Two-body separated-aileron surface aerodynamic efficiency modeling method, system, device and apparatus

Abstract

The application provides a two-body separation control surface aerodynamic efficiency modeling method, system, device and medium. The method according to the application comprises the following steps: obtaining aircraft grids under different relative attitudes, performing numerical calculation on the aircraft grids under different relative attitudes, obtaining aerodynamic forces under different relative attitudes, relative displacements and control surface deflection angles, then obtaining control surface aerodynamic efficiency data, and dividing the control surface aerodynamic efficiency data into a sample set and a verification set according to a preset ratio after preprocessing; training a two-body separation control surface aerodynamic efficiency prediction model based on the sample set, obtaining a trained two-body separation control surface aerodynamic efficiency prediction model; verifying the trained two-body separation control surface aerodynamic efficiency prediction model based on the verification set, and verifying the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model. The scheme according to the application can quickly predict the control surface aerodynamic efficiency in the two-body separation process, and improve the prediction efficiency.

Description

Technical Field

[0001] This document relates to the field of aerodynamic efficiency modeling technology, and in particular to a method, system, equipment and device for modeling the aerodynamic efficiency of two-body separated control surfaces. Background Technology

[0002] Existing nonlinear unsteady aerodynamic modeling methods mainly fall into two categories: one is to establish traditional mathematical aerodynamic models (such as algebraic models, step response models, etc.) related to aerodynamic forces and flight physics; the other is intelligent learning-based aerodynamic models (such as fuzzy logic methods, support vector machines (SVMs), etc.). Traditional mathematical methods involve piecewise linear aerodynamic modeling using large amounts of aerodynamic data, resulting in low model accuracy and difficulty in parameter identification, which is increasingly failing to meet current engineering needs. Intelligent learning-based methods, on the other hand, can establish high-precision multi-input multi-output nonlinear aerodynamic models, making them highly suitable for nonlinear aerodynamic modeling.

[0003] In the process of predicting the separation trajectory in a two-body separation scenario, real-time control of two-stage control surfaces is required. Therefore, accurate prediction of control surface efficiency is a key factor in the design of the two-stage separation process. However, during the separation process, control surface efficiency is affected by the relative distance, relative attitude, and control surface deflection angle between the two bodies, resulting in very significant nonlinear aerodynamic characteristics. Using traditional numerical computation (CFD) methods to calculate the separation process under multi-dimensional conditions would incur enormous computational resources and time overhead (each state requires several hours of computation), posing significant challenges to the design and evaluation of the separation trajectory in a two-body separation scenario. Therefore, it is necessary to leverage the high modeling accuracy of intelligent learning methods to establish a control surface aerodynamic efficiency model for a two-body separation scenario. This model can quickly predict (within one second of computation time) the aerodynamic efficiency of the first and second-stage control surfaces, thus providing a rapid aerodynamic prediction method for the design and control of the separation trajectory and safety boundaries in a two-body separation scenario. Summary of the Invention

[0004] This invention provides a method for modeling the aerodynamic efficiency of two-body separated control surfaces, including:

[0005] S1. Obtain the aircraft mesh under different relative attitudes, perform numerical calculations on the aircraft mesh under different relative attitudes, obtain the aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles, and then obtain control surface aerodynamic efficiency data. After preprocessing the control surface aerodynamic efficiency data, divide it into a sample set and a verification set according to a preset ratio.

[0006] S2. Based on the sample set, train the aerodynamic efficiency prediction model of the two-body separation control surface to obtain the trained aerodynamic efficiency prediction model of the two-body separation control surface.

[0007] S3. Based on the validation set, the trained two-body separation control surface aerodynamic efficiency prediction model is validated to verify the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model.

[0008] This invention provides a two-body separated control surface aerodynamic efficiency modeling system, comprising:

[0009] The data acquisition module is used to acquire the aircraft mesh under different relative attitudes, perform numerical calculations on the aircraft mesh under different relative attitudes, obtain aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles, and then obtain control surface aerodynamic efficiency data. After preprocessing the control surface aerodynamic efficiency data, it is divided into a sample set and a verification set according to a preset ratio.

