A method, control method and equipment for identifying laminar flow separation bubbles at the trailing edge of an airfoil

Abstract

This invention discloses a method, control method, and device for identifying laminar flow separation bubbles at the trailing edge of an airfoil. By deploying pressure sensors on the trailing edge face of the airfoil and utilizing the characteristic that the face pressure equivalently represents the pressure on the upper surface of the trailing edge, this invention achieves accurate and effective acquisition of pressure data in the key monitoring area of ​​the laminar flow separation bubble at the trailing edge, providing a reliable data foundation for bubble identification. Simultaneously, this method determines real-time peak frequency data based on the acquired pressure data and identifies laminar flow separation bubbles by combining the time stability judgment condition of the peak frequency of the trailing edge face pressure signal under low Reynolds number and small angle of attack conditions. Based on the physical law of the invariance of the peak frequency of the trailing edge pressure at low Reynolds number, a specific bubble identification criterion is constructed, providing accurate and real-time judgment basis for subsequent closed-loop control of the laminar flow separation bubble at the trailing edge of the airfoil.

Description

Technical Field

[0001] This invention relates to the technical field of aircraft control, specifically to a method, control method, and device for identifying laminar flow separation bubbles on the trailing edge of an airfoil. Background Technology

[0002] In near-space cruise or high-altitude, low-speed flight scenarios of high-aspect-ratio UAVs, their wings are often in low Reynolds number and low angle-of-attack conditions. Under these conditions, stable laminar separation bubbles easily form. These separation bubbles disrupt the flow field structure at the wing's trailing edge, leading to nonlinear deterioration of wing aerodynamics. This manifests as a sharp drop in lift, a sharp increase in drag, and a significant decrease in the lift-to-drag ratio. This not only drastically reduces the UAV's cruise efficiency but may also trigger flight attitude instability, severely restricting the UAV's flight performance and safety. Therefore, real-time and accurate identification of the laminar separation bubble state at the wing's trailing edge is a crucial prerequisite for achieving active flow control and improving the aerodynamic performance of UAVs.

[0003] In related technologies, there is a technical problem that it is impossible to identify whether laminar flow separation bubbles appear on the trailing edge of an airfoil in real time and accurately under low Reynolds number and small angle of attack conditions. Summary of the Invention

[0004] The technical problem this invention aims to solve is that, in related technologies, under low Reynolds number and small angle of attack conditions, it is impossible to accurately and in real time identify whether laminar flow separation bubbles appear at the wing trailing edge. The objective is to provide a method, control method, and device for identifying laminar flow separation bubbles at the wing trailing edge. This solves the technical problem of being unable to accurately and in real time identify whether laminar flow separation bubbles appear at the wing trailing edge.

[0005] This invention is achieved through the following technical solution:

[0006] In a first aspect, the present invention provides a method for identifying laminar flow separation bubbles at the trailing edge of an airfoil, comprising:

[0007] At least pressure data is acquired; wherein the pressure data is pressure data acquired through a preset target pressure sensing device; wherein the target sensing device is a pressure sensing device installed on the trailing edge end face of the wing; wherein the pressure data is pressure data used to equivalently characterize the upper surface of the trailing edge of the wing.

[0008] Based on the pressure data, determine the real-time peak frequency data;

[0009] Based at least on the real-time peak frequency data and the preset time stability judgment condition, it is identified whether laminar flow separation bubbles appear at the trailing edge of the wing; wherein, the time stability judgment condition is used to characterize the state in which the peak frequency of the pressure signal at the trailing edge remains stable in the time dimension under the condition of low Reynolds number and small angle of attack.

[0010] Furthermore, the pressure sensing device includes at least one pressure detection point, a pressure measuring pipeline, and a dynamic pressure sensor body. The at least one pressure detection point is located on the trailing edge of the wing. The pressure measuring pipeline is embedded inside the trailing edge of the wing and extends a predetermined distance from the trailing edge to the leading edge of the wing. One end of the pressure measuring pipeline is connected to the pressure detection point, and the other end is connected to the dynamic pressure sensor body to establish a signal transmission connection.

[0011] The step of acquiring at least pressure data includes:

[0012] Receives pressure data transmitted from the dynamic pressure sensor body.

[0013] Further, the step of determining the real-time peak frequency data based on the pressure data includes:

[0014] The pressure data is filtered to retain signal components within a preset frequency band.

[0015] Separate the AC component from the filtered pressure data;

[0016] The AC component is subjected to spectral analysis to determine the frequency component with the largest amplitude in its power spectrum, and the frequency component with the largest amplitude is determined as the real-time peak frequency data.

[0017] Furthermore, the step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes:

[0018] Calculate the stability metric of the real-time peak frequency data within a continuous time window; wherein the stability metric includes at least one of variance, standard deviation, range, or coefficient of variation;

[0019] The stability metric is compared with a preset stability threshold.

[0020] If the stability metric indicates that the change in the real-time peak frequency data is less than the stability threshold, the time stability judgment condition is determined to be met, and laminar flow separation bubbles are identified.

[0021] Furthermore, the step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes:

[0022] Obtain a preset target frequency threshold and a frequency deviation threshold; wherein, the target frequency threshold is a reference value of the peak frequency of the pressure signal when laminar separation bubbles appear at the trailing edge of the wing under low Reynolds number and small angle of attack conditions;

[0023] Calculate the frequency difference between the real-time peak frequency data and the target frequency threshold, and compare the frequency difference with the frequency deviation threshold;

[0024] If the frequency difference is less than or equal to the frequency deviation threshold, the time stability judgment condition is determined to be met, and laminar flow separation bubbles are identified.

[0025] Furthermore, acquiring at least the pressure data also includes:

[0026] Receive real-time flight attitude data detected by a preset attitude detection device; wherein, the real-time flight attitude data includes at least wing angle of attack data.

[0027] Furthermore, the step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes:

[0028] Acquire real-time peak frequency data sequences and wing angle of attack data sequences synchronously collected within the target time period;

[0029] Determine whether the wing angle of attack data sequence meets the preset increase condition within the target time period;

[0030] Determine whether the real-time peak frequency data sequence meets the time stability judgment condition within the target time period;

[0031] If the wing angle of attack data sequence satisfies the increase condition and the real-time peak frequency data sequence satisfies the time stability judgment condition, the laminar flow separation bubble is identified as having appeared.

[0032] Secondly, the present invention provides a control method for a trailing laminar separation bubble based on plasma excitation, comprising:

[0033] The method described above for identifying laminar flow separation bubbles at the wing trailing edge is used to identify whether laminar flow separation bubbles appear at the wing trailing edge.

[0034] If a laminar flow separation bubble is detected at the trailing edge of the wing, the plasma actuator is activated to suppress the laminar flow separation bubble at the trailing edge of the wing.

[0035] If no laminar separation bubble is detected at the trailing edge of the wing, the plasma actuator is kept off.

[0036] Thirdly, the present invention provides an electronic device, comprising: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by the one or more processors, the instructions being executed by the one or more processors to cause the one or more processors to implement the method described above.

[0037] Fourthly, the present invention provides an aircraft comprising: an electronic device as described above.

[0038] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0039] This invention is adapted to the cruise application conditions of high-aspect-ratio UAVs at low Reynolds numbers and small angles of attack. It effectively solves the technical problems in related technologies, such as the difficulty in arranging pressure measurement points due to insufficient wing trailing edge thickness and narrow installation space, as well as the inability to accurately obtain the pressure data corresponding to the trailing edge laminar flow separation bubble. By deploying pressure sensing devices on the trailing edge end face of the wing and utilizing the characteristic of end face pressure equivalently representing the pressure on the upper surface of the wing trailing edge, accurate and effective pressure data acquisition of the key monitoring area of ​​the trailing edge laminar flow separation bubble is achieved, providing a reliable data foundation for bubble identification. Simultaneously, this method determines real-time peak frequency data based on the acquired pressure data and identifies laminar flow separation bubbles by combining the time stability judgment condition of the peak frequency of the trailing edge end face pressure signal under low Reynolds number and small angle of attack conditions. Based on the physical law of the invariance of the peak frequency of the low Reynolds number trailing edge pressure, a specific bubble identification criterion is constructed, providing accurate and real-time judgment basis for subsequent closed-loop control of the wing trailing edge laminar flow separation bubble. This lays an important foundation for improving the wing aerodynamic performance of high-aspect-ratio UAVs under low Reynolds number conditions and enhancing the cruise flight performance of UAVs. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0041] Figure 1 A flowchart illustrating a method for identifying laminar flow separation bubbles at the trailing edge of a wing, as provided in the embodiments of this specification.

