A multi-fidelity composite material strength verification method, equipment, medium and product

Abstract

The present application discloses a multi-fidelity composite material strength verification method, equipment, medium and product, relating to the field of composite material technology. The method comprises: using a finite element model to construct a three-dimensional unit cell model of an aviation composite material, and then constructing a unit failure judgment data set, using the unit failure judgment data set to train a first fully connected neural network to obtain a low-fidelity neural network, performing a structural strength test on the aviation composite material to obtain test data, and using the test data and the low-fidelity neural network to train a second fully connected neural network to obtain a high-fidelity neural network; according to the generalized internal force state of the working condition to be predicted, using the low-fidelity neural network and the high-fidelity neural network to complete the strength verification of the aviation composite material under the working condition to be predicted. The present application constructs a multi-fidelity strength model of the aviation composite material based on the test data and simulation data, thereby improving the efficiency and accuracy of the strength verification of the aviation composite material.

Description

Technical Field

[0001] The present application relates to the technical field of composite materials, and in particular to a multi-fidelity composite material strength verification method, equipment, medium and product. Background Art

[0002] Composite structures are widely used in advanced aircraft structures. These structures often possess complex internal topologies or incorporate materials with complex internal topologies. Therefore, detailed full-scale model simulations are often not feasible for structural analysis. Instead, shell elements are used for modeling. However, due to the lack of strength criteria for composite shell models, the only option is to calculate the Cauchy stress within each layer based on the shell's cross-sectional forces, and then apply stress-based strength models for strength verification. The scale discrepancy between the structural response based on shell elements and the material-level failure criteria used for strength assessment makes it crucial to develop reliable strength models that can be directly applied to shell element structural failure prediction. Due to the diverse internal structures of composite materials, their strength properties are difficult to describe using a unified mathematical expression. Over the past few decades, data-driven approaches have emerged as an alternative approach to constructing strength models for complex material systems and have been applied to develop strength models for a variety of composite materials and structures. With sufficient training data, data-driven strength models can describe complex strength behaviors and provide highly accurate strength predictions for composite structures.

[0003] However, collecting sufficient data remains a key challenge in developing data-driven models. Experimental test data is reliable, but the process is costly and time-consuming. Numerical simulation, on the other hand, can generate vast amounts of data while significantly reducing both time and cost compared to experiments. However, for complex structures or loading conditions, the accuracy of numerical simulation results can be difficult to guarantee. Leveraging the advantages of multi-source data is crucial. Summary of the Invention

[0004] The purpose of this application is to provide a multi-fidelity composite material strength verification method, equipment, medium and product, to construct a multi-fidelity strength model of aviation composite materials based on test data and simulation data, and to improve the efficiency and accuracy of aviation composite material strength verification.

[0005] To achieve the above objectives, the present application provides the following solutions: In a first aspect, the present application provides a multi-fidelity composite material strength verification method, comprising: using a finite element model to construct a three-dimensional unit cell model of an aviation composite material.

[0006] Multiple sampling simulations are performed based on the three-dimensional unit cell model to construct a unit failure judgment data set. The unit failure judgment data set includes the strength margin of the aviation composite material under different simulation conditions. The generalized internal force state of the aviation composite material under different simulation conditions is different.

[0007] Construct a first fully connected neural network and a second fully connected neural network.

[0008] The first fully connected neural network is trained using the unit failure judgment data set to obtain a low-fidelity neural network.

[0009] Structural strength tests are conducted on aviation composite materials to obtain test data. The test data includes strength margins of the aviation composite materials under different test conditions. The generalized internal force states of the aviation composite materials under different test conditions are different.

[0010] The second fully connected neural network is trained using the test data and the low-fidelity neural network to obtain a high-fidelity neural network.

[0011] Obtain the generalized internal force state of the working condition to be predicted.

[0012] According to the generalized internal force state of the working condition to be predicted, the low-fidelity neural network and the high-fidelity neural network are used to complete the strength verification of the aviation composite material under the working condition to be predicted.

[0013] Optionally, the first fully connected neural network is trained using the unit failure judgment data set to obtain a low-fidelity neural network, including: taking the generalized internal force state of the aviation composite material under different simulation conditions as input, and taking the strength margin of the aviation composite material under the corresponding simulation conditions as output, to train the first fully connected neural network to obtain a low-fidelity neural network.

