Method for verifying feasibility of simulating atmospheric upflow and collecting wind energy by unmanned aerial vehicle

Abstract

The invention discloses a feasibility verification method for simulating atmospheric upflow and collecting wind energy by an unmanned aerial vehicle, and the method comprises the steps: firstly, providing an energy collection framework, firstly obtaining elevation data of a terrain of a region of interest, sorting the elevation data, and drawing a structured grid in pointwise software; secondly, grid information corresponding to the real terrain is imported into an OpenFoam platform, the airflow speed of the whole flow field domain is calculated through a large-eddy simulation numerical calculation method, and a corresponding unsteady wind field is obtained; constructing a mathematical equation of a dynamic system of the fixed-wing unmanned aerial vehicle; meanwhile, through simulation and simulation, the energy collection mechanism is discussed, the potential of collecting the upflow from the hillside can be found, and then a feasible energy supply mode is provided for the unmanned aerial vehicle. The method verifies the feasibility of collecting the upflow from the mountainous region, and is of great significance for improving the adaptive capacity of the unmanned aerial vehicle in the mountainous region environment.

Description

Technical Field

[0001] The present invention relates to the technical field of feasibility verification of unmanned aerial vehicle (UAV) wind energy collection, and in particular to a feasibility verification method for simulating atmospheric updrafts and unmanned aerial vehicle (UAV) wind energy collection. Background Art

[0002] Inspired by the flying skills of intelligent animals in nature, researchers have gradually understood how birds obtain gradient wind fields. In order to utilize shear winds, large birds such as albatrosses have developed energy in various forms of atmospheric flows. They usually use updrafts, sea shear winds and hillsides to fly thousands of kilometers over the ocean without flapping their wings. The "power soaring" strategy. By tracking 16 albatrosses equipped with logging devices on their back feathers, it was verified that the energy acquisition behavior of albatrosses is divided into four stages. Compared with albatrosses, birds living in forests use thermal currents and wave updrafts caused by temperature differences and barriers to forage, respectively. Typical examples are peregrine falcons and frigate birds, which have been shown to successfully use thermal energy for static soaring, thereby reducing energy consumption. Therefore, the effective use of convection caused by heat exchange or natural obstacles will be a breakthrough in the research of long-endurance fixed-wing micro-UAVs (FMUAUs).

[0003] In order to imitate birds flying in cities or mountains with FMUAU, a lot of simulation and experimental research has been carried out. In terms of controlling the UAV to collect energy, an adaptive LQ controller is used to automatically hover around the thermal center to utilize the heat. The coordinated flight mission of multiple gliders has also been studied, and static soaring is used in the absence of thermal clouds. In summary, static soaring has always been one of the topics that researchers are keen to study, and great progress has been made.

[0004] However, current research on energy harvesting has mainly focused on thermal updrafts, while less research has been done on wave updrafts. Although some have successfully captured updrafts on mountains, terrain updrafts generated by wind hitting obstacles are determined by potential flow calculations. However, potential flow theory fails to capture the effects of non-steady vortices in simulations. In addition, in actual flight tests, the uncertainty of wind speed, the estimation error of the aircraft state, and the measurement noise of the sensors hinder the realization of energy harvesting. Therefore, it is obviously crucial to explore the feasibility of using updrafts caused by complex terrain to extend endurance to a certain extent.

[0005] Fixed-wing micro-UAVs soar in geographical environments and play an irreplaceable role in the civil and military fields. In order to complete the mission efficiently and completely, it is crucial to extend the range and endurance. However, their endurance has always been a challenge. Inspired by static and dynamic soaring, this invention aims to explore the feasibility of energy utilization on hillsides. This invention will use the longitudinal dynamics formula of the aircraft, consider the wind field simulation and energy collection process of the interaction between the airflow environment and the glider, and verify the feasibility of UAVs collecting wind energy in a specific area. Summary of the invention

[0006] The purpose of the present invention is to address the deficiencies of the prior art and propose a method for simulating atmospheric updrafts and verifying the feasibility of using drones to collect wind energy. The present invention verifies the feasibility of using drones to collect wind energy in a specific area by simulating the terrain environment.