[0010] The model training module is used to train the two-body separation control surface aerodynamic efficiency prediction model based on the sample set, and obtain the trained two-body separation control surface aerodynamic efficiency prediction model.

[0011] The model validation module validates the trained two-body separation control surface aerodynamic efficiency prediction model based on the validation set, thereby verifying the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model.

[0012] This invention provides an electronic device, comprising:

[0013] Processor; and,

[0014] A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the two-body separation control surface aerodynamic efficiency modeling method described above.

[0015] This invention provides a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the two-body separation control surface aerodynamic efficiency modeling method described above.

[0016] By employing embodiments of the present invention, a neural network-based aerodynamic efficiency model for the control surface in a two-body separation scenario is established. This model can quickly predict the first and second-order aerodynamic forces, avoiding a large amount of CFD numerical simulation work (the calculation time for each condition is several hours), and providing a rapid aerodynamic force prediction method for the design of separation trajectory and safety boundary in a two-body separation scenario. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the aerodynamic efficiency modeling method for two-body separated control surfaces according to an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of the aerodynamic efficiency modeling system for two-body separated control surfaces according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram illustrating the two-body separation scenario and the definition of the rudder deflection angle in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the neural network input for the aerodynamic efficiency modeling method of the two-body separated control surface according to an embodiment of the present invention.

[0022] Figure 5 This is a schematic diagram of the neural network framework according to an embodiment of the present invention;

[0023] Figure 6 This is a schematic diagram illustrating the influence of neural network neurons and layer number in an embodiment of the present invention.

[0024] Figure 7 This is a schematic diagram of the axial force coefficient of the aerodynamic model according to an embodiment of the present invention;

[0025] Figure 8 This is a schematic diagram of the normal force coefficient of the aerodynamic model according to an embodiment of the present invention;

[0026] Figure 9 This is a schematic diagram of the pitching moment coefficient of the aerodynamic model according to an embodiment of the present invention;

[0027] Figure 10 This is a schematic diagram showing the distribution of modeled and sample values ​​of the axial force coefficient in an embodiment of the present invention.

[0028] Figure 11 This is a schematic diagram of the modeling values ​​and sample value distribution of the normal force coefficient in an embodiment of the present invention;

[0029] Figure 12 This is a schematic diagram of the modeling values ​​and sample value distribution of the pitch moment coefficient in an embodiment of the present invention;

[0030] Figure 13 This is a schematic diagram illustrating the variation of the rudder surface efficiency with the relative distance between the two bodies in an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0032] Method Implementation Examples

[0033] According to an embodiment of the present invention, a method for modeling the aerodynamic efficiency of two-body separated control surfaces is provided. Figure 1 This is a schematic diagram of the aerodynamic efficiency modeling method for two-body separated control surfaces according to an embodiment of the present invention. Figure 1 As shown, the aerodynamic efficiency modeling method for two-body separated control surfaces in this embodiment of the invention specifically includes:

[0034] S1. Obtain the aircraft mesh under different relative attitudes, perform numerical calculations on the aircraft mesh under different relative attitudes, obtain the aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles, and then obtain control surface aerodynamic efficiency data. After preprocessing the control surface aerodynamic efficiency data, divide it into a sample set and a verification set according to a preset ratio.

[0035] The steps for generating the aircraft mesh are as follows:

[0036] Step 1: Pointwise mesh generation software was used to generate the mesh. The two-body separation aircraft model, including the first and second levels, was imported.

[0037] Step 2: Generate mesh line connectors at the edges of the aircraft digital model;

[0038] Step 3: Distribute grid points on each grid line connector and densify the grid in areas with more separated flows;

[0039] Step 4: Generate a mesh domain using the mesh line connector and project the domain onto the digital model surface to make the mesh fit the body.