[0014] Optionally, the first fully connected neural network or the second fully connected neural network is: . .

[0015] ;

[0016] in, represents the first Tier neurons; Represents the generalized internal force state of aviation composite materials under simulation conditions; Indicates the Tier neuron bias parameters; Indicates the The width of the layer; Indicates the Tier The neuron for the Tier The weights of neurons; After nonlinear activation, Tier neurons; After nonlinear activation, Tier neurons; represents a nonlinear activation function; represents the output of the first fully connected neural network; represents the first Tier neurons; Indicates the Tier neuron bias parameters; Indicates the The width of the layer; Indicates the Tier neuron weights; Indicates the first hidden layer in the last neurons.

[0017] Optionally, training the second fully connected neural network using the test data and the low-fidelity neural network to obtain a high-fidelity neural network includes: expanding the test data to obtain an expanded strength margin of the aviation composite material under different expanded working conditions. The generalized internal force state of the aviation composite material under different expanded working conditions is different.

[0018] The generalized internal force state of aviation composite materials under different extended working conditions is input into the low-fidelity neural network to obtain the low-fidelity strength margin.

[0019] The generalized internal force state and low-fidelity strength margin of aerospace composite materials under the same extended working condition are taken as input data pairs.

[0020] The second fully connected neural network is trained with the input data pair as input and the extended strength margin of the aviation composite material under the corresponding extended working condition as output to obtain a high-fidelity neural network.

[0021] Optionally, the test data is expanded to obtain the expansion strength margin of the aviation composite material under different expansion conditions, including: obtaining a scaling interval.

[0022] Extracts a preset number of amplitudes from the zoom interval.

[0023] Determine any amplitude as the current amplitude.

[0024] Determine any test condition as the current test condition.

[0025] Multiply the generalized internal force state corresponding to the current test condition by the current amplitude to obtain the current extended condition.

[0026] The extended strength margin of the aerospace composite material under the current test condition is multiplied by the current amplitude to obtain the extended strength margin of the aerospace composite material under the current extended condition.

[0027] Update the current test condition and return to step "multiply the generalized internal force state corresponding to the current test condition by the current amplitude to obtain the current extended condition" until all test conditions are traversed.

[0028] Update the current amplitude and return to the step of "determining any test condition as the current test condition" until all amplitudes are traversed to obtain the extended strength margin of the aviation composite material under different extended conditions.

[0029] Optionally, based on the generalized internal force state of the working condition to be predicted, the low-fidelity neural network and the high-fidelity neural network are used to complete the strength verification of the aviation composite material under the working condition to be predicted, including: inputting the generalized internal force state of the working condition to be predicted into the low-fidelity neural network to obtain a low-fidelity strength margin prediction value.

[0030] The low-fidelity strength margin prediction value and the generalized internal force state of the working condition to be predicted are input into the high-fidelity neural network to obtain the high-fidelity strength margin prediction value.

[0031] Complete the strength verification of aviation composite materials under the predicted working conditions based on high-fidelity strength margin prediction values.

[0032] Optionally, completing strength verification of the aviation composite material under the working condition to be predicted based on the high-fidelity strength margin prediction value includes: when the high-fidelity strength margin prediction value is greater than or equal to a strength margin threshold, determining that the aviation composite material is in a failure state under the working condition to be predicted.

[0033] When the high-fidelity strength margin prediction value is less than the strength margin threshold, it is determined that the aviation composite material is in a non-failure state under the working condition to be predicted.

[0034] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned multi-fidelity composite material strength verification method.

[0035] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned multi-fidelity composite material strength verification method when executed by a processor.

[0036] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which implements the above-mentioned multi-fidelity composite material strength verification method when executed by a processor.

[0037] According to the specific embodiments provided in this application, this application discloses the following technical effects: This application provides a multi-fidelity composite material strength verification method, equipment, medium and product, which quickly obtains a large amount of low-fidelity strength evaluation data through numerical simulation, and then uses an artificial neural network to establish a low-fidelity data-driven composite material shell strength model. On this basis, a "two-step" neural network (i.e., a low-fidelity neural network and a high-fidelity neural network) is applied to fuse the low-fidelity data with the high-fidelity data obtained from the experiment, while retaining the failure behavior in the full load space of the structure learned from the simulation data, ensuring numerical consistency with the experimental test results, and achieving a balance between efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 This is a flow chart of a multi-fidelity composite material strength verification method in one embodiment of the present application.

