Unmanned aerial vehicle wing deformation monitoring sensor layout method based on modal analysis

Abstract

The invention relates to an unmanned aerial vehicle wing deformation monitoring sensor layout method based on modal analysis, and the method comprises the steps: determining the modal parameter detection of a wing under different working conditions through modal analysis, and employing a modal component sum index to recognize a key degree of freedom so as to optimize the position of a sensor; the method is mainly used for verifying the deformation of the wing part in the overall design process of the unmanned aerial vehicle in a laboratory. According to the method, the modal shape and the inherent frequency are obtained through finite element analysis, the modal amplitude and the participation factor of each degree of freedom are calculated, the optimal arrangement position of the sensor is further optimized through an iterative algorithm, and the arranged fiber bragg grating sensor can accurately monitor the deformation information of the wing. The data processing unit collects data and evaluates wing performance by applying a data analysis algorithm. The method improves the accuracy and comprehensiveness of deformation measurement, and provides guarantee for the flight safety and efficiency of the unmanned aerial vehicle.

Description

Technical Field

[0001] The invention relates to the technical field of unmanned aerial vehicles, and in particular to a sensor layout method in a wing deformation test of an unmanned aerial vehicle. Background Art

[0002] In the field of drone technology, the health and performance of the wings are crucial to flight safety. When a drone is performing a mission, its wings are affected by various forces, including lift, drag, gravity, and dynamic loads caused by changes in flight speed and flight attitude. These forces will cause the wings to deform, and the deformation of the wings is directly related to the flight performance, stability and safety of the drone.

[0003] In order to ensure the flight safety and improve the flight efficiency of the drone, it is a crucial step to test the deformation of the wing. However, the position of the sensor is crucial to accurately capture the deformation information of the entire wing. If the position of the sensor is not set properly, it may cause the following problems: 1. Incomplete coverage: When the sensor does not cover the key areas of the wing, such as the wing root, wing tip or specific structural features, the deformation in these areas will not be captured. This may lead to a misunderstanding of the overall stiffness and stress distribution of the wing.

[0004] 2. Lack of modal information: If the sensor is not placed at the modal nodes or areas with large amplitude of the wing, important dynamic information may be missed. Since different vibration modes affect the wing to different degrees, appropriate sensor arrangement is required to capture key modal information.

[0005] 3. Measurement accuracy is affected: Incorrect sensor location may lead to inaccurate measurement data. For example, if the sensors are too concentrated in one part, they may not accurately reflect the deformation in other areas. In addition, if there is not enough density of sensors in areas with high curvature or concentrated strain, the measurement results will deviate greatly from the actual situation.

[0006] 4. Data redundancy and insufficiency: Placing too many sensors in some areas may lead to data redundancy, while lack of sufficient sensors in key areas may lead to data loss. This unbalanced sensor layout will affect the efficiency and accuracy of data analysis.

[0007] 5. Misunderstanding of structural dynamic characteristics: If the sensor layout does not take into account the dynamic characteristics of the wing, such as natural frequencies and damping characteristics, it may lead to a misunderstanding of the dynamic response of the wing under actual flight conditions.

[0008] In order to avoid the problems of inaccurate measurement and incomplete coverage of wing shape caused by improper sensor placement, it is necessary to comprehensively consider the structural characteristics, dynamic response and testing requirements of the wing, and use scientific methods and advanced technologies to optimize the sensor layout. Summary of the invention

[0009] The present invention aims to design a sensor layout method for a UAV wing deformation test, so as to solve the problem of inaccurate and incomplete wing deformation measurement caused by improper sensor position layout in traditional test methods.

[0010] This application uses an optimized layout based on modal analysis, specifically by performing modal analysis to determine the modal shape and natural frequency of the wing, and then arranges sensors in areas with larger modal amplitudes to ensure that key dynamic information is captured. This method can ensure that the sensor covers the key areas of the wing, thereby detecting deformation information more comprehensively. At the same time, a fiber Bragg grating sensor is used, and a method for reconstructing the wing trailing edge morphology based on a fiber Bragg grating sensor is studied specifically for the real-time monitoring needs of the trailing edge morphology and deflection angle of a variable curvature wing. This sensor has the advantages of small size, light weight, and strong anti-interference ability, and is suitable for accurate measurement of wing deformation.