[0040] Step 5: Generate grid blocks through the grid domain to complete the spatial grid generation.

[0041] Step Six: Considering the relative attitude changes between the second and first stages in the two-body separation scenario, when generating the mesh, the first stage should remain stationary, while the second stage adjusts its position according to the relative attitude (such as relative pitch angle, relative X-direction displacement, relative Y-direction displacement, etc.). Repeat steps one to five to finally form the aircraft mesh under different relative attitudes.

[0042] In one specific implementation of this invention, CFL3D software is first installed on the local computer, and then the above-mentioned aircraft mesh is imported. The mesh format is plot3d. In the CFL3D input file, the SST turbulence model is used, and the calculation method is set to the DDES method.

[0043] Numerical calculations were then performed on the aircraft mesh under different relative attitudes to obtain aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles. The aerodynamic forces with and without control surface deflection angles were subtracted from the aerodynamic forces with and without control surface deflection angles to obtain control surface efficiency data. The aerodynamic forces under different relative attitudes were then organized and sorted according to relative attitude to obtain a control surface aerodynamic efficiency dataset. 80% of the data in this dataset was used as the sample set, and the remaining 20% ​​was used as validation data to form the validation set.

[0044] S2. Based on the sample set, train the aerodynamic efficiency prediction model of the two-body separation control surface to obtain the trained aerodynamic efficiency prediction model of the two-body separation control surface.

[0045] The specific steps to form a preliminary neural network model are as follows:

[0046] 1. Establish a neural network framework;

[0047] 2. Set the number of layers in the neural network;

[0048] 3. Set the number of neurons in each layer of the neural network;

[0049] 4. The loss function optimization method for neural networks is selected as MSG;

[0050] The specific steps for optimizing the neural network model are as follows:

[0051] Step 1: Use the neural network framework described above for learning;

[0052] Step 2: Adjust the number of neurons, number of layers, loss function optimization method, and step size according to the learning accuracy;

[0053] Step 3: Repeat steps 1 and 2 until the neural network model achieves good accuracy and efficiency, thus obtaining the final aerodynamic model.

[0054] S3. Validate the trained two-body separated control surface aerodynamic efficiency prediction model based on the validation set to verify the effectiveness of the model. S3 specifically includes:

[0055] Select several points from the validation set as validation inputs;

[0056] Substitute several points into the optimized two-body separation aerodynamic model;

[0057] The accuracy of the model is judged by the mean square error between several points and the fitted points.

[0058] A specific implementation step of this invention is as follows:

[0059] (1) Generate the aerodynamic calculation grid required for CFD numerical simulation based on the characteristics of the two-body separation aircraft;

[0060] (2) Based on the characteristics of the two-body separation aircraft, select aerodynamic settings such as spatial discretization format, time propagation format, turbulence model, preprocessing, and entropy correction to perform preliminary aerodynamic calculations and obtain aerodynamic forces and flow fields. The flow field should include basic information such as pressure, density, temperature, energy, and velocity.

[0061] (3) Based on the preliminary flow field and aerodynamic force obtained in step (2), the grid is refined until the aerodynamic force remains almost unchanged, proving that the grid size is reasonable and reliable.

[0062] (4) Simulate the flow field of the two-body separation aircraft under relative attitude (relative pitch angle, relative displacement, etc.) to obtain the static aerodynamic forces and torques of the first and second stages of the aircraft.

[0063] (5) Based on the aerodynamic data obtained in step (4), subtract the aerodynamic force without rudder deflection from the aerodynamic force with rudder deflection angle to obtain the aerodynamic efficiency of the control surface under different relative positions and attitudes during the two-body separation process, such as... Figure 13 The diagram shown is a schematic diagram of the change of control surface efficiency with the relative distance between the two bodies in an embodiment of the present invention. The control surface aerodynamic efficiency data is organized into the dataset required by the neural network.

[0064] (6) Take 80% of the data in the dataset from step (5) as the sample set and the remaining 20% ​​as the validation data to form the validation set.

