Complex wind field simulation method suitable for small and medium-sized unmanned aerial vehicle wind resistance test

Abstract

The invention discloses a complex wind field simulation method suitable for a small and medium-sized unmanned aerial vehicle wind resistance test, and relates to the technical field of intelligent wind control, and the method comprises the steps: based on a plasma exciter control instruction set, generating a directional micro vortex group in a wind tunnel through a plasma exciter array, meanwhile, the spatial distribution of the directional micro vortex group is regulated and controlled by adopting an ultrasonic interference method, and a three-dimensional turbulent flow field is generated; an unmanned aerial vehicle anti-risk test is executed, anti-wind performance data is collected and combined with PIV to verify the stability of the nest ring structure, and dynamic adjustment is carried out through a fuzzy PID control algorithm; and constructing a judgment matrix based on the adjusted wind resistance test data, calculating a wind resistance performance index of the unmanned aerial vehicle by adopting a dynamic time warping algorithm, and generating a performance evaluation report. According to the invention, through vortex ring stability feature extraction and fuzzy PID dynamic adjustment based on the deep space-time convolutional network, closed-loop stability control in a complex wind field environment is realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent wind control technology, and in particular to a complex wind field simulation method suitable for wind resistance tests of small and medium-sized unmanned aerial vehicles. Background Art

[0002] In the field of wind resistance test of small and medium-sized UAVs, wind field simulation technology mainly uses oscillating airfoil arrays to

[0003] and the passive generation method of turbulent grids. The former is based on the periodic disturbance of the airflow by mechanically adjustable blades to generate a quasi-steady-state wind field with a preset turbulence scale; the latter uses a porous plate structure to induce airflow separation to form a vortex group, and its turbulence intensity is determined by the aperture distribution and the Reynolds number. Current technology usually combines the particle image velocimetry (PIV) system for flow field calibration, and relies on fixed wind tunnel facilities to achieve wind speed direction control, which has been widely used in the verification of UAV aerodynamic stability. NASA's vertical wind tunnel experimental system uses this method to simulate level II-IV atmospheric turbulence in the wind speed range of 20m / s.

[0004] However, conventional methods have two significant limitations: First, the control accuracy of the spatial distribution of micro-vortices is insufficient. Passive turbulence grids rely on the self-organization effect of the fluid, making it difficult to accurately control the vortex position and circulation intensity, resulting in inaccurate three-dimensional turbulent field structure (error > 15%). Second, there is a lack of a dynamic stability maintenance mechanism. The existing open-loop control cannot respond to the flow field distortion caused by drone disturbances, and the test needs to be interrupted for manual intervention (as described in the AFRL-2021 technical memorandum). In response to the demand for refined wind field control, although plasma excitation technology has been applied to airfoil flow separation control, its application in the field of coordinated generation of three-dimensional vortex groups still needs to be broken through. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a complex wind field simulation method suitable for wind resistance tests of small and medium-sized UAVs to solve the problems of insufficient spatial distribution accuracy of passive turbulent micro-vortices and lack of dynamic stability of the flow field under open-loop control.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, the present invention provides a complex wind field simulation method suitable for wind resistance tests of small and medium-sized unmanned aerial vehicles, which includes defining target wind field parameters and performing preprocessing, generating a turbulence spectrum using an empirical mode decomposition algorithm, discretizing through a finite volume method, and generating a plasma actuator control instruction set; based on the plasma actuator control instruction set, generating a directional micro-vortex group in a wind tunnel through a plasma actuator array, and simultaneously using an ultrasonic interferometry method to regulate the spatial distribution of the directional micro-vortex group to generate a three-dimensional turbulent field; performing a UAV risk resistance test, collecting wind resistance performance data and combining it with PIV to verify the stability of the nest ring structure, and dynamically adjusting it through a fuzzy PID control algorithm; constructing a judgment matrix based on the adjusted wind resistance test data, and using a dynamic time warping algorithm to calculate the UAV wind resistance performance index to generate a performance evaluation report.

[0009] As a preferred solution of the complex wind field simulation method suitable for the wind resistance test of small and medium-sized UAVs of the present invention, the target wind field parameters include three-dimensional wind speed, turbulence intensity and vortex scale;

[0010] The preprocessing includes Kalman filtering denoising, outlier removal, normalization and time-frequency alignment.

