Active flow control system for aircraft wake flow optimization

Abstract

The invention discloses an active flow control system for aircraft wake flow optimization, and relates to the technical field of aircraft wake flow optimization, and the system comprises a monitoring module which is used for obtaining a trailing vortex induced velocity based on an H-B trailing vortex induced velocity model; the acquisition module is used for calculating the thrust change quantity of the unmanned aerial vehicle for washing the wake flow on the aerial carrier during fixed straight and level flight; the optimization module is used for optimizing the unmanned aerial vehicle cluster based on a particle swarm optimization algorithm and calculating the optimized trailing vortex induced velocity variation and the rolling torque coefficient control ratio; and the evaluation module is used for evaluating a wake flow optimization effect based on fuzzy reasoning according to the rolling torque coefficient control ratio and the trailing vortex velocity variable quantity, and improving a particle swarm optimization algorithm according to an evaluation result. Through reasonable wake flow resistance reduction optimization, the energy consumption of the unmanned aerial vehicle in the flight process can be effectively reduced, the endurance time is prolonged, and the problem that the wake flow generated by an aerial carrier in the prior art affects the flight resistance and the flight safety of the unmanned aerial vehicle is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft wake optimization, and more particularly to an active flow control system for aircraft wake optimization. Background Art

[0002] When an aircraft is flying, the air flowing over the wing will form a strong air disturbance, forming a wake. The formation of the wake is due to the pressure difference on the wing, which causes the air to swirl, eddy and other complex flow phenomena behind the wing. Wake disturbances may affect other aircraft in flight, especially in busy airspace, and may cause flight safety hazards, especially during takeoff and landing. Wakes increase the drag of the aircraft, causing the aircraft to consume more fuel to maintain flight. The interaction between the wake and the air will cause increased noise, especially when flying at low altitudes, causing trouble to residents. Active flow control (AFC) refers to actively intervening and changing the air flow state through external devices or methods (such as mechanical devices, airflow injection, suction, vibration control, etc.) to achieve specific flow control goals. Unlike passive flow control (such as wing design optimization), active flow control can adjust and control the flow state in real time to adapt to different flight conditions.

[0003] Deficiencies of existing technologies:

[0004] Traditional wake control methods mainly rely on the passive design of the aircraft, such as wing shape, tail configuration, etc. These designs can only reduce the impact of wake to a certain extent. However, with the development of aviation technology, the existing passive control methods can no longer meet the higher requirements of modern aircraft for fuel efficiency, flight safety and environmental impact. Therefore, it is particularly important to propose a system that can actively adjust the flow state and optimize the wake characteristics.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an active flow control system for optimizing aircraft wake, which solves the problems raised in the above-mentioned background technology by optimizing the aircraft wake.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An active flow control system for aircraft wake optimization comprises a monitoring module, an acquisition module, an optimization module and an evaluation module, wherein the modules are connected: the monitoring module is used to monitor the air flow state around a UAV in the upwash zone of the carrier aircraft wake in real time, and to acquire the wake vortex induced velocity based on the HB wake vortex induced velocity model according to the air flow state; the acquisition module is used to acquire the thrust of the UAV in the upwash zone of the carrier aircraft wake for fixed straight and level flight, and to calculate the thrust change of the UAV in the upwash zone of the carrier aircraft wake in combination with the thrust of the UAV in the cruise flight state; the optimization module is used to generate the carrier aircraft wake adjustment coefficient based on the machine learning algorithm according to the wake vortex induced velocity and the thrust change, optimize the UAV cluster based on the particle swarm optimization algorithm according to the carrier aircraft wake adjustment coefficient, and calculate the optimized wake vortex induced velocity change and the rolling moment coefficient control ratio; the evaluation module is used to evaluate the wake optimization effect based on fuzzy reasoning according to the rolling moment coefficient control ratio and the wake vortex velocity change, and improve the particle swarm optimization algorithm according to the evaluation result.