[0065] (7) Form a preliminary machine learning model and initially set key parameters such as the number of learning layers, the number of neurons in each layer, and the loss function optimization method;

[0066] (8) Substitute the sample set formed in step (6) into the preliminary machine learning model formed in step (7) to evaluate the machine learning efficiency and accuracy.

[0067] (9) Adjust the number of learning layers, the number of neurons, and the loss function optimization method until the learning efficiency and accuracy reach the optimal level, thereby initially establishing the aerodynamic model of the two-body separation scenario.

[0068] (10) Select several points in the validation set as validation inputs and input them into the aerodynamic model in step (9). Determine the model accuracy based on the mean square error between the several points and the fitting points, and obtain the final aerodynamic model of the two-body separation scenario.

[0069] The following is based on Figure 3 The two-body separation scenario shown will be explained in detail:

[0070] from Figure 3 As can be seen, the two-body separation vehicle consists of a first stage and a second stage, with the control surfaces of the second stage located at its tail. Because the second stage is within the flow interference zone of the first stage (the shock wave reflection pattern is shown in the figure), there is relatively complex aerodynamic interference between the second stage and the first stage. Therefore, the prediction of the aerodynamic efficiency of the second stage control surfaces is particularly important.

[0071] First, a preliminary plan for the inputs and outputs of the aerodynamic model is needed. Since this scenario only considers aerodynamic force prediction in the longitudinal plane, four quantities are sufficient to clearly describe the scenario. Therefore, there are four inputs, such as... Figure 4 These are dy (relative y-displacement), α (angle of attack of the incoming flow), az (relative pitch angle), and delta (deflection angle of the second-stage vehicle control surfaces). Since the aerodynamic efficiency of the control surfaces is achieved by subtracting the aerodynamic forces and moments of the first and second stages, the model outputs six quantities: first-stage normal force, first-stage axial force, first-stage pitch moment, second-stage normal force, second-stage axial force, and second-stage pitch moment.

[0072] Then, the aerodynamic forces corresponding to different input quantities (dy, α, az, delta) are calculated, with 5 points taken for each dimension, and the values ​​are as follows:

[0073] dy = 1m, 2m, 3m, 4m, 5m

[0074] α=-10°,-5°,0°,5°,10°

[0075] az=0°, 4°, 8°, 12°, 16°

[0076] delta=0°, 10°, 20°, 25°, 30°

[0077] Therefore, a total of 4 calculations are required. 5 =625 computational states. CFD numerical simulation was used to calculate the aerodynamic forces and moments under different operating conditions, forming a control surface aerodynamic efficiency dataset. 80% of the dataset was randomly selected as the sample set and 20% as the validation set.

[0078] Next, a preliminary aerodynamic model based on neural networks was established, such as... Figure 5 The input layer contains the model's input values, the output layer contains the model's output values, and the hidden layers in between require further testing and confirmation.

[0079] To allow for appropriate settings of the number of layers and neurons in the neural network, the fitting accuracy of the neural network was tested using 4, 8, 16, 24, and 32 neurons, and 1, 2, 3, and 4 hidden layers, respectively. Figure 6 As can be seen from the figure, a good fitting accuracy can be achieved when the number of hidden layers is 2 and the number of neurons is 16. Using more hidden layers and neurons will not further improve the fitting accuracy. Therefore, the number of neurons is 16 and the number of hidden layers is 2.

[0080] The aerodynamic model was then iterated further, with the sample set input into the model to obtain the fitted values, and the validation set input into the aerodynamic model to obtain the predicted values. As can be seen from the figure, the fitted values ​​and predicted values ​​gradually converged after sufficient iterations, and the neuron parameters in the model were adjusted to their optimal values. Figures 10-11 This is a schematic diagram of the distribution of modeling values ​​and sample values ​​in an embodiment of the present invention.

