An active flow control system for aircraft wake optimization

Abstract

The application discloses an active flow control system for optimizing aircraft wake, and relates to the technical field of aircraft wake optimization, and comprises a monitoring module, a obtaining module and an optimization module.The monitoring module is used for obtaining wake vortex induced velocity based on an H-B wake vortex induced velocity model.The obtaining module is used for calculating the thrust change of a tail-washing unmanned aerial vehicle in straight and level flight on an aircraft.The optimization module is used for optimizing the unmanned aerial vehicle cluster based on a particle swarm optimization algorithm, and calculating the wake vortex induced velocity change and the roll moment coefficient control ratio after optimization.The evaluation module is used for evaluating the wake optimization effect based on fuzzy reasoning according to the roll moment coefficient control ratio and the wake vortex velocity change, and improving the particle swarm optimization algorithm according to the evaluation result.The energy consumption of the unmanned aerial vehicle in the flight process can be effectively reduced, and the endurance time can be improved through reasonable wake drag reduction optimization, so that the problem that the flight resistance and flight safety of the unmanned aerial vehicle are influenced by the wake generated by the aircraft in the prior art 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 in flight, the air flowing over the wing creates a strong air disturbance, known as a wake. Wakes are formed due to pressure differences across the wing, causing the air to swirl and eddy behind the wing, creating complex flow phenomena such as vortices and eddies. Wake disturbances can affect other aircraft in flight, especially in busy airspace, and can pose a safety hazard, particularly during takeoff and landing. Wakes increase aircraft drag, causing the aircraft to consume more fuel to maintain flight. The interaction between wakes and air can lead to increased noise, especially at low altitudes, causing disturbances to residents. Active flow control (AFC) involves actively intervening and changing air flow conditions through external devices or methods (such as mechanical devices, air injection, suction, vibration control, etc.) to achieve specific flow control objectives. Unlike passive flow control (such as wing design optimization), active flow control can adjust and control flow conditions in real time to adapt to different flight conditions.

[0003] Deficiencies in existing technologies:

[0004] Traditional wake control methods rely primarily on passive aircraft design, such as wing shape and tail configuration, which can only mitigate the effects of wake turbulence to a certain extent. However, with the advancement of aviation technology, existing passive control methods are no longer able to meet the higher demands of modern aircraft for fuel efficiency, flight safety, and environmental impact. Therefore, it is particularly important to develop 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 includes 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 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; the acquisition module is used to obtain the thrust of the UAV in the carrier aircraft wake upwash area during straight and level flight, and calculate the thrust change of the UAV in the carrier aircraft wake upwash area during straight and level flight based on 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 wake vortex induced velocity and the thrust change based on a machine learning algorithm, optimize the UAV cluster based on the carrier aircraft wake adjustment coefficient based on the particle swarm optimization algorithm, 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 results.

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

[0010] In a preferred embodiment, the calculation process of the thrust change of the drone in the upwash vortex of the carrier aircraft during straight and level flight is as follows: the drone is in the upwash area of ​​the carrier aircraft's 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 drone in the flight direction is calculated in combination with the known drone weight and drone resistance; the thrust of the drone in the upwash area of ​​the carrier aircraft during straight and level flight is subtracted from the thrust of the drone in the single-machine cruising flight state to obtain the thrust change of the drone in the upwash vortex of the carrier aircraft during straight and level flight.

[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 carrier 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 updated position of the particle is the deployment position of the drone.

[0012] In a preferred embodiment, the roll moment coefficient control ratio is obtained by the following steps: obtaining the lift variation of the left wing and the right wing of the unmanned aerial vehicle under the wake disturbance respectively, subtracting the lift variation to obtain the lift difference between the left wing and the right wing; multiplying the lift difference between the left wing and the right wing by the average wing span radius to obtain the roll moment generated by the wake of the loaded aircraft; taking the ratio of the roll moment to the maximum deflection angle of the aileron as the roll restoring moment coefficient generated by the maximum deflection angle of the aileron of the unmanned aerial vehicle; combining the air density and the incoming flow velocity at infinity, the wing area and the span length of the unmanned aerial vehicle to calculate the roll moment coefficient control ratio.