[0011] The specific steps are as follows: Step 1: Obtain the modal parameters of the wing structure through finite element analysis or experimental modal analysis; Step 2: Calculate the modal component sum index according to the modal parameters to obtain the MISS value of the degree of freedom of each considered mode; Step 3: According to the MISS value, determine the degree of freedom that contributes most to the wing vibration response to select the best sensor location; Step 4: Use an iterative or other optimization algorithm to optimize the location of the best sensor; Step 5: Use the data processing unit to receive data from the sensor, obtain the wing deformation results, and apply the data analysis algorithm to evaluate the wing performance.

[0012] Among them, the degree of freedom refers to the minimum number of independent coordinates required for the various structural parts of the wing to move in space, which is used to describe the behavior of the wing in dynamic response.

[0013] In step 1, the modal parameters include a modal shape, which is used to describe the vibration form of the wing structure in a specific mode; the modal shape is represented by a modal participation factor, and its calculation formula is as follows:

[0014] in, is the modal participation factor of the kth mode, is the modal participation factor corresponding to the ith degree of freedom, is the modal amplitude of the kth mode at the ith degree of freedom, and N is the total number of degrees of freedom.

[0015] In step 2, for each considered mode, the modal amplitude of the mode in all degrees of freedom is calculated; the modal amplitude refers to the relative displacement amplitude of a certain degree of freedom relative to a certain reference point under a specific mode; The calculation formula of the modal component sum index in step 2 is:

[0016] Among them, MISS is the modal component sum index value; is the modal amplitude of the kth mode at the ith degree of freedom, is the natural frequency of the kth mode.

[0017] A further improvement is to sort according to the MISS values ​​of the k-th mode, select the one with the largest MISS value in the i-th degree of freedom, determine it as the key area, and arrange the sensors.

[0018] A further improvement is that the sensor adopts a fiber Bragg grating sensor. In the present application, the fiber Bragg grating sensor has the advantages of small size, light weight, strong anti-interference ability, etc., and is suitable for accurate measurement of wing deformation.

[0019] A further improvement is that the optimization algorithm in step 4 includes but is not limited to genetic algorithm, simulated annealing, etc. Through the iterative optimization method, the arrangement position of the sensor is continuously adjusted to further improve the accuracy and comprehensiveness of deformation monitoring.

[0020] A further improvement is that the data analysis algorithm in step 5 can identify the size, position and direction of the deformation, thereby providing a quantitative analysis result.

[0021] Beneficial effects of this application: 1. Accurate sensor layout: The key areas of the wing are determined through modal analysis, and the sensors are precisely arranged at the degrees of freedom that contribute most to the dynamic response. This directly improves the quality of data acquisition and reduces the burden of subsequent data processing.

[0022] 2. Optimize monitoring efficiency: Use optimization algorithms such as genetic algorithms to select the best sensor locations, reducing the trial and error process and improving the efficiency of monitoring layout.

[0023] 3. Efficient data acquisition: Using fiber Bragg grating sensors, they are small in size, light in weight, and have strong anti-interference ability, and can collect deformation data quickly and accurately.

[0024] 4. Real-time performance evaluation: Combined with data analysis algorithms, sensor data is processed and analyzed in real time to quickly identify deformation size, location and direction, and timely evaluate wing performance, thereby shortening the feedback cycle.

[0025] 5. Reduce the number of unnecessary tests: Due to the optimization of sensor layout and data collection, it is possible to maximize the useful information obtained from each test, reducing repeated tests caused by improper layout.

[0026] 6. Reduce maintenance costs: Accurate sensor layout and efficient data acquisition can reduce unnecessary repeated tests and adjustments, thereby reducing the cost of the entire test and maintenance.

