Fixed-wing drone method of flight to exploit monsoon weather to collect mountain terrain updrafts

Abstract

The application discloses a flight method for a fixed-wing unmanned aerial vehicle to collect ascending air flow on mountain terrain by using monsoon climate, and the method comprises the following steps: firstly, a numerical simulation method is used to pre-calculate the wind speed of a region of interest, and weather conditions capable of utilizing the ascending air flow are determined, i.e., the monsoon speed when the unmanned aerial vehicle is capable of flying without energy consumption; then, a suitable flight time is determined, and PID control method is used to adjust the flight control parameters of the unmanned aerial vehicle according to the mathematical equation of the dynamic system of the unmanned aerial vehicle, so that the unmanned aerial vehicle can keep stable horizontal flight, and the heading direction of the unmanned aerial vehicle tends to be opposite to the monsoon direction; and finally, in actual flight, an extended Kalman filter is used to estimate the ascending air flow, and the energy gain in the task execution process is analyzed by quantifying the flight data. The application can utilize the ascending air flow to perform long-endurance flight, saves a large amount of resources, and verifies the feasibility of collecting wind energy in the flight process, thereby providing experience support for developing new ascending air flow.

Description

Technical Field

[0001] This invention relates to the field of updraft collection technology, and more particularly to a flight method for a fixed-wing unmanned aerial vehicle to collect updrafts from mountainous terrain using monsoon climate. Background Technology

[0002] Marine birds such as albatrosses utilize the shear wind field over the sea surface for long-endurance flight, while birds inhabiting valleys and forests forage using thermal updrafts caused by temperature differences and dynamic updrafts created by natural obstacles such as valleys. In natural landscapes, updrafts are classified into two types based on their causes: thermal updrafts and dynamic updrafts. Thermal updrafts are formed due to uneven heating of the Earth's surface caused by solar radiation and unstable energy in the atmosphere. Dynamic updrafts and undulating updrafts are generated when wind, flowing horizontally, is forced to move upwards along the mountainside due to the obstruction of tall obstacles such as mountains. However, current bird flight records in updrafts focus primarily on thermal updrafts, with fewer records in the areas of dynamic and undulating updrafts. In summary, the troposphere, driven by uneven heating of the ground due to clouds or uneven pressure fields created by natural obstacles such as cliffs, mountains, and valleys, contains various forms of atmospheric energy available for use by organisms in nature. Effectively utilizing this energy under different flight environments will be a breakthrough point in the research of long-endurance unmanned aerial vehicles (UAVs).

[0003] Small fixed-wing UAVs are characterized by low Reynolds numbers (10⁻¹⁰). 5 (Approximately). Under these flight conditions, the relatively high ratio of viscous forces results in a lower lift-to-drag ratio for the airfoil. Statistical data from aircraft at different Reynolds numbers show that the maximum profile lift-to-drag ratio of conventional airfoils near low Reynolds numbers is approximately the same as that at high Reynolds numbers (10). 6Half of the range of fixed-wing UAVs. The increase in range of fixed-wing UAVs is greatly limited by the aerodynamic efficiency of the airfoil profile, i.e., the lift-to-drag ratio of the aircraft. Considering that birds fly at low speeds and are similar in size to small UAVs, many researchers have drawn inspiration from birds in nature to design flexible or deformable wings to improve the lift-to-drag ratio of fixed-wing aircraft under low-speed conditions, thereby further increasing the loiter time of fixed-wing aircraft in the air at low speeds. High lift of the wing is achieved by forcing flow separation at the leading edge of the airfoil, and the flow-separated airfoil can resist gusts and free-flow turbulence without sacrificing aerodynamic efficiency, thus increasing its flight time limit to four times that of existing UAVs. In recent years, researchers have focused on developing energy sources in nature, hoping to bring new hope to the development of long-endurance UAVs. Nature contains various forms of energy for our use, among which solar-powered flight is a typical example. The advantages of solar-powered UAVs in long-endurance flight, economy, and environmental friendliness are becoming increasingly popular. Airbus's Zephyr S solar-powered drone reportedly set a record for the longest flight time in Earth's atmosphere after nearly 26 days of operation. However, long-endurance solar-powered drones must fly at altitudes of tens of thousands of meters to ensure the utilization of solar energy and avoid gas disturbances caused by complex weather conditions in the troposphere. On the other hand, some solar-powered drones are designed for low-altitude flight. These drones are often affected by weather conditions; the energy harvesting efficiency of their solar cells drops significantly on cloudy days. Therefore, it is essential to find and develop new energy utilization methods to improve the flight performance of these aircraft.