[0040] Figure 2 This is a flow chart of a multi-fidelity composite material strength verification method in another embodiment of the present application.

[0041] Figure 3 This is a schematic diagram of the multi-fidelity neural network model architecture in one embodiment of the present application.

[0042] Figure 4 Schematic diagram of strength test specimens and test settings under different working conditions in one embodiment of the present application.

[0043] Figure 5 This is a diagram of the final failure results of laminate specimens under different working conditions in one embodiment of the present application.

[0044] Figure 6 Graphs showing load-displacement data for each test set in one embodiment of the present application; Figure 6 Part (a) is a load-displacement curve of a 0° tensile test in an embodiment of the present application; Figure 6 Part (b) is a load-displacement curve of a 90° tensile test in an embodiment of the present application; Figure 6 Part (c) is a load-displacement curve diagram of a 0° compression test in an embodiment of the present application; Figure 6 Part (d) is a load-displacement curve diagram of a 90° compression test in an embodiment of the present application; Figure 6 Part (e) is a load-displacement curve of a 0° bending test in an embodiment of the present application; Figure 6 Part (f) is a load-displacement curve of a 90° bending test in an embodiment of the present application; Figure 6 Part (g) is a load-displacement curve of an in-plane shear test in one embodiment of the present application; Figure 6 Part (h) is a load-displacement curve diagram of an out-of-plane shear test in an embodiment of the present application.

[0045] Figure 7 This is a diagram of the low-fidelity model strength prediction results in one embodiment of the present application. Figure 7 Part (a) is an embodiment of the present application Structural failure prediction result diagram in load space; Figure 7 Part (b) is an embodiment of the present application Structural failure prediction result diagram in load space; Figure 7 Part (c) is an embodiment of this application Structural failure prediction result diagram in load space; Figure 7 Part (d) is an embodiment of the present application. Graph of structural failure prediction results within the load space.

[0046] Figure 8 This is a comparison chart of the prediction results of the low-fidelity model and the multi-fidelity model in one embodiment of the present application. Figure 8 Part (a) is an embodiment of the present application Structural strength envelope diagram within the load space; Figure 8 Part (b) is an embodiment of the present application Structural strength envelope diagram within the load space; Figure 8 Part (c) is an embodiment of this application Structural strength envelope diagram within the load space; Figure 8 Part (d) is an embodiment of the present application. Structural strength envelope diagram within the load space.

[0047] Figure 9 This is a structural diagram of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0049] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0050] In an exemplary embodiment, Figure 1 As shown, a multi-fidelity composite material strength verification method is provided, including: steps 101 to 108.

[0051] Step 101: Use the finite element model to construct a three-dimensional unit cell model of the aviation composite material.

[0052] Step 102: Perform multiple sampling simulations based on the three-dimensional unit cell model to construct a unit failure judgment dataset. The unit failure judgment dataset includes the strength margin of the aviation composite material under different simulation conditions. The generalized internal force state of the aviation composite material under different simulation conditions varies.

[0053] Step 103: Construct a first fully connected neural network and a second fully connected neural network.

[0054] Step 104: The first fully connected neural network is trained using the unit failure judgment dataset to obtain a low-fidelity neural network. The low-fidelity neural network is obtained by training the first fully connected neural network using the generalized internal force state of the aviation composite material under different simulation conditions as input and the strength margin of the aviation composite material under the corresponding simulation conditions as output.

[0055] Step 105: Conduct a structural strength test on the aviation composite material to obtain test data. The test data includes the strength margin of the aviation composite material under different test conditions. The generalized internal force state of the aviation composite material under different test conditions is different.

[0056] Step 106: Use the test data and the low-fidelity neural network to train the second fully connected neural network to obtain a high-fidelity neural network.

[0057] Step 107: Obtain the generalized internal force state of the working condition to be predicted.