[0042] Figure 2 This is a schematic diagram showing the location of the trailing edge laminar flow separation bubble and the pressure measurement point provided in the embodiments of this specification;

[0043] Figure 3This is a schematic diagram comparing the pressure at the upper surface and end face of the trailing edge in the embodiments provided in this specification, where Re = 34000 and α = 4 degrees. Figure 3 a is the time-domain waveform of the pressure signal at the upper surface of the trailing edge. Figure 3 b is the time-domain waveform of the pressure signal at the trailing edge end face;

[0044] Figure 4 This is a schematic diagram of the pressure measurement point arrangement and the internal structure of the airfoil trailing edge provided in the embodiments of this specification;

[0045] Figure 5 This is a schematic diagram of the trailing edge pressure signal spectrum at different angles of attack provided in the embodiments of this specification, wherein laminar separation bubbles begin to appear at 5 degrees. Figure 5 a is the time-domain waveform of the original pressure signal on the trailing edge of the wing at an angle of attack of 3 degrees; Figure 5 b is a single-sided amplitude spectrum of the pressure signal on the trailing edge of the wing at an angle of attack of 3 degrees; Figure 5 c is the time-domain waveform of the original pressure signal on the trailing edge of the wing at an angle of attack of 4 degrees; Figure 5 d is the single-sided amplitude spectrum of the pressure signal on the trailing edge of the wing at an angle of attack of 4 degrees; Figure 5 e is the time-domain waveform of the original pressure signal on the trailing edge of the wing at an angle of attack of 5 degrees; Figure 5 f is a single-sided amplitude spectrum of the pressure signal at the trailing edge of the wing at an angle of attack of 5 degrees; Figure 5 g is the time-domain waveform of the original pressure signal on the trailing edge of the wing at an angle of attack of 6 degrees; Figure 5 h is the single-sided amplitude spectrum of the pressure signal on the trailing edge of the wing at an angle of attack of 6 degrees.

[0046] Figure 6 This is a schematic diagram of a plasma-excited trailing edge laminar flow separation bubble identification and closed-loop control system provided in the embodiments of this specification;

[0047] Figure 7 This is a block diagram of an electronic device provided in the embodiments of this specification. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0049] like Figure 1 As shown, this embodiment provides a method for identifying laminar flow separation bubbles on the trailing edge of an airfoil. The subject executing the identification method can be an unmanned aerial vehicle (UAV) flight control computer, an airborne measurement and control unit, an embedded processor integrated into a sensor system, a ground-based industrial computer, or a simulation platform, etc.

[0050] The method may include:

[0051] Step S12: Acquire at least pressure data; wherein the pressure data is pressure data acquired through a preset target pressure sensing device; wherein the target sensing device is a pressure sensing device installed on the trailing edge end face of the wing; wherein the pressure data is pressure data used to equivalently characterize the upper surface of the trailing edge of the wing.

[0052] In this embodiment, the pressure data is a physical quantity reflecting the real-time change of fluid pressure at the trailing edge of the wing, and is the basic data for subsequent identification of laminar flow separation bubbles. The target pressure sensing device is a sensing device used to collect pressure data at the trailing edge of the wing. The trailing edge of the wing is the end plane at the chordal position x / c=1, which is the physical end face of the wing's trailing edge.

[0053] In this embodiment, the equivalent characterization can be expressed as the pressure variation law at the trailing edge end face of the wing being basically consistent with the pressure variation law in the interval 0.98 < x / c ≤ 1 on the upper surface of the trailing edge of the wing. The end face pressure data can replace the pressure data on the upper surface of the trailing edge for the identification of laminar flow separation bubbles.

[0054] In a specific implementation scheme, the target pressure sensing device can adopt various structural forms, including at least a pressure detection point, a pressure measuring pipeline, and a pressure sensor body. The pressure detection point can be arranged in one or more ways. If there are multiple points, they can be spaced along the wing span at intervals on the trailing edge of the wing to achieve synchronous acquisition of pressure data at different locations along the wing span. The pressure measuring pipeline is a rigid or flexible pressure-conducting pipeline, embedded inside the wing trailing edge, extending a predetermined distance from the wing trailing edge towards the wing leading edge. The value of this predetermined distance is determined based on the principle of avoiding bending of the pressure measuring pipeline and meeting the installation space requirements inside the wing; there is no fixed value limit, and it can be adaptively adjusted according to the structural dimensions of different wings. One end of the pressure measuring pipeline is sealed to the pressure detection point, and the other end is sealed to the pressure sensor body, achieving effective transmission of the pressure signal.

[0055] In one specific implementation, the pressure sensor body can be a dynamic pressure sensor or other pressure sensing elements with high sampling frequency and fast response characteristics. The range of the dynamic pressure sensor can be adaptively selected according to the pressure variation range of the wing trailing edge under low Reynolds number conditions. For example, a small-range dynamic pressure sensor can be selected to improve the acquisition accuracy. The pressure detection point is in direct contact with the fluid on the wing trailing edge end face, converting the physical change of fluid pressure into a transmittable pressure signal. This pressure signal is transmitted to the pressure sensor body through the pressure measurement pipeline, where it is converted into an electrical signal. This electrical signal is then transmitted to the actuator via wired or wireless transmission, forming pressure data that can be processed by subsequent steps. In actual implementation, in addition to acquiring pressure data, this step can also simultaneously acquire other auxiliary data as needed, such as wing flight attitude data and ambient wind speed data.

[0056] Step S14: Based on the pressure data, determine the real-time peak frequency data.

[0057] In a specific implementation plan, the pressure data can first be preprocessed. Preprocessing includes filtering, which aims to remove noise interference from the pressure data and retain the effective pressure signal components. The filtering type can be either low-pass filtering or band-pass filtering. The preset frequency band range for filtering can be determined based on the frequency range of the wing trailing edge pressure signal under low Reynolds number conditions, with the principle of retaining pressure signal components that reflect the characteristics of laminar flow separation bubbles.

[0058] Then, after the filtering process is completed, the AC component can be separated from the filtered pressure data. Specifically, the separation can be achieved by calculating the average value of the pressure data and then subtracting the average value from the filtered pressure data point by point. Alternatively, other processing methods that can effectively separate AC and DC components can be used. The removed DC component is the static reference value in the pressure data, while the retained AC component is the core part that reflects the dynamic changes in pressure.

[0059] Finally, spectral analysis is performed on the separated AC components. This spectral analysis can be achieved through Fourier transform or wavelet transform, etc., to convert the time-domain data of the AC components into frequency-domain data, obtaining the power spectrum of the pressure signal. The power spectrum includes the amplitude information of the pressure signal at different frequencies. The frequency component with the largest amplitude is determined from the power spectrum; this frequency component is the real-time peak frequency data.

[0060] During the determination process, continuous time segments can be selected to segment the pressure data and obtain a continuous real-time peak frequency data sequence, or the pressure data can be processed point by point in real time to obtain the real-time peak frequency data of a single point.

[0061] Step S16: Based at least on the real-time peak frequency data and the preset time stability judgment condition, identify whether the laminar flow separation bubble appears at the trailing edge of the wing; wherein, the time stability judgment condition is used to characterize the state in which the peak frequency of the trailing edge end face pressure signal remains stable in the time dimension under the condition of low Reynolds number and small angle of attack.

[0062] In this embodiment, the time stability judgment condition can be a preset logical criterion. That is, under the specific flight condition of low Reynolds number and small angle of attack, when the trailing edge laminar separation bubble appears, the peak frequency extracted from the trailing edge end face pressure signal will exhibit a stable characteristic in the time dimension, that is, its value does not change significantly. This stability can be a relative concept, that is, the degree of fluctuation is lower than a certain acceptable threshold.

[0063] In one specific implementation scheme, the presence of laminar separation bubbles at the wing trailing edge can be identified solely based on the real-time peak frequency data and preset time stability judgment conditions. This implementation scheme is applicable to typical operating conditions of large aspect ratio UAVs in stable cruise at low Reynolds numbers and small angles of attack. In this case, the UAV's flight attitude shows no significant fluctuations, and the external atmospheric environment is not significantly disturbed. Accurate identification of laminar separation bubbles can be achieved solely through the frequency characteristics of the pressure signal. Specifically, firstly, real-time peak frequency data at multiple moments are continuously collected or received to form a data sequence. The time length covered by this sequence is the analysis time window. Then, the stability metric of the peak frequency data sequence within the analysis time window is calculated. This stability metric is a numerical indicator used to quantify the dispersion or fluctuation amplitude of the sequence. Its specific form can be: the variance, standard deviation, range (i.e., the difference between the maximum and minimum values ​​in the sequence), or coefficient of variation (i.e., the ratio of the standard deviation to the mean), etc. The above statistics can reflect the fluctuation of data around its central trend from different perspectives.