[0009] In a preferred embodiment, the method for obtaining the tail vortex induced velocity is as follows: monitor the air flow state, obtain the lift distribution of the wing according to the air flow state, and determine the vortex source position and vortex distribution of the tail vortex; based on the HB tail vortex induced velocity model, integrate the velocity field generated by the vortex source, substitute the vortex distribution into the integral calculation formula, integrate according to the geometric shape and vortex distribution of the wing, and obtain the tail vortex induced velocity;

[0010] In a preferred embodiment, the calculation process of the thrust change of the UAV in the upwash vortex of the carrier aircraft is as follows: the UAV is in the upwash area of ​​the carrier aircraft wake, flying forward at a speed v0, and is subject to the upwash speed v1. The change in angle of attack is obtained according to the upwash speed, and the thrust of the UAV in the flight direction is calculated in combination with the known weight of the UAV and the resistance of the UAV; the thrust of the UAV in the upwash area of ​​the carrier aircraft wake is subtracted from the thrust of the UAV in the single-aircraft cruising flight state to obtain the thrust change of the UAV in the upwash vortex of the carrier aircraft.

[0011] In a preferred embodiment, the particle swarm optimization algorithm steps are as follows: randomly generate a group of particle positions and initial velocities, and calculate the initial fitness value, which is the aircraft wake adjustment coefficient, and set the initial position of the particle as the individual extreme position of each particle; traverse the individual extreme fitness values ​​of all particles, find the maximum fitness value, and set the individual extreme position of the corresponding particle as the global extreme position; for each particle, update its own position according to its current speed and position, calculate the fitness value of the new position, and update the individual optimal position and global optimal position of the particle based on the fitness value; when the maximum number of iterations is reached, stop the iteration, and the particle updated position is the deployment position of the drone.

[0012] In a preferred embodiment, the steps for obtaining the roll moment coefficient control ratio are as follows: respectively obtain the lift changes of the left and right wings of the UAV under the wake disturbance, subtract the lift changes to obtain the lift difference between the left and right wings; multiply the lift difference between the left and right wings by the average wingspan radius to obtain the roll moment generated by the UAV under the influence of the wake of the carrier aircraft; take the ratio of the roll moment to the maximum deflection angle of the aileron as the roll restoring moment coefficient generated when the UAV aileron is deflected at the maximum angle; combine the air density and incoming flow velocity at infinity, the wing area and span of the UAV, and calculate the roll moment coefficient control ratio.

[0013] In a preferred embodiment, the specific process of evaluating the wake optimization effect based on fuzzy reasoning according to the rolling moment coefficient control ratio and the tail vortex velocity change is as follows: the rolling moment coefficient control ratio and the tail vortex velocity change are defined as input variables, and they are divided into different fuzzy sets respectively; the wake optimization coefficient is defined as an output variable, and it is divided into fuzzy sets; fuzzy rules are formulated to describe the influence of the rolling moment coefficient control ratio and the tail vortex velocity change on the wake optimization coefficient; fuzzy reasoning is performed according to the fuzzy rules to evaluate the wake optimization effect.

[0014] In a preferred embodiment, the process of improving the particle swarm optimization algorithm according to the evaluation results is as follows: the wake optimization coefficient is compared and analyzed with a preset threshold; when the wake optimization coefficient is less than the preset threshold, the wake optimization effect is not good, and the particle swarm optimization algorithm needs to be improved by increasing the maximum number of iterations; when the wake optimization coefficient is greater than the preset threshold, the wake optimization effect is good, and the particle swarm optimization algorithm does not need to be improved.

[0015] Technical effects and advantages of an active flow control system for aircraft wake optimization according to the present invention:

[0016] 1. The present invention can reduce the air resistance caused by the wake by optimizing the flow characteristics of the wake, thereby reducing the total resistance of the aircraft, which not only improves fuel efficiency but also reduces the operating cost of the aircraft; since the wake is often accompanied by air turbulence and pressure fluctuations, optimizing the wake can effectively reduce the noise level, especially for civil aircraft, noise control is of great importance to environmental protection and residents around the airport; optimizing the wake flow helps to improve the stability of the aircraft in flight, reduce the impact of wake interference on the wings and tail, and improve the maneuverability of the aircraft, especially under low-speed or complex flight conditions.