[0004] The problem of thermal updrafts has long been a popular research topic for pilots seeking to increase glider energy. Significant progress has been made in actual flight, but there are few ways to utilize dynamic updrafts for gliding in valleys. Therefore, this invention focuses on dynamic updrafts caused by natural obstacles in valleys. First, it simulates the energy utilization of fixed-wing UAVs in non-real-world terrain environments, and then determines the critical wind speed for glider flight through a series of numerical calculations. This invention will determine the critical conditions for wind energy harvesting through numerical simulation, directly using the Global Weather Forecast System (GFS) provided by the meteorological bureau to determine whether energy can be obtained from the monsoon, and use the Extended Kalman Filter (EKF) to estimate the updrafts. Experiments will be used to verify the advantages of harvesting updrafts from mountain slopes. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a flight method for fixed-wing unmanned aerial vehicles (UAVs) to collect updrafts from mountainous terrain using monsoon climates. This invention simulates the wind field of a specific region in a non-surface setting; reconstructs a selected UAV through reverse engineering, interacts with the wind field, and plots the collected energy; determines the critical conditions for wind energy collection through numerical interpolation; and conducts wind energy harvesting experiments in advance based on weather conditions using the GFS (Geophysical Field Model). This non-surface simulation and confirmation of wind energy utilization greatly facilitates glider missions in mountainous areas, saving significant manpower and time resources.

[0006] The objective of this invention is achieved through the following technical solution: a flight method for a fixed-wing unmanned aerial vehicle (UAV) to collect updrafts from mountainous terrain using monsoon climate, comprising the following steps:

[0007] (1) Extract the three-dimensional topographic data of the hillside impacted by the monsoon in the region of interest from the open geographic information system, then set the incoming wind speed, and then use the large eddy simulation numerical calculation method to calculate the updraft in the region of interest.

[0008] (2) Input the updraft of the region of interest into the simulation system to interact with the UAV, use the UAV to perform flight simulation in the region of interest, and then calculate the energy gain of the UAV during flight according to the energy formula;

[0009] (3) By changing the monsoon speed in the simulation system multiple times, multiple flight simulations were carried out to calculate the energy gain obtained by the UAV during each flight, and then the relationship between the energy gain obtained by the UAV and the monsoon speed was obtained. The interpolation method was used to find the monsoon speed when the UAV flew without energy consumption as the optimal monsoon speed.

[0010] (4) Based on the relationship between the energy gain obtained by the UAV and the monsoon speed, construct the mathematical equations of the UAV dynamic system;

[0011] (5) Check the monsoon speed of the area of ​​interest according to the global forecast system provided by the meteorological bureau, and select the flight time according to the optimal monsoon speed; and adjust the flight control parameters of the UAV according to the mathematical equation of the UAV dynamics system so that the UAV can fly stably;

[0012] (6) Conduct flight experiments of the UAV based on the flight time and flight control parameters determined in step (5), and estimate the updraft and the energy state of the UAV during the experiment.

[0013] Furthermore, in step (1), when extracting the three-dimensional topographic data of the hillside impacted by the monsoon in the region of interest from the open geographic information system, it is necessary to import the elevation data into the map drawing software and select the hillside to provide topographic support for the generation of updrafts; wherein, the elevation data is obtained by extracting from the open geographic information system.

[0014] Furthermore, in step (1), when calculating the updraft in the region of interest using the large eddy simulation numerical method, the following steps are also included:

[0015] The simulation requires using real UAV parameters, then reconstructing the UAV's shape through reverse engineering, and then constructing the UAV's parameters by measuring its parameters. Finally, the aerodynamic coefficient and torque coefficient of the UAV are calculated using the vortex lattice method. The aerodynamic coefficient and torque coefficient of the UAV are then used for simulation to obtain the updraft in the region of interest.

[0016] Furthermore, the expression for the energy formula is:

[0017]

[0018] In the formula, E represents the energy gain of the drone. a Let m represent energy, g represent mass, g represent gravitational acceleration, D represent drag, and w represent force. z Indicates an updraft, w x γ represents the incoming wind speed, γ represents the track angle, and V represents the direction of travel. a It is relative to the speed of the wind, and the superscript · indicates the corresponding derivative.

[0019] Furthermore, the mathematical equations of the UAV dynamics system are expressed as follows:

[0020]

[0021] In the formula, v a α represents the speed relative to the wind, θ represents the angle of attack, θ represents the pitch angle, Q represents the angular acceleration, q represents the incoming dynamic pressure, S represents the airfoil area, Sc represents the airfoil area chord, and C represents the airfoil area chord. T C represents the thrust coefficient. D C represents the drag coefficient. L C represents the lift coefficient. m I represents the torque coefficient, t represents time, and I represents the torque coefficient. yy It represents the moment of inertia.

[0022] Furthermore, in step (5), adjusting the flight control parameters of the UAV specifically includes:

[0023] By using PID control to adjust the flight control parameters of the drone, the drone can fly stably. When adjusting the flight control parameters, the glider's maneuverability is not required. The core of adjusting the flight control parameters is to ensure that the turbulent wind speed at high altitude does not affect the normal and stable flight of the drone.