[0013] In a preferred embodiment, the specific process of evaluating the wake optimization effect based on the roll moment coefficient control ratio and the wake velocity variation according to fuzzy reasoning is as follows: defining the roll moment coefficient control ratio and the wake velocity variation as input variables, and dividing them into different fuzzy sets respectively; defining the wake optimization coefficient as an output variable, and dividing it into a fuzzy set; formulating fuzzy rules to describe the influence of the roll moment coefficient control ratio and the wake velocity variation on the wake optimization coefficient; and evaluating the wake optimization effect according to fuzzy reasoning based on the fuzzy rules.

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

[0015] The technical effects and advantages of the active flow control system for optimizing the wake of an aircraft of the present application are as follows:

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

[0017] 2.The present application helps to improve the aerodynamic performance of the aircraft, including the optimization of lift and lift-drag ratio, making the aircraft more efficient in flight; for multiple aircraft formation flight, optimizing the wake can reduce the interference of the wake on the subsequent aircraft, increase the flight safety and efficiency, especially in the high-density air transport environment, it can better arrange the route and flight spacing; the active flow control system can cope with different flight stages and external environment changes through real-time feedback and adjustment, improving the adaptability and flexibility of the system, which means that the optimal wake optimization effect can be maintained under different flight conditions;

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

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

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0021] Embodiment 1, Figure 1 An active flow control system for optimizing the wake of an aircraft according to the present application is given.

[0022] The monitoring module is configured to monitor the air flow state around the UAV in the wash zone of the wake of the carrier aircraft in real time, and obtain the wake-induced velocity based on the H-B wake-induced velocity model according to the air flow state.

[0023] It should be noted that the use of the wash wake of the carrier aircraft can reduce the required thrust of the UAV in straight and level flight, thereby achieving the effect of reducing fuel consumption per unit time, and the amount of reduction in the throttle thrust in the single-aircraft cruising state can be calculated quantitatively. However, flying in the wake of the carrier aircraft will increase the fuel consumption of the UAV in the companion flight state. Therefore, in order to reduce energy consumption, the air-based formation should be designed in the wash wake area of the carrier aircraft and away from the wake area.

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

[0025] Wake area: The air flow in the aircraft's wake upwash is more complex, mainly due to the airflow changes caused by the wake vortex and vortex structure. The wake vortex will cause disturbances in the air flow rate, creating a complex aerodynamic environment within a certain range behind the 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 based on 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 integrated according to the geometric shape of the wing and the vorticity distribution to obtain the wake vortex induced velocity.

[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 enhancing 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 intensity of the wake vortex: Calculating the wake vortex-induced velocity can help determine the intensity distribution of the wake vortex, thereby identifying areas with greater wake vortex intensity. The intensity of the wake 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] Controlling the position and distribution of the wake vortex: Based on the HB model, calculation of the wake vortex-induced velocity can reveal the spatial distribution of the wake vortex. By simulating the induced velocity field generated by the wake vortex, more efficient airfoils can be designed and the lift distribution on the wing surface can be adjusted to position the wake vortex 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 minimize interference between aircraft.

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

[0034] Improving flight safety: The influence of the wake vortex on the subsequent aircraft can cause aerodynamic disturbance, even making the aircraft out of control, especially when flying at low altitude. By calculating the wake vortex induced velocity, the pilot can better predict the distribution and intensity of the wake vortex, so as to adjust the flight path and avoid the aircraft entering the strong wake vortex area. Wake 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 cooperative optimization: In the case of multi-aircraft cooperative combat or navigation, the interaction of the wake vortex between different aircraft can be studied through the analysis of the wake vortex induced velocity. Through accurate wake vortex calculation, aircraft can avoid the wake interference of each other and reduce the vortex interaction effect between aircraft, improving the air maneuverability.

[0036] The acquisition module is used to acquire the thrust of the unmanned aerial vehicle in the straight and level flight state in the upper wash area of the wake vortex of the carrier aircraft, and the thrust of the unmanned aerial vehicle in the single aircraft cruising flight state is combined to calculate the thrust change amount of the unmanned aerial vehicle in the straight and level flight state in the upper wash area of the wake vortex of the carrier aircraft.