[0017] 2. The present invention helps to improve the aerodynamic performance of aircraft by adjusting the structure of the wake, including the optimization of lift and lift-to-drag ratio, making the aircraft more efficient in flight; for multiple aircraft flying in formation, optimizing the wake can reduce the interference of the wake on subsequent aircraft, increase flight safety and efficiency, and better arrange routes and flight spacing, especially in high-density air transport environments; the active flow control system can respond to different flight phases and external environmental changes through real-time feedback and adjustment, and improve the adaptability and flexibility of the system, which means that the best wake optimization effect can be maintained under different flight conditions;

[0018] By optimizing the flow state of the wake, the long-term impact and wear of the wake on the aircraft structure can be reduced, thereby extending the service life of the fuselage and its important components and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a schematic diagram of the structure of an active flow control system for aircraft wake optimization according to the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, Figure 1 The invention provides an active flow control system for optimizing aircraft wake.

[0022] The monitoring module is used to monitor the air flow state around the UAV in the carrier aircraft wake upwash area in real time, and obtain the wake vortex induced velocity based on the HB wake vortex induced velocity model according to the air flow state;

[0023] It should be noted that the use of the carrier aircraft's upwash wake can reduce the thrust required for the drone's fixed straight and level flight, thereby reducing the fuel consumption per unit time. The reduction in throttle thrust compared to the single-aircraft cruising state can be quantitatively calculated by the formula. Flying in the carrier aircraft's downwash wake will increase the fuel consumption of the drone in the accompanying flight state. Therefore, the air-based formation aimed at reducing energy consumption should be designed with the carrier aircraft's upwash wake action area as the design area, and stay away from the downwash wake action area.

[0024] When the UAV is in the wake upwash area of ​​the carrier aircraft, the state of air flow is determined by multiple factors, including the flight state of the carrier aircraft, the wake intensity, the flight speed and the meteorological conditions of the environment. The wake area is generally a section of space behind and below the carrier aircraft, and its main feature is the vortex effect of the airflow.

[0025] Wake area: The air flow in the wake upwash area of ​​the carrier aircraft is relatively complex, mainly due to the air flow changes caused by the tail vortex and vortex structure. The tail vortex will cause disturbances in the air flow rate, which forms a complex aerodynamic environment within a certain range behind the carrier aircraft;

[0026] The method for obtaining the tail vortex induced velocity is as follows:

[0027] The laser Doppler velocimeter is used to monitor the air flow state, obtain the lift distribution of the wing according to the air flow state, and determine the vortex source position and vorticity distribution of the tail vortex;

[0028] Based on the HB wake vortex induced velocity model, the velocity field generated by the vortex source is calculated by integration, the vorticity distribution is substituted into the integral calculation formula, and the wake vortex induced velocity is obtained by integration according to the geometric shape of the wing and the vorticity distribution.

[0029] The specific calculation formula of the tail vortex induced velocity is as follows: Where v is the wake vortex induced velocity, α is the vorticity, r is the position of the UAV, g is the vortex source position, and I is the differential length of the vortex source element;

[0030] Wake optimization aims to reduce the negative impact of the wake vortex, thereby improving the aircraft's aerodynamic efficiency, reducing drag and improving flight safety. By calculating the wake vortex induced velocity based on the HB model, the wake can be optimized in many aspects:

[0031] Reducing the strength of the tail vortex: The calculation of the tail vortex induced velocity can help determine the strength distribution of the tail vortex, thereby identifying the area with a large tail vortex strength. The strength of the tail vortex can be reduced by optimizing the aircraft design or adjusting the flight attitude (such as adjusting the lift distribution of the wing, changing the wing geometry or airfoil);

[0032] Control the position and distribution of the tail vortex: Based on the HB model, the calculation of the tail vortex induced velocity can reveal the position distribution of the tail vortex in space. By simulating the induced velocity field generated by the tail vortex, a more efficient airfoil can be designed and the lift distribution on the wing surface can be adjusted to make the tail vortex move toward the tail of the aircraft or away from other aircraft. This optimization measure helps to reduce the impact of the wake on subsequent aircraft and reduce interference between aircraft.