[0024] Furthermore, in step (6), before conducting the flight experiment of the UAV, it is necessary to ensure that the UAV can fly. Specifically, this is determined by the following method: using a handheld airspeed meter to measure the wind speed at the top of the hillside. When the wind speed is greater than the theoretically calculated optimal monsoon speed, it means that the UAV can fly, and only then can the flight experiment of the UAV be carried out.

[0025] Furthermore, in step (6), the estimation of the updraft and the energy state of the UAV is specifically achieved through the following method:

[0026] First, the wind speed at the top of the hillside is measured using a handheld airspeed meter; then, the measured wind speed is optimized using an extended Kalman filter to obtain the updraft; finally, the energy gain is calculated using the energy formula based on the updraft, which is the energy state.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] (1) This invention uses numerical simulation to determine the critical conditions for collecting wind energy using drones in a certain region under the influence of monsoon climate, thus saving a lot of resources.

[0029] (2) This invention reverse-engineers existing gliders to determine the feasibility of gliders collecting energy from monsoon climates. In this process, no design parameters or aerodynamic parameters of the gliders are required, providing a paradigm for verifying energy collection for different aircraft.

[0030] (3) Even at relatively low wind speeds, the UAV can collect a certain amount of energy, a characteristic that depends on the aerodynamic characteristics of the fixed wing; when the cruising speed is close to the incoming flow, the glider hovers over the hillside area with an appropriate slope without moving forward, thereby maintaining a constant airspeed and slowly ascending; therefore, the present invention is crucial for improving the endurance of future tests by designing a fixed wing suitable for specific terrains.

[0031] (4) The method of the present invention includes: extracting high-precision three-dimensional terrain data and using the large eddy simulation (LES) numerical calculation method to calculate the updraft; setting up an interaction between the UAV and the airflow in the environment to estimate the monsoon climate energy collected by the fixed-wing UAV; finding the monsoon speed at which the glider can fly without energy consumption; predicting weather conditions based on the global weather forecast system provided by the meteorological bureau, estimating the updraft through EKF, conducting flight experiments, estimating the energy state of the real glider, and verifying the method of collecting monsoon climate energy using a fixed-wing UAV. Attached Figure Description

[0032] Figure 1 This is a schematic diagram showing the specific power and net power of a glider under different speed conditions;

[0033] Figure 2This is a verification framework diagram for the mountaintop flight test;

[0034] Figure 3 This is a schematic diagram of the space trajectory for a high-altitude outdoor experimental flight.

[0035] Figure 4 This is a filtered graph showing the changes in wind direction and the glider's heading; among them, Figure 4 (a) is a filtered wind direction change diagram. Figure 4 (b) is a diagram showing the glider's heading changes;

[0036] Figure 5 This is a graph showing the parameter changes of a glider during an outdoor energy harvesting experiment; among them, Figure 5 (a) is a graph showing the altitude changes of the glider during an outdoor energy harvesting experiment. Figure 5 (b) is a graph showing the change in the glider's climb rate during the outdoor energy harvesting experiment. Figure 5 (c) is a graph showing the pitch angle changes of the glider during the outdoor energy harvesting experiment;

[0037] Figure 6 This is an estimated wind field variation diagram; among which, Figure 6 (a) is a diagram showing the wind field variation in the northerly direction in the estimated wind field. Figure 6 (b) is a diagram showing the wind field variation in the westerly direction in the estimated wind field. Figure 6 (c) is a graph showing the velocity variation along the z-axis recorded by the flight control barometer;

[0038] Figure 7 It uses measured and estimated data to calculate the specific kinetic and specific potential energy time histories during the harvest process; among them, Figure 7 (a) is the time history diagram of specific kinetic energy throughout the entire harvest process. Figure 7 (b) is the time history diagram of specific potential energy throughout the entire harvest process. Figure 7 (c) is the instantaneous specific power calculated throughout the entire harvest process. Figure 7 (d) is a time history diagram of the power consumed by the glider's resistance during the entire harvest process. Detailed Implementation

[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not intended to limit this application.

[0040] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0041] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "in response to determination," or "includes." Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process or method. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0042] The present invention will now be described in detail with reference to the accompanying drawings. Unless otherwise specified, the features of the following embodiments and implementations can be combined with each other.

[0043] The superior speed, maneuverability, and range of fixed-wing UAVs have expanded their application in scientific research. Overcoming the limitation of short range will further broaden the practical applications of UAVs. This invention aims to improve the flight time of UAVs by collecting updrafts in mountainous areas. These updrafts, specifically their speed, are caused by the lifting of monsoon winds by terrain. Fixed-wing UAVs can achieve long-endurance flight by utilizing updrafts.

[0044] Example 1:

[0045] The fixed-wing UAV of the present invention utilizes the monsoon climate to collect updrafts from mountainous terrain for flight, specifically including the following steps:

[0046] (1) Extract the three-dimensional topographic data of the hillside impacted by the monsoon in the region of interest from the open geographic information system (GIS), then set the incoming wind speed, and then use the large eddy simulation (LES) numerical calculation method to calculate the updraft in the region of interest.