[0058] Step 108: Based on the generalized internal force state of the working condition to be predicted, the low-fidelity neural network and the high-fidelity neural network are used to complete the strength verification of the aviation composite material under the working condition to be predicted.

[0059] Among them, the first fully connected neural network or the second fully connected neural network is as follows.

[0060] .

[0061] .

[0062] .

[0063] in, represents the first Tier neurons; Represents the generalized internal force state of aviation composite materials under simulation conditions; Indicates the Tier neuron bias parameters; Indicates the The width of the layer; Indicates the Tier The neuron for the Tier The weights of neurons; After nonlinear activation, Tier neurons; After nonlinear activation, Tier neurons; represents a nonlinear activation function; represents the output of the first fully connected neural network; represents the first Tier neurons; Indicates the Tier neuron bias parameters; Indicates the The width of the layer; Indicates the Tier neuron weights; Indicates the first hidden layer in the last neurons.

[0064] Step 106 includes: step 106-1 to step 106-4.

[0065] Step 106-1: Expand the test data to obtain the expansion strength margin of the aviation composite material under different expansion conditions. The generalized internal force state of the aviation composite material under different expansion conditions is different.

[0066] Step 106-2: Input the generalized internal force state of the aviation composite material under different extended working conditions into the low-fidelity neural network to obtain the low-fidelity strength margin.

[0067] Step 106-3: The generalized internal force state and low-fidelity strength margin of the aviation composite material under the same extended working condition are used as input data pairs.

[0068] Step 106 - 4 : Using the input data pair as input and the extended strength margin of the aviation composite material under the corresponding extended working condition as output, the second fully connected neural network is trained to obtain a high-fidelity neural network.

[0069] Step 106-1 includes: step 106-1-1 to step 106-1-8.

[0070] Step 106-1-1: Obtain the zoom range.

[0071] Step 106-1-2: extract a preset number of amplitudes from the zoom interval.

[0072] Step 106-1-3: Determine any amplitude as the current amplitude.

[0073] Step 106-1-4: Determine any test condition as the current test condition.

[0074] Step 106-1-5: Multiply the generalized internal force state corresponding to the current test condition by the current amplitude to obtain the current extended condition.

[0075] Step 106-1-6: Multiply the strength margin of the aviation composite material under the current test condition by the current amplitude to obtain the extended strength margin of the aviation composite material under the current extended condition.

[0076] Step 106-1-7: Update the current test condition and return to step 106-1-6 until all test conditions are traversed.

[0077] Step 106-1-8: Update the current amplitude and return to step 106-1-4 until all amplitudes are traversed to obtain the expansion strength margin of the aviation composite material under different expansion conditions.

[0078] Step 108 includes: step 108-1 to step 108-3.

[0079] Step 108 - 1 : Input the generalized internal force state of the working condition to be predicted into the low-fidelity neural network to obtain a low-fidelity strength margin prediction value.

[0080] Step 108 - 2 : Input the low-fidelity strength margin prediction value and the generalized internal force state of the working condition to be predicted into the high-fidelity neural network to obtain a high-fidelity strength margin prediction value.

[0081] Step 108-3: Complete strength verification of the aviation composite material under the predicted operating condition based on the high-fidelity strength margin prediction value. If the high-fidelity strength margin prediction value is greater than or equal to the strength margin threshold, the aviation composite material is determined to be in a failed state under the predicted operating condition. If the high-fidelity strength margin prediction value is less than the strength margin threshold, the aviation composite material is determined to be in a non-failed state under the predicted operating condition.

[0082] Based on neural networks, the fusion of test and simulation data is achieved to construct a multi-fidelity data-driven strength model for aviation composite materials. The generalization of limited test data in the full load space is achieved, and while retaining the failure behavior of the structure in the full load space learned by simulation data, the numerical consistency with the experimental test results is guaranteed. It effectively solves the contradiction between the insufficient accuracy of low-cost simulation data and the difficulty in obtaining high-fidelity simulation data in the construction of traditional data-driven composite material strength models, so as to achieve a balance between efficiency and accuracy in the construction of strength models. In actual engineering applications, this application effectively improves the analysis efficiency while ensuring the analysis accuracy, and can help achieve high-reliability, real-time, rapid failure prediction and strength verification of aviation composite plate and shell structures under complex external load conditions. In addition, this application has good versatility. In addition to the laminated plate structure given in the case, it is also applicable to various forms of aviation plate and shell structures such as metamaterials, sandwich, and reinforcement, and has great application potential.