[0081] Finally, the validation set is input into the aerodynamic model for validation, such as... Figures 7-9 ,from Figures 7-9 As can be seen, the aerodynamic model makes good predictions of the first and second stage aerodynamic moments in this tailrace scenario, verifying that the aerodynamic model is reasonable.

[0082] The above is a preliminary test of the aerodynamic modeling method for two-body separation scenarios based on neural networks. The test results show that the aerodynamic model framework established by this method has strong aerodynamic force prediction capabilities. Content not described in detail in this specification is well-known to those skilled in the art.

[0083] By employing the embodiments of the present invention, the following beneficial effects are achieved:

[0084] A method for modeling the aerodynamic efficiency of control surfaces in a two-body separation scenario is provided. This method establishes an aerodynamic efficiency model of control surfaces in a two-body separation scenario. This model can quickly predict the first and second-order aerodynamic forces (the calculation time is on the order of one second), avoiding a large amount of CFD numerical simulation work (the calculation time for each condition is several hours). This provides a fast aerodynamic force prediction method for the design of separation trajectory and safety boundary in a two-body separation scenario.

[0085] System Implementation Examples

[0086] According to an embodiment of the present invention, a two-body separated control surface aerodynamic efficiency modeling system is provided. Figure 2 This is a schematic diagram of the aerodynamic efficiency modeling system for two-body separated control surfaces according to an embodiment of the present invention. Figure 2 As shown, the aerodynamic efficiency modeling system for two-body separated control surfaces in this embodiment of the invention specifically includes:

[0087] Data acquisition module 20 is used to acquire aircraft meshes under different relative attitudes, perform numerical calculations on the aircraft meshes under different relative attitudes, obtain aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles, and then obtain control surface aerodynamic efficiency data. After preprocessing the control surface aerodynamic efficiency data, it is divided into a sample set and a verification set according to a preset ratio.

[0088] Model training module 22 is used to train the two-body separation control surface aerodynamic efficiency prediction model based on the sample set, and obtain the trained two-body separation control surface aerodynamic efficiency prediction model.

[0089] The model verification module 24 verifies the trained two-body separation control surface aerodynamic efficiency prediction model based on the verification set, thereby verifying the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model.

[0090] Device Example 1

[0091] According to an embodiment of the present invention, an electronic device is provided, comprising:

[0092] Processor; and,

[0093] A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the two-body separation control surface aerodynamic efficiency modeling method described above.

[0094] Device Example 2

[0095] According to an embodiment of the present invention, a storage medium is provided for storing computer-executable instructions, which, when executed, implement the steps of the above-described two-body separation control surface aerodynamic efficiency modeling method.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for modeling the aerodynamic efficiency of two-body separated control surfaces, characterized in that... include: S1. Obtain the aircraft mesh under different relative attitudes, perform numerical calculations on the aircraft mesh under different relative attitudes, obtain the aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles, and then obtain control surface aerodynamic efficiency data. After preprocessing the control surface aerodynamic efficiency data, divide it into a sample set and a verification set according to a preset ratio. S2. Based on the sample set, train the aerodynamic efficiency prediction model of the two-body separation control surface to obtain the trained aerodynamic efficiency prediction model of the two-body separation control surface. S3. Validate the trained two-body separation control surface aerodynamic efficiency prediction model based on the validation set to verify the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model. The steps for obtaining the control surface efficiency data are as follows: Numerical calculations were performed on the aircraft mesh under different relative attitudes to obtain the aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles. Subtracting the aerodynamic force without the deflection angle from the aerodynamic force obtained by the deflection angle of the control surface, we can obtain the control surface aerodynamic efficiency data under different relative positions and attitudes during the separation process of the two bodies. The inputs and outputs of the two-body separation control surface aerodynamic efficiency prediction model are planned according to the specific physical scenario of two-body separation. The inputs of the two-body separation control surface aerodynamic efficiency prediction model include: relative pitch angle, relative roll angle, relative displacement, and control surface deflection angle; the outputs of the two-body separation control surface aerodynamic efficiency prediction model include: first-level normal force, first-level axial force, first-level pitch moment, second-level normal force, second-level axial force, and second-level pitch moment.