[0047] It should be understood that by setting the incoming flow conditions (i.e., incoming wind speed), the updraft generated by the monsoon being affected by the hillside can be calculated, which is the updraft of the region of interest.

[0048] Furthermore, when extracting 3D topographic data of monsoon-impacted hillsides in a region of interest from an open geographic information system (GIS), the elevation data needs to be imported into mapping software, such as Pointwise, to select suitable hillsides that provide good topographic support for the generation of updrafts. The elevation data is obtained by extracting it from the open GIS.

[0049] Furthermore, when using the LES numerical calculation method to calculate the updraft in the region of interest, the following steps are also required: to use real UAV parameters for simulation, then to reconstruct the UAV shape through reverse engineering, then to construct the UAV parameters by measuring its parameters, and finally to calculate the UAV's aerodynamic coefficient and moment coefficient using the vortex lattice method, and then to use the UAV's aerodynamic coefficient and moment coefficient for simulation to obtain the updraft in the region of interest.

[0050] (2) Input the updraft of the region of interest into the simulation system to interact with the UAV, use the UAV to perform flight simulation in the region of interest, and then calculate the energy gain of the UAV during flight according to the energy formula.

[0051] Furthermore, the expression for the energy formula is:

[0052]

[0053] In the formula, E represents the energy gain of the drone. a Let m represent energy, g represent mass, g represent gravitational acceleration, D represent drag, and w represent force. z Indicates an updraft, w x γ represents the incoming wind speed, γ represents the track angle, and V represents the direction of travel. a It is relative to the speed of the wind, and the superscript · indicates the corresponding derivative.

[0054] (3) By changing the monsoon speed in the simulation system multiple times, multiple flight simulations were carried out to calculate the energy gain obtained by the UAV during each flight, and then the relationship between the energy gain obtained by the UAV and the monsoon speed was obtained. The interpolation method was used to find the monsoon speed when the UAV flew with no energy consumption as the optimal monsoon speed.

[0055] It should be noted that changing the monsoon speed in the simulation system will change the energy gain acquired by the UAV. Therefore, by repeatedly changing the monsoon speed in the simulation system and conducting multiple flight simulations, the energy gain of the UAV for each flight simulation is calculated, and then the relationship is fitted to obtain the relationship between the energy gain acquired by the UAV and the monsoon speed. By further observing the speed range of net energy crossing, an interpolation method can be used to find the monsoon speed at which the UAV flies with no energy consumption.

[0056] (4) Based on the relationship between the energy gain obtained by the UAV and the monsoon speed, construct the mathematical equations of the UAV dynamic system.

[0057] Furthermore, the mathematical equations of the UAV dynamics system are expressed as follows:

[0058]

[0059] In the formula, v a α represents the speed relative to the wind, θ represents the angle of attack, θ represents the pitch angle, Q represents the angular acceleration, q represents the incoming dynamic pressure, S represents the airfoil area, Sc represents the airfoil area chord, and C represents the airfoil area chord. T C represents the thrust coefficient. D C represents the drag coefficient. L C represents the lift coefficient. m I represents the torque coefficient, t represents time, and I represents the torque coefficient. yy It represents the moment of inertia.

[0060] In the above formula, the interaction force between the UAV and the airflow is represented by the lift coefficient C. L and drag coefficient C D The calculation was approximated using the open-source software XFLR5. The energy generated by the interaction between the drone and the updraft was calculated using an energy formula.

[0061] (5) Check the monsoon speed of the region of interest according to the Global Forecast System (GFS) provided by the meteorological bureau, and select the flight time according to the optimal monsoon speed; and adjust the flight control parameters of the UAV according to the mathematical equation of the UAV dynamics system so that the UAV can fly stably.

[0062] It should be understood that monsoon speed changes constantly, but the changes in monsoon speed within a certain period of time are based on evidence and vary within a certain range. Therefore, the monsoon speed of the region can be viewed through the global forecast system, and then a suitable flight time can be selected for the flight experiment. Specifically, the optimal monsoon speed can be used to determine which time period is the appropriate time for the flight experiment.

[0063] Furthermore, the flight control parameters of the UAV are adjusted, specifically by using PID control methods to adjust the flight control parameters of the UAV so that the UAV can fly stably. When adjusting the flight control parameters, the glider's high maneuverability is not required. The core of adjusting the flight control parameters is to ensure that the turbulent wind speed at high altitudes does not affect the normal and stable flight of the UAV.

[0064] It should be understood that when adjusting the flight control parameters of a drone, in addition to enabling the drone to fly stably, the drone's heading can also be made to be in the opposite direction of the monsoon, which makes it easier to collect wind energy.