[0064] Next, the calculated stability metric is compared with a preset stability threshold. The stability threshold is a pre-set limit value that represents the maximum allowable level of fluctuation for determining frequency stability.

[0065] Finally, the judgment logic is executed. If the stability metric is less than or equal to the preset stability threshold, the time stability judgment condition is met, and the laminar separation bubble at the wing trailing edge is identified as having appeared. Otherwise, it is identified as not having appeared. For example, when the standard deviation is used as the stability metric, if the calculated standard deviation is less than 5Hz, the peak frequency is determined to be stable, indicating that a separation bubble has appeared.

[0066] In this embodiment, the presence of a laminar flow separation bubble at the wing trailing edge can be identified based on the real-time peak frequency data and preset time stability judgment conditions. Alternatively, the presence of a laminar flow separation bubble at the wing trailing edge can be identified based on the real-time peak frequency data, flight attitude data, and preset time stability judgment conditions.

[0067] In this implementation scheme, in addition to peak frequency stability, the identification operation can also incorporate flight attitude data as a joint criterion to improve identification accuracy and environmental adaptability. The flight attitude data includes at least wing angle of attack data, which can be provided by a pre-defined inertial measurement unit or attitude heading reference system.

[0068] In one specific implementation, the presence of laminar flow separation bubbles at the wing trailing edge can be identified based on the real-time peak frequency data, flight attitude data, and preset time stability judgment conditions. This implementation can be applied to low Reynolds number and low angle of attack conditions where the flight attitude of a large aspect ratio UAV changes slightly and the external atmospheric environment is slightly disturbed. By introducing the joint judgment of flight attitude data and frequency characteristic data, the accuracy of laminar flow separation bubble identification can be further improved, and misjudgments caused by single data feature judgment can be reduced.

[0069] Specifically, firstly, real-time peak frequency data sequences and wing angle of attack data sequences are acquired simultaneously within a target time period. Secondly, two parallel and related judgments are performed. Judgment 1: Determine whether the wing angle of attack data sequence meets a preset increase condition within the target time period. The increase condition is used to confirm that the angle of attack is in an increasing trend, and its specific form can be: the increment of the final value of the sequence relative to the initial value exceeds a positive threshold. Alternatively, the sequence is linearly fitted, and its slope (rate of change) is positive and exceeds a certain threshold. This corresponds to the flight state that laminar separation bubbles are prone to occur during the process of increasing angle of attack. Judgment 2: Determine whether the real-time peak frequency data sequence meets the time stability judgment condition within the same target time period. This condition has the same logic as in the above implementation, that is, the stability metric value calculated based on the frequency sequence itself does not exceed its corresponding stability threshold.

[0070] Finally, the joint judgment logic is executed. Only when both judgment one and judgment two above are true—that is, when the wing angle of attack data sequence satisfies the increase condition and the real-time peak frequency data sequence simultaneously satisfies the stability condition—is the laminar separation bubble at the wing trailing edge ultimately identified as present. If the two conditions cannot be simultaneously met, it is identified as not present.

[0071] The wing trailing edge laminar flow separation bubble identification method of this embodiment is suitable for the cruise application conditions of large aspect ratio UAVs with low Reynolds number and small angle of attack. It effectively solves the technical problems in related technologies, such as the difficulty in arranging pressure measurement points due to insufficient wing trailing edge thickness and narrow installation space, as well as the inability to accurately obtain the pressure data corresponding to the trailing edge laminar flow separation bubble. By deploying pressure sensing devices on the end face of the wing trailing edge and utilizing the characteristic of end face pressure to equivalently represent the pressure on the upper surface of the wing trailing edge, accurate and effective pressure data acquisition of the key monitoring area of ​​the trailing edge laminar flow separation bubble is achieved, providing reliable data for separation bubble identification. This method is fundamental; it determines real-time peak frequency data based on acquired pressure data, and identifies laminar separation bubbles by combining the time stability judgment condition of peak frequency of trailing edge pressure signal under low Reynolds number and small angle of attack conditions. Based on the physical law of the invariance of peak frequency of trailing edge pressure under low Reynolds number conditions, a unique separation bubble identification criterion is constructed. The identification logic is simple and the judgment is accurate, providing a precise and real-time judgment basis for the closed-loop control of laminar separation bubbles at the trailing edge of the wing. It can lay an important foundation for improving the aerodynamic performance of the wing under low Reynolds number conditions and enhancing the cruise flight performance of the UAV.

[0072] In some embodiments, the pressure sensing device includes at least one pressure detection point, a pressure measuring pipeline, and a dynamic pressure sensor body. The at least one pressure detection point is located on the trailing edge of the wing. The pressure measuring pipeline is embedded inside the trailing edge of the wing and extends a predetermined distance from the trailing edge to the leading edge of the wing. One end of the pressure measuring pipeline is connected to the pressure detection point, and the other end is connected to the dynamic pressure sensor body to establish a signal transmission connection.

[0073] The step of acquiring at least pressure data includes:

[0074] Step S122: Receive pressure data transmitted by the dynamic pressure sensor body.

[0075] In this embodiment, the pressure detection point is a tiny hole directly drilled into the structure on the trailing edge end face of the wing. Its function is to be directly exposed to the airflow environment at that end face, sensing and transmitting the dynamic pressure fluctuations generated by the airflow movement. The at least one point can be a single measuring point arranged on the end face, or multiple measuring points can be arranged at intervals along the wing span to monitor the trailing edge flow state at different spanwise positions.

[0076] In this embodiment, the pressure-measuring conduit is a slender pipe used to transmit pressure fluctuations. Its material can be rigid or semi-flexible, for example, a stainless steel capillary tube or an engineering plastic tube. This conduit is embedded within the internal structure of the wing's trailing edge, for example, arranged between wing ribs or fixed to the inner wall of the skin. It should be noted that the conduit starts from the pressure detection point on the trailing edge end face, and its main extension direction is towards the leading edge of the wing (i.e., roughly opposite to the incoming flow direction), continuing for a predetermined distance. The value of this predetermined distance is determined to avoid bending or twisting of the pressure-measuring conduit, and can be set according to the structural dimensions and installation space adaptability of the trailing edge of the large aspect ratio UAV wing for different airfoils; there is no fixed numerical limitation. One end of the pressure-measuring conduit is sealed to the pressure detection point to ensure no leakage during pressure signal transmission, and the other end is sealed to the dynamic pressure sensor body, thus forming a complete pressure signal transmission channel from the pressure detection point to the dynamic pressure sensor body, ensuring that the pressure signal sensed by the pressure detection point can be stably and losslessly transmitted to the dynamic pressure sensor body.

[0077] In this embodiment, the dynamic pressure sensor body is a sensing component with pressure signal conversion and conditioning functions, and a small-range dynamic pressure sensor can be selected.

[0078] In this embodiment, during the actual pressure data acquisition process, the fluid pressure at the trailing edge of the wing acts on the pressure detection point. The pressure detection point converts the real-time changes in fluid pressure into a pressure signal. This pressure signal is transmitted to the dynamic pressure sensor body through a sealed pressure measuring pipeline along a preset path. After receiving the pressure signal, the dynamic pressure sensor body completes the conversion from a physical pressure signal to an electrical signal and signal conditioning, generating pressure data that reflects the real-time pressure changes at the trailing edge of the wing. This pressure data can equivalently characterize the pressure data on the upper surface of the trailing edge of the wing and can be directly used for subsequent laminar flow separation bubble identification and related processing.

[0079] In related technologies, to monitor the pressure on the wing surface, measuring points are often placed on the upper or lower surface of the wing. However, in the specific scenario of monitoring the low Reynolds number trailing edge laminar flow separation bubble, which is the focus of this embodiment, the area directly affected by the separation bubble and needing to be sensed is located near the trailing edge of the wing (e.g., chord position x / c > 0.98). In this region, the wing structure becomes extremely thin, making it virtually impossible or extremely difficult in terms of physical space to drill holes and bury pressure measuring pipes on the upper and lower surfaces using conventional methods. This creates a fundamental contradiction: "the monitoring requirement is clear, but there is nowhere to install the sensor."