[0083] In an exemplary embodiment, a multi-fidelity composite material strength verification method is provided, comprising: finite element model simulation of composite material structure, low-fidelity model construction, structural strength test under typical working conditions, multi-fidelity model construction and structural strength verification. A composite material strength model that can integrate different fidelity data obtained from experiments and simulations is first established based on a large number of low-cost low-fidelity data sets obtained from simulations, which is suitable for real-time, fast and efficient simulation. The strength verification agent model can perform structural failure judgment based on the cross-sectional internal forces of the shell unit. On this basis, a small number of structural tests are conducted to obtain high-fidelity test results of structural strength under typical working conditions, and the low-fidelity model is corrected using the test data through a multi-fidelity model construction method. The limited test data is generalized in the full load space, and the efficiency of data-driven composite shell unit strength model construction is effectively improved while ensuring accuracy, which can help achieve high-reliability real-time fast failure prediction and strength verification under complex external load conditions for aviation composite plate and shell structures. In addition, this application has good versatility. In addition to the laminate structure given in the case, it is also applicable to various forms of aviation plate and shell structures such as metamaterials, sandwich, and reinforcement. It has great application potential. The implementation flow chart is shown in Figure 2 and Figure 3 .

[0084] Step 1. Simulate the finite element model of the composite structure.

[0085] Step 1.1: Construct a three-dimensional unit cell model of the composite structure. This is accomplished by constructing a finite element model of the structural unit cell and applying equation constraints to impose periodic boundary conditions on the unit cell.

[0086] Step 1.2: Perform a large number of sampling simulations to construct a unit failure judgment data set.

[0087] Unit forces are applied to each reference node in turn, and the stress and interface force responses of the unit cell under eight unit generalized stress conditions, including tension, bending, torsion, and in-plane shear, are simulated and calculated. On this basis, random sampling is performed in the 8-dimensional generalized internal force space. Based on the linear superposition of the fields, the structural response under different generalized internal force states in each sample is calculated. The strength judgment criterion based on Cauchy stress at the material scale is applied to judge the failure state of each layer of the structure. The interlayer interface strength judgment criterion is applied to judge the delamination failure state, and a strength simulation data set is obtained.

[0088] Step 2. Low-fidelity model (i.e., low-fidelity neural network) construction.

[0089] Step 2.1 Build a fully connected neural network.

[0090] Build includes layer neural network, The width of the layer is The neural network input is the generalized force state: ,in, is the pulling force in one direction, For 2-direction tension, is the in-plane shear force, is the bending moment in direction 1, is the bending moment in two directions, is the torque, is the shear force in one direction, The output is the strength margin. , which is obtained based on the strength criterion of structural details, and a value greater than or equal to 1 indicates structural failure.

[0091] .

[0092] .

[0093] .

[0094] in, The value range is ; The value range is .

[0095] Step 2.2: Train the model parameters based on the simulation dataset.

[0096] Low-fidelity neural networks It is based on low-fidelity data obtained from simulation For training. is the training data, is the data size, Indicates the Data points. Set the loss function and continuously reduce the loss function , train the parameters of each hidden layer of the neural network, that is: .

[0097] in, are vectors consisting of all weight and bias parameters respectively.

[0098] Step 3. Structural strength test under typical working conditions.

[0099] Conduct specimen-level tests to test the structural strength of the structure under a single generalized force and obtain high-fidelity data.

[0100] This is usually a standard test under a single load, such as uniaxial tension, compression, bending, or shear in various directions. Furthermore, this method aims to establish an initial failure strength criterion, meaning that the test focuses on obtaining the generalized load state at which the structure begins to fail, rather than the ultimate load at final failure.

[0101] Step 4. Multi-fidelity model (i.e., high-fidelity neural network) construction.

[0102] Step 4.1: Expand the high-fidelity data.

[0103] Here, it is assumed that the damage mode does not change within a small range of the initial damage load, that is, the structural strength margin It changes approximately linearly with the load. Therefore, by setting a certain scaling interval , evenly take the preset number in the interval Amplitude , expand each set of test data: , To test the data size, Indicates the data points.