[0065] (6) Conduct flight experiments of the UAV based on the flight time and flight control parameters determined in step (5), and estimate the updraft and the energy state of the UAV during the experiment.

[0066] Furthermore, before conducting flight experiments on drones, it is necessary to ensure that the drones are capable of flight. This is determined by the following method: using a handheld airspeed meter to measure the wind speed at the top of the hillside. When the wind speed is greater than the theoretically calculated optimal monsoon speed, it means that the drones are capable of flight. Only then can the drone flight experiment, i.e., the energy harvesting experiment, be carried out.

[0067] It should be understood that only when the measured wind speed is greater than the theoretically calculated optimal monsoon speed can the drone be guaranteed to fly; only after ensuring the drone's flight capability can flight experiments be conducted. Energy can be harvested during flight; therefore, flight experiments can also be considered energy harvesting experiments.

[0068] Furthermore, the energy state of the updraft and the UAV is estimated using the following method: First, the wind speed at the top of the hill is measured using a handheld airspeed meter; then, the measured wind speed is optimized using an extended Kalman filter (EKF) to obtain the updraft; finally, the energy gain is calculated using the energy formula based on the updraft, which is the energy state.

[0069] It should be noted that the wind speed measured by the airspeed meter is first used to estimate the updraft, and then to further estimate the energy state. The calculated energy gain is positive, indicating that the UAV can collect energy. Furthermore, the feasibility of wind energy harvesting was verified during the UAV's flight.

[0070] Example 2:

[0071] The fixed-wing UAV used in this implementation utilizes the monsoon climate to collect updrafts from mountainous terrain, and specifically includes the following steps:

[0072] S1. Input the data on the interaction between the wind field and the terrain into the simulation system to interact with the aircraft. The data on the interaction between the wind field and the terrain is the updraft calculated by the LES numerical calculation method in Example 1.

[0073] S2. Investigate the critical conditions for unpowered long-term flight of an aircraft in natural terrain. Simulate the characteristics of natural wind fields under different speed conditions in the OpenFoam platform to obtain the corresponding wind field data. Then input the wind field data into the simulation system to interact with the aircraft for flight simulation.

[0074] S3. With the increase of the incoming wind speed, maintain a stable pitch angle for flight. Plot the absolute value of the average power calculated according to the energy utilization principle formula under different speed conditions, that is, the relationship between energy gain and monsoon speed, to further determine the critical flight condition, that is, the monsoon speed at which the aircraft flies with no energy consumption.

[0075] S4. The standard-class glider ASW28, with a span of 2.540 meters, was used as the aircraft described in this embodiment to verify that the aircraft could obtain energy from the updrafts in front of the ridge.

[0076] S5. To reduce the complexity of the sensing system and decrease the glider's weight during flight, this embodiment employs a method based on an extended Kalman filter (EKF), using a GNSS sensing system and a Pitot tube to estimate wind field information under complex terrain. The EKF, as a highly efficient recursive filter capable of estimating the state of a dynamic system from a series of noisy measurements, is used in GNSS sensing systems for fast and accurate satellite phase and attitude determination, and in aircraft attitude estimation for fusing data from different sensors. In the flight experiment of this embodiment, the GNSS sensing system is used to provide the aircraft's velocity relative to the inertial coordinate system, while the Pitot tube is used to measure the dynamic pressure of the aircraft's velocity relative to the airflow. Unlike traditional frequency domain filters, the extended Kalman filter is a time-domain state predictor that considers errors in the system modeling process and measurement noise from the sensing system, and models an arbitrary state-space model as follows:

[0077] x k =f(x) k-1 )+w k

[0078] z k =h(x k )+v k

[0079] In the formula, x k Let f represent the system state at time k, and let w represent the system function. k Indicates process noise; z k v represents the observation result of the nonlinear measurement function h. kThis indicates the impact of measurement noise and interference. In experiments predicting wind fields, this includes: a Pitot tube scale factor γ, used for real-time line calibration and fault diagnosis of the sensor; and process noise w. k It is a correction to imperfect mathematical models and system perturbations; z k It is the observation result of the nonlinear measurement function h, which includes the effects of measurement noise and interference, with v k This indicates that, since GNSS sensing systems are already capable of measuring glider glider speeds with high precision compared to Pitot tubes, GPS data will not be considered as observation data here.

[0080] Further, the prior estimate of the system state and its error covariance matrix are obtained, and their expressions are as follows:

[0081]

[0082] In the formula, Represents the prior estimate of the system state; The first part represents the error covariance matrix of the prior estimate. Since there is no information about the wind field, this embodiment will consider that the wind field does not change over time; Q represents the noise covariance matrix, which represents the error between the state transition matrix and the actual process; A represents the state transition matrix of the system, and the superscript T indicates the transpose of the matrix.