[0080] Faced with this contradiction, related technologies often adopt two solutions, both of which have proven infeasible. Specifically, the first approach is to abandon contact pressure measurement and instead use non-contact measurement techniques (e.g., particle image velocimetry (PIV) or pressure-sensitive pressure sensor (PSP)). However, while these techniques overcome spatial limitations, their low sampling frequency, slow dynamic response, and limited measurement accuracy fail to meet the requirements for real-time, high-precision identification and control of laminar flow separation bubbles. The second approach, without changing the measurement principle (i.e., still using a high-frequency response dynamic pressure sensor), attempts to move the measuring point from the impassable trailing edge location towards the wing leading edge (i.e., "to the left" or towards the fuselage) to obtain sufficient installation space. This approach aligns with the engineering compromise logic: "If it can't fit here, find a nearby place that can." However, simulations and experiments show that once the measuring point leaves the critical influence area of ​​the trailing edge, the measured pressure signal cannot effectively characterize the appearance of laminar flow separation bubbles at the trailing edge, leading to monitoring failure.

[0081] This implementation breaks away from the aforementioned framework and proposes a third, previously unthinkable solution: placing the pressure detection points on the trailing edge of the wing (i.e., the narrow end plane connecting the upper and lower surfaces). The ingenuity of this arrangement lies in:

[0082] 1. Although the end face is narrow, it provides a feasible layout plane for the measuring points. More importantly, by starting the pressure measuring line from the measuring point on this end face and extending it directly towards the leading edge of the wing, the line can be laid and connected to the sensor body in a relatively spacious area inside the wing. This cleverly avoids the limitations of the extremely confined space inside the trailing edge on traditional line bending and sensor installation without completely damaging the aerodynamic shape and structure of the wing.

[0083] 2. The physical basis for equivalent measurement was discovered and utilized. The idea of ​​placing points on the end face was not arbitrary; its feasibility is based on a key discovery that had not been used to solve this problem before: simulation studies have confirmed that, under low Reynolds number and small angle of attack conditions, the pressure signal at the trailing edge end face and the pressure signal at the last edge position of the trailing edge upper surface of interest exhibit highly consistent (especially spectral characteristics) dynamic characteristics (i.e., equivalent) when laminar separation bubbles appear. This discovery provides the theoretical basis for this implementation method.

[0084] 3. This implementation method, through the technical means of "distributing points on the end face and utilizing the equivalence of the pressure on the end face and the upper surface", simultaneously satisfies three seemingly contradictory requirements: (1) It maintains the advantages of high frequency response and high precision of the dynamic pressure measurement method (meeting the real-time requirements of the control system). (2) It realizes effective monitoring of key areas that cannot be directly distributed physically (through equivalent measurement). (3) It avoids moving the measuring points to invalid positions due to the search for installation space (the end face position is equivalent to the target position in terms of fluid influence). This ultimately makes reliable and real-time monitoring of the trailing edge laminar separation bubble possible, laying the hardware foundation for subsequent accurate identification and closed-loop control.

[0085] In some implementations, the step of determining the real-time peak frequency data based on the pressure data includes:

[0086] Step S142: Filter the pressure data to retain signal components within a preset frequency band.

[0087] In this embodiment, the preset frequency band range can be a frequency range pre-set based on simulation test data and wind tunnel test data under low Reynolds number and small angle of attack conditions. The value of this range can be such that it covers the frequency variation range of the pressure signal on the trailing edge of the wing when laminar flow separation bubbles appear, while avoiding the frequency range corresponding to external noise (noise generated by fuselage vibration and airflow disturbance), and there is no fixed numerical limitation. The signal components can be the signal portions corresponding to different frequencies in the pressure data, wherein the signal components within the preset frequency band range are valid signal components, and the signal components outside the preset frequency band range are noise interference signals.

[0088] In this embodiment, digital filtering, analog filtering, or a combination of both can be used. Digital filtering can employ various algorithms such as low-pass filtering, band-pass filtering, and high-pass filtering. Specifically, a band-pass filtering algorithm can be used to retain the effective signal within a preset frequency band while eliminating high-frequency noise (high-frequency interference signals generated by fuselage structural vibration) and low-frequency interference (low-frequency signals generated by steady-state fluctuations in ambient airflow). Analog filtering can be implemented using filtering circuits; for example, RC or LC filtering circuits can be used to perform preliminary filtering of the electrical signals corresponding to the pressure data.

[0089] Step S144: Separate the AC component from the filtered pressure data.

[0090] In this embodiment, the AC component can be represented as the signal portion in the filtered pressure data that reflects the dynamic changes in fluid pressure at the trailing edge of the wing and is directly related to the formation and development of laminar separation bubbles.

[0091] In a specific implementation scheme, the executing entity can calculate the average value of the filtered pressure data to obtain the average value of the filtered pressure data within a preset time period. This average value is the DC component in the filtered pressure data. Then, the average value is subtracted from each data point in the filtered pressure data, and the difference is the AC component. The preset time period is selected based on the principle of accurately calculating the DC component. It can be adaptively set according to the pressure data acquisition frequency. For example, if the pressure data acquisition frequency is 1000Hz, 100ms can be selected as the preset time period to calculate the average value of the filtered pressure data within this time period, ensuring the accuracy of the DC component calculation and thus improving the accuracy of AC component separation.

[0092] In a specific implementation plan, an AC / DC separation circuit or a corresponding signal processing algorithm can be used to directly separate the filtered pressure data into AC and DC components. The DC component is removed by grounding or filtering circuit, while the AC component is extracted by signal coupling.

[0093] Step S146: Perform spectrum analysis on the AC component to determine the frequency component with the largest amplitude in its power spectrum, and determine the frequency component with the largest amplitude as the real-time peak frequency data.

[0094] In this embodiment, the spectrum analysis is a process in which the executing entity converts the time-domain AC component obtained in step S144 into frequency-domain data, analyzes the signal amplitude corresponding to different frequencies, and then extracts the core frequency features. The power spectrum is a curve showing the correspondence between frequency and signal amplitude obtained after frequency-domain analysis, which can intuitively reflect the signal strength of the AC component at different frequencies.

[0095] In a specific implementation plan, the purpose of this step is to extract the core frequency characteristics of the AC component through spectrum analysis to obtain real-time peak frequency data. The implementation process can be divided into two stages: a frequency domain conversion stage and a peak frequency extraction stage, which are completed collaboratively. In the frequency domain conversion stage, the executing entity can perform spectrum analysis on the AC component separated in step S144. Specifically, a Fourier transform algorithm can be used, or other spectrum analysis algorithms such as wavelet transform or short-time Fourier transform can be employed. The executing device converts the time-domain AC component into frequency-domain data using the corresponding spectrum analysis algorithm, generating a power spectrum of the AC component. The power spectrum includes multiple frequency components, each corresponding to a signal amplitude. The higher the signal amplitude, the more pronounced the pressure fluctuation at that frequency. In the peak frequency extraction stage, the executing entity can analyze the generated power spectrum, traversing all frequency components and selecting the frequency component with the largest signal amplitude. The value corresponding to this frequency component is then determined as the real-time peak frequency data.

[0096] In some embodiments, the step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes:

[0097] Step S1602: Calculate the stability metric of the real-time peak frequency data within a continuous time window; wherein the stability metric includes at least one of variance, standard deviation, range, or coefficient of variation.

[0098] In this embodiment, the continuous time window can be a pre-set continuous time interval used to calculate the stability metric. Its length is designed to accurately reflect the fluctuation characteristics of the real-time peak frequency data and meet the real-time requirements of laminar flow separation bubble identification. Specifically, it can be a preset fixed value or a dynamic value. Specifically, the continuous time window can be adjusted according to the acquisition frequency of the real-time peak frequency data. For example, if the acquisition frequency of the real-time peak frequency data is 100Hz, the length of the continuous time window can be set to 1 second (including 100 data points) or 2 seconds (including 200 data points). The continuous time window can be a sliding time window or a fixed time window.

[0099] In this embodiment, the stability metric can be a parameter used to quantify the degree of fluctuation of real-time peak frequency data within a continuous time window. Its value is positively correlated with the degree of fluctuation of the peak frequency; that is, the smaller the stability metric, the smaller the degree of change and the stronger the stability of the real-time peak frequency data within the time window. This stability metric can be any one of variance, standard deviation, range, or coefficient of variation, or two or more can be used simultaneously to improve the accuracy of fluctuation quantification.

[0100] In one specific implementation, the average value of all real-time peak frequency data within a continuous time window can be calculated first. Then, the deviation of each real-time peak frequency data from this average value can be calculated. All deviation values ​​are squared and summed. Finally, the summation result is divided by the number of real-time peak frequency data within the time window to obtain the variance value. The square root of the variance value can be calculated based on the variance calculation result to obtain the standard deviation value.

[0101] In one specific implementation, the maximum and minimum values ​​of the real-time peak frequency data within a continuous time window can be directly extracted, and the range can be obtained by subtracting the minimum value from the maximum value. Alternatively, the coefficient of variation can be obtained by dividing the standard deviation by the average value of the real-time peak frequency data within the continuous time window.