[0033] Reduce induced drag: The induced velocity caused by the tail vortex will lead to changes in air flow, which in turn causes induced drag. Induced drag is the phenomenon that the tail vortex causes air flow instability and increases drag. After obtaining the tail vortex induced velocity through the HB model, optimizing the wing shape and lift distribution can effectively reduce the strength of the tail vortex and reduce the induced drag, thereby improving the aircraft's fuel efficiency and range;

[0034] Improve flight safety: The impact of the tail vortex on the following aircraft may cause aerodynamic disturbances and even cause the aircraft to lose control, especially when flying at low altitudes. By calculating the tail vortex induced speed, the pilot can better predict the distribution and strength of the tail vortex, thereby adjusting the flight path to avoid the aircraft entering the strong tail vortex area. The tail vortex optimization also helps to arrange the flight path in the busy area of ​​the airport, reduce the wake interference between aircraft, and ensure flight safety;

[0035] Multi-aircraft collaborative optimization: In the case of multi-aircraft collaborative operations or navigation, the interaction of wakes between different aircraft can be studied by analyzing the wake vortex induced velocity. Through accurate wake vortex calculation, aircraft can avoid each other's wake interference, reduce the vortex interaction effect between aircraft, and improve air maneuverability.

[0036] An acquisition module is used to acquire the thrust of the UAV in the upwash zone of the carrier aircraft wake when it is in a straight and level flight, and calculate the thrust change of the UAV in the upwash zone of the carrier aircraft wake when it is in a straight and level flight, combined with the thrust of the UAV in a single-aircraft cruise flight state;

[0037] The process of obtaining the thrust of the UAV in the wake upwash area of ​​the carrier aircraft when it is in a straight and level flight is as follows:

[0038] When the UAV is in the upwash area of ​​the carrier aircraft wake, the UAV flies forward at a speed v0 and is subject to an upwash speed v1. The change in angle of attack is obtained based on the upwash speed, and the thrust of the UAV in a straight and level flight in the flight direction is calculated by combining the known weight of the UAV and the resistance of the UAV.

[0039] The thrust calculation formula for obtaining the UAV in the upwash zone of the carrier aircraft's wake is as follows: Where T is the thrust of the drone in straight and level flight, D is the drag of the drone, G is the weight of the drone, v1 is the upwash speed, v0 is the speed of the drone, and Δa is the change in the angle of attack;

[0040] The thrust of a single-unit UAV cruising flight is the resistance encountered by the UAV during flight. The thrust of the UAV in the upwash zone of the carrier aircraft's wake is subtracted from the thrust of the UAV in the single-unit cruising flight to obtain the thrust change of the UAV in the upwash zone of the carrier aircraft's wake. The specific calculation formula is as follows: Where ΔT is the thrust change of the UAV in the upwash wake of the carrier aircraft, T is the thrust of the UAV in the upwash wake, is the drag of the UAV, G is the weight of the UAV, v1 is the upwash speed, and v0 is the speed of the UAV.

[0041] Optimization module: used to generate the aircraft wake adjustment coefficient based on the machine learning algorithm according to the wake vortex induced speed and thrust change, optimize the UAV cluster based on the particle swarm optimization algorithm according to the aircraft wake adjustment coefficient, and calculate the optimized wake vortex induced speed change and rolling moment coefficient control ratio;

[0042] The specific calculation formula of the carrier aircraft wake adjustment coefficient is as follows: Z = μ1*ΔT-μ2*v; wherein Z is the carrier aircraft wake adjustment coefficient, ΔT is the thrust change of the UAV in the carrier aircraft wake wash straight and level flight, μ1 is the thrust change weight factor, v is the wake vortex induced speed, μ2 is the wake vortex induced speed weight factor, and μ1 and μ2 are both greater than 0;

[0043] The particle swarm optimization algorithm steps are as follows:

[0044] Firstly, a group of particle positions and initial velocities are randomly generated, and an initial fitness value is calculated, the fitness value being the carrier wake adjustment coefficient, and the initial position of the particle is set to be the individual extreme position of each particle;

[0045] Traverse the individual extreme fitness values ​​of all particles, find the maximum fitness value, and set the individual extreme position of the corresponding particle as the global extreme position;

[0046] For each particle, update its position according to its current speed and position, calculate the fitness value of the new position, and update the individual best position and global best position of the particle based on the fitness value;

[0047] The specific formula for updating the particle speed and position is as follows:

[0048] v id (t+1)=wv id (t)+c1r1(p id (t)-x id (t)+c2r2(p gd (t)-x id (t))

[0049] x id (t+1)=x id (t)+v id (t+1)

[0050] In the formula, v id (t) and x id (t) represents the velocity and position of particle i in the tth iteration in d dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between 0 and 1, and p id (t) represents the individual extreme position of particle i at the tth iteration, p gd(t) represents the global extreme position of the entire particle swarm at the tth iteration;

[0051] When the maximum number of iterations is reached, the iteration is stopped, and the particle update position is the UAV deployment position;

[0052] The core idea of ​​the particle swarm optimization algorithm is to represent the potential solutions of the problem as "particles". These particles search for the optimal solution by updating their speed and position in the search space. Each particle has two important properties:

[0053] Position: represents the current solution;

[0054] Speed: controls the direction and amplitude of particle movement in the search space;

[0055] Each particle adjusts its speed and position based on two things:

[0056] Individual optimal solution: the best position found by the particle itself in history;

[0057] Global optimal solution: the best position found in the entire particle swarm;

[0058] The tail vortex induced velocity obtained initially is subtracted from the tail vortex induced velocity after optimization to obtain a change in the tail vortex induced velocity;

[0059] When the drone is within the range of the carrier's tail vortex, its left and right wings will be affected by different airflows, resulting in lift differences between the left and right wings, which in turn generates a large rolling moment. If the rolling moment exceeds the aircraft's maximum roll control capability, the drone will lose control.

[0060] The steps for obtaining the rolling moment coefficient control ratio are as follows:

[0061] The lift changes of the left and right wings of the UAV under the wake disturbance are obtained respectively, and the lift changes are subtracted to obtain the lift difference between the left and right wings. The lift difference between the left and right wings is multiplied by the average wingspan radius to obtain the rolling moment of the UAV affected by the wake of the carrier aircraft. The ratio of the rolling moment to the maximum deflection angle of the aileron is used as the rolling restoring moment coefficient generated when the UAV aileron is deflected at the maximum angle; the rolling moment coefficient control ratio is calculated by combining the air density and the incoming flow velocity at infinity, the wing area and the span of the UAV;

[0062] The specific calculation formula of the rolling moment coefficient control ratio is as follows: Where R is the rolling moment coefficient control ratio, ΔL l is the lift change of the left wing, ΔL2 is the lift change of the right wing, e is the average wingspan radius, B is the span of the UAV, S is the wing area of ​​the UAV, ρ ∞ is the air density at infinity, V ∞is the incoming flow velocity, C is the roll restoring moment coefficient generated when the UAV aileron is deflected to the maximum angle;

[0063] When R is greater than 1, the drone cannot balance the rolling moment caused by the carrier aircraft's wake by aileron deflection, which may cause the aircraft to lose control and crash. Therefore, there is a risk of loss of control when the drone flies in an area where R is close to 1.

[0064] The evaluation module is used to evaluate the wake optimization effect based on fuzzy reasoning according to the rolling moment coefficient control ratio and the change of the wake vortex speed, and to improve the particle swarm optimization algorithm according to the evaluation results.

[0065] In step C1, the rolling moment coefficient control ratio and the change in the tail vortex velocity are defined as input variables, and they are divided into different fuzzy sets respectively.

[0066] For example, "Low", "Medium", and "High" are for the roll moment coefficient control ratio, and "Low", "Medium", and "High" are for the trailing vortex velocity change.

[0067] Step C2, define the wake optimization coefficient as the output variable and divide it into fuzzy sets, for example, "Low", "High", for the wake optimization effect.

[0068] Step C3, formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0069] The rolling moment coefficient control ratio is marked as R, the tail vortex velocity change is marked as Δv, and the wake optimization coefficient is marked as W, then it can be defined as

[0070] Rule 1:IF(R is High)AND(Δv is Low)THEN(W is Low)

[0071] Rule 2:IF(R is Low)AND(Δv is High)THEN(W is High) ...

[0073] Step C4, performing fuzzy reasoning according to fuzzy rules to determine the wake optimization effect.

[0074] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although the present embodiment takes three fuzzy sets as examples, in fact, the rolling moment coefficient control ratio, the tail vortex velocity change, and the wake optimization effect coefficient can be divided into more than three sets to facilitate better precise adjustment.