[0083] S6. By measuring the observation data of the system, the optimal estimate of the system state is obtained, and its posterior estimate is expressed as follows:

[0084]

[0085] In the formula, The posterior estimate representing the system state is derived from the fusion of predicted and observed data; K k represents the Kalman gain coefficient, which is an intermediate result of filtering and is determined by minimizing the sum of the squares of the L2 norms of the errors between the true state and the estimated state; H represents the observation matrix of the system; R represents the covariance matrix of the measurement error, which is related to the measuring instrument.

[0086] S7. Update the formula for the error covariance matrix of the posterior estimate and use it as the prior estimate input value for the next time step. Therefore, each updated state estimate is calculated from the estimate of the previous time step and the new input data.

[0087] S8. Due to the presence of many unmeasurable and unpredictable turbulences in the atmosphere, relying solely on the inherent stability of the aircraft and manual actuator input is insufficient for stable flight under complex conditions. Uncertain gusts of wind on the windward slope of a ridge can render the aircraft uncontrollable. Therefore, a cascaded attitude controller is needed to convert roll and pitch angles into actuator inputs, ensuring stable flight even when not visually inspected. The outer loop of the controller consists of a PI controller, which calculates the current angle error to generate the desired pitch angle and roll angular velocity, inputting them into the inner loop. The inner loop consists of a PD controller, which inputs PWM signals to the actuator distributor based on the current and desired angular velocities.

[0088] S9. Following step (6) of Example 1, conduct flight experiments on the aircraft.

[0089] Example 3:

[0090] This embodiment provides a flight method for a fixed-wing unmanned aerial vehicle to collect updrafts from mountainous terrain using monsoon climate.

[0091] The characteristics of natural wind fields under different speed conditions were simulated in the OpenFoam platform. Data on the interaction between the wind field and the terrain were input into the simulation environment to interact with the glider. The glider took off at an altitude of 710m and began power-off flight 200m in front of the hillside.

[0092] Figure 1 The average values ​​of the specific power and net power of the glider under different speed conditions are given. Figure 1 The orange line represents the work done by the updraft on the glider. It shows that as the incoming airflow speed increases, the updraft formed by the interaction of terrain and wind strengthens, and the energy collected increases significantly. Figure 1 This shows a lack of clear correlation between drag and wind speed, which is reasonable given the limited airspeed required for stable ascent or soaring through the air. Furthermore, the increased incoming flow velocity has a far greater impact on ground speed than on airspeed. It is worth noting that... Figure 1 The yellow and purple lines are almost zero, which means that the work of horizontal and vertical time-varying winds is negligible, so the two lines almost overlap. Figure 1 The red line represents net power, indicating that the glider can fly unpowered for extended periods when the ambient wind speed is greater than 6 m / s. It can be seen that the critical velocity is very low, likely because the effects of the fuselage, tires, engine, and propeller are ignored in the current simulation. Therefore, the critical incident velocity can serve as a reference for comparison with meteorological forecast data, preparing for flight experiments on mountains. The specific power lines for horizontal and vertical time-varying winds coincide because they both tend towards zero. Figure 1 As shown in the red dashed box in the image.

[0093] When the incoming airflow velocity increases again, although the glider maintains a stable pitch angle, the updraft increases the glider's local angle of attack, allowing the glider's energy to steadily increase. Additionally, because the glider's takeoff area is located in a region with strong updrafts on a hillside, when the incoming airflow velocity is high, although the glider's airspeed remains constant, it is affected by the wind ahead and the glider's aerodynamic characteristics, preventing it from penetrating the wind field and causing it to remain in the area with strong updrafts, thus maintaining a stable climb rate.

[0094] The energy harvesting mechanism was evaluated through flight experiments. The flight test framework in an outdoor environment is as follows: Figure 2 As shown. A key factor in energy harvesting is wind field estimation. Specifically, wind speed is estimated using an EKF (Electronic Kinematics Flow) system combined with glider state information and airspeed measured by the onboard avionics system. During flight, a PID (Pilot-Device Controller) is employed to consider the glider's yaw stability.

[0095] The glider is equipped with a 13x6.5mm propeller mounted on its nose, powered by a 1000kV Sunnysky X2814 motor, enabling it to glide in strong winds. An inertial measurement unit (IMU) and magnetometer are embedded to estimate ground position and attitude angles. Similarly, altitude relative to sea level is measured by a barometer, and GPS provides sufficiently accurate ground velocity information. The avionics systems shown in Table 1 are integrated into the CUAV V5+ autopilot.

[0096] Table 1: Various Avionics Systems

[0097] equipment describe autopilot CUAV V5+ microcontroller Arm Cortex M7 barometer MS5611 GPS Neo-M8N Accelerometer / Gyroscope ICM-20689 brushless motor 1000KV

[0098] Next, we will use flight test data and estimated wind fields to evaluate the energy gain and assess the potential benefits of unpowered glider flight in updrafts on hillsides.