[0011] As a preferred solution of the complex wind field simulation method suitable for the wind resistance test of small and medium-sized unmanned aerial vehicles described in the present invention, the steps of generating a turbulence spectrum using an empirical mode decomposition algorithm, performing discretization processing using a finite volume method, and generating a plasma actuator control instruction set are as follows:

[0012] Based on the processed target wind field parameters, the high-frequency turbulence components are obtained through empirical mode decomposition, and the turbulence energy spectrum is extracted using Hilbert transform.

[0013] The wind tunnel flow field is divided into finite volume grids by the finite volume method, and the turbulence energy spectrum is mapped to the finite volume grid nodes by using the spatial interpolation method.

[0014] The discretized Navier-Stokes equations are solved to obtain the vortex core parameters, which are then converted into a plasma actuator control instruction set through a plasma flow field control method based on the Biot-Savart law.

[0015] As a preferred solution of the complex wind field simulation method suitable for the wind resistance test of small and medium-sized unmanned aerial vehicles described in the present invention, wherein: the generation of a directional micro-vortex group in a wind tunnel by a plasma actuator array refers to simultaneously using ultrasonic interferometry to control the spatial distribution of the directional micro-vortex group to generate a three-dimensional turbulent field, the steps are as follows:

[0016] The real-time flow field solver engine analyzes the actuator coordinates, voltage, frequency and phase offset parameters in the plasma actuator control instruction set, and applies high-voltage pulses through the dielectric barrier discharge array at the fan outlet to generate a directional micro-vortex group.

[0017] Laser Doppler velocimetry is used to scan and obtain the spatial distribution of directional micro-vortex groups, and the phase difference of ultrasonic array control is calculated through acoustic interferometry algorithm;

[0018] Based on the control of the phase difference of the ultrasonic array, the ultrasonic interferometry method is used to adjust the sound wave emission angle and sound pressure level to generate a vortex ring structure;

[0019] The vortex ring structure and the directional micro-vortex group are superimposed on the initial wind field to generate a three-dimensional turbulence field that meets the target wind field parameters, and the three-dimensional turbulence field data is verified by particle image velocimetry.

[0020] As a preferred solution of the complex wind field simulation method for the wind resistance test of small and medium-sized UAVs described in the present invention, the steps of performing the UAV risk resistance test, collecting wind resistance performance data and combining PIV to verify the stability of the ring-and-gutter structure are as follows:

[0021] Initialize the initial position and non-linear velocity of the UAV based on the three-dimensional turbulence field data, perform wind resistance tests in the three-dimensional flow field, and collect wind resistance performance data;

[0022] Wind resistance performance data includes blade torque, fuselage three-axis vibration acceleration, pitch and roll angles, and vortex ring diameter deviation and position offset;

[0023] A deep spatiotemporal convolutional network is used to fuse wind resistance performance data to generate the vortex ring stability feature vector, and the stability score of the pit-ring structure is predicted using the vortex ring dynamics equation.

[0024] As a preferred solution of the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles described in the present invention, the dynamic adjustment through the fuzzy PID control algorithm refers to defining the vortex temperature threshold based on historical vortex collapse critical data and comparing it with the stability score of the pit ring structure, and dynamically adjusting the plasma exciter voltage and ultrasonic phase through the fuzzy PID control algorithm according to the comparison results.

[0025] As a preferred solution of the complex wind field simulation method for the wind resistance test of small and medium-sized UAVs described in the present invention, the steps of extracting the static performance characteristics of the UAV and calculating the UAV wind resistance performance index using the dynamic time warping algorithm to generate a performance evaluation report are as follows:

[0026] The outliers in the adjusted wind resistance test data were eliminated using the 3σ criterion and normalized to generate a standardized data set. The static performance characteristics of the UAV were extracted using the principal component analysis method.

[0027] Based on the static performance characteristics of the UAV, the dynamic time warping (DTW) algorithm is used to evaluate the dynamic response characteristics, and the UAV wind resistance performance index is calculated in combination with linear weighted fusion.

[0028] Generate a performance evaluation report based on the wind resistance performance index, vortex ring stability score and adjusted wind resistance test data.

