range of the means of the two sets of random variables a and b;

[0026] Step 3.2: Use the rand function to generate m random number sample sets;

[0027] Step 3.3: Based on the random standard deviation and mean set in step 3.1, use Gaussian distribution to generate n, n∈m wing length and wing width data on the right side of the wing, and obtain the wing length and wing width data on the left side of the wing by reflecting the wing length and wing width data on the right side of the wing. Repeat the above steps to iteratively generate wing size data;

[0028] Step 3.4: Save the wing size data to an Excel file;

[0029] Step 3.5: Read the wing size data from the Excel file and call the Nastran software to batch calculate the first six natural frequencies.

[0030] Preferably, step 3.4 specifically includes:

[0031] Step 3.4.1: After completing each iteration of wing size data generation, save the currently calculated wing length a and wing width b data to a local file;

[0032] Step 3.4.2: Use the pandas library to read and process the wing size data of each iteration, calculate the summary wing length a and wing width b, and save the summary data to an Excel file named after the iteration number;

[0033] Step 3.4.3: Use the shutil module and os library to manage the generated Excel files, including copying and deleting operations.

[0034] Preferably, step 3.5 specifically includes:

[0035] Step 3.5.1: Read the wing size data from the Excel file, generate the mesh coordinates of the finite element model, create the aircraft wing model bdf file, and use the generated finite element model mesh coordinates to replace the corresponding mesh coordinates in the bdf file;

[0036] Step 3.5.2: Use Python script to call Nastran software to perform finite element analysis of the wing using the aircraft wing model bdf file, and calculate the first six natural frequencies;

[0037] Step 3.5.2: Read and process the output of Nastran software, extract the natural frequency data, and store the natural frequency data and the generated wing geometry parameters in the corresponding Excel file to complete the construction of the training data set.

[0038] Preferably, in step 4, the convolutional neural network includes an input layer, a convolutional layer, a pooling layer, a DroOut layer, a flattening layer and a fully connected layer, and the loss function of the convolutional neural network is a mean square error function;

[0039] The input layer is used to receive data of shape (n, 5), where n is the number of wing natural frequency data and 5 is the first five natural frequency data of the wing;

[0040] The convolution layer includes the first convolution layer and the second convolution layer, which are used to extract the features of the first five natural frequency data;

[0041] The first convolutional layer has a layer type of Conv1D, a number of neurons of 64, a convolution kernel size of 3, and an activation function of ReLU.

[0042] The layer type of the second convolutional layer is Conv1D, the number of neurons is 128, the convolution kernel size is 3, and the activation function is the ReLU function;

[0043] The flattening layer is used to flatten the convolutional data output and convert it into one-dimensional data;

[0044] The pooling layer is used to reduce the dimension of the first five natural frequency data. The layer type of the pooling layer is MaxPooling1D, and the pooling window size is 2;

[0045] The fully connected layer is used to process the first five natural frequency data after pooling to obtain the predicted value of the natural frequency data. The number of neurons in the fully connected layer is 128, and the activation function is the ReLU function.

[0046] The DroOut layer is set to a ratio of 0.5, which is used to discard the neuron output at a ratio of 0.5;

[0047] The output layer is used to output the predicted value of the natural frequency data. The number of neurons in the output layer is 4, and the activation function is linear. The 4 neurons correspond to the mean value of wing length a, the variance of wing length a, the mean value of wing width b, and the variance of wing width b.

[0048] The expression of the mean square error function is:

[0049]

[0050] In formula (1), n ​​is the number of wing natural frequency data, y i is the true value of the natural frequency data, ypred is the predicted value of the natural frequency data.

[0051] Preferably, the step of using the training data set to train the convolutional neural network in step 5 includes:

[0052] Step 5.1: Perform minimum-maximum normalization on the input training data and scale the training data to the range of (0,1) to facilitate processing of data of different magnitudes and improve the accuracy of the algorithm;

[0053] Step 5.2: Train using early stopping.