[0007] The object of the present invention is achieved through the following technical solution: a feasibility verification method for simulating atmospheric updrafts and collecting wind energy by unmanned aerial vehicles, comprising the following steps:

[0008] (1) Extract elevation data of the terrain of the area of ​​interest from the National Geographic Information System, organize it and draw a structured grid in pointwise software;

[0009] (2) importing the grid information in step (1) into the OpenFoam platform, and based on the grid information, using the large eddy simulation numerical calculation method in the OpenFoam platform to calculate the airflow velocity of the entire flow field, and collect the velocity information of the large airflow;

[0010] (3) Construct mathematical equations for the dynamics system of fixed-wing UAVs;

[0011] (4) Based on the velocity information of the atmospheric flow and the mathematical equations of the fixed-wing UAV dynamics system, simulation is performed to verify whether the UAV can collect wind energy by calculating the energy during the simulation process.

[0012] Furthermore, in step (1), the accuracy of the elevation data is 12.5 meters;

[0013] In the step (1), during the process of drawing the structured grid, it can also replace the unstructured grid.

[0014] Furthermore, the use of a large eddy simulation numerical calculation method to calculate the airflow velocity of the entire flow field specifically includes:

[0015] The momentum conservation of viscous fluid is described by filtering the NS equation in the physical space, and the calculation formula is:

[0016]

[0017] In the formula, ρ represents density, x represents physical quantity, p represents pressure, μ represents subgrid turbulent viscosity, τ ij represents the subgrid-scale stress at the i-th row and j-th column, u represents the velocity vector, and the superscript ~ indicates a filtering operation;

[0018] The above calculation formula is modified to calculate the airflow velocity of the entire flow field.

[0019] Furthermore, the construction of the mathematical equation of the fixed-wing UAV dynamics system specifically includes:

[0020] First, refer to the longitudinal two-dimensional dynamics formula to determine that the absolute motion of the fixed-wing drone is equal to the motion of the airflow coordinate system plus the relative motion, expressed as:

[0021]

[0022] In the formula, The vector representing the velocity of the UAV in the inertial coordinate system, and the subscript I represents the inertial coordinate system where the UAV is located; The vector representing the velocity of the UAV relative to the airspeed in the airflow coordinate system, and the subscript a represents the airflow coordinate system in which the UAV is located; A vector representing wind speed;

[0023] Then, taking the derivative with respect to time on both sides of the equation and shifting the terms, we get:

[0024]

[0025] Where ω represents the airflow coordinate system along j a The angular velocity of the axis, × represents the cross product of the vector, v a represents the airspeed in the airflow coordinate system, and the superscript · represents the derivative;

[0026] Reset And using Newton's second law in the airflow coordinate system, we get:

[0027]

[0028] In the formula, T represents thrust, and the thrust of an unpowered glider is T=0; m represents mass, g represents gravitational acceleration, and k a represents the basis vector in the airflow coordinate system, L represents the lift in the direction perpendicular to the airflow, and D represents the drag along the incoming flow direction. Their expressions are:

[0029]

[0030] g=gk i

[0031] In the formula, ρ represents density, S represents wing area, k i represents the vertical basis vector, C L is the lift coefficient, C D represents the drag coefficient;

[0032] Secondly, the wind field in the inertial coordinate system is converted to the airflow coordinate system. The two-dimensional conversion matrix between the two is as follows:

[0033]

[0034] In the formula, S γ represents the two-dimensional transformation matrix, γ represents the track angle;

[0035] Then, the pitch angle θ is defined, and the relationship between the pitch angle θ and the track angle γ is obtained according to the two-dimensional transformation matrix, which is expressed as θ = α-γ, where α represents the angle of attack, and the pitch angle rate is set Substituting it into Newton's second law equation, the two-dimensional longitudinal dynamic equation under the action of wind field is derived, which is expressed as:

[0036]

[0037] In the formula, q represents the dynamic pressure, C T represents the thrust coefficient, w x Indicates the incoming wind speed, w z represents the updraft, Q represents the angular acceleration, Sc represents the chord length of the wing area, and C m Indicates the moment coefficient, I yy Represents the moment of inertia.