[0027] 7. Improve the reliability of UAV performance evaluation: Real-time and accurate deformation data makes UAV performance evaluation more reliable, helping to discover potential problems in advance and make corrections. Through effective monitoring methods, the safety of the UAV's wing structure in various flight conditions can be ensured, providing additional protection for flight safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0029] Figure 1 : A flow chart of a method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis provided in this application; Figure 2 : The simulated coordinate system of the UAV-1 wing; Figure 3 : Flowchart of the method for optimizing the best sensor position. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] The following examples are intended to further illustrate but not limit the sensor layout method in the UAV wing deformation test described in the present invention.

[0033] For the wings of a drone, the degrees of freedom may include different deformation modes such as bending and twisting of the wing. These deformation modes can occur at different locations with different amplitudes and directions, so each part that may deform is considered as a degree of freedom.

[0034] In this application, degrees of freedom refer to the minimum number of independent coordinates required for the spatial motion position of each part of the wing structure. It is used to describe the behavior of the wing in dynamic response and is a key factor in determining the sensor layout.

[0035] In the embodiment of the present application, firstly, the modal parameters of the wing structure are obtained by finite element analysis, as follows: Modal parameters include modal shapes, which are used to describe the vibration form of the wing structure in a specific mode. The modal shape is represented by a vector. The dynamic change of the wing can be regarded as a complex multi-degree-of-freedom system. In this application, the modal coordinate system is used to convert the complex multi-degree-of-freedom system into a series of single-degree-of-freedom systems, so that the dynamic characteristics of the system can be more easily analyzed.

[0036] When performing modal analysis, we first mesh the wing in the defined coordinate system and then use the finite element method to calculate the local modal shapes of each element. These local modal shapes are then combined into global modal shapes, which describe the vibration characteristics of the entire wing structure in a specific mode. Based on this, the geometric modeling of the wing structure of the UAV (model UAV-1) is carried out as follows Figure 2 The coordinate system Oxyz is the three-dimensional absolute coordinate system of the design space, and the default origin is the front point of the aircraft fuselage; ’ x ’ y ’ z ’ is the three-dimensional coordinate system of the wing, with the origin being the leading edge of the wing root; ’’ cs is the wing two-dimensional dimensionless coordinate system, including the following points: the origin and O ’ x ’ y ’ z ’ same; s is the spanwise relative position parameter, which is 0 at the root and 1 at the tip; c is the chordwise relative position parameter at a given position, which is 0 at the leading edge and 1 at the trailing edge; the relative position of a single point in the wing plane is described by (c, s); the arrangement of a single structural component inside the wing is described by a set of relative position points {P n This two-dimensional dimensionless coordinate system helps to relate the different parts of the wing to the modal amplitudes.

[0037] First, we assume that the UAV wing has been divided into finite element meshes and the modal amplitude of each node has been obtained through finite element analysis. , these modal amplitudes describe the vibration amplitude of each degree of freedom in the kth mode. We also assume that we have the modal participation factor for each degree of freedom .

[0038] Therefore, the modal parameters are determined, which are specifically expressed by the modal participation factor, as shown in the following formula (1):

[0039] in, is the modal participation factor of the kth mode, is the modal participation factor corresponding to the ith degree of freedom, is the modal amplitude of the k-th mode at the i-th degree of freedom, and N is the total number of degrees of freedom; the k-th mode here refers to the vibration form of the wing structure at the k-th natural frequency. In the embodiment of the present application, the position of each degree of freedom is defined by a coordinate system. For example, in finite element analysis, the wing model is divided into finite element grids. Each grid degree of freedom will have a corresponding (c, s) coordinate. We can ’’ Select several specific coordinate points (c, s) in the cs coordinate system and find the corresponding modal amplitudes and modal participation factors; Suppose we have the following simplified data: Degrees of freedom i = 1, 2, 3 The corresponding modal amplitude

[0040] The corresponding modal participation factor

[0041] We can substitute these data into the modal participation factor formula (1) and calculate: =0.46; this result indicates that the modal participation factor of the k-th mode in the entire wing structure is 0.46, which also indicates the contribution of this mode to the overall dynamic response.