[0054] Step 5.3: Use the LOSS function and the mean absolute error function to evaluate the predicted value of the natural frequency data output by the training;

[0055] The expression of minimum-maximum normalization is:

[0056]

[0057] In formula (2), x′ is the normalized training data, x min is the minimum value of the training data, x max is the maximum value of the training data.

[0058] Preferably, the processing of the random samples and the convolutional neural network in step 7 includes:

[0059] The training data set of the convolutional neural network is expanded, the structure of the convolutional neural network is adjusted, and the convolutional neural network is retrained.

[0060] The beneficial effects of the present invention are:

[0061] 1. The present invention uses CNN for parameter inversion, which significantly reduces the calculation and correction time, improves the speed of model correction, and enables it to be applied to design adjustments in real time.

[0062] 2. The present invention constructs a CNN network, which can extract and learn complex data features, improve the accuracy of prediction, make the correction of the mean and variance of model parameters more precise, and improve the reliability of the design.

[0063] 3. The present invention constructs Python and Nastran interfaces to achieve one-click data generation and analysis, realizes process automation, reduces manual intervention, and improves work efficiency.

[0064] 4. The present invention utilizes the structural adaptability of the CNN network to process diverse input parameters, adapt to different modeling requirements, and easily cope with a variety of design changes.

[0065] 5. The present invention reduces sample requirements and improves data utilization through deep learning, thereby effectively reducing experimental data requirements and controlling cost consumption.

[0066] 6. The present invention overcomes the problem of high computational complexity in traditional methods and improves correction accuracy and efficiency by adopting a convolutional neural network to construct a mapping model between the random response of structural dynamics and the random characteristics of model parameters. In addition, by generating a large number of random samples and corresponding structural natural frequency data sets and training them with a neural network, a rapid and accurate correction of the random characteristics of model parameters is achieved. The present invention significantly improves the deficiencies of traditional methods in dealing with random characteristics and supports efficient decision-making and improvements in practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic flow chart of a method for quickly correcting an aircraft model wing taking random uncertainty into consideration provided by the present invention;

[0068] Figure 2 A schematic diagram of the finite element model mesh and specific mesh parameters provided by the present invention;

[0069] Figure 3 A schematic diagram of the finite element model of an aircraft wing provided by the present invention;

[0070] Figure 4 The simulation result diagram of the natural frequency of the wing and the corresponding modal vibration shape provided by the present invention;

[0071] Figure 5 The CNN network architecture diagram provided by the present invention;

[0072] Figure 6 A schematic diagram showing the comparison between the random parameter values ​​and the target values ​​of the wing model provided by the present invention;

[0073] Figure 7 A schematic diagram showing the comparison between the corrected wing length a provided by the present invention and the random distribution of the target value;

[0074] Figure 8 A schematic diagram showing the comparison between the corrected wing width b provided by the present invention and the random distribution of the target value;

[0075] Fig. 9 This is a random distribution diagram of the first five corrected natural frequencies provided by the present invention. DETAILED DESCRIPTION

[0076] Combination Figure 1-3 To illustrate this embodiment, Figure 1 As shown, the steps of a method for quickly correcting an aircraft model wing considering random uncertainty described in this embodiment include:

[0077] S1: Finite element modeling of the aircraft wing model;

[0078] S101: Select unit type: Common unit types include tetrahedral unit, hexahedral unit, etc. In this embodiment, quadrilateral unit (Quad) is used;

[0079] S102: Meshing step: Global mesh generation: Use the Paver mesh generator to perform global meshing on the wing model to ensure that the mesh is evenly distributed;

[0080] S103: Local mesh optimization: Dense meshing is performed in stress concentration areas of the wing model to improve the calculation accuracy of these areas. For example, the mesh near the wing root and wing tip needs to be particularly refined.