[0029] In a second aspect, the present invention provides a complex wind field simulation system suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles, comprising a data processing module for defining target wind field parameters and performing preprocessing, generating a turbulence spectrum using an empirical mode decomposition algorithm, performing discretization processing using a finite volume method, and generating a plasma actuator control instruction set;

[0030] The turbulence field generation module is used to generate a group of directional micro-vortices in the wind tunnel through the plasma actuator array based on the plasma actuator control instruction set. At the same time, the ultrasonic interferometry method is used to control the spatial distribution of the directional micro-vortex group to generate a three-dimensional turbulence field.

[0031] The wind resistance test module is used to perform UAV risk resistance tests, collect wind resistance performance data, combine it with PIV to verify the stability of the ring-and-dimple structure, and dynamically adjust it through the fuzzy PID control algorithm;

[0032] The performance evaluation module is used to extract the static performance characteristics of the UAV based on the adjusted wind resistance test data, calculate the UAV wind resistance performance index using a dynamic time warping algorithm, and generate a performance evaluation report.

[0033] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles as described in the first aspect of the present invention.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles as described in the first aspect of the present invention.

[0035] The beneficial effects of the present invention are: through the coordinated control of the plasma actuator array and the ultrasonic interferometer method, a directional micro-vortex group is generated, thereby realizing high-precision spatial distribution control of the three-dimensional turbulent field; through the vortex ring stability feature extraction and fuzzy PID dynamic adjustment based on the deep spatiotemporal convolution network, closed-loop stability control in a complex wind field environment is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is a flow chart of a complex wind field simulation method suitable for wind resistance testing of small and medium-sized UAVs.

[0038] Figure 2 Schematic diagram of a complex wind field simulation system suitable for wind resistance testing of small and medium-sized UAVs.

[0039] Figure 3 Flowchart of vortex ring stability control. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0043] Reference Figures 1 to 3 , is an embodiment of the present invention, which provides a complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles, comprising the following steps:

[0044] S1. Define the target wind field parameters and perform preprocessing, generate the turbulence spectrum using the empirical mode decomposition algorithm, perform discretization using the finite volume method, and generate the plasma actuator control instruction set;

[0045] Target wind field parameters include three-dimensional wind speed, turbulence intensity, and eddy size;

[0046] It should be noted that the target wind field parameters are defined based on historical wind tunnel test data (such as PIV flow field measurement records and plasma actuator response test reports) and UAV flight condition data (such as attitude angle, three-axis vibration acceleration, blade torque, and vortex ring parameters);

[0047] Preprocessing includes Kalman filtering for noise reduction, outlier removal, normalization, and time-frequency alignment;

[0048] Furthermore, the target wind field parameters (three-dimensional wind speed, turbulence intensity, and eddy size) were first subjected to Kalman filtering for noise reduction. A state-space model was used to eliminate measurement noise while retaining effective turbulence characteristics. A boxplot method was then used to identify and remove outliers, ensuring that the data distribution conformed to physical laws. The normalization phase used a maximum-minimum scaling method to map each parameter to the [0, 1] interval, eliminating dimensional differences. The time-frequency alignment phase unified the sampling frequency through a short-time Fourier transform, and a dynamic time warping algorithm was employed to compensate for time delays between sensors, ultimately forming a temporally and spatially consistent wind field parameter dataset.

[0049] Based on the processed target wind field parameters, the high-frequency turbulence components are obtained through empirical mode decomposition, and the turbulence energy spectrum is extracted using Hilbert transform.

[0050] Furthermore, the processed target wind field parameters are first subjected to multi-scale decomposition using the empirical mode decomposition algorithm. An adaptive screening process is used to decompose the signal into several intrinsic mode function components, where the high-frequency components correspond to turbulent pulsation characteristics. The Hilbert transform is applied to the extracted high-frequency turbulence components, and the instantaneous amplitude and phase information are obtained through analytical signal construction, and the Hilbert energy spectrum is then calculated. The energy spectrum analysis is smoothed using the Welch periodogram method, identifying the dominant turbulent modes in the 0.1-10 Hz frequency band, ultimately forming a turbulent energy spectrum containing frequency-energy distribution characteristics (quantifying the energy distribution characteristics of vortices of different scales).

[0051] The wind tunnel flow field is divided into finite volume grids using the finite volume method, and the turbulent energy spectrum is mapped to the finite volume grid nodes using the spatial interpolation method to provide the initial flow field conditions (velocity field / vorticity field) for the discretized Navier-Stokes equations.