[0075] Furthermore, for the judgment of the wake optimization effect, a threshold value can be set according to the actual situation. For example, when the rolling moment coefficient control ratio exceeds 0.3, it is calibrated as "High"; when the tail vortex speed change is higher than 20, it is calibrated as "High", and so on. I will not go into details here.

[0076] Compare W with the preset threshold. When W is less than the preset threshold, the wake optimization effect is not good, and the particle swarm optimization algorithm needs to be improved by increasing the maximum number of iterations. When W is greater than the preset threshold, the wake optimization effect is good, and the particle swarm optimization algorithm does not need to be improved.

[0077] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0078] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0079] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0080] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0081] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0082] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An active flow control system for aircraft wake optimization, characterized in that: It includes monitoring module, acquisition module, optimization module and evaluation module. There are connections between the modules: The monitoring module is used to monitor the air flow state around the UAV in the carrier aircraft wake upwash area in real time, and obtain the wake vortex induced velocity based on the HB wake vortex induced velocity model according to the air flow state; An acquisition module is used to acquire the thrust of the UAV in the upwash zone of the carrier aircraft wake when it is in a straight and level flight, and calculate the thrust change of the UAV in the upwash zone of the carrier aircraft wake when it is in a straight and level flight, combined with the thrust of the UAV in a single-aircraft cruise flight state; The optimization module is used to generate the aircraft wake adjustment coefficient based on the machine learning algorithm according to the wake vortex induced speed and thrust change, optimize the UAV cluster based on the particle swarm optimization algorithm according to the aircraft wake adjustment coefficient, and calculate the optimized wake vortex induced speed change and rolling moment coefficient control ratio; The evaluation module is used to evaluate the wake optimization effect based on fuzzy reasoning according to the rolling moment coefficient control ratio and the change of the wake vortex speed, and to improve the particle swarm optimization algorithm according to the evaluation results.

2. An active flow control system for aircraft wake optimization according to claim 1, characterized in that: The method for obtaining the tail vortex induced velocity is as follows: Monitor the air flow state, obtain the lift distribution of the wing according to the air flow state, and determine the vortex source position and vorticity distribution of the tail vortex; Based on the HB wake vortex induced velocity model, the velocity field generated by the vortex source is integrated and calculated. The vorticity distribution is substituted into the integral calculation formula, and the wake vortex induced velocity is obtained by integrating according to the geometric shape of the wing and the vorticity distribution. The specific calculation formula of the tail vortex induced velocity is as follows: Where v is the wake vortex induced velocity, α is the vorticity, r is the UAV position, g is the vortex source position, and I is the differential length of the vortex source element.

3. An active flow control system for aircraft wake optimization according to claim 2, characterized in that: The calculation process of the thrust change of the UAV in the wake of the carrier aircraft is as follows: The UAV is in the upwash area of ​​the carrier aircraft wake, flying forward at a speed v0 and subject to the upwash speed v1. The change in angle of attack is obtained based on the upwash speed, and the thrust of the UAV in a straight and level flight in the flight direction is calculated by combining the known weight of the UAV and the resistance of the UAV. The thrust calculation formula for obtaining the UAV in the upwash zone of the carrier aircraft's wake is as follows: Where T is the thrust of the drone in straight and level flight, D is the drag of the drone, G is the weight of the drone, v1 is the upwash speed, v0 is the speed of the drone, and Δa is the change in the angle of attack; The thrust of a drone in a single-machine cruising flight state is the resistance encountered by the drone during flight; The thrust change of the UAV in the upwash of the carrier aircraft is obtained by subtracting the thrust of the UAV in the straight and level flight state from the thrust of the UAV in the single-aircraft cruising flight state. The specific calculation formula is as follows: Where ΔT is the thrust change of the UAV in the upwash wake of the carrier aircraft when it is in straight and level flight, T is the thrust of the UAV in straight and level flight, is the drag of the UAV, G is the weight of the UAV, v1 is the upwash speed, and v0 is the speed of the UAV.