[0081] S104: Mesh quality check: Use the quality check function in the finite element software to check the mesh quality, such as the unit distortion rate, aspect ratio, mesh angle, etc., to ensure that the mesh quality meets the requirements;

[0082] The specific parameters of the finite element model and mesh are as follows: Figure 2 As shown in the figure, the specific parameters of the mesh are as follows: (1) Number of nodes: 1979; (2) Number of elements: 1337; (3) Element shape: quadrilateral (Quad); (4) Mesher type: Paver; (5) Topology type: Quad4; (6) Global edge length: automatically calculated, value 0.01; (7) Surface list: Surface 1:14.

[0083] S105: Material property setting: Material properties are an important part of the finite element analysis model. In this embodiment, aluminum is used as the material. Aluminum is widely used in the aerospace field and has excellent specific strength and specific stiffness. The setting of material properties directly affects the accuracy of the model and the reliability of the analysis results. According to the characteristics of aluminum, relevant material parameters are set to accurately simulate its mechanical behavior. The following are the setting steps:

[0084] S10501: Elastic modulus: The elastic modulus of aluminum is set at 70 GPa, which is a measure of its stiffness;

[0085] S10502: Poisson's ratio: The Poisson's ratio of aluminum is usually 0.33, which represents the ratio of the lateral deformation to the longitudinal deformation of the material;

[0086] S10503: Density: Set the density of aluminum to 2.7g / cm 3 , which is used to calculate the model mass and inertial properties.

[0087] The wing finite element model of the aircraft model established later in this embodiment is as follows Figure 3As shown, for parameterization, this embodiment selects the geometric dimensions of the aircraft wing, wing length a and wing width b, as uncertain parameters.

[0088] S2: Perform dynamic simulation on the finite element model of the aircraft wing;

[0089] In order to study the dynamics of the aircraft model structure, this implementation method first performs dynamic finite element simulation on the aircraft model based on Patran and Nastran software. The specific steps are as follows:

[0090] S201: Analysis type: Select the appropriate analysis type according to the research purpose. This paper mainly conducts modal analysis to obtain the natural frequency and vibration mode of the wing, and conducts static analysis to evaluate the strength and displacement of the wing under various loads;

[0091] S202: Simulation software: Use Nastran software for finite element analysis, which has powerful solving capabilities and rich analysis functions.

[0092] S203: Calculation process:

[0093] S20301: Initial setup: Check the integrity of the model and the correctness of parameter settings.

[0094] S20302: Load simulation file: Import the set finite element model file, set calculation options and solution parameters.

[0095] S20303: Run simulation: Start the solver and monitor the calculation status and convergence in real time during the calculation process.

[0096] S204: Result processing: import the output result file into Patran software to read and obtain the natural frequency and corresponding vibration mode of the wing;

[0097] like Figure 4 As shown, this embodiment calculates and analyzes the first six natural frequencies of the wing, and obtains the first six natural frequencies of the wing and their corresponding modal vibration shape results by simulation.

[0098] S3: Input the parameters of the wing model to be corrected into the finite element model for simulation calculation to obtain the corresponding structural characteristic quantities;

[0099] S4: Generate multiple random samples of wing structural parameters in Python and Nastran environment, calculate natural frequencies in batches, and complete the construction of training data set;

[0100] S401: Build Python and Nastran interface;

[0101] The main goal of building the Python and Nastran interface is to realize the automation process and perform dynamic finite element simulation on the aircraft model. The implementation steps are as follows:

[0102] S40101: Prepare and parameterize the aircraft wing model: First, create an initial finite element model of the aircraft wing.

[0103] The geometric parameters of the model (wing length, wing width) are set as variable parameters to facilitate subsequent adjustment through the program.

[0104] S40102: Python Program Development: Write Python programs to implement the following functions:

[0105] S40103: Parameter generation: Generate different combinations of wing geometry parameters based on research requirements.

[0106] S40104: Model File Modification: The program automatically modifies the Nastran input model file based on the generated parameters to reflect different geometry changes.

[0107] S40105: Batch Nastran: Automatically start Nastran for batch analysis by calling system commands or using the appropriate API interface.