2. The method according to claim 1, characterized in that, The steps for generating the aircraft mesh include: The two-body separation aircraft model was imported using Pointwise mesh generation software. The two-body separation aircraft model includes a first-stage aircraft model and a second-stage aircraft model. Generate mesh lines as connectors at the edges of the binary aircraft digital model; Grid points are distributed on each grid line connector to densify areas where the separation flow value is greater than a preset value; A mesh domain is generated using a mesh line connector, and the domain is projected onto the surface of the digital model to make the mesh fit the body. The aircraft mesh is generated by generating mesh blocks through mesh domains.

3. The method according to claim 1, characterized in that, The process of obtaining aircraft meshes under different relative attitudes specifically includes: keeping the primary aircraft model unchanged, adjusting the position of the secondary aircraft model according to the relative attitude to perform the aircraft mesh generation steps, and finally forming aircraft meshes under different relative attitudes.

4. The method according to claim 1, characterized in that, The step of preprocessing the aerodynamic efficiency data of the control surfaces and dividing it into training and validation sets according to a preset ratio specifically includes: The aerodynamic forces under different relative attitudes are organized and sorted according to the relative attitude to obtain the aerodynamic efficiency data of the control surface. 80% of the aerodynamic efficiency data of the control surface was used as the sample set, and the remaining 20% ​​was used as the validation set.

5. The method according to claim 1, characterized in that, The construction of the preset two-body separation control surface aerodynamic efficiency prediction model specifically includes: Establishing a neural network framework includes: setting the number of neural network layers, setting the number of neurons in each layer, and selecting MSG as the neural network loss function optimization method; Learning is performed using a neural network framework; Adjust the number of neurons, layers, loss function optimization method, and step size according to the learning accuracy until the preset stopping condition is met to obtain the trained two-body separation control surface aerodynamic efficiency prediction model.

6. A two-body separated control surface aerodynamic efficiency modeling system, characterized in that, include: The data acquisition module is used to acquire the aircraft mesh under different relative attitudes, perform numerical calculations on the aircraft mesh under different relative attitudes, obtain aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles, and then obtain control surface aerodynamic efficiency data. After preprocessing the control surface aerodynamic efficiency data, it is divided into a sample set and a verification set according to a preset ratio. The model training module is used to train the two-body separation control surface aerodynamic efficiency prediction model based on the sample set, and obtain the trained two-body separation control surface aerodynamic efficiency prediction model. The model validation module validates the trained two-body separation control surface aerodynamic efficiency prediction model based on the validation set, thereby verifying the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model. The steps for obtaining the control surface efficiency data are as follows: Numerical calculations were performed on the aircraft mesh under different relative attitudes to obtain the aerodynamic forces under different relative attitudes, relative displacements, and control surface deflection angles. Subtracting the aerodynamic force without the deflection angle from the aerodynamic force obtained by the deflection angle of the control surface, we can obtain the control surface aerodynamic efficiency data under different relative positions and attitudes during the separation process of the two bodies. The inputs and outputs of the two-body separation control surface aerodynamic efficiency prediction model are planned according to the specific physical scenario of two-body separation. The inputs of the two-body separation control surface aerodynamic efficiency prediction model include: relative pitch angle, relative roll angle, relative displacement, and control surface deflection angle; the outputs of the two-body separation control surface aerodynamic efficiency prediction model include: first-level normal force, first-level axial force, first-level pitch moment, second-level normal force, second-level axial force, and second-level pitch moment.

7. An electronic device, comprising: processor; as well as, A memory is configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the two-body separation control surface aerodynamic efficiency modeling method as described in any one of claims 1-5.

8. A storage medium for storing computer-executable instructions, which, when executed, implement the steps of the two-body separation control surface aerodynamic efficiency modeling method as described in any one of claims 1-5.

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