[0042] In this application, through the above modal analysis, it is found that the main deformation areas of the wing are concentrated in the wing beam, wing ribs, long stringers and surface skin. In this application, for each mode in which the wing may deform, its modal amplitude in all degrees of freedom is calculated, that is, the sum index of its modal components in this mode is calculated, as shown in formula (2):

[0043] Among them, MISS is the modal component sum index value; is the modal amplitude of the kth mode at the ith degree of freedom, is the natural frequency of the kth mode.

[0044] Next, in step 3, based on the size of the MISS value, we will determine the degrees of freedom that contribute most to the wing vibration response and select these degrees of freedom as the optimal sensor locations. According to formula (2), we can calculate the MISS value of the kth mode on the i-th degree of freedom. Then, we will sort the MISS values ​​of all degrees of freedom to determine which degrees of freedom contribute most to the wing vibration response. When selecting the optimal sensor location, we will give priority to degrees of freedom with larger MISS values. These degrees of freedom are usually located in key areas of the wing, such as spars, ribs, long stringers, etc. By arranging sensors in these key areas, we can capture the deformation information of the wing more accurately.

[0045] Table 1 Sensor layout position reference table Location Sensor arrangement describe Wing spar Arrange to scale The wing spar is the main load-bearing component of the wing and should be arranged at a location with a larger cross-sectional height. Its axis should be kept as free of bending as possible to increase rigidity and strength. There should be at least one sensor on each wing spar to monitor the stress conditions. Wing rib Along-course arrangement or vertical spar arrangement The wing rib is a longitudinal component that can be arranged along the heading or perpendicular to the wing beam. There should be at least one sensor on each wing rib to monitor its stress condition. Long Stringer Arrange in proportion or parallel to an element The long stringer is a span-wise load-bearing member that can be arranged in proportion or parallel to an element. Each long stringer should have at least one sensor to monitor its stress condition. Wing tip Specific layout The forces at the wing tip are complex and require special attention. Multiple sensors should be arranged at this location to comprehensively monitor the forces. Wing root Specific layout The wing root is close to the fuselage and the stress conditions are more complicated. Multiple sensors should be arranged at this location to comprehensively monitor its stress conditions. Specifically, we can refer to the sensor layout position reference table in Table 1 to select the appropriate sensor layout. For example, for the wing spar, we can arrange the sensors in proportion and ensure that there is at least one sensor on each wing spar. For the ribs, we can arrange the sensors along the heading or perpendicular to the wing spar and arrange at least one sensor on each rib. Similarly, for the long stringer and wing tip, we can also arrange the sensors according to the corresponding layout.

[0046] By selecting the best sensor position according to the MISS value and making appropriate sensor layout, we can ensure that the sensor covers the key areas of the wing, thereby detecting deformation information more comprehensively. This layout method can improve the accuracy and reliability of UAV wing deformation tests and provide better protection for the flight safety and performance of UAVs.

[0047] After determining the best sensor locations, we can use an iterative algorithm to further optimize these locations. A commonly used optimization algorithm is the genetic algorithm. The genetic algorithm is a search algorithm that simulates the biological evolution process in nature, and finds the optimal solution by simulating operations such as gene selection, crossover, and mutation. The specific implementation method is as follows: 1. Initialize the population: Randomly generate a certain number of sensor locations as the initial population.

[0048] 2. Evaluate fitness: Calculate the fitness of each sensor position based on its MISS value, that is, the degree of contribution to the wing vibration response.

[0049] 3. Selection operation: Perform selection operation based on fitness and select sensor positions with higher fitness to enter the next generation.

[0050] 4. Crossover operation: Randomly select two positions from the selected sensor positions for crossover operation to generate new sensor positions.

[0051] 5. Mutation operation: Perform mutation operation on the newly generated sensor positions with a certain probability to increase the diversity of the population.

