Two-body separation control surface aerodynamic efficiency modeling method, system, equipment and device

Abstract

The invention provides a two-body separation control surface aerodynamic efficiency modeling method, system and device and a medium, and the method comprises the steps: obtaining aircraft grids under different relative attitudes, carrying out the numerical calculation of the aircraft grids under different relative attitudes, obtaining aerodynamic forces under different relative attitudes, relative displacements and control surface deflection angles, and carrying out the modeling of the aerodynamic efficiency of the two-body separation control surface. Preprocessing the control surface aerodynamic efficiency data, and dividing the preprocessed control surface aerodynamic efficiency data into a sample set and a verification set according to a preset proportion; training a two-body separation control surface aerodynamic efficiency prediction model based on the sample set to obtain a trained two-body separation control surface aerodynamic efficiency prediction model; and 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. According to the scheme, the control surface aerodynamic efficiency in the two-body separation process can be quickly predicted, and the prediction efficiency is improved.

Description

Technical Field

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

[0002] There are two main types of research methods commonly used in the prior art for nonlinear unsteady aerodynamic modeling: one is to establish a traditional mathematical aerodynamic model related to aerodynamic forces and flight physical quantities (such as algebraic models, step response models, etc.), and the other is an intelligent learning aerodynamic model (such as fuzzy logic method, support vector machine (SVM), etc.). Among them, the traditional mathematical method is to perform piecewise linear aerodynamic modeling based on a large amount of aerodynamic data. The model accuracy is low and the parameter identification is difficult, which has gradually failed to meet the existing engineering needs. The intelligent learning method can establish a high-precision multi-input and multi-output nonlinear aerodynamic model, which is very suitable for nonlinear aerodynamic modeling.

[0003] In the process of separation trajectory prediction of two-body separation scenario, two-stage control surfaces are required to be controlled in real time. Therefore, in the design of two-stage separation process, accurate prediction of control surface efficiency is a key factor. However, in the process of two-body separation, control surface efficiency is affected by the relative distance, relative attitude and control surface deflection angle between the two bodies, which makes the control surface efficiency of the two bodies present very obvious nonlinear aerodynamic characteristics in the separation process. If the traditional numerical calculation method (CFD) is used to calculate the separation process under multi-dimensional working conditions, it will bring huge computing resources and time overhead (each state needs to be calculated for several hours), which will bring great difficulties to the design and evaluation of separation trajectory of two-body separation scenario. Therefore, it is necessary to take advantage of the high modeling accuracy of intelligent learning methods to establish a control surface aerodynamic efficiency model for two-body separation scenario. Through this model, the aerodynamic efficiency of the first and second control surfaces can be quickly predicted (the calculation time is on the order of one second), thereby providing a fast aerodynamic prediction method for the separation trajectory and safety boundary design and control in the two-body separation scenario. Summary of the invention

[0004] An embodiment of the present invention provides a method for modeling aerodynamic efficiency of a two-body separation control surface, comprising:

[0005] S1, obtaining the aircraft meshes under different relative postures, performing numerical calculations on the aircraft meshes under the different relative postures, obtaining the aerodynamic forces under different relative postures, relative displacements, and deflection angles of the control surface, and then obtaining the aerodynamic efficiency data of the control surface, and pre-processing the aerodynamic efficiency data of the control surface and dividing it into a sample set and a verification set according to a preset ratio;

[0006] S2. Training a two-body separation control surface aerodynamic efficiency prediction model based on the sample set to obtain a trained two-body separation control surface aerodynamic efficiency prediction model;

[0007] S3. Verifying the trained two-body separation control surface aerodynamic efficiency prediction model based on the verification set to verify the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model.

[0008] An embodiment of the present invention provides a two-body separation control surface aerodynamic efficiency modeling system, comprising:

[0009] A data acquisition module is used to acquire the aircraft meshes under different relative postures, perform numerical calculations on the aircraft meshes under the different relative postures, obtain the aerodynamic forces under different relative postures, relative displacements, and deflection angles of the control surface, and then obtain the aerodynamic efficiency data of the control surface, and divide the aerodynamic efficiency data of the control surface into a sample set and a verification set according to a preset ratio after preprocessing;

[0010] A model training module, used for training a two-body separation control surface aerodynamic efficiency prediction model based on the sample set to obtain a trained two-body separation control surface aerodynamic efficiency prediction model;

[0011] The model verification module verifies the trained two-body separation control surface aerodynamic efficiency prediction model based on the verification set to verify the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model.