[0099] The outdoor test site is located at the top of a hillside (30°30'34.32"N, 119°47'50.00"E), in a subtropical monsoon climate. Under the influence of a high-pressure system, frequent cold northwesterly winds develop in winter, creating conditions for the formation of updrafts on the windward slope. The GFS weather forecast operated by the meteorological bureau indicated a wind speed of 3.85 m / s during the test period, providing favorable conditions for energy harvesting verification.

[0100] The trajectory diagram of gliding is as follows Figure 3 As shown, the red triangle represents the glider starting to collect energy from the updraft, while the red pentagram represents the endpoint. Figure 3 The upper right corner shows the eastern and northern tracks of the process. Figure 3 The black arrow in the image indicates that the wind direction is northwest.

[0101] Figure 4 (a) shows the wind direction estimated based on Kalman filtering, which increases slightly before 8.1 s, then remains constant around 28°; after 26.60 s, the direction gradually decreases, which is basically consistent with the data from the ECMWF Weather Forecasting Center. Generally, the glider experienced almost the same wind direction. Figure 3 The top right corner is a top view of the trajectory, indicating that the glider is moving in a southwest direction. The heading angle is estimated using the Attitude Heading Reference System (AHRS). Figure 4 As shown in (b), although the flight path is not along the wind, it tends to align with the wind direction. During the period from 8.1s to 26.6s, due to the increase in wind direction, the heading angle increases, and the vertical tail changes the heading angle, thereby eliminating sideslip.

[0102] The glider took off from near the mountaintop at an altitude of 712m, initially climbing slowly, then accelerating noticeably around 6 and 14 seconds. Figure 5 As shown in (a). The climb rate and pitch angle are respectively as follows: Figure 5 (b) and Figure 5 As shown in (c), although the pitch angle is consistently negative, which would lead to a rapid descent, the updraft steadily lifts the glider; another important reason must be mentioned is that the wing's angle of attack is approximately 7° to maintain a positive angle of attack. It is noteworthy that the throttle is almost zero throughout the process, except for a slight input in the first 3 seconds. GPS data shows that the glider reached a maximum altitude of approximately 742 meters without propulsion. This potential energy efficiency makes it highly worthwhile to develop an effective and mature control system that relies on real-time airflow sensing. Figure 5 The orange line in the diagram represents throttle input, and the entire process is considered unpowered flight, although there is a slight throttle input of less than 20% in the first 3 seconds, which can be ignored.

[0103] The northerly wind direction estimated by the glider, such as Figure 6 As shown in (a), the negative signal indicates that a southerly wind was blowing at the time, with an average wind speed of 4.80 m / s. Figure 6 In (b), the initial velocity is approximately 9 m / s. Due to the slow ascent, the initial velocity increases slightly, resulting in an average wind speed of 10.94 m / s, which is higher than the data from the ECMWF weather forecast center. Abrupt changes occur in the estimated data, such as at 6.5 s, 17 s, 22 s, or 26 s, which could explain wind shear or turbulent fluctuations. These data allow for the estimation of the wind field in the NED coordinate system from wind speeds measured by EKF and GPS.

[0104] Figure 6(c) shows the velocity along the z-axis recorded by the flight control barometer. The blue line represents the estimated vertical airflow, with negative values ​​indicating an updraft of approximately 4 m / s between 0-3 s and 7-10 s. Around 14 s and 22 s, the updraft is less pronounced, resulting in a slight downward motion. As the simulation above shows, the updraft perceived by the glider weakens as it ascends and moves away from the slope. Each time the ground velocity tends towards negative, the glider is again affected by a larger updraft to compensate for the previous energy loss, ensuring a sustained ascent throughout the process.

[0105] like Figure 7 As shown, the red line represents the instantaneous energy of the updraft, the orange area represents the collected energy, and the blue area at the bottom represents the energy consumed by the glider. Simulation results indicate that energy gains and losses mainly originate from the updraft, induced drag, and viscous drag; the effect of time-varying wind on energy is uncertain, which may lead to energy increases or decreases, or they may cancel each other out; however, in either case, the effect is negligible over long periods.

[0106] Figure 7 (c) is the calculated instantaneous specific power, as mentioned above, the abrupt change is caused by the pulsation of the shear wind. Figure 7 The area enclosed by the red line in (c) and its horizontal and vertical coordinates represent the integration of energy related to updrafts. To conservatively assess the efficiency of flying uphill, the power consumed is determined using the drag generated by the critical stall and the maximum actuator output; this yields the power consumed by glider drag, such as... Figure 7 As shown in (d).

[0107] Except for 15s and 20.4s, the net power is positive for most of the time when the power consumed is slightly greater than the energy gained from the updraft, such as... Figure 7 (a) and Figure 7 As shown in (b). Figure 7 (a) and Figure 7 (b) shows the time histories of specific kinetic energy and specific potential energy for the entire process. It can be seen that the specific kinetic energy relative to airspeed at the initial moment is 97.35 J / kg, decreasing to 76 J / kg, and finally reaching 396.93 J / kg. In summary, the total energy increased by 242.32 J / kg, a total increase of 104.85%, which is very consistent with the calculated result of the formula, with a net value of 256 J / kg. The negligible error may be related to neglecting time-varying winds.