[0038] Furthermore, in step (4), the total energy of the drone is defined as:

[0039]

[0040] In the formula, E a represents the total energy of the drone in the airflow coordinate system, subscript a represents the airflow coordinate system, h represents the height, V a represents the airspeed; based on the total energy acquisition rate of the drone relative to the rate of change of energy over time, its expression is:

[0041]

[0042] In the formula, Represents the rate of change of energy; based on the rate of change of specific energy over time and the mathematical equation of the fixed-wing UAV dynamics system, we can get:

[0043]

[0044] In the above formula, when the calculated , it means that the drone can collect wind energy.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] (1) In numerical simulation, the present invention proposes an energy collection framework to simulate the updraft caused by the complex terrain used by the UAV, and uses the hillside elevation data with a resolution of 12.5 m as the physical information of the simulated flight test site.

[0047] (2) The present invention uses large eddy simulation to calculate the turbulent wind field around the hillside so as to perform a more realistic simulation under specific terrain conditions. Practice has shown that a glider can utilize the updraft on the hillside to increase the total energy without the support of a power system and fuel consumption.

[0048] (3) The present invention experimentally discovered that the main sources of energy variation include vertical direction, wind speed and drag; and that the effect of time-varying wind on energy is limited compared to updraft and drag; the results show that they cancel each other out even though they are characterized by uncertainty due to the randomness of turbulent pulsation.

[0049] (4) In the simulation, the method described in the present invention obtains three-dimensional real terrain from an open data set, imports a two-dimensional terrain map into the OpenFOAM platform, and uses a large eddy simulation method to calculate the unsteady wind field around the terrain; then the UAV motion equation is used to simulate the glider motion; and by analyzing the collected thermal updrafts, an overall energy collection framework is obtained to develop new ways to utilize energy on the hillside. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the three wind forms currently used for wind energy;

[0051] Figure 2 This is a schematic diagram of constructing the UAV motion equation; it involves the airflow and fuselage coordinate systems. The NED coordinate system is shown on the left, and all directions shown are positive;

[0052] Figure 3 This is a diagram of the energy harvesting framework in simulation;

[0053] Figure 4 The DEM of the mountain range is composed of ridges and valleys with a resolution of 12.5m. The yellow ellipse is the area of ​​interest for slope flight. The contour map is shown in Figure 4 The lower right corner of the figure shows the yellow straight line, which is the top view of the extracted terrain profile.

[0054] Figure 5 It is the velocity time history diagram of four characteristic points under different grids;

[0055] Figure 6 is the sampling spectrum and power spectrum;

[0056] Figure 7 It is the pressure field cloud map around the hillside under exponential wind conditions;

[0057] Figure 8 It is the airflow distribution at the point where the maximum updraft value is 7.27 meters in front of the mountain;

[0058] Fig. 9 is a time history diagram of the flight parameters of an unpowered glider in the process of acquiring energy in the windward slope area; Fig. 9 (a) is the time course of airspeed, Fig. 9 (b) is the time history diagram of the track angle, Fig. 9 (c) is the time history diagram of vertical airflow. Fig. 9 (d) is the time course diagram of height;

[0059] Fig.10 It is a composition diagram of specific power and harvested energy during unpowered flight of the longitudinal wind profile; among them, Fig.10 (a) is the change diagram of resistance specific work, Fig.10 (b) is the change diagram of vertical time-varying wind specific work, Fig.10 (c) is the change diagram of horizontal time-varying wind specific work, Fig.10 (d) is the variation diagram of the specific work of the rising airflow. Fig.10 (e) is the change diagram of total energy. DETAILED DESCRIPTION

[0060] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers 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 the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims. It should be understood that the above general description and the detailed description that follows are merely exemplary and explanatory and cannot limit the present application.