[0104] Step 4.2: Using the low-fidelity model, obtain low-fidelity strength prediction results based on the generalized force input in each set of high-fidelity data.

[0105] The high-fidelity dataset is obtained in step 4.1 In this step, As input, the low-fidelity neural network model constructed in step 2 is used for prediction to obtain the low-fidelity failure prediction result of the structure under this load state. ,Right now: .

[0106] Step 4.3: Based on the high-fidelity data and the low-fidelity model prediction results, train a second neural network for model correction.

[0107] The input of training data consists of two parts: Part 1 Input comes from high frequency data, i.e., section forces from structural tests. Part 2 This is the predicted value of the low-fidelity model obtained in step 4.2 under the input of high-fidelity data. The output of the training data is the expanded data of each group of test results. , that is, the data set finally used for training the neural network model is: The model construction and training methods are the same as step 2.

[0108] Step 5. Structural strength check.

[0109] Based on the given generalized internal force state, the failure of plate and shell elements is judged through a multi-fidelity surrogate model, and the structural strength is checked.

[0110] Given the generalized internal force state As input, it first passes through the first neural network to make a low-fidelity prediction. Then, this prediction is submitted to the second neural network along with the initial input, which modifies the result, that is: .

[0111] Get the final high-fidelity prediction results.

[0112] Taking a 24-ply ZT7H5429 material laminate as an example, the strength verification method of a multi-fidelity composite material provided in this embodiment is specifically described.

[0113] Step 1. Simulate the finite element model of the composite structure.

[0114] A finite element model of the structural unit cell is constructed, and periodic boundary conditions are imposed on the unit cell using equation constraints. Unit forces are applied to each reference node in turn, and the stress and interface force responses of the unit cell under eight unit generalized stress conditions, including tension, bending, torsion, and in-plane shear, are simulated and calculated. On this basis, random sampling is performed in the 8-dimensional generalized internal force space, and based on the linear superposition of the fields, the structural response under different generalized internal force states in each sample is calculated. The strength criterion of the material is applied to verify the failure state of the structure. In this case, the Hashin criterion and the quadratic interface force criterion are used to judge the failure of the laminate.

[0115] .

[0116] in, Characterize fiber breakage under tension, Characterize fiber buckling and kinking under compression, Characterize matrix failure under tension and compression, Characterizes delamination failure between layers. The action represents the Cauchy stress. They represent fiber tension, fiber compression, matrix tension, matrix compression, in-plane shear, and in-plane tensile strength, respectively.

[0117] Based on this, the intensity simulation data set can be calculated .

[0118] Step 2. Low-fidelity model construction.

[0119] Construct a fully connected neural network and train the model based on the simulation data set. In this case, the nonlinear activation function uses a linear rectifier function, that is, The loss function used for parameter training uses the mean square error loss criterion, namely: , This represents the neural network.

[0120] Step 3. Structural strength test under typical working conditions.

[0121] Eight sets of laminate strength tests were conducted to measure the tensile, compressive, flexural, and shear strengths of the laminates. The test results were high-fidelity data. The types and standards of the eight laminate strength tests are summarized in Table 1.

[0122] Table 1 Strength test and test standards

[0123]

[0124] Each test set consists of three samples. Figure 4 The load is increased in each test until the specimen is destroyed. The failure mode diagram of each group of tests is shown in Figure 5 The load-displacement curves of the 8 groups of tests obtained in the experiment are shown in Figure 6 As shown, for clarity, the test curves of different specimens in each figure are offset along the x-axis.

[0125] Since this is the initial failure strength of the present application, when the load-displacement curve begins to deviate from the initial slope (indicated by the dotted line), it is Figure 6 The test results and the corresponding shell generalized section forces are shown in Table 2.

[0126] Table 2 Strength test results

[0127]

[0128] Based on the test results, 8 pairs of high-fidelity data were obtained. The input of each pair is the section force , the corresponding output is (indicates damage initiation), calculated from the test load.

[0129] Step 4. Multi-fidelity model construction.