[0037] The process of acquiring the thrust of the unmanned aerial vehicle in the straight and level flight state in the upper wash area of the wake vortex of the carrier aircraft is as follows:

[0038] When the unmanned aerial vehicle is in the upper wash area of the wake vortex of the carrier aircraft, the unmanned aerial vehicle flies forward at a speed v0 and is subjected to an upper wash speed v1. According to the upper wash speed, the angle of attack change amount is acquired, and the thrust of the unmanned aerial vehicle in the flight direction in the straight and level flight state is calculated in combination with the known weight of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle.

[0039] The calculation formula of the thrust of the unmanned aerial vehicle in the straight and level flight state in the upper wash area of the wake vortex of the carrier aircraft is as follows: In the formula, T is the thrust of the unmanned aerial vehicle in the straight and level flight state, D is the resistance of the unmanned aerial vehicle, G is the weight of the unmanned aerial vehicle, v1 is the upper wash speed, v0 is the speed of the unmanned aerial vehicle, and Δa is the angle of attack change amount.

[0040] The thrust of the unmanned aerial vehicle in the single aircraft cruising flight state is the resistance of the unmanned aerial vehicle in the flight process. The thrust change amount of the unmanned aerial vehicle in the straight and level flight state in the upper wash area of the wake vortex of the carrier aircraft is obtained by subtracting the thrust of the unmanned aerial vehicle in the single aircraft cruising flight state from the thrust of the unmanned aerial vehicle in the straight and level flight state in the upper wash area of the wake vortex of the carrier aircraft. The specific calculation formula is as follows: In the formula, ΔT is the thrust change amount of the unmanned aerial vehicle in the straight and level flight state in the upper wash area of the wake vortex of the carrier aircraft, T is the thrust of the unmanned aerial vehicle in the straight and level flight state, D is the resistance of the unmanned aerial vehicle, G is the weight of the unmanned aerial vehicle, v1 is the upper wash speed, and v0 is the speed of the unmanned aerial vehicle.

[0041] Optimization module: used to generate the aircraft wake adjustment coefficient based on the wake vortex induced speed and thrust change based on the machine learning algorithm, optimize the UAV cluster based on the aircraft wake adjustment coefficient based on the particle swarm optimization algorithm, 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; where Z is the carrier aircraft wake adjustment coefficient, ΔT is the thrust change of the UAV in the carrier aircraft wake wash in fixed straight and level flight, μ1 is the thrust change weighting factor, v is the wake vortex induced velocity, and μ2 is the wake vortex induced velocity weighting factor, and both μ1 and μ2 are greater than 0;

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

[0044] First, a set of particle positions and initial velocities are randomly generated, and the initial fitness value, which is the carrier wake adjustment coefficient, is calculated. The initial position of the particles is set to 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 optimal position and global optimal 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] Where, v id (t) and x id (t) represents the velocity and position of particle i at 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 updated position is the UAV deployment position;

[0052] The core idea of ​​the particle swarm optimization algorithm is to represent the potential solutions to 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 is initially obtained and subtracted from the optimized tail vortex induced velocity to obtain a tail vortex induced velocity change;

[0059] When a drone is within the range of the carrier aircraft's wake vortex, its left and right wings are affected by different airflows, resulting in different lift forces on the left and right wings, which in turn generates a large rolling moment. If this 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 generated by the UAV under the influence of the carrier aircraft's wake. The ratio of the rolling moment to the maximum aileron deflection angle is used as the roll restoring moment coefficient generated when the UAV aileron deflects the maximum angle. The roll moment coefficient control ratio is calculated by combining the air density and incoming flow velocity at infinity, the UAV wing area and span.

[0062] The specific calculation formula of the rolling moment coefficient control ratio is as follows: Where R is the roll 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 velocity, 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 trailing vortex velocity variation are defined as input variables, and they are divided into different fuzzy sets.

[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] In step C2, the wake optimization coefficient is defined as the output variable and divided into fuzzy sets, for example, "Low" and "High", for the wake optimization effect.

[0068] Step C3: Develop a set of fuzzy rules to describe the impact of different input variables on the 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 tail flow 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: Perform fuzzy reasoning based on 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 this embodiment takes three fuzzy sets as an example, 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 roll moment coefficient control ratio exceeds 0.3, it is calibrated as "High", and when the tail vortex speed change is higher than 20, it is calibrated as "High", etc., which will not be elaborated here.