[0052] Furthermore, the finite volume method first established a structured hexahedral grid system in the wind tunnel test section. Non-uniform mesh refinement was then used to locally refine the boundary layer region, with the grid size decreasing from 50 mm in the core region to 5 mm at the walls. The turbulent energy spectrum was mapped to the grid nodes using a three-dimensional inverse distance weighted interpolation algorithm. The interpolation weight function took the form of a Gaussian kernel function, with the kernel radius set to 1.5 times the average spacing between adjacent grid points. The initial conditions for the discretized Navier-Stokes equations consisted of two physical fields: the velocity field, generated by inverse Fourier transforming the turbulent energy spectrum to generate a random pulsating component, superimposed on the mean wind speed profile; and the vorticity field, obtained by calculating the curl of the velocity field, using the vorticity-stream function method to ensure divergence freedom. The initial parameter errors at the grid nodes were kept within 5%, meeting the convergence requirements of the CFD solver.

[0053] The discretized Navier-Stokes equations are solved to obtain the vortex core parameters, which are then converted into a plasma actuator control instruction set through a plasma flow field control method based on the Biot-Savart law.

[0054] Furthermore, the discretized Navier-Stokes equations are solved using an iterative pressure-velocity coupling algorithm, analyzing the flow field evolution process on a finite volume grid. The vortex core parameters are extracted using the vortex isosurface identification method to obtain the spatial distribution characteristics of the rotating fluid structure. A plasma flow field control method based on the Biot-Savart law establishes a mapping relationship between vortex motion and actuator parameters: the induced velocity distribution is calculated by inverting the vortex field, and combined with the physical model of dielectric barrier discharge, the fluid dynamic characteristics are converted into the spatial layout parameters and electrical parameters of the actuator. Finally, a plasma actuator control instruction set containing actuator coordinates, voltage, frequency, and phase offset is generated.

[0055] It should be noted that the vortex core refers to the tubular area formed by the concentrated rotating fluid in the three-dimensional turbulent field. The vortex core parameters include the position coordinates of the vortex core, the unit vector of the rotation direction, the circulation intensity and the vortex core radius.

[0056] S2. Based on the plasma actuator control instruction set, a directional micro-vortex group is generated in the wind tunnel through the plasma actuator array. At the same time, ultrasonic interferometry is used to control the spatial distribution of the directional micro-vortex group to generate a three-dimensional turbulent field;

[0057] The real-time flow field solver engine analyzes the actuator coordinates, voltage, frequency and phase offset parameters in the plasma actuator control instruction set, and applies high-voltage pulses through the dielectric barrier discharge array at the fan outlet to generate a directional micro-vortex group.

[0058] Furthermore, the real-time flow field solution engine uses a parallel computing architecture to process the plasma actuator control instruction set, and uses the eigenvalue decomposition algorithm to analyze the coupling relationship between the actuator coordinates, voltage, frequency and phase offset parameters. In the fan outlet area, the dielectric barrier discharge array is spatially configured according to the analytical results, and the electrodes are arranged to form an interlaced asymmetric topology. The high-voltage pulse generator generates a nanosecond rising edge electrical signal according to the instruction parameters, and generates surface discharge on the surface of the medium through capacitive coupling. During the discharge process, the plasma sheath formed by the ionized gas interacts with the mainstream field, and induces a rotating flow structure in the boundary layer based on the momentum transfer principle. The collaborative work of multiple excitation units forms a directional micro-vortex group with a specific circulation distribution.

[0059] It should be noted that a dielectric barrier discharge array refers to a plasma excitation device composed of multiple dielectric barrier discharge units arranged in a specific spatial configuration. Its core features are reflected in three aspects: Its structure adopts a sandwich configuration of high-voltage electrode, dielectric layer, and ground electrode, with the electrodes arranged in an interdigitated pattern; the discharge characteristics are manifested as a cluster of micro-discharge channels confined by the dielectric layer, with plasma coupling effects between the units; and the flow field is controlled by matching the array arrangement with the target vortex scale and utilizing the ion wind effect to generate momentum transfer in the boundary layer. When the array is in operation, it generates surface discharge plasma, which interacts with the fluid through Coulomb force, ultimately inducing a directional vortex structure in the flow field that meets the control instructions.