[0108] S40106: Data Collection and Processing: Extract key analysis results such as natural frequencies from Nastran output files, and perform preliminary data cleaning and formatting to prepare data for neural network training.

[0109] S402: Data preparation and storage of random parameters of wing geometry;

[0110] The random parameters of the wing length and wing width b of the aircraft model are generated as follows:

[0111] S40201: Define parameter range: First, set the standard deviations of two sets of random variables a and b (standard deviation A and standard deviation B) and the generation range of two sets of means (mean A and mean B). These parameters are designed to reflect the physical properties related to the wing size and are derived from a uniform distribution;

[0112] S40202: Generate random numbers: Use the rand function of numpy to generate 1000 random number sample sets. These values ​​are used as the basic parameters for generating wing dimensions, ensuring the randomness and diversity of dimension data a and b.

[0113] S40203: Wing size data generation:

[0114] S4020301: Wing size calculation: In the program loop, based on the previously generated random mean and standard deviation,

[0115] Gaussian distribution (normal distribution) is used to generate specific wing length and wing width data of 60 wings, and each set of data specifically reflects the mean and variance of the wing length and wing width.

[0116] S4020302: Data reflection processing: To achieve symmetry of the wing, the size data of the left wing part is obtained by reflecting the data of the right part.

[0117] S40204: Data storage and processing:

[0118] S4040401: Save calculation results: After completing each data generation iteration, save the wing length and wing width data to a text file to preserve the details of data generation and facilitate subsequent analysis.

[0119] S4020402: Export to Excel: Use the pandas library to read and process the previous wing size data, calculate and summarize the length and width of the wing, and save these data in an Excel file named with the number of iterations to ensure that each data is recorded independently to avoid data overwriting.

[0120] S40205: File management: Use shutil and os libraries to manage generated Excel files, including copying

[0121] Create and delete operations to ensure the organization of data files and effective use of storage space.

[0122] S40206: Subsequent applications of random data:

[0123] S4020601: Data reading and processing: Read wing size data from Excel files, generate mesh coordinates of finite element models, create aircraft wing model bdf files, and replace corresponding mesh coordinates in bdf files for subsequent Nastran calculations.

[0124] S4020602: Natural frequency calculation: Use Python script to call Nastran software to perform finite element analysis of the wing using the aircraft wing model bdf file and calculate its natural frequency.

[0125] S4020603: Results Summary: Read and process the Nastran output (.f06 file), extract the natural frequency data, and store it and the generated wing geometry parameters in the corresponding Excel table to complete the data set.

[0126] Through the above steps, not only the randomness and systematicity of data generation are guaranteed, but also the generated dataset can play a key role in the uncertainty assessment of aircraft design and analysis. The format of the generated dataset is shown in Table 1;

[0127] Table 1

[0128]

[0129]

[0130] S5: Construct a convolutional neural network to learn the mapping relationship between wing dynamic response and parameters;

[0131] The convolutional neural network constructed in this embodiment is as follows Figure 5 As shown, it includes input layer, convolution layer, pooling layer, DroOut layer, flattening layer and fully connected layer. The loss function of the convolutional neural network is the mean square error function.

[0132] The expression of the mean square error function is:

[0133]

[0134] In formula (1), n ​​is the number of wing natural frequency data, y i is the true value of the natural frequency data, y pred is the predicted value of natural frequency data;

[0135] The input layer is used to receive data of shape (n, 5), where n is the number of wing natural frequency data and 5 is the first five natural frequency data of the wing;

[0136] The convolution layer includes the first convolution layer and the second convolution layer, which are used to extract the features of the first five natural frequency data;

[0137] The first convolutional layer has a layer type of Conv1D, a number of neurons of 64, a convolution kernel size of 3, and an activation function of ReLU.