[0076] Compare W with the preset threshold. When W is less than the preset threshold, the wake optimization effect is poor, 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 formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

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

[0079] Those skilled 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 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 description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection 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 scope of protection 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; The acquisition module is used to obtain the thrust of the UAV in the carrier aircraft wake upwash area during straight and level flight, and calculate the thrust change of the UAV in the carrier aircraft wake upwash during straight and level flight based on the thrust of the UAV in the single-aircraft cruise flight state; The optimization module is used to generate the aircraft wake adjustment coefficient based on the wake vortex induced speed and thrust change based on the machine learning algorithm, optimize the UAV cluster based on the aircraft wake adjustment coefficient based on the particle swarm optimization algorithm, and calculate the optimized wake vortex induced speed change and rolling moment coefficient control ratio; An evaluation module is used to evaluate the wake optimization effect based on fuzzy reasoning according to the roll moment coefficient control ratio and the change in the wake vortex velocity, and to improve the particle swarm optimization algorithm based on the evaluation results; The specific calculation formula of the carrier aircraft wake adjustment coefficient is as follows: ;Where Z is the wake adjustment coefficient of the carrier aircraft, It is the thrust change of the UAV in the wake of the carrier aircraft when it is in straight and level flight. is the thrust change weight factor, v is the vortex induced velocity, is the wake vortex induced velocity weight factor, and are all greater than 0; The particle swarm optimization algorithm steps are as follows: Randomly generate a set of particle positions and initial velocities, calculate an initial fitness value, which is the carrier wake adjustment coefficient, and set the initial position of the particles to 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 optimal position and global optimal position of the particle based on the fitness value; When the maximum number of iterations is reached, the iteration is stopped and the particle updated position is the UAV deployment position.

2. The 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 based on 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 the vorticity distribution is substituted into the integral calculation formula. The wake vortex induced velocity is obtained by integrating according to the geometric shape and vorticity distribution of the wing. The specific calculation formula of the tail vortex induced velocity is as follows: ; where v is the wake vortex induced velocity, 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. The 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 wash of the carrier aircraft is as follows: The UAV is in the upwash area of ​​the carrier aircraft wake, with a speed of Flying forward, subject to upwash speed , the change in angle of attack is obtained according to the upwash speed, and the thrust of the drone in a fixed straight and level flight in the flight direction is calculated by combining the known drone weight and drone resistance; The thrust calculation formula for the UAV in the upwash zone of the carrier aircraft 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, Is the washing speed, is the drone speed, is the change in angle of attack; The thrust of a drone in a single-plane 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, is the thrust change of the UAV in the wake of the carrier aircraft, T is the thrust of the UAV in the straight and level flight, D is the drag of the UAV, G is the weight of the UAV, Is the washing speed, It's the drone speed.

4. The active flow control system for aircraft wake optimization according to claim 1, characterized in that: The steps for obtaining the rolling moment coefficient control ratio are as follows: Obtain the lift changes of the left and right wings of the UAV under the wake disturbance respectively, and subtract the lift changes to obtain the lift difference between the left and right wings; 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 by the average wingspan radius; The ratio of the rolling moment to the maximum aileron deflection angle is used 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.

5. The active flow control system for aircraft wake optimization according to claim 4, 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 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 effects of the rolling moment coefficient control ratio and the change in the wake vortex velocity on the wake optimization coefficient. Fuzzy reasoning is performed based on fuzzy rules to evaluate the wake optimization effect.

6. The active flow control system for aircraft wake optimization according to claim 5, characterized in that: The process of improving the particle swarm optimization algorithm based on 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.

7. The active flow control system for aircraft wake optimization according to claim 6, characterized in that: The specific formula for updating the particle speed and position is as follows: ； ； Where, and Represent particles exist On the first The speed and position at the iteration, is the inertia weight, and is the learning factor, and is a random number between 0 and 1, Represents particles In the The individual extreme value position at the iteration, Indicates that the entire particle swarm is The global extremum position at the iteration.

8. The active flow control system for aircraft wake optimization according to claim 7, 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, is the lift change on the left wing, 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, is the density of air at infinity, 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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