[0060] Laser Doppler velocimeter scanning is used to obtain the spatial distribution of the directional micro-vortex group, and the phase difference of the ultrasonic array control is calculated by the acoustic interference algorithm. The expression is:

[0061]

[0062] Where Δφ is the phase difference of the ultrasonic array, λ is the wavelength of the sound wave, d is the spacing of the ultrasonic array, and θ is the target deflection angle of the directional micro-vortex group;

[0063] Furthermore, an ultrasonic array refers to an acoustic control device composed of multiple ultrasonic transducers arranged in a specific spatial configuration. It controls the spatial distribution of directional micro-vortex groups through the acoustic interference effect, thereby achieving precise generation and dynamic stability control of three-dimensional turbulent fields.

[0064] It should be noted that before calculating the ultrasonic array control phase difference, all parameters of the expression are standardized by Z-score to unify the dimensions.

[0065] Based on the control of the phase difference of the ultrasonic array, the ultrasonic interferometry method is used to adjust the sound wave emission angle and sound pressure level to generate a vortex ring structure;

[0066] Furthermore, based on the calculation results of the phase difference controlled by the ultrasonic array, the ultrasonic interferometry method first configures the emission parameters of the transducer unit: the phase difference Δφ is precisely synchronized by a digital phase controller, and the sound wave emission angle θ is adjusted to the target direction by the mechanical rotation mechanism of the array. The sound pressure level is regulated by pulse width modulation technology, which controls the amplitude of the sound wave by changing the duty cycle of the driving voltage. In the flow field, the interfering sound field generated by the ultrasonic array forms a three-dimensional standing wave structure, and the acoustic radiation force gradient field acts on the directional micro-vortex group, causing the micro-vortices to reorganize and arrange along the plane of the sound pressure node. The periodic pressure distribution of the interfering sound field induces the fluid to produce rotational motion, and multiple micro-vortices merge at the extreme sound pressure point to form a closed vortex ring structure.

[0067] The vortex ring structure and the directional micro-vortex group are superimposed on the initial wind field to generate a three-dimensional turbulence field that meets the target wind field parameters, and the three-dimensional turbulence field data is verified by particle image velocimetry.

[0068] Furthermore, the superposition process of the vortex ring structure and the directional micro-vortex group strictly follows the principles of fluid dynamics, and the precise construction of the three-dimensional turbulent field is achieved through the linear superposition of the flow field. The initial wind field establishes a reference velocity profile as the basic environment, and the vortex ring structure generated by the ultrasonic interferometer method and the directional micro-vortex group generated by the plasma actuator array are vector-superimposed as the disturbance component. The superposition process adopts the vorticity conservation principle and ensures the divergence-free characteristics of the synthetic flow field by solving the Poisson equation. During the particle image velocimetry verification stage, a dual-pulse laser sheet light source works in conjunction with a high-speed CCD camera, and the laser plane illuminates the tracer particles at a specific angle to the flow field axis. Two consecutive frames of particle images are processed by the cross-correlation algorithm to calculate the instantaneous velocity vector distribution of the three-dimensional turbulent field. The verification data includes key parameters such as the vortex ring center coordinates, velocity gradient field, pressure distribution, and micro-vortex group density distribution to ensure that the generated three-dimensional turbulent field strictly matches the target wind field parameter requirements.

[0069] The three-dimensional flow field data include the coordinates of the vortex ring center, velocity gradient field, pressure distribution, and micro-vortex group density distribution.

[0070] S3: Perform UAV risk resistance testing based on a three-dimensional turbulence field, collect wind resistance data and verify the stability of the ring-shaped structure using PIV, and dynamically adjust it using a fuzzy PID control algorithm;

[0071] Furthermore, based on three-dimensional turbulent field data, the UAV's wind resistance testing process was strictly carried out according to the following process: First, the UAV's initial spatial position was precisely set based on the vortex ring center coordinates and velocity gradient field parameters using a six-degree-of-freedom positioning platform, ensuring that the position error was controlled to the millimeter level. Simultaneously, initial nonlinear velocity parameters were configured based on the local velocity profile of the turbulent field, and precise matching of velocity vectors was achieved using differential GPS and an inertial measurement unit. During the test, the UAV executed a preset flight trajectory in a three-dimensional turbulent field environment, collecting real-time wind resistance performance data such as blade torque, three-axis vibration acceleration of the fuselage, and pitch / roll angles. Particle image velocimetry was also used to simultaneously monitor vortex ring diameter deviation and position offset.