[0102] In a specific implementation plan, peak frequency data of laminar flow separation bubbles appearing at the wing trailing edge under different flight conditions can be collected in advance through simulation experiments, wind tunnel tests, and flight tests to construct a "flight condition-continuous time window length" mapping data table. The flight condition dimension can include the UAV's real-time flight altitude and real-time flight speed, and can also be supplemented with parameters such as atmospheric Reynolds number and wing angle of attack as needed. The continuous time window length is the length of time that can accurately capture the peak frequency fluctuation characteristics under the corresponding flight condition. For example, at a flight altitude of 8km and a flight speed of 80m / s, the window length is set to 1 second; at a flight altitude of 10km and a flight speed of 70m / s, the window length is adjusted to 1.5 seconds. Before calculating the stability metric, the execution entity first receives real-time flight condition data (flight altitude, flight speed, etc.) transmitted from the UAV's onboard navigation system and attitude detection device, matches the real-time condition data with the condition dimensions in the preset data table, and looks up the corresponding continuous time window length for that flight condition. If there is no perfectly matching entry in the real-time operating condition data, the window length corresponding to the closest operating condition entry in the data table is selected, or the appropriate window length is calculated through linear interpolation.

[0103] Step S1604: Compare the stability metric value with a preset stability threshold.

[0104] In this embodiment, the preset stability threshold is a judgment benchmark value pre-set based on simulation data and wind tunnel test data under low Reynolds number and small angle of attack conditions. Its value corresponds to the upper limit of the fluctuation of real-time peak frequency data when laminar separation bubbles appear. Different types of stability measurement values ​​correspond to different stability thresholds. There is no uniform fixed value. It can be set according to the type of stability measurement value.

[0105] In a specific implementation scheme, the executing entity first retrieves a preset stability threshold corresponding to the type of stability metric calculated in step S1602. If step S1602 calculates the standard deviation, the stability threshold corresponding to the standard deviation is retrieved. If both the standard deviation and the coefficient of variation are calculated, the thresholds corresponding to the standard deviation and the coefficient of variation are retrieved respectively. Then, the executing entity compares the stability metric with the corresponding stability threshold. The comparison process is a direct numerical comparison, and the core judgment logic is: whether the stability metric is lower than the stability threshold. It can be understood that the smaller the stability metric, the smaller the fluctuation of the real-time peak frequency data, which is more consistent with the characteristic of stable peak frequency when laminar separation bubbles appear.

[0106] If step S1602 calculates multiple types of stability metrics simultaneously, any one of them must be satisfied; that is, as long as one of the stability metrics is lower than the corresponding threshold, the volatility requirement is considered to be initially met. Alternatively, all of them must be satisfied; that is, all calculated stability metrics must be lower than the corresponding thresholds for the volatility requirement to be considered met.

[0107] Step S1606: If the stability metric indicates that the change in the real-time peak frequency data is lower than the stability threshold, it is determined that the time stability judgment condition is met, and the laminar flow separation bubble is identified as appearing.

[0108] In this embodiment, the stability metric indicates that the degree of change of the real-time peak frequency data is lower than the stability threshold, that is, the comparison result of step S1604 is that the value of the stability metric is less than (or less than or equal to) the preset stability threshold.

[0109] In some embodiments, the step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes:

[0110] Step S1622: Obtain the preset target frequency threshold and frequency deviation threshold; wherein, the target frequency threshold is the reference value of the peak frequency of the pressure signal when laminar flow separation bubbles appear at the trailing edge of the wing under low Reynolds number and small angle of attack conditions.

[0111] In this embodiment, the target frequency threshold can be a pre-calibrated reference value of the peak frequency of the pressure signal at the trailing edge when laminar flow separation bubbles appear at the trailing edge of the wing under low Reynolds number and low angle of attack conditions. It is a reference for determining whether the peak frequency is stable, and the value can be determined by the wing airfoil, low Reynolds number environmental parameters (atmospheric density, temperature), etc.

[0112] In this embodiment, the frequency deviation threshold can be a pre-set maximum absolute value range that allows real-time peak frequency data to deviate from the target frequency threshold. Its value characterizes the allowable fluctuation of the peak frequency when laminar separation bubbles appear. The smaller the deviation threshold, the stricter the judgment standard and the higher the recognition accuracy.

[0113] In a specific implementation plan, under typical cruise conditions with low Reynolds number and small angle of attack, the flow field environment for the formation of laminar separation bubbles at the wing's trailing edge can be simulated. Pressure signals from the trailing edge face can be collected, and the peak frequency extracted. The average of multiple tests is taken as the target frequency threshold for this typical condition, for example, calibrated to 50Hz. Based on the experimental data from the above typical condition, the fluctuation range of the peak frequency when the laminar separation bubble stably appears can be statistically analyzed, and the maximum value of this fluctuation range is taken as the frequency deviation threshold, for example, calibrated to 2Hz (i.e., a real-time peak frequency within the range of 48Hz-52Hz is considered stable).

[0114] In a specific implementation plan, to adapt to the differences in peak frequency reference values ​​under different flight conditions, the target frequency threshold and frequency deviation threshold can be dynamically adjusted based on the real-time flight conditions of the UAV. This is achieved through a combination of a pre-set data table and real-time table lookup. Specifically, peak frequency characteristics of laminar flow separation bubbles appearing at the trailing edge of the wing can be collected in advance through multi-condition simulation and wind tunnel testing under different flight conditions (including flight altitude, flight speed, and additional parameters such as atmospheric Reynolds number and wing angle of attack), constructing a mapping data table of "flight condition - target frequency threshold - frequency deviation threshold". For example, at an altitude of 8km and a speed of 80m / s, the target frequency threshold is 50Hz and the deviation threshold is 2Hz. At an altitude of 10km and a speed of 70m / s, the target frequency threshold is 45Hz and the deviation threshold is 1.5Hz; at an altitude of 6km and a speed of 90m / s, the target frequency threshold is 55Hz and the deviation threshold is 2.5Hz.

[0115] Step S1624: Calculate the frequency difference between the real-time peak frequency data and the target frequency threshold, and compare the frequency difference with the frequency deviation threshold.

[0116] In this embodiment, the frequency difference can be the absolute difference between the real-time peak frequency data and the target frequency threshold. Using the absolute difference can eliminate the influence of positive and negative deviations in the frequency value, and only the degree of deviation from the reference value needs to be considered.

[0117] In a specific implementation scheme, the executing entity can obtain the real-time peak frequency data obtained in step S14. This data can be a single-point real-time value or the average peak frequency within a continuous time window (the length of the time window can be preset or dynamically adjusted; taking the average value can reduce misjudgments caused by fluctuations in single-point data). Then, the executing entity can calculate the difference between the real-time peak frequency data (or the average value) and the target frequency threshold obtained in step S1622, and take the absolute value of the calculation result as the frequency difference.

[0118] Then, the executing entity can compare the calculated frequency difference with the frequency deviation threshold obtained in step S1622, and the judgment logic is whether the frequency difference is less than or equal to the frequency deviation threshold.

[0119] If the real-time peak frequency data is a continuous sequence, the frequency difference can be calculated for the peak frequency at each time point and compared with the deviation threshold, or the mean of all frequency differences in the sequence can be calculated and then compared.

[0120] Step S1626: If the frequency difference is less than or equal to the frequency deviation threshold, determine that the time stability judgment condition is met, and identify the laminar flow separation bubble as appearing.

[0121] In a specific implementation scheme, the executing entity can make a determination based on the comparison result of step S1624. If the frequency difference is less than or equal to the frequency deviation threshold (both single data points and sequence means meet the threshold), the real-time peak frequency data is determined to meet the preset time stability judgment condition, and thus identified as laminar separation bubbles appearing at the wing trailing edge. If the frequency difference is greater than the frequency deviation threshold, the real-time peak frequency data is determined not to meet the time stability judgment condition, and thus identified as no laminar separation bubbles appearing at the wing trailing edge.

[0122] In some implementations, acquiring at least the pressure data further includes:

[0123] Step S124: Receive real-time flight attitude data detected by a preset attitude detection device; wherein the real-time flight attitude data includes at least wing angle of attack data.

[0124] In this embodiment, the preset attitude detection device may include an angle-of-attack sensor. It can be used as a device to detect wing angle-of-attack data, specifically a weather vane-type angle-of-attack sensor, a differential pressure angle-of-attack sensor, or an optical angle-of-attack sensor.