4. An active flow control system for aircraft wake optimization according to claim 3, characterized in that: The specific calculation formula of the carrier aircraft wake adjustment coefficient is as follows: Z=μ1*ΔT-μ2*v; wherein, Z is the carrier aircraft wake adjustment coefficient, ΔT is the thrust change of the UAV in the carrier aircraft wake wash straight and level flight, μ1 is the thrust change weight factor, v is the wake vortex induced speed, μ2 is the wake vortex induced speed weight factor, and both μ1 and μ2 are greater than 0.

5. An active flow control system for aircraft wake optimization according to claim 4, characterized in that: The particle swarm optimization algorithm steps are as follows: Randomly generate a group of particle positions and initial velocities, and calculate an initial fitness value, which is the carrier wake adjustment coefficient, and set the initial position of the particle to be the individual extreme position of each particle; Traverse the individual extreme fitness values ​​of all particles, find the maximum fitness value, and set the individual extreme position of the corresponding particle as the global extreme position; For each particle, update its position according to its current speed and position, calculate the fitness value of the new position, and update the individual best position and global best position of the particle based on the fitness value; When the maximum number of iterations is reached, the iteration is stopped, and the particle update position is the UAV deployment position.

6. An active flow control system for aircraft wake optimization according to claim 5, characterized in that: The steps for obtaining the rolling moment coefficient control ratio are as follows: The lift changes of the left and right wings of the UAV under the wake disturbance are obtained respectively, and the lift difference between the left and right wings is obtained by subtracting the lift changes; The rolling moment of the UAV caused by the wake of the carrier aircraft is obtained by multiplying the lift difference between the left and right wings with the average wingspan radius; The ratio of the rolling moment to the maximum deflection angle of the aileron is taken as the rolling restoring moment coefficient generated when the aileron of the UAV is deflected at the maximum angle; Combining the air density and incoming flow velocity at infinity, the wing area and span of the UAV, the rolling moment coefficient control ratio is calculated.

7. An active flow control system for aircraft wake optimization according to claim 6, characterized in that: The specific process of evaluating the wake optimization effect based on fuzzy reasoning according to the rolling moment coefficient control ratio and the change in the wake vortex velocity is as follows: The rolling moment coefficient control ratio and the change of the trailing vortex velocity are defined as input variables, and they are divided into different fuzzy sets respectively. The wake optimization coefficient is defined as the output variable and divided into fuzzy sets; Fuzzy rules are formulated to describe the influence of the rolling moment coefficient control ratio and the change of the wake vortex velocity on the wake optimization coefficient; Fuzzy reasoning is performed based on fuzzy rules to evaluate the wake optimization effect.

8. An active flow control system for aircraft wake optimization according to claim 7, characterized in that: The process of improving the particle swarm optimization algorithm according to the evaluation results is as follows: Compare and analyze the wake optimization coefficient with the preset threshold; When the wake optimization coefficient is less than the preset threshold, the wake optimization effect is not good, and the particle swarm optimization algorithm needs to be improved by increasing the maximum number of iterations; When the wake optimization coefficient is greater than the preset threshold, the wake optimization effect is good and there is no need to improve the particle swarm optimization algorithm.

9. An active flow control system for aircraft wake optimization according to claim 8, characterized in that: The specific formula for updating the particle speed and position is as follows: v id (t+1)=wv id (t)+c1r1(p id (t)-x id (t)+c2r2(p gd (t)-x id (t)); xid(t+1)=xid(t)+vid(t+1); In the formula, v id (t) and x id (t) represents the velocity and position of particle i in the tth iteration in d dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between 0 and 1, and p id (t) represents the individual extreme position of particle i at the tth iteration, p gd (t) represents the global extreme position of the entire particle swarm at the tth iteration.

10. An active flow control system for aircraft wake optimization according to claim 9, characterized in that: The specific calculation formula of the rolling moment coefficient control ratio is as follows: Where R is the rolling moment coefficient control ratio, ΔL l is the lift change of the left wing, ΔL2 is the lift change of the right wing, e is the average wingspan radius, B is the span of the UAV, S is the wing area of ​​the UAV, ρ ∞ is the air density at infinity, V ∞ is the incoming flow velocity, and C is the roll restoring moment coefficient generated when the UAV aileron is deflected to the maximum angle.

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