[0072] Initialize the initial position and non-linear velocity of the UAV based on the three-dimensional turbulence field data, perform wind resistance tests in the three-dimensional flow field, and collect wind resistance performance data;

[0073] Furthermore, based on three-dimensional turbulent field data, the UAV wind resistance test process is strictly carried out according to the following process: First, the initial spatial position of the UAV is precisely set based on the vortex ring center coordinates and velocity gradient field parameters using a six-degree-of-freedom positioning platform to ensure that the position error is controlled to the millimeter level. At the same time, the initial nonlinear velocity parameters are configured based on the local velocity profile of the turbulent field, and the velocity vector is accurately matched using differential GPS and an inertial measurement unit. During the test, the UAV executes a preset flight trajectory in a three-dimensional turbulent field environment, collecting wind resistance performance data such as blade torque, three-axis vibration acceleration of the fuselage, and pitch / roll angle in real time. The vortex ring diameter deviation and position offset are simultaneously monitored using particle image velocimetry. All data is transmitted to the processing terminal via a high-speed data bus to form time-synchronized wind resistance performance data.

[0074] Wind resistance performance data includes blade torque, fuselage three-axis vibration acceleration, pitch and roll angles, and vortex ring diameter deviation and position offset;

[0075] The deep spatiotemporal convolutional network (DSTCN) is used to fuse the wind resistance performance data to generate the vortex ring stability feature vector, and the stability score of the pit ring structure is predicted by the vortex ring dynamics equation. The expression is:

[0076]

[0077] Where S is the stability score of the vortex ring structure, τ is the length of time the vortex ring maintains stable rotation in the airflow, τ0 is the benchmark decay time of the vortex ring structure, Δp is the spatial distance between the UAV and the center of the vortex ring structure, R is the actual size of the current vortex ring structure, ΔT is the instantaneous fluctuation value of the UAV motor torque, and T max It is the maximum torque value that the drone motor can withstand.

[0078] It should be noted that before predicting the stability score of the pit-ring structure, all parameters of the expression were standardized by Z-score to unify the dimensions.

[0079] Based on the historical vortex collapse critical data, the vortex temperature threshold is defined and compared with the stability score of the vortex ring structure. According to the comparison results, the plasma actuator voltage and ultrasonic phase are dynamically adjusted through the fuzzy PID control algorithm to maintain the stability of the vortex ring structure.

[0080] Furthermore, the researchers extracted a vortex temperature threshold based on historical vortex collapse critical data. Real-time vortex temperatures were compared with the threshold and analyzed. Combined with the vortex ring stability score generated by a deep spatiotemporal convolutional network, a two-parameter decision matrix was established. A fuzzy PID control algorithm dynamically adjusts parameters based on the matrix output: when the vortex temperature exceeds the threshold and the vortex ring stability score falls below the set standard, the plasma actuator voltage is gradually increased to the critical discharge threshold, while the ultrasonic phase difference is recalibrated. When the parameter combination falls within the stable range, the plasma actuator voltage is maintained within the normal operating range, and the ultrasonic phase difference is fixed.

[0081] S4. Extract the static performance characteristics of the UAV based on the adjusted wind resistance test data, and use the dynamic time warping algorithm to calculate the UAV wind resistance performance index and generate a performance evaluation report.

[0082] The outliers in the adjusted wind resistance test data were eliminated using the 3σ criterion and normalized to generate a standardized data set. The static performance characteristics of the UAV were extracted using the principal component analysis method.

[0083] Furthermore, the rectified wind resistance test data was first tested for outliers using the 3σ criterion. After calculating the mean and standard deviation of each parameter, data points exceeding three times the standard deviation were removed. The remaining data were then linearly mapped to the [0, 1] interval using maximum-minimum normalization to form a standardized dataset. Principal component analysis was then used to reduce the dimensionality of the standardized dataset. Eigenvalue decomposition was used to obtain the eigenvectors of the covariance matrix. Principal components whose cumulative contribution exceeded a set threshold were selected as the static performance characteristics of the drone.

[0084] The static performance characteristics of the drone include the average blade torque, the proportion of the three-axis vibration acceleration energy of the fuselage, and the rate of change of the pitch angle and roll angle.