[0125] In this embodiment, the preset attitude detection device may also include an inertial measurement unit. It may also include a combined attitude detection system. Specifically, this combined attitude detection system may consist of an angle-of-attack sensor, an inertial measurement unit, and a Global Navigation Satellite System (GNSS) receiver. The GNSS receiver assists in acquiring the UAV's real-time flight speed, altitude, and other motion parameters, complementing the detection data from the angle-of-attack sensor and the inertial measurement unit to improve the detection accuracy of the wing angle-of-attack data.

[0126] In this embodiment, the real-time flight attitude data may further include: real-time flight altitude data, real-time flight speed data, and pitch / roll angle data.

[0127] In some embodiments, the step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes:

[0128] Step S1642: Acquire the real-time peak frequency data sequence and wing angle of attack data sequence synchronously collected within the target time period.

[0129] In this embodiment, the target time period can be a pre-set continuous time interval used for synchronous analysis of real-time peak frequency data and wing angle of attack data.

[0130] In this embodiment, the synchronous acquisition can be achieved by keeping the acquisition timestamps of the real-time peak frequency data and the wing angle of attack data consistent.

[0131] In a specific implementation plan, a fixed time length can be preset based on test data under low Reynolds number and small angle of attack conditions, such as 1 second, 2 seconds or 0.5 seconds. This length must ensure that the slight increase in angle of attack can be captured, while meeting the real-time requirements of identification.

[0132] In a specific implementation plan, firstly, the real-time peak frequency data generated in step S14 and the wing angle of attack data received in step S124 can be retrieved from the local cache unit. Both types of data carry timestamps. Then, based on a preset target time period (e.g., tracing back 1 second from the current moment), all real-time peak frequency data and wing angle of attack data with valid timestamps within that time period can be filtered out. Next, based on the timestamps, the two types of data are matched one-to-one in chronological order. If a certain timestamp contains only one type of data, all data corresponding to that timestamp are removed to ensure sequence synchronization. Finally, the aligned real-time peak frequency data is arranged in chronological order to form a real-time peak frequency data sequence, and the aligned wing angle of attack data is arranged in the same chronological order to form a wing angle of attack data sequence.

[0133] Step S1644: Determine whether the wing angle of attack data sequence meets the preset increase condition within the target time period.

[0134] In this embodiment, the increasing condition can specifically be a trend determination condition, an increment determination condition, or a comprehensive determination condition. Specifically, the trend determination condition can be: linearly fitting the wing angle of attack data sequence and calculating the slope of the fitted line; if the slope is positive and the absolute value of the slope is greater than or equal to a preset slope threshold, then the increasing condition is satisfied. The slope threshold can be calibrated experimentally. The increment determination condition can be: calculating the difference between the angle of attack value at the start and end times in the wing angle of attack data sequence (end value - start value); if the difference is greater than or equal to a preset increment threshold, then the increasing condition is satisfied. The comprehensive determination condition can simultaneously satisfy both the trend determination and the increment determination.

[0135] Step S1646: Determine whether the real-time peak frequency data sequence meets the time stability judgment condition within the target time period.

[0136] In this embodiment, the time stability judgment condition can be the same as that in the above-described embodiment.

[0137] Step S1648: If the wing angle of attack data sequence satisfies the increase condition and the real-time peak frequency data sequence satisfies the time stability judgment condition, the laminar flow separation bubble is identified as having appeared.

[0138] In this implementation, a logical AND rule can be used, meaning that the combined conditions for laminar flow separation bubble identification are only met when both the "increased angle of attack condition" and the "frequency stability condition" are satisfied. If either condition is not met, it is determined that no laminar flow separation bubble has appeared. The specific determination scenarios are as follows:

[0139] If the conditions for increased angle of attack and frequency stability are met, it can be identified as laminar flow separation bubbles appearing at the trailing edge of the wing.

[0140] If the condition for increasing angle of attack is met but the condition for frequency stability is not met, it can be identified that no laminar separation bubble has appeared.

[0141] If the condition for increasing angle of attack is not met while the condition for frequency stability is met, it can be identified that no laminar separation bubble has appeared.

[0142] If the conditions for increasing angle of attack and frequency stability are not met, it can be identified that laminar separation bubbles have not appeared.

[0143] This embodiment provides a control method for a trailing laminar separation bubble based on plasma excitation, including:

[0144] Step S22: Using the above-described method for identifying laminar flow separation bubbles at the trailing edge of the wing, identify whether laminar flow separation bubbles appear at the trailing edge of the wing.

[0145] Step S24: If laminar separation bubbles are detected at the trailing edge of the wing, the plasma actuator is activated to suppress the laminar separation bubbles at the trailing edge of the wing.

[0146] In this embodiment, a dielectric barrier discharge (DBD) plasma actuator or a surface dielectric barrier discharge actuator can be selected. The excitation area is concentrated on the wing surface and can be precisely applied to the formation area of ​​the trailing edge laminar separation bubble.

[0147] In this embodiment, the plasma actuator can be located in the laminar separation bubble formation region of the wing's trailing edge, specifically in the chordal position of the upper surface of the wing's trailing edge where 0.98 < x / c ≤ 1, or at the junction of the wing's trailing edge end face and upper surface. The plasma actuator can establish a power supply connection with the UAV's power supply system and a signal transmission connection with the actuator (using wired transmission, such as CAN bus or RS485 bus), receiving on / off control commands output by the actuator.

[0148] In one specific implementation, the executing entity first generates a plasma actuator activation command according to preset control rules. The activation command can be a standardized electrical signal or a digital command (e.g., a high-level signal, a pulse signal), and may include preset excitation parameters (e.g., excitation voltage, excitation frequency, excitation duty cycle). Then, the activation command is transmitted in real-time to the plasma actuator's drive module via a preset signal transmission channel. Upon receiving the activation command, the plasma actuator's drive module starts the power supply circuit, outputting electrical energy of preset parameters to the actuator's electrodes. The actuator then starts and generates a plasma flow field. After the plasma actuator is activated, the generated plasma flow field acts on the boundary layer airflow at the wing's trailing edge. Through the momentum injection effect, it improves the airflow's adhesion ability, breaks the stable structure of the laminar separation bubble, and causes the separated laminar airflow to re-attach to the wing's trailing edge surface, thereby suppressing the laminar separation bubble. During the suppression process, the executing entity continuously monitors the state of the laminar separation bubble in real-time using the identification method in step S22. If the separation bubble is detected to have disappeared, the actuator can be kept on for a period of time (1-2 seconds) before being turned off to prevent the separation bubble from reappearing and improve control stability.

[0149] Step S26: If no laminar flow separation bubble is detected at the trailing edge of the wing, control the plasma actuator to remain in the off state.

[0150] The closed-loop control method of this embodiment forms a complete closed-loop feedback system through the sensing in step S22, the execution in step S24, and the standby in step S26. Its collaborative logic aligns with the actual cruise requirements of high aspect ratio UAVs. Specifically, under low Reynolds number and low angle-of-attack cruise conditions, the actuator continuously identifies separation bubbles. When no separation bubble appears, the exciter is turned off to reduce energy consumption. When a separation bubble appears, the exciter is quickly activated to precisely suppress it. After the separation bubble disappears, the exciter is promptly turned off to avoid interfering with the normal flow field.

[0151] In one specific implementation, a closed-loop control method for low Reynolds number trailing laminar separation bubbles based on plasma excitation is provided.

[0152] High-aspect-ratio UAVs are primarily used for long-endurance missions in near space. Due to the thin air and low flight speed at cruising altitudes, they operate in typical low Reynolds number atmospheric environments. Under low Reynolds number conditions, laminar flow separation easily occurs on the wing surface, inducing laminar flow separation bubbles. This leads to a sharp deterioration in wing aerodynamic performance, rapidly reducing the overall performance of the UAV and significantly shortening its loiter time and mission radius. Research shows that under low Reynolds number conditions, when the wing is at a small angle of attack, the presence of trailing-edge laminar flow separation bubbles causes nonlinear aerodynamic phenomena, which differs from the principle of reduced lift due to airflow deceleration and separation at large angles of attack. Furthermore, since high-aspect-ratio UAVs mostly cruise at small angles of attack, trailing-edge laminar flow separation bubbles become a crucial factor affecting their cruise flight.

[0153] Dielectric barrier discharge (DBD) plasma flow control technology is an active flow control technique based on plasma excitation. This technology induces disturbances through plasma, providing technical support for improving the aerodynamic performance of high-aspect-ratio UAVs at low Reynolds numbers. This invention addresses the nonlinear aerodynamic problem caused by laminar separation bubbles on wings at small angles of attack under low Reynolds numbers by proposing an equivalent measurement method for the wing trailing edge and a method for identifying and controlling trailing-edge laminar separation bubbles. By placing pressure measuring points at the wing trailing edge end face, the limitations of traditional methods, which cannot place measuring points as close to the trailing edge as possible due to the thinness of the wing trailing edge, are overcome. Utilizing the relatively fixed peak frequency of the pressure signal at the wing trailing edge end face when laminar separation bubbles appear, a plasma-excited closed-loop control method is proposed. Leveraging the advantages of plasma control, such as flexible control position, simple structure, and short response time, this method achieves precise control of the trailing-edge laminar separation bubbles, thereby suppressing nonlinear changes in airfoil aerodynamics.