[0138] The layer type of the second convolutional layer is Conv1D, the number of neurons is 128, the convolution kernel size is 3, and the activation function is the ReLU function;

[0139] The flattening layer is used to flatten the convolutional data output and convert it into one-dimensional data;

[0140] The pooling layer is used to reduce the dimension of the first five natural frequency data. The layer type of the pooling layer is MaxPooling1D, and the pooling window size is 2;

[0141] The fully connected layer is used to process the first five natural frequency data after pooling to obtain the predicted value of the natural frequency data. The number of neurons in the fully connected layer is 128, and the activation function is the ReLU function.

[0142] The DroOut layer is set to a ratio of 0.5, which is used to discard the neuron output at a ratio of 0.5;

[0143] The output layer is used to output the predicted value of the natural frequency data. The number of neurons in the output layer is 4, and the activation function is linear. The 4 neurons correspond to the mean value of wing length a, the variance of wing length a, the mean value of wing width b, and the variance of wing width b.

[0144] S6: training the convolutional neural network using the training data set, inputting the parameters of the wing model to be corrected into the trained convolutional neural network for parameter inversion, and obtaining the model parameters generated by the inversion;

[0145] S601: Perform minimum-maximum normalization processing on the input training data, and scale the training data to the range of (0,1) to facilitate processing of data of different magnitudes and improve the accuracy of the algorithm;

[0146] The expression of minimum-maximum normalization is:

[0147]

[0148] In formula (2), x′ is the normalized training data, x m i n is the minimum value of the training data, x max is the maximum value of the training data.

[0149] S602: Use early stopping method for training. The early stopping method stops training when the validation loss no longer decreases significantly, thereby avoiding the problem of overfitting the model on the training set. Specifically, it monitors the loss function of the validation set and terminates training early when it no longer decreases in several consecutive training rounds;

[0150] S603: Use the LOSS function and the mean absolute error function to evaluate the predicted value of the natural frequency data output by the training, and the evaluation results are shown in Table 2;

[0151] Table 2

[0152]

[0153]

[0154] It can be seen from Table 2 that the convolutional neural network constructed in this embodiment has high accuracy and efficiency when processing test data. The low value of the mean error rate (Mean Error Rates) indicates that the average deviation between the predicted parameters and the target parameters is small, indicating that the convolutional neural network has high reliability and generalization ability.

[0155] S7: inputting the model parameters generated by inversion into the finite element model to calculate the corrected structural characteristic quantities;

[0156] S8: Calculate the error between the corrected structural feature quantity and the actual structural feature quantity. If the error is greater than a preset value, expand the training data set of the convolutional neural network, adjust the structure of the convolutional neural network, and retrain the convolutional neural network until the error between the corrected structural feature quantity and the actual structural feature quantity is less than the preset value, thereby completing the rapid correction of the aircraft model wing.

[0157] The comparison between the random parameter values ​​and target values ​​of the wing of the aircraft model after correction in this embodiment is shown in Table 3.

[0158] Table 3

[0159] parameter Target value Correction value Error rate <![CDATA[μ a ]]> 0.298078297 0.298486398 0.1369％ <![CDATA[σ a ]]> 0.003927089 0.003787407 3.5576％ <![CDATA[μ b ]]> 0.025039665 0.025018017 0.0864％ <![CDATA[σ b ]]> 0.001696540 0.001735816 2.3146％

[0160] As shown in Table 3, in this embodiment, the average error of the corrected wing geometric dimension mean is 0.1117%, the average error of the wing geometric dimension variance is 2.9361%, and the fitting results of the corrected value and the target value distribution are as follows: Figure 6 As shown, it can be seen that the correction value of this embodiment is relatively well matched with the target value distribution, and the corrected model has achieved a good correction effect.

[0161] In this embodiment, the corrected mean and variance of the wing length and wing width are used to regenerate the wing length (a) and wing width (b) values ​​and import them into the Nastran software to calculate the corresponding fifth-order natural frequency. The corrected wing length (a) and wing width (b) values ​​and natural frequencies are compared with the target values. The comparison results are shown in Figure 2. Figure 7 , Figure 8 , as shown in Table 4 and Table 5.