[0085] Based on the static performance characteristics of the UAV, the dynamic time warping (DTW) algorithm is used to evaluate the dynamic response characteristics, and the UAV wind resistance performance index is calculated by combining linear weighted fusion. The expression is:

[0086]

[0087] Among them, W is the UAV wind resistance performance index, α is the weight coefficient of the UAV static performance characteristics, and λ i is the variance contribution rate of the i-th static feature, C i is the i-th static eigenvalue, D is the dynamic time warping distance;

[0088] It should be noted that before calculating the UAV wind resistance performance index, all parameters of the expression were standardized through Z-score to unify the dimensions.

[0089] Generate a performance evaluation report based on the wind resistance performance index, vortex ring stability score and adjusted wind resistance test data.

[0090] This embodiment also provides a complex wind field simulation system suitable for wind resistance testing of small and medium-sized UAVs, including:

[0091] The data processing module is used to define the target wind field parameters and perform preprocessing, generate the turbulence spectrum using the empirical mode decomposition algorithm, perform discretization using the finite volume method, and generate the plasma actuator control instruction set;

[0092] The turbulence field generation module is used to generate a group of directional micro-vortices in the wind tunnel through the plasma actuator array based on the plasma actuator control instruction set. At the same time, the ultrasonic interferometry method is used to control the spatial distribution of the directional micro-vortex group to generate a three-dimensional turbulence field.

[0093] The wind resistance test module is used to perform UAV risk resistance tests, collect wind resistance performance data, combine it with PIV to verify the stability of the ring-and-dimple structure, and dynamically adjust it through the fuzzy PID control algorithm;

[0094] The performance evaluation module is used to extract the static performance characteristics of the UAV based on the adjusted wind resistance test data, calculate the UAV wind resistance performance index using a dynamic time warping algorithm, and generate a performance evaluation report.

[0095] This embodiment also provides a computer device, which is suitable for the case of a complex wind field simulation method for wind resistance testing of small and medium-sized unmanned aerial vehicles, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles proposed in the above embodiment.

[0096] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0097] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0098] In summary, the present invention achieves high-precision spatial distribution control of three-dimensional turbulent fields by: generating directional micro-vortex groups through the coordinated regulation of plasma actuator arrays and ultrasonic interferometry; and achieving closed-loop stability control in complex wind field environments through vortex ring stability feature extraction and fuzzy PID dynamic adjustment based on deep spatiotemporal convolutional networks.

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles, characterized by: include, Define target wind field parameters and perform preprocessing, generate turbulence spectrum using empirical mode decomposition algorithm, discretize using finite volume method, and generate plasma actuator control instruction set; Based on the plasma actuator control instruction set, a plasma actuator array is used to generate a group of directional micro-vortices in a wind tunnel. At the same time, ultrasonic interferometry is used to control the spatial distribution of the directional micro-vortex group to generate a three-dimensional turbulent field. Perform UAV risk resistance testing, collect wind resistance data and combine PIV to verify the stability of the ring-and-gutter structure, and dynamically adjust it through the fuzzy PID control algorithm; Based on the adjusted wind resistance test data, the static performance characteristics of the UAV are extracted and the dynamic time warping algorithm is used to calculate the UAV wind resistance performance index to generate a performance evaluation report.

2. The complex wind field simulation method for wind resistance testing of small and medium-sized UAVs according to claim 1, characterized in that: The target wind field parameters include three-dimensional wind speed, turbulence intensity and vortex scale; The preprocessing includes Kalman filtering denoising, outlier removal, normalization and time-frequency alignment.

3. The complex wind field simulation method for wind resistance testing of small and medium-sized UAVs according to claim 2, characterized in that: The turbulence spectrum is generated by using the empirical mode decomposition algorithm, and the finite volume method is used for discretization to generate the plasma actuator control instruction set. The steps are as follows: Based on the processed target wind field parameters, the high-frequency turbulence components are obtained through empirical mode decomposition, and the turbulence energy spectrum is extracted using Hilbert transform. The wind tunnel flow field is divided into finite volume grids by the finite volume method, and the turbulence energy spectrum is mapped to the finite volume grid nodes by using the spatial interpolation method. The discretized Navier-Stokes equations are solved to obtain the vortex core parameters, which are then converted into a plasma actuator control instruction set through a plasma flow field control method based on the Biot-Savart law.