[0012] An embodiment of the present invention provides an electronic device, including:

[0013] processor; and,

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

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

[0016] By adopting the embodiment of the present invention, a neural network-based aerodynamic efficiency model of the control surface in the two-body separation scenario is established. This model can quickly predict the primary and secondary aerodynamic forces, avoiding a large amount of CFD numerical simulation workload (the calculation time for each working condition is several hours), and providing a fast aerodynamic force prediction method for the separation trajectory and safety boundary design in the two-body separation scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 This is a flow chart of a method for modeling aerodynamic efficiency of a two-body separation control surface according to an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a two-body separation control surface aerodynamic efficiency modeling system according to an embodiment of the present invention;

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

[0021] Figure 4 A schematic diagram of a neural network input quantity of a two-body separation control surface aerodynamic efficiency modeling method according to an embodiment of the present invention;

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

[0023] Figure 6 A schematic diagram of the analysis of the impact of the number of neurons and layers in a neural network according to an embodiment of the present invention;

[0024] Figure 7 Schematic diagram of the axial force coefficient of the aerodynamic model of an embodiment of the present invention;

[0025] Figure 8 Schematic diagram of normal force coefficient of the aerodynamic model of an embodiment of the present invention;

[0026] Fig. 9 Schematic diagram of the pitching moment coefficient of the aerodynamic model of an embodiment of the present invention;

[0027] Fig.10 is a schematic diagram of the distribution of modeled values ​​and sample values ​​of the axial force coefficient according to an embodiment of the present invention;

[0028] Fig.11 Schematic diagram of the modeling value and sample value distribution of the normal force coefficient according to an embodiment of the present invention;

[0029] Fig.12 Schematic diagram of the distribution of modeling values ​​and sample values ​​of the pitching moment coefficient according to an embodiment of the present invention;

[0030] Fig.13 Schematic diagram of the change of the control surface efficiency with the relative distance between two bodies according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0032] Method Embodiment

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

[0034] S1, obtaining the aircraft meshes under different relative postures, performing numerical calculations on the aircraft meshes under the different relative postures, obtaining the aerodynamic forces under different relative postures, relative displacements, and deflection angles of the control surface, and then obtaining the aerodynamic efficiency data of the control surface, and pre-processing the aerodynamic efficiency data of the control surface and dividing it into a sample set and a verification set according to a preset ratio;

[0035] The steps to generate the aircraft mesh are as follows:

[0036] Step 1: Pointwise mesh generation software is used to generate the mesh, and the two-body separation aircraft digital model is imported, including the first and second stages;

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

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

[0039] Step 4: Generate a mesh domain through 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 surface domain to complete the spatial grid generation.

[0041] Step 6: Considering that the relative posture of the second level and the first level changes in the two-body separation scenario, when generating the mesh, the first level should be kept stationary, and the second level should be adjusted according to the relative posture (such as relative pitch angle, relative X-direction displacement, relative Y-direction displacement, etc.). Repeat steps 1 to 5 to finally form the aircraft mesh under different relative postures.

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

[0043] After that, numerical calculations were performed on the aircraft grids under different relative attitudes to obtain the aerodynamic forces under different relative attitudes, relative displacements, and rudder deflection angles; the aerodynamic forces obtained with the rudder deflection angle were subtracted from the aerodynamic forces without the rudder deflection angle to obtain the rudder efficiency data; the aerodynamic forces under different relative attitudes were sorted and sorted according to the relative attitudes to obtain the rudder aerodynamic efficiency data set. 80% of the data in the data set was used as the sample set, and the remaining 20% ​​of the data was used as the verification data to form a verification set.

[0044] S2. Training a two-body separation control surface aerodynamic efficiency prediction model based on the sample set to obtain a trained two-body separation control surface aerodynamic efficiency prediction model;

[0045] To form a preliminary neural network model, the specific steps are as follows:

[0046] 1. Establish a neural network framework;

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

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

[0049] 4. Select the neural network loss function optimization method as MSG;

[0050] To optimize the neural network model, the specific steps are as follows:

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

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

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

[0054] S3, verifying the trained two-body separation control surface aerodynamic efficiency prediction model based on the verification set to verify the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model. S3 specifically includes:

[0055] Select several points in the validation set as validation input;

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

[0057] The accuracy of the model is determined based on the mean square error between several points and the fitting points.

[0058] A specific implementation step of the embodiment of the present 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) According to the characteristics of the two-body separation aircraft, aerodynamic settings such as spatial discrete format, time advancement format, turbulence model, preprocessing, and entropy correction are selected to perform preliminary aerodynamic calculations to 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 encrypted until the aerodynamic force remains almost unchanged, proving that the grid size is reasonable and reliable.

[0062] (4) Simulate the flow field of a two-body separated aircraft in relative posture (relative pitch angle, relative displacement, etc.) to obtain the static aerodynamic forces and moments of the first and second stages of the aircraft.