[0130] By constructing Figure 3 The two-step multi-fidelity model shown in the figure is used to achieve the fusion of experimental data and simulation data. First, it is expanded based on the existing 8 pairs of high-frequency data. This application is in [0.9, 1.1] to The amplitude is uniformly taken within the interval , expand each set of test data: .

[0131] Based on this High-fidelity data , train the second neural network As a correction model, it forms a multi-fidelity model together with the first neural network.

[0132] Step 5. Structural strength check.

[0133] In the application of the model, given the generalized internal force state The multi-fidelity surrogate model is used as input to determine the failure state of plate and shell elements and perform structural strength verification. This completes the application of the multi-fidelity strength model construction method for aviation composite materials that integrates test and simulation data.

[0134] The implementation of the large-scale sampling simulation in step 1.2 includes but is not limited to the method of applying linear superposition. Any other method that can simulate the response results of the unit cell model under a specific generalized plate and shell load state can use this application.

[0135] The data obtained from the specimen-level test in step 3 include but are not limited to standard tests under a single load. Any strength test, as long as a specific generalized load state at which the structure begins to fail is obtained, can be used as high-fidelity data for subsequent multi-fidelity model training.

[0136] This case study presents the strength prediction results of low-fidelity and multi-fidelity data-driven strength models for ZT7H / 5429 laminates. The proposed strength model is then applied to the strength assessment of a composite wing structure.

[0137] Based on low-fidelity RVE simulation data, a neural network with 4 layers and 64-132-64-8 units was constructed and trained. The model prediction results in some typical load section spaces are as follows: Figure 7 As shown. Figure 7 (a) and Figure 7(b) It can be seen that under biaxial tension, compression, or bending, the strength envelope, i.e., the boundary between the blue and orange regions, takes the form of a diagonally oriented diamond. This is because deformation in one direction is compensated by deformation in the other direction due to the Poisson effect. Figure 7 The strength envelope in (c) is roughly circular. This is because under out-of-plane shear loading, the failure of the laminate is mainly caused by delamination. Since the interlaminar shear strength is independent of direction, the out-of-plane shear strength of the laminate is the same in all directions. Figure 7 (d) shows the results of the coupled axial and bending loading. Under this loading, the laminate close to one laminate surface fails first due to the combined effects of the axial force and bending tensile load. In some loading cases, such as Figure 7 (b) and 7(d), the predictions agree well with the test data, but for some other cases, e.g. Figure 7 (a) and 7(c), the predictions are not accurate enough. But in all cases, the predicted intensity envelopes capture the pattern of the test data well.

[0138] On this basis, a modified neural network model with 3 layers and 64-32-8 neurons was trained based on the test data, achieving a loss of 0.00001 on the test data and a failure prediction accuracy of 99.8%. Figure 8 As shown in the figure, the overall pattern of the strength envelope remains almost unchanged from the low-fidelity to the multi-fidelity prediction, but the multi-fidelity prediction agrees better with the test data. These results demonstrate that the proposed multi-fidelity model can effectively measure the strength of laminates using shell cross-sectional forces and can efficiently construct the model using a small amount of test data and a large amount of simulation data.

[0139] In addition, this application has good versatility. In addition to the laminate structure given in the case, it is also applicable to various forms of aviation plate and shell structures such as metamaterials, sandwich, and reinforcement, and has great application potential.

[0140] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-fidelity composite material strength verification method is implemented.