4. The complex wind field simulation method for wind resistance testing of small and medium-sized UAVs according to claim 3, characterized in that: The method of generating a directional micro-vortex group in a wind tunnel by using a plasma actuator array is to simultaneously use ultrasonic interferometry to control the spatial distribution of the directional micro-vortex group and generate a three-dimensional turbulent field. The steps are as follows: The real-time flow field solver engine analyzes the actuator coordinates, voltage, frequency and phase offset parameters in the plasma actuator control instruction set, and applies high-voltage pulses through the dielectric barrier discharge array at the fan outlet to generate a directional micro-vortex group. Laser Doppler velocimetry is used to scan and obtain the spatial distribution of directional micro-vortex groups, and the phase difference of ultrasonic array control is calculated through acoustic interferometry algorithm; Based on the control of the phase difference of the ultrasonic array, the ultrasonic interferometry method is used to adjust the sound wave emission angle and sound pressure level to generate a vortex ring structure; The vortex ring structure and the directional micro-vortex group are superimposed on the initial wind field to generate a three-dimensional turbulence field that meets the target wind field parameters, and the three-dimensional turbulence field data is verified by particle image velocimetry.

5. The complex wind field simulation method for wind resistance testing of small and medium-sized UAVs according to claim 4, characterized in that: The steps for performing the UAV risk resistance test, collecting wind resistance data and combining PIV to verify the stability of the nest-ring structure are as follows: Initialize the initial position and non-linear velocity of the UAV based on the three-dimensional turbulence field data, perform wind resistance tests in the three-dimensional flow field, and collect wind resistance performance data; Wind resistance performance data includes blade torque, fuselage three-axis vibration acceleration, pitch and roll angles, and vortex ring diameter deviation and position offset; A deep spatiotemporal convolutional network is used to fuse wind resistance performance data to generate the vortex ring stability feature vector, and the stability score of the pit-ring structure is predicted using the vortex ring dynamics equation.

6. The complex wind field simulation method for wind resistance testing of small and medium-sized UAVs according to claim 5, characterized in that: The dynamic adjustment through the fuzzy PID control algorithm refers to defining the vortex temperature threshold based on historical vortex collapse critical data and comparing it with the stability score of the dimple-ring structure, and dynamically adjusting the plasma actuator voltage and ultrasonic phase through the fuzzy PID control algorithm according to the comparison results.

7. The complex wind field simulation method for wind resistance testing of small and medium-sized UAVs according to claim 6, characterized in that: The steps of extracting the static performance characteristics of the UAV and using the dynamic time warping algorithm to calculate the UAV wind resistance performance index and generate a performance evaluation report are as follows: The outliers in the adjusted wind resistance test data were eliminated using the 3σ criterion and normalized to generate a standardized data set. The static performance characteristics of the UAV were extracted using the principal component analysis method. Based on the static performance characteristics of the UAV, the dynamic time warping (DTW) algorithm is used to evaluate the dynamic response characteristics, and the UAV wind resistance performance index is calculated in combination with linear weighted fusion. Generate a performance evaluation report based on the wind resistance performance index, vortex ring stability score and adjusted wind resistance test data.

8. A complex wind field simulation system suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles, based on the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles according to any one of claims 1 to 7, characterized in that: include, The data processing module is used to define the target wind field parameters and perform preprocessing, generate the turbulence spectrum using the empirical mode decomposition algorithm, perform discretization using the finite volume method, and generate the plasma actuator control instruction set; The turbulence field generation module is used to generate a group of directional micro-vortices in the wind tunnel through the plasma actuator array based on the plasma actuator control instruction set. At the same time, the ultrasonic interferometry method is used to control the spatial distribution of the directional micro-vortex group to generate a three-dimensional turbulence field. The wind resistance test module is used to perform UAV risk resistance tests, collect wind resistance performance data, combine it with PIV to verify the stability of the ring-and-dimple structure, and dynamically adjust it through the fuzzy PID control algorithm; The performance evaluation module is used to extract the static performance characteristics of the UAV based on the adjusted wind resistance test data, calculate the UAV wind resistance performance index using a dynamic time warping algorithm, and generate a performance evaluation report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the complex wind field simulation method suitable for wind resistance testing of small and medium-sized unmanned aerial vehicles as described in any one of claims 1 to 7 are implemented.

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