[0063] (5) Based on the aerodynamic data obtained in step (4), the aerodynamic efficiency of the rudder surface at different relative positions and attitudes during the separation process of the two bodies is obtained by subtracting the aerodynamic force with the rudder deflection angle from the aerodynamic force without the rudder deflection angle, as follows: Fig.13 The figure shows a schematic diagram of the change of the control surface efficiency with the relative distance between two bodies in an embodiment of the present invention, and the control surface aerodynamic efficiency data is sorted into the data set required by the neural network.

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

[0065] (7) Form a preliminary machine learning model and preliminarily 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 efficiency and accuracy of machine learning.

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

[0068] (10) Select several points from the verification set as verification input and bring them into the aerodynamic model in step (9). The model accuracy is judged based on the mean square error between the several points and the fitting points to obtain the final aerodynamic model of the two-body separation scene.

[0069] Below Figure 3 The two-body separation scenario shown is specifically described:

[0070] from Figure 3 As can be seen in the figure, the two-body separated aircraft consists of a first stage and a second stage, and the control surface of the second stage is located at the tail of the second stage. Since the second stage is in the flow interference area of ​​the first stage (the figure shows the form of shock wave reflection), there is a more complex aerodynamic interference between the second stage and the first stage, so the aerodynamic efficiency prediction of the second stage control surface is particularly important.

[0071] First, it is necessary to make a preliminary plan for the input and output of the aerodynamic model. Since this scenario only considers the aerodynamic force prediction in the longitudinal plane, four quantities are needed to describe the scenario clearly, so there are 4 input quantities, such as Figure 4 , respectively dy (relative y displacement), α (incoming flow angle of attack), az (relative pitch angle), and delta (secondary aircraft control surface deflection angle). Because the control surface aerodynamic efficiency is achieved by subtracting the aerodynamic forces and moments of the first and second levels, the output of the model is 6 quantities, namely the first-level normal force, the first-level axial force, the first-level pitch moment, the second-level normal force, the second-level axial force, and the second-level pitch moment.

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

[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 5 = 625 calculation states. CFD numerical simulation is used to calculate 625 states, and the corresponding aerodynamic forces and moments under different working conditions are obtained to form a data set of aerodynamic efficiency of the control surface. 80% of them are randomly selected as sample sets and 20% as validation sets.

[0078] Next, a preliminary aerodynamic model based on neural network is established, such as Figure 5 , where the input layer is the input of the model, the output layer is the output of the model, and the hidden layer in the middle needs further testing and confirmation.

[0079] In order to properly set the number of layers and neural units of the neural network, the number of neural units is 4, 8, 16, 24, and 32, and the number of hidden layers of the neural network is 1, 2, 3, and 4 to test the fitting accuracy of the neural network. Figure 6 ,From the figure, we can see that when the number of hidden layers is 2 and the number of neurons is 16, a good fitting accuracy can be achieved. 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] Then the aerodynamic model is further iterated, where the sample set is brought into the model to obtain the fitting value, and the validation set is brought into the aerodynamic model to obtain the predicted value. As can be seen from the figure, the fitting value and the predicted value gradually converge after sufficient iterations, and the neuron parameters in the model are adjusted to the best. Figure 10-11 Schematic diagram of distribution of modeling values ​​and sample values ​​according to an embodiment of the present invention.

[0081] Finally, the validation set is brought into the aerodynamic model for verification, such as Figure 7-Figure 9 ,from Figure 7-Figure 9 It can be seen that the aerodynamic model has a good prediction of the aerodynamic torque of the first and second stages in the tail retreat scenario, which verifies that the aerodynamic model is reasonable.

[0082] The above is a preliminary test of the aerodynamic modeling method for the two-body separation scenario based on a neural network. From the test results, we can see that the aerodynamic model framework established by this method has a strong aerodynamic prediction capability. The contents not described in detail in the specification of the present invention belong to the well-known technology of those skilled in the art.

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

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

[0085] System Example

[0086] According to an embodiment of the present invention, a two-body separation control surface aerodynamic efficiency modeling system is provided. Figure 2 Schematic diagram of a two-body separation control surface aerodynamic efficiency modeling system according to an embodiment of the present invention. Figure 2 As shown, the two-body separation control surface aerodynamic efficiency modeling system of the embodiment of the present invention specifically includes:

[0087] The data acquisition module 20 is used to acquire the aircraft meshes under different relative postures, perform numerical calculations on the aircraft meshes under the different relative postures, obtain the aerodynamic forces under different relative postures, relative displacements, and deflection angles of the control surface, and then obtain the aerodynamic efficiency data of the control surface, and divide the aerodynamic efficiency data of the control surface into a sample set and a verification set according to a preset ratio after preprocessing;

[0088] A model training module 22 is used to train the two-body separation control surface aerodynamic efficiency prediction model based on the sample set to obtain a 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 to verify the validity of the two-body separation control surface aerodynamic efficiency prediction model.