[0162] Table 4

[0163]

[0164]

[0165] Table 5

[0166] Mean natural frequency Target value Correction value Error rate <![CDATA[f1]]> 0.151648 0.146359 3.489％ <![CDATA[f2]]> 0.406761 0.389584 4.218％ <![CDATA[f3]]> 3.990503 4.100371 2.752％ <![CDATA[f4]]> 3.778299 3.985891 5.497％ <![CDATA[f5]]> 4.161709 4.308347 3.526％

[0167] like Fig. 9 As shown, the distribution of the first five natural frequencies after correction in this embodiment is highly fitted to the first five natural frequencies of the target structure. It can be seen from Tables 4 and 5 that the mean average error between the corrected natural frequencies and the target values ​​is 0.450%, and the variance average error is 3.896%, indicating that the constructed convolutional neural network has a good correction effect.

[0168] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for rapid correction of aircraft model wings considering random uncertainty, characterized in that: The steps of the method for quickly correcting the wing of an aircraft model considering random uncertainty include: Step 1: Perform finite element modeling on the aircraft wing model, perform dynamic simulation on the finite element model of the aircraft wing, and complete the parameterization setting of the aircraft wing model, wherein the geometric dimensions of the aircraft wing, wing length a and wing width b, are selected as uncertain parameters in the parameterization setting; Step 2: Input the parameters of the wing model to be corrected into the finite element model for simulation calculation to obtain the corresponding structural characteristic quantities; Step 3: In the Python and Nastran environments, generate multiple random samples of wing structural parameters, calculate the natural frequencies in batches, and complete the construction of the training data set; Step 4: Construct a convolutional neural network to learn the mapping relationship between wing dynamic response and parameters; Step 5: Use the training data set to train the convolutional neural network, input the parameters of the wing model to be corrected into the trained convolutional neural network for parameter inversion, and obtain the model parameters generated by the inversion; Step 6: Input the model parameters generated by inversion into the finite element model to calculate the corrected structural characteristic quantities; Step 7: Calculate the error between the corrected structural feature and the actual structural feature. If the error is greater than the preset value, process the random sample and the convolutional neural network until the error between the corrected structural feature and the actual structural feature is less than the preset value, thus completing the rapid correction of the aircraft model wing.

2. A method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 1, characterized in that: The steps to build the finite element model of the aircraft wing in step 1 include: Step 1.1.1: Select quadrilateral elements as mesh elements, use the mesh generator to perform global mesh division on the wing model, and ensure that the mesh is evenly distributed after division; Step 1.1.2: Dense meshing is performed in the stress concentration area of ​​the wing model, and the mesh quality is tested using finite element software to complete the meshing of the wing model; Step 1.1.3: Select aluminum as the material property of the wing model, set the elastic modulus, Poisson's ratio and density of the aluminum, and complete the establishment of the finite element model.

3. A method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 1, characterized in that: The steps of performing dynamic simulation on the finite element model of the aircraft wing in step 1 include: Step 1.2.1: Perform finite element analysis using Nastrn software; Step 1.2.2: Read the finite element analysis results and obtain the first six natural frequencies of the wing; Step 1.2.3: Perform static analysis on the finite element model of the aircraft wing to evaluate the strength and displacement of the wing under all loads and complete the parameterization of the aircraft wing model.

4. A method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 3, characterized in that: Step 1.2.1 specifically includes: Step 1.2.1.1: Check the integrity of the wing model and the correctness of the finite element model parameter settings; Step 1.2.1.2: Import the set finite element model, set the calculation options and solution parameters; Step 1.2.1.3: Start the solver to solve the problem and monitor the calculation status and convergence in real time during the finite element analysis process.

5. The method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 1, characterized in that: Step 3 specifically includes: Step 3.1: Set the standard deviation of the two sets of random variables a and b and the generation range of the means of the two sets of random variables a and b; Step 3.2: Use the rand function to generate m random number sample sets; Step 3.3: Based on the random standard deviation and mean set in step 3.1, use Gaussian distribution to generate n, n∈m wing length and wing width data on the right side of the wing, and obtain the wing length and wing width data on the left side of the wing by reflecting the wing length and wing width data on the right side of the wing. Repeat the above steps to iteratively generate wing size data; Step 3.4: Save the wing size data to an Excel file; Step 3.5: Read the wing size data from the Excel file and call the Nastran software to batch calculate the first six natural frequencies.