[0141] Those skilled in the art will understand that Figure 9 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0142] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0143] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0144] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0145] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0146] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0147] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A multi-fidelity composite material strength verification method, characterized in that: include: Use finite element models to construct three-dimensional unit cell models of aviation composite materials; Perform multiple sampling simulations based on the three-dimensional unit cell model to construct a unit failure judgment data set; The unit failure judgment data set includes the strength margin of the aviation composite material under different simulation conditions; the generalized internal force state of the aviation composite material under different simulation conditions is different; Constructing a first fully connected neural network and a second fully connected neural network; Training the first fully connected neural network using the unit failure judgment data set to obtain a low-fidelity neural network; The first fully connected neural network is trained using the generalized internal force state of the aviation composite material under different simulation conditions as input and the strength margin of the aviation composite material under the corresponding simulation conditions as output to obtain a low-fidelity neural network; Conducting structural strength tests on aviation composite materials to obtain test data; the test data includes strength margins of the aviation composite materials under different test conditions; the generalized internal force states of the aviation composite materials under different test conditions are different; Training the second fully connected neural network using the test data and the low-fidelity neural network to obtain a high-fidelity neural network; Obtain the generalized internal force state of the working condition to be predicted; According to the generalized internal force state of the working condition to be predicted, the low-fidelity neural network and the high-fidelity neural network are used to complete the strength verification of the aviation composite material under the working condition to be predicted; The second fully connected neural network is trained using the test data and the low-fidelity neural network to obtain a high-fidelity neural network, comprising: By expanding the test data, the expansion strength margin of aviation composite materials under different expansion conditions is obtained; the generalized internal force state of aviation composite materials under different expansion conditions is different; Inputting the generalized internal force state of the aviation composite material under different extended working conditions into the low-fidelity neural network to obtain the low-fidelity strength margin; The generalized internal force state and low-fidelity strength margin of aerospace composite materials under the same extended working condition are used as input data pairs; The second fully connected neural network is trained with the input data pair as input and the extended strength margin of the aviation composite material under the corresponding extended working condition as output to obtain a high-fidelity neural network.

2. The multi-fidelity composite material strength verification method according to claim 1, characterized in that: The first fully connected neural network or the second fully connected neural network is: ； ； ； in, represents the first Tier neurons; Represents the generalized internal force state of aviation composite materials under simulation conditions; Indicates the Tier neuron bias parameters; Indicates the The width of the layer; Indicates the Tier The neuron for the Tier The weights of neurons; After nonlinear activation, Tier neurons; After nonlinear activation, Tier neurons; represents a nonlinear activation function; represents the output of the first fully connected neural network; represents the first Tier neurons; Indicates the Tier neuron bias parameters; Indicates the The width of the layer; Indicates the Tier The neuron for the Tier neuron weights; Indicates the first hidden layer in the last neurons.

3. The multi-fidelity composite material strength verification method according to claim 1, characterized in that: The test data is expanded to obtain the expansion strength margin of aviation composite materials under different expansion conditions, including: Get the zoom range; Extract a preset number of amplitudes from the zoom interval; Determine any amplitude as the current amplitude; Determine any test condition as the current test condition; Multiply the generalized internal force state corresponding to the current test condition by the current amplitude to obtain the current extended condition; Multiplying the strength margin of the aviation composite material under the current test condition by the current amplitude to obtain the extended strength margin of the aviation composite material under the current extended condition; Update the current test condition and return to step "multiply the generalized internal force state corresponding to the current test condition by the current amplitude to obtain the current extended condition" until all test conditions are traversed; Update the current amplitude and return to step "Determine any test condition as the current test condition" until all amplitudes are traversed to obtain the extended strength margin of the aviation composite material under different extended conditions.

4. The multi-fidelity composite material strength verification method according to claim 1, characterized in that: According to the generalized internal force state of the working condition to be predicted, the low-fidelity neural network and the high-fidelity neural network are used to complete the strength verification of the aviation composite material under the working condition to be predicted, including: Inputting the generalized internal force state of the working condition to be predicted into the low-fidelity neural network to obtain a low-fidelity strength margin prediction value; Inputting the low-fidelity strength margin prediction value and the generalized internal force state of the working condition to be predicted into the high-fidelity neural network to obtain a high-fidelity strength margin prediction value; Complete the strength verification of aviation composite materials under the predicted working conditions based on high-fidelity strength margin prediction values.

5. The multi-fidelity composite material strength verification method according to claim 4, characterized in that: Complete strength verification of aviation composite materials under predicted working conditions based on high-fidelity strength margin predictions, including: When the high-fidelity strength margin prediction value is greater than or equal to the strength margin threshold, it is determined that the aviation composite material is in a failure state under the working condition to be predicted; When the high-fidelity strength margin prediction value is less than the strength margin threshold, it is determined that the aviation composite material is in a non-failure state under the working condition to be predicted.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-fidelity composite material strength verification method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-fidelity composite material strength verification method according to any one of claims 1 to 5 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-fidelity composite material strength verification method according to any one of claims 1 to 5 is implemented.

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