[0108] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of flying a fixed-wing drone to collect mountainous terrain updrafts using monsoon climate, characterized in that, The method comprises the following steps: (1) extracting three-dimensional terrain data of monsoon-impacted slopes in a region of interest from an open geographic information system, setting an incoming wind speed, and then calculating updraft in the region of interest by using a large eddy simulation numerical calculation method; (2) inputting the updraft in the region of interest into a simulation system to interact with a UAV, using the UAV to perform flight simulation in the region of interest, and then calculating energy gain of the UAV during flight according to an energy formula; the expression of the energy formula is: wherein represents the energy gain of the drone, E a represents the energy, m is the mass, g is the gravitational acceleration, D represents the drag, w z represents the updraft, w x represents the incoming wind speed, γ represents the track angle, V a is the speed relative to the wind, the superscript • represents the corresponding derivative; (3) changing the monsoon speed in the simulation system multiple times to perform multiple flight simulations, calculating energy gain of the UAV during each flight, and then obtaining a relationship between the energy gain of the UAV and the monsoon speed, and finding the monsoon speed at which the UAV flies without energy consumption as an optimal monsoon speed by using an interpolation method; (4) constructing a mathematical equation of a UAV dynamics system according to the relationship between the energy gain of the UAV and the monsoon speed; (5) checking the monsoon speed of the region of interest according to a global forecast system provided by a meteorological bureau, and selecting a flight time according to the optimal monsoon speed; and adjusting flight control parameters of the UAV according to the mathematical equation of the UAV dynamics system, so that the UAV can fly stably; (6) performing a flight experiment of the UAV based on the flight time and the flight control parameters determined in step (5), and estimating the updraft and the energy state of the UAV during the experiment.

2. The method of claim 1, wherein the fixed-wing drone utilizes monsoon climate to collect mountainous terrain updrafts. In step (1), when extracting three-dimensional terrain data of monsoon-impacted slopes in a region of interest from an open geographic information system, the elevation data is imported into a mapping software, and the slopes are selected to provide terrain support for the generation of updraft; the elevation data is obtained by extraction from an open geographic information system.

3. The method of claim 1, wherein the fixed-wing drone utilizes monsoon climate to collect mountainous terrain updrafts. In step (1), when calculating the updraft in the region of interest by using a large eddy simulation numerical calculation method, the following steps are further included: Real UAV parameters are used for simulation, then the UAV shape is restored through reverse engineering, the parameters of the UAV are constructed by measuring the parameters, and finally the aerodynamic coefficients and moment coefficients of the UAV are calculated by the vortex lattice method, and the aerodynamic coefficients and moment coefficients of the UAV are used for simulation to obtain the updraft in the region of interest.

4. The method of claim 1, wherein the fixed-wing drone utilizes monsoon climate to collect mountainous terrain updrafts. The expression of the mathematical equation of the UAV dynamics system is: where v a denotes the speed relative to the wind, a denotes the angle of attack, Q denotes the angular acceleration, q denotes the dynamic pressure of the flow, S denotes the wing area, Sc denotes the wing area chord, C T denotes the thrust coefficient, C D denotes the drag coefficient, C L denotes the lift coefficient, C m denotes the moment coefficient, t denotes the time, I yy denotes the moment of inertia.

5. The method of claim 1, wherein the fixed-wing drone utilizes monsoon climate to collect mountainous terrain updrafts. In step (5), the adjustment of the flight control parameters of the UAV specifically includes: The flight control parameters of the UAV are adjusted by using a PID control method, so that the UAV can fly stably; when adjusting the flight control parameters, the maneuverability of the glider is not required to be high, and the core of adjusting the flight control parameters is to ensure that the turbulent wind speed in the high altitude does not affect the normal stable flight of the UAV.

6. The method of claim 1, wherein the fixed-wing drone utilizes monsoon climate to collect mountainous terrain updrafts. In step (6), before performing the flight experiment of the UAV, it is further ensured that the UAV can fly, which is specifically determined by the following method: a handheld airspeed meter is used to measure the wind speed at the top of the slope, and when the wind speed is greater than the optimal monsoon speed value calculated theoretically, it represents that the UAV can fly, and then the flight experiment of the UAV can be performed.

7. The method of claim 1, wherein the fixed-wing drone utilizes monsoon climate to collect mountainous terrain updrafts. In step (6), the estimation of the updraft and the energy state of the UAV is specifically realized by the following method: First, the wind speed of the hilltop is measured by hand-held anemometer; then the wind speed is optimized by extended Kalman filter to obtain the updraft; finally, the energy gain is calculated by energy formula according to the updraft, which is the energy state.

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