[0052] 6. Termination condition judgment: If the preset number of iterations is reached or the fitness reaches the preset threshold, the iteration is stopped; otherwise, return to step 2 to continue the iteration.

[0053] Through genetic algorithm optimization, we can continuously adjust the position of sensors to improve the accuracy and comprehensiveness of deformation monitoring.

[0054] In step 5 of this application, after determining the optimal sensor positions, we need to arrange fiber Bragg grating sensors at these positions. Fiber Bragg grating sensors have the advantages of small size, light weight, and strong anti-interference ability, and are suitable for accurate measurement of wing deformation. For example, according to the optimal sensor position, drill holes or grooves in the corresponding parts of the wing to install the fiber Bragg grating sensor. Fix the fiber Bragg grating sensor on the wing to ensure that it fits tightly against the wing surface. Connect the fiber Bragg grating sensor to the data acquisition system to receive the sensor data and perform subsequent processing.

[0055] After completing the sensor layout, we need to receive the sensor data and analyze it to evaluate the performance of the drone wing. The data from the fiber Bragg grating sensor is received through the data acquisition system, including deformation information and temperature data. The collected data is processed and analyzed, and data analysis algorithms can be used to identify information such as the size, location, and direction of the deformation. Based on the analysis results, the performance of the wing is evaluated, such as whether the deformation exceeds the design limit and whether there is a potential fault. Improvement measures are proposed based on the analysis results to further improve the safety and reliability of the drone wing.

[0056] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0057] The embodiments described above are only descriptions of the preferred methods of the present application, and are not intended to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements made to the technical solutions of the present application by ordinary technicians in this field should fall within the protection scope determined by the claims of the present application.

Claims

1. A method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis, characterized in that: The following steps are involved: Step 1: Obtain the modal parameters of the wing structure through finite element analysis or experimental modal analysis; Step 2: Calculate the modal component sum index according to the modal parameters to obtain the MISS value of the degree of freedom of each considered mode; Step 3: According to the MISS value, determine the degree of freedom that contributes most to the wing vibration response to select the best sensor location; Step 4: Use optimization algorithm to optimize the position of the best sensor; Step 5: Use the data processing unit to receive data from the sensor, obtain the wing deformation results, and apply the data analysis algorithm to evaluate the wing performance.

2. The method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis according to claim 1 is characterized in that: Degrees of freedom refer to the minimum number of independent coordinates required for the various structural parts of the wing to move in space, and are used to describe the behavior of the wing in dynamic response.

3. The method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis according to claim 1 is characterized in that: In step 1, the modal parameters include modal shapes, which are used to describe the vibration form of the wing structure in a specific mode; the modal shape is represented by a modal participation factor, and its calculation formula is as follows: is the modal participation factor of the kth mode, is the modal participation factor corresponding to the ith degree of freedom, is the modal amplitude of the kth mode at the ith degree of freedom, and N is the total number of degrees of freedom.

4. The method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis according to claim 1 is characterized in that: In step 2, for each considered mode, the modal amplitude of the mode in all degrees of freedom is calculated; the modal amplitude refers to the relative displacement amplitude of a certain degree of freedom relative to a certain reference point under a specific mode; The calculation formula of the modal component sum index in step 2 is: Among them, MISS is the modal component sum index value; is the modal amplitude of the kth mode at the ith degree of freedom, is the natural frequency of the kth mode.

5. The method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis according to claim 4 is characterized in that: The MISS values ​​of the k-th mode are sorted, and the one with the largest MISS value in the i-th degree of freedom is selected as the key area, and the sensors are arranged.

6. The method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis according to claim 5 is characterized in that: The sensor adopts a fiber Bragg grating sensor.

7. The method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis according to claim 1 is characterized in that: The optimization algorithm in step 4 includes but is not limited to genetic algorithm, simulated annealing, etc.

8. The method for arranging sensors for monitoring wing deformation of unmanned aerial vehicles based on modal analysis according to claim 1 is characterized in that: The data analysis algorithm in step 5 can identify the size, location and direction of the deformation, thereby providing quantitative analysis results.

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