[0154] Under low Reynolds number conditions, the trailing edge laminar separation bubble just begins to appear at the trailing edge of the wing. Figure 2 As shown in the figure, the affected range is approximately the 0.98 < x / c ≤ 1 interval of the wing. Currently, techniques such as PIV, pressure-sensitive paint, and dynamic pressure measurement can be used to measure laminar flow separation bubbles. However, PIV and pressure-sensitive paint have limitations such as low sampling frequency and insufficient accuracy, making it difficult to meet the requirements of real-time performance and accuracy. Dynamic pressure measurement, on the other hand, has the advantages of high sampling frequency, fast response, and high accuracy, making it a better choice for measuring laminar flow separation bubbles.

[0155] Typically, to obtain real-time dynamic pressure data on the wing surface, pressure measurement points need to be placed on the upper and lower surfaces and holes need to be drilled. Figure 2As shown in the diagram, the dynamic pressure sensor and pressure measurement port are then connected via a pressure measurement pipe inside the wing. Due to the small size of the wing trailing edge structure and the narrow internal installation space, the location of the dynamic pressure measurement point can only cover the area of ​​the wing surface where x / c ≤ 0.90 (approximately 90% of the area). Current research mainly focuses on how to control wing stall at high angles of attack and optimize the wing lift-to-drag ratio. The 10% area of ​​the wing trailing edge has a weak impact on the overall aerodynamic performance, and ignoring the influence of this area will not bring about significant changes in the overall performance. Therefore, existing research focuses on the 90% area that accounts for the vast majority of the wing area, so there is almost no research on the measurement of dynamic pressure at the wing trailing edge.

[0156] However, since the trailing edge laminar separation bubble can cause large fluctuations in the instantaneous pressure at the trailing edge of the wing by influencing the flow field at the trailing edge, it has a significant impact on the aerodynamic performance of the wing under low Reynolds number and low angle of attack conditions. Therefore, if we want to carry out research on the control of the trailing edge laminar separation bubble, the influence of the trailing edge region of the wing cannot be ignored. In particular, how to accurately measure the pressure information in the region of 0.98 < x / c ≤ 1 has become the key to carrying out research on the control of the trailing edge laminar separation bubble.

[0157] Among these technologies, PIV and pressure-sensitive paint can overcome the limitations of trailing edge size and achieve measurement of the entire trailing edge region, but they have shortcomings such as low sampling frequency and insufficient measurement accuracy. To control laminar separation bubbles at the trailing edge, it is not necessary to monitor the entire region of 0.98 < x / c ≤ 1. It is only necessary to arrange at least one pressure measuring point within this region, and the presence of laminar separation bubbles can be identified by measuring the pressure information fed back from this point.

[0158] Simulation studies show that, because the measuring points on the upper surface and the end face of the trailing edge are very close, the influence of the laminar separation bubble on the pressure measuring points on the upper surface and the end face of the trailing edge is basically the same. Figure 3 As shown in the figure, the pressure on the trailing edge end face can be equivalently characterized by the pressure on the upper surface of the trailing edge, and can be used for the identification and monitoring of laminar separation bubbles at the trailing edge. Arranging pressure measuring points on the trailing edge end face can avoid pipe bending and reduce the requirements and difficulty of installation space.

[0159] Therefore, to address the challenge of measuring laminar separation bubbles at the trailing edge, this invention, based on simulation research, proposes an equivalent measurement method for the wing trailing edge. It employs a dynamic pressure sensor (KuliteXCQ series small-range sensor) to collect transient pressure changes at the wing's trailing edge end face, using this as a basis to equivalently measure the occurrence of laminar separation bubbles. This is achieved by arranging pressure measurement points on the trailing edge end face (…). Figure 2 and Figure 4 As shown), the pressure testing pipeline is buried inside the rear edge, and the pipeline extends a certain distance from the rear edge end to the front edge (as shown). Figure 4(As shown) Then, a dynamic pressure sensor is connected, which avoids pipe bending, reduces the requirements for installation space, and realizes the equivalent measurement of laminar flow separation bubbles by collecting the pressure at the trailing edge.

[0160] Simulation and experimental studies show that plasma excitation utilizes plasma-induced vortices to transfer induced momentum from the leading edge to the trailing edge of the airfoil. By "dispatch" the separation bubble, it can essentially achieve the control effect of controlling the laminar separation bubble at the trailing edge and eliminating aerodynamic nonlinearity. However, currently, this is only applied to open-loop control scenarios, where the occurrence of the laminar separation bubble at the trailing edge is known in advance, and then the plasma excitation is manually activated. Open-loop control has significant limitations in adapting to numerous uncertainties in engineering applications, such as wind speed, atmospheric conditions, external disturbances, and flight attitude. Leveraging the advantages of closed-loop control, the controller issues a control command when the laminar separation bubble at the trailing edge is detected, triggering the plasma exciter to start working. At other times, the plasma exciter remains off, thereby achieving precise control of the laminar separation bubble at the trailing edge while reducing power consumption and improving energy efficiency.

[0161] Simulation studies show that when the wing is under low Reynolds number and small angle of attack conditions, the peak frequency of the pressure signal at the trailing edge is... The frequency decreases with increasing airfoil angle of attack; however, this peak frequency increases when laminar separation bubbles appear. It will stabilize at a certain value, and this peak frequency will occur before stall at high angles of attack. It no longer decreases with increasing angle of attack. Figure 5 As shown in the figure, this law can be called the invariance of the peak frequency of the low Reynolds number trailing edge pressure. Let the peak frequency at this time be the target frequency. For example, under typical operating conditions, as the wing angle of attack increases, the peak frequency of the pressure signal increases. The frequency will decrease from 139.86 Hz (3-degree angle of attack) to 129.87 Hz (4-degree angle of attack). As the angle of attack continues to increase, the peak frequency of the pressure signal will decrease. It remains relatively stable at 99.9Hz (peak frequency at 5 degrees and 6 degrees). To maintain consistency, laminar flow separation bubbles begin to appear at a 5-degree angle of attack, therefore the target frequency ftarget under this typical operating condition is 99.9Hz.

[0162] Based on the above principles, this implementation plan proposes a trailing laminar separation bubble identification method. The core of this method is the utilization of the invariance of the trailing pressure peak frequency at low Reynolds numbers. When a laminar separation bubble appears, the trailing pressure peak frequency remains at a fixed value (the target frequency). This means that the presence of laminar separation bubbles at the trailing edge can be determined by whether the peak frequency of the trailing edge pressure is stable. By acquiring and analyzing the trailing edge pressure signal in real time, the real-time peak frequency of the trailing edge pressure signal can be obtained. and the peak frequency With target frequency For comparison, when the peak frequency With target frequency When the bubble approaches, it indicates that a trailing-edge laminar separation bubble is about to appear or has already appeared. Furthermore, combining the advantages of equivalent measurement methods for the wing trailing edge and plasma-excited control, a closed-loop control method for trailing-edge laminar separation bubbles based on plasma excitation is proposed. Figure 6 (As shown).

[0163] Specific implementation: First, acquire the raw transient pressure signal at the trailing edge of the wing for a sampling time of T. Then, perform low-pass filtering on the signal (the filter cutoff frequency is greater than the target frequency). ), to obtain the filtered signal Calculate the average value of the signal. , will signal minus The signal obtained has had its DC component removed. Use FFT to convert the signal By converting to the frequency domain (s), the peak frequency of the signal can be obtained through analysis. When the peak frequency With target frequency (e.g., 99.9Hz) difference Less than the trigger frequency (For example, at 5Hz), it is considered that laminar separation bubbles have appeared or are about to appear. At this time, plasma excitation is turned on, and when the difference is... Greater than the trigger frequency (For example, at 5Hz) it is assumed that no laminar separation bubble has appeared, and plasma excitation is turned off at this time.