[0090] Device Example 1

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

[0092] processor; and,

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

[0094] Device Example 2

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

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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 a two-body separated control surface, characterized in that include: S1, obtaining the aircraft meshes under different relative postures, performing numerical calculations on the aircraft meshes under the different relative postures, obtaining the aerodynamic forces under different relative postures, relative displacements, and deflection angles of the control surface, and then obtaining the aerodynamic efficiency data of the control surface, and pre-processing the aerodynamic efficiency data of the control surface and dividing it into a sample set and a verification set according to a preset ratio; S2. Training a two-body separation control surface aerodynamic efficiency prediction model based on the sample set to obtain a trained two-body separation control surface aerodynamic efficiency prediction model; S3. Verifying the trained two-body separation control surface aerodynamic efficiency prediction model based on the verification set to verify the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model.

2. The method according to claim 1, characterized in that: The step of generating the aircraft grid comprises: The Pointwise mesh generation software is used to import the two-body separation aircraft digital model, which includes the first-stage aircraft digital model and the second-stage aircraft digital model; Generate grid line connectors at the edge of the two-body aircraft digital model; Distribute grid points on each grid line connector and encrypt areas where the separation flow value is greater than the preset value; Generate a mesh domain through the mesh line connector, and project the domain onto the digital model surface to make the mesh fit the body; The grid block is generated through the grid surface domain to complete the generation of the aircraft grid.

3. The method according to claim 1, characterized in that: The acquisition of the aircraft mesh under different relative postures specifically includes: keeping the primary aircraft digital model unchanged, adjusting the position of the secondary aircraft digital model according to the relative posture, executing the aircraft mesh generation step, and finally forming the aircraft mesh under different relative postures.

4. The method according to claim 1, characterized in that: The steps for obtaining the control surface efficiency data are as follows: Numerical calculation is performed on the aircraft grids under the different relative postures to obtain aerodynamic forces under different relative postures, relative displacements, and control surface deflection angles; The aerodynamic force obtained with the deflection angle of the rudder surface is subtracted from the aerodynamic force without the rudder surface to obtain the aerodynamic efficiency data of the rudder surface at different relative positions and attitudes during the separation process of the two bodies.

5. The method according to claim 4, characterized in that The pre-processing of the control surface aerodynamic efficiency data and dividing it into a training set and a validation set according to a preset ratio specifically includes: The aerodynamic forces under different relative attitudes are sorted and sorted according to the relative attitudes to obtain the aerodynamic efficiency data of the control surface; 80% of the control surface aerodynamic efficiency data are used as a sample set, and the remaining 20% ​​of the data are used as a verification set.

6. 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: Establish a neural network framework, including: setting the number of neural network layers, setting the number of neurons in each layer of the neural network, and selecting the neural network loss function optimization method as MSG; Use a neural network framework for learning; The number of neurons, the number of layers, the loss function optimization method and the step size are adjusted according to the learning accuracy until the preset stop condition is met to obtain the trained two-body separation control surface aerodynamic efficiency prediction model.

7. The method according to claim 1, characterized in that The input and output of the two-body separation control surface aerodynamic efficiency prediction model are planned according to the specific physical scenario of the two-body separation. The input of the two-body separation control surface aerodynamic efficiency prediction model includes: relative pitch angle, relative roll angle, relative displacement, and control surface deflection angle; the output of the two-body separation control surface aerodynamic efficiency prediction model includes: primary normal force, primary axial force, primary pitch moment, secondary normal force, secondary axial force, and secondary pitch moment.

8. A two-body separated control surface aerodynamic efficiency modeling system, characterized in that: include: A data acquisition module is used to acquire the aircraft meshes under different relative postures, perform numerical calculations on the aircraft meshes under the different relative postures, obtain the aerodynamic forces under different relative postures, relative displacements, and deflection angles of the control surface, and then obtain the aerodynamic efficiency data of the control surface, and divide the aerodynamic efficiency data of the control surface into a sample set and a verification set according to a preset ratio after preprocessing; A model training module, used for training a two-body separation control surface aerodynamic efficiency prediction model based on the sample set to obtain a trained two-body separation control surface aerodynamic efficiency prediction model; The model verification module verifies the trained two-body separation control surface aerodynamic efficiency prediction model based on the verification set to verify the effectiveness of the two-body separation control surface aerodynamic efficiency prediction model.

9. An electronic device, comprising: processor; as well as, A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the steps of the method for modeling the aerodynamic efficiency of a two-body separated control surface as claimed in any one of claims 1 to 7.

10. A storage medium for storing computer executable instructions, wherein the computer executable instructions, when executed, implement the steps of the method for modeling the aerodynamic efficiency of a two-body separated control surface as claimed in any one of claims 1 to 7.

Images