6. A method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 5, characterized in that: Step 3.4 specifically includes: Step 3.4.1: After completing each iteration of wing size data generation, save the currently calculated wing length a and wing width b data to a local file; Step 3.4.2: Use the pandas library to read and process the wing size data of each iteration, calculate the summary wing length a and wing width b, and save the summary data to an Excel file named after the iteration number; Step 3.4.3: Use the shutil module and os library to manage the generated Excel files, including copying and deleting operations.

7. A method for rapid correction of aircraft model wings taking into account random uncertainty according to claim 5, characterized in that: Step 3.5 specifically includes: Step 3.5.1: Read the wing size data from the Excel file, generate the mesh coordinates of the finite element model, create the aircraft wing model bdf file, and use the generated finite element model mesh coordinates to replace the corresponding mesh coordinates in the bdf file; Step 3.5.2: Use Python script to call Nastran software to perform finite element analysis of the wing using the aircraft wing model bdf file, and calculate the first six natural frequencies; Step 3.5.2: Read and process the output of Nastran software, extract the natural frequency data, and store the natural frequency data and the generated wing geometry parameters in the corresponding Excel file to complete the construction of the training data set.

8. The method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 1, characterized in that: In step 4, the convolutional neural network includes an input layer, a convolutional layer, a pooling layer, a DroOut layer, a flattening layer, and a fully connected layer. The loss function of the convolutional neural network is a mean square error function. The input layer is used to receive data of shape (n, 5), wherein n is the number of wing natural frequency data, and 5 is the first five order natural frequency data of the wing; The convolution layer includes the first convolution layer and the second convolution layer, which are used to extract the features of the first five natural frequency data; The first convolutional layer has a layer type of Conv1D, a number of neurons of 64, a convolution kernel size of 3, and an activation function of ReLU. The layer type of the second convolutional layer is Conv1D, the number of neurons is 128, the convolution kernel size is 3, and the activation function is the ReLU function; The flattening layer is used to flatten the convolutional data output and convert it into one-dimensional data; The pooling layer is used to reduce the dimension of the first five natural frequency data. The layer type of the pooling layer is MaxPooling1D, and the pooling window size is 2; The fully connected layer is used to process the first five natural frequency data after pooling to obtain the predicted value of the natural frequency data. The number of neurons in the fully connected layer is 128, and the activation function is the ReLU function. The DroOut layer is set to a ratio of 0.5, which is used to discard the neuron output at a ratio of 0.5; The output layer is used to output the predicted value of the natural frequency data. The number of neurons in the output layer is 4, and the activation function is linear. The 4 neurons correspond to the mean value of wing length a, the variance of wing length a, the mean value of wing width b, and the variance of wing width b. The expression of the mean square error function is: In formula (1), n ​​is the number of wing natural frequency data, y i is the true value of the natural frequency data, y pred is the predicted value of the natural frequency data.

9. The method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 1, characterized in that: The steps of training the convolutional neural network using the training data set in step 5 include: Step 5.1: Perform minimum-maximum normalization on the input training data and scale the training data to the range of (0,1); Step 5.2: Train using early stopping. Step 5.3: Use the LOSS function and the mean absolute error function to evaluate the predicted value of the natural frequency data output by the training; The expression of minimum-maximum normalization is: In formula (2), x′ is the normalized training data, x min is the minimum value of the training data, x max is the maximum value of the training data.

10. The method for rapidly correcting an aircraft model wing taking random uncertainty into consideration according to claim 1, characterized in that: The processing of random samples and convolutional neural networks in step 7 includes: The training data set of the convolutional neural network is expanded, the structure of the convolutional neural network is adjusted, and the convolutional neural network is retrained.

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