[0164] To address the issue of nonlinear changes in the lift linear segment at low angles of attack due to laminar flow separation bubbles at the trailing edge of high-aspect-ratio UAVs under low Reynolds number conditions, leading to deterioration in aerodynamic performance, this invention proposes a plasma-excited closed-loop control method and implementation device for low Reynolds number trailing edge laminar flow separation bubbles. First, an equivalent measurement method for the wing trailing edge is proposed, solving the problem of difficulty in measuring trailing edge laminar flow separation bubbles. Then, utilizing the relatively fixed frequency of the trailing edge pressure peak when laminar flow separation bubbles appear, a method for identifying trailing edge laminar flow separation bubbles and a plasma-excited closed-loop control method are proposed, achieving closed-loop control of the trailing edge laminar flow separation bubbles. This provides a solution for improving the flight performance of high-aspect-ratio UAVs at low Reynolds numbers.

[0165] This implementation scheme utilizes the trailing edge pressure of the wing to equivalently characterize the pressure on the upper surface of the trailing edge. Figure 3 As shown), an equivalent measurement method for the wing trailing edge is proposed, placing the pressure monitoring point at the wing trailing edge end face (…). Figure 4 As shown in the figure, this solves the problem of difficulty in monitoring pressure and identifying laminar flow separation bubbles due to the limited space at the trailing edge of the wing. It realizes the equivalent measurement of the trailing edge laminar flow separation bubble and provides a basis for the identification of laminar flow separation bubbles.

[0166] This implementation scheme utilizes the principle that the peak frequency of the pressure at the trailing edge end face is fixed when the trailing edge laminar separation bubble appears. Figure 5 As shown in the figure, a method for identifying laminar flow separation bubbles using pressure peak frequency is proposed, providing a basis for the control of laminar flow separation bubbles.

[0167] This invention proposes using plasma excitation for closed-loop control of trailing laminar separation bubbles, which has advantages such as flexible control position, simple structure, and short response time. Figure 6 (As shown).

[0168] This invention proposes to use plasma excitation for closed-loop control of trailing edge laminar separation bubbles, which remains applicable even when external conditions (wind speed, atmospheric conditions, external disturbances, flight attitude, etc.) change, thereby improving the control system's adaptability to the environment and its engineering application value.

[0169] By adjusting the position and number of the plasma exciter and pressure monitoring points proposed in this invention, distributed independent / cooperative control of multiple positions in the spanwise direction of the wing can be achieved, thereby improving control effect and efficiency.

[0170] like Figure 4 As shown, in order to identify the appearance of laminar flow separation bubbles at the trailing edge of the wing, pressure measurement points are arranged on the trailing edge end face. The pressure measurement pipeline is buried inside the trailing edge. The pipeline extends a certain distance from the trailing edge end face to the leading edge before connecting to the dynamic pressure sensor. Because the pipeline bends are avoided, the requirements for installation space are reduced. The pressure on the trailing edge end face can be measured to monitor the pressure signal at the end face position and capture the characteristics of the appearance of laminar flow separation bubbles at the trailing edge.

[0171] like Figure 6 As shown, to realize the closed-loop control method of trailing edge laminar separation bubble based on plasma excitation, the transient pressure signal at the trailing edge end face of the wing is first acquired. The sampling time is T, and the average value x0 of the signal is obtained. minus The signal obtained has had its DC component removed. Use FFT to convert the signal By converting to the frequency domain (s), the peak frequency of the signal can be obtained through analysis. When the peak frequency With target frequency (e.g., 99.9Hz) difference Less than the trigger frequency (For example, at 5Hz), laminar flow separation bubbles are considered to have appeared, and plasma excitation is then activated. When the difference... Greater than the trigger frequency (For example, at 5Hz) it is assumed that no laminar separation bubble has appeared, and plasma excitation is turned off at this time.

[0172] According to an embodiment of the present invention, an electronic device is provided; please refer to... Figure 7 The electronic device in this embodiment may include one or more of the following components: a processor, a network interface, memory, non-volatile memory, and one or more application programs, wherein the one or more application programs may be stored in non-volatile memory and configured to be executed by one or more processors, and the one or more programs are configured to perform the methods as described in the foregoing method embodiments.

[0173] According to an embodiment of the present invention, an aircraft is provided, comprising: an electronic device as described above. The aircraft may be a high-aspect-ratio unmanned aerial vehicle, a small, light general-purpose aircraft, etc.

[0174] According to embodiments of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a computer, causes the computer to perform the method described in any of the above embodiments.

[0175] According to embodiments of the present invention, a computer program product comprising instructions is also provided, which, when executed by a computer, cause the computer to perform a method in any of the above embodiments.

[0176] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying laminar flow separation bubbles at the trailing edge of an airfoil, characterized in that, include: At least pressure data is acquired; wherein the pressure data is pressure data acquired through a preset target pressure sensing device; wherein the target sensing device is a pressure sensing device installed on the trailing edge end face of the wing; wherein the pressure data is pressure data used to equivalently characterize the upper surface of the trailing edge of the wing. Based on the pressure data, determine the real-time peak frequency data; Based at least on the real-time peak frequency data and the preset time stability judgment condition, it is identified whether laminar flow separation bubbles appear at the trailing edge of the wing; wherein, the time stability judgment condition is used to characterize the state in which the peak frequency of the pressure signal at the trailing edge remains stable in the time dimension under the condition of low Reynolds number and small angle of attack.

2. The method according to claim 1, characterized in that, The pressure sensing device includes at least one pressure detection point, a pressure measuring pipeline, and a dynamic pressure sensor body. The at least one pressure detection point is located on the trailing edge end face of the wing. The pressure measuring pipeline is buried inside the trailing edge of the wing and extends a predetermined distance from the trailing edge end face to the leading edge of the wing. One end of the pressure measuring pipeline is connected to the pressure detection point, and the other end is connected to the dynamic pressure sensor body to establish a signal transmission connection. The step of acquiring at least pressure data includes: Receives pressure data transmitted from the dynamic pressure sensor body.

3. The method according to claim 2, characterized in that, The step of determining the real-time peak frequency data based on the pressure data includes: The pressure data is filtered to retain signal components within a preset frequency band. Separate the AC component from the filtered pressure data; The AC component is subjected to spectral analysis to determine the frequency component with the largest amplitude in its power spectrum, and the frequency component with the largest amplitude is determined as the real-time peak frequency data.

4. The method according to claim 3, characterized in that, The step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes: Calculate the stability metric of the real-time peak frequency data within a continuous time window; wherein the stability metric includes at least one of variance, standard deviation, range, or coefficient of variation; The stability metric is compared with a preset stability threshold. If the stability metric indicates that the change in the real-time peak frequency data is less than the stability threshold, the time stability judgment condition is determined to be met, and laminar flow separation bubbles are identified.

5. The method according to claim 3, characterized in that, The step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes: Obtain a preset target frequency threshold and a frequency deviation threshold; wherein, the target frequency threshold is a reference value of the peak frequency of the pressure signal when laminar separation bubbles appear at the trailing edge of the wing under low Reynolds number and small angle of attack conditions; Calculate the frequency difference between the real-time peak frequency data and the target frequency threshold, and compare the frequency difference with the frequency deviation threshold; If the frequency difference is less than or equal to the frequency deviation threshold, the time stability judgment condition is determined to be met, and laminar flow separation bubbles are identified.

6. The method according to claim 2, characterized in that, The acquisition of at least pressure data also includes: Receive real-time flight attitude data detected by a preset attitude detection device; wherein, the real-time flight attitude data includes at least wing angle of attack data.

7. The method according to claim 6, characterized in that, The step of identifying whether laminar separation bubbles appear at the wing trailing edge, based at least on the real-time peak frequency data and preset time stability judgment conditions, includes: Acquire real-time peak frequency data sequences and wing angle of attack data sequences synchronously collected within the target time period; Determine whether the wing angle of attack data sequence meets the preset increase condition within the target time period; Determine whether the real-time peak frequency data sequence meets the time stability judgment condition within the target time period; If the wing angle of attack data sequence satisfies the increase condition and the real-time peak frequency data sequence satisfies the time stability judgment condition, the laminar flow separation bubble is identified as having appeared.

8. A control method for a trailing-edge laminar separation bubble based on plasma excitation, characterized in that, include: The method for identifying laminar flow separation bubbles at the trailing edge of a wing as described in any one of claims 1 to 7 is used to identify whether laminar flow separation bubbles appear at the trailing edge of the wing. If a laminar flow separation bubble is detected at the trailing edge of the wing, the plasma actuator is activated to suppress the laminar flow separation bubble at the trailing edge of the wing. If no laminar separation bubble is detected at the trailing edge of the wing, the plasma actuator is kept off.

9. An electronic device, characterized in that, include: A memory, and one or more processors communicatively connected to the memory; The memory stores instructions that can be executed by the one or more processors to cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. An aircraft, characterized in that, include: Including an electronic device as described in claim 9.

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