[0061] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0062] It should be understood that, although the term first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of..." or "when..." or "in response to determination". Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process and method including a series of elements include not only those elements, but also include other elements not clearly listed, or also include elements inherent to such process and method. In the absence of more restrictions, the elements limited by the statement "including one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements.

[0063] The present invention is described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the features of the following embodiments and implementations can be combined with each other.

[0064] Embodiment 1:

[0065] The feasibility verification method of simulating atmospheric updrafts and collecting wind energy by unmanned aerial vehicles of the present invention specifically comprises the following steps:

[0066] (1) The elevation data of the terrain of the area of ​​interest were extracted from the national geographic information system (GIS), sorted and drawn into a structured grid using pointwise software.

[0067] Furthermore, the accuracy of the elevation data is 12.5 meters, and different geographical locations can be appropriately selected as the region of interest according to the calculation requirements. In the process of drawing the structured grid, it can be replaced with an unstructured grid due to its complexity.

[0068] (2) Importing the grid information in step (1) into OpenFoam, and based on the grid information, using the large eddy simulation (LES) numerical calculation method in OpenFoam to calculate the airflow velocity of the entire flow field, and collect the velocity information of the atmospheric airflow.

[0069] It should be understood that OpenFoam is a large open source computing program, through which airflow velocity can be calculated. The LES numerical calculation method is used to calculate the unsteady wind field. There is a lot of information in the wind field, part of which is velocity information. In this embodiment, only the LES numerical calculation method is used to calculate the airflow velocity to describe the unsteady wind field.

[0070] Furthermore, the LES numerical calculation method is used to calculate the airflow velocity of the entire flow field, specifically including: describing the momentum conservation of viscous fluid by filtering the NS equation in the physical space, and the calculation formula is:

[0071]

[0072] In the formula, ρ represents density, x represents physical quantity, p represents pressure, μ represents subgrid turbulent viscosity, τ ij represents the sub-grid-scale stress at the i-th row and j-th column, u represents the velocity vector, and the superscript ~ represents the filtering operation. Based on the above calculation formula, the airflow velocity of the entire flow field is calculated by deformation.

[0073] (3) Construct the mathematical equations of the fixed-wing UAV dynamics system.

[0074] Specifically, first refer to the longitudinal two-dimensional dynamics formula to determine that the absolute motion of the fixed-wing UAV is equal to the motion of the airflow coordinate system plus the relative motion, expressed as:

[0075]

[0076] In the formula, The vector representing the velocity of the UAV in the inertial coordinate system, and the subscript I represents the inertial coordinate system where the UAV is located; The vector representing the velocity of the UAV relative to the airspeed in the airflow coordinate system, and the subscript a represents the airflow coordinate system in which the UAV is located; A vector representing wind speed.

[0077] Then, taking the derivative with respect to time on both sides of the equation and shifting the terms, we get:

[0078]

[0079] Where ω represents the airflow coordinate system along j a The angular velocity of the axis, × represents the cross product of the vector, v a represents the airspeed in the airflow coordinate system, and the superscript · represents the derivative.

[0080] Reset And using Newton's second law in the airflow coordinate system, we get:

[0081]

[0082] In the formula, T represents thrust. Since we consider an unpowered glider, thrust T = 0; m represents mass, g represents gravitational acceleration, and k a represents the basis vector in the airflow coordinate system, L represents the lift in the direction perpendicular to the airflow, and D represents the drag along the incoming flow direction. Their expressions are:

[0083]

[0084] g=gk i

[0085] In the formula, ρ represents density, S represents wing area, k i represents the vertical basis vector, C L is the lift coefficient, C D Represents the drag coefficient.

[0086] Secondly, since the wind field is defined in the inertial coordinate system, it is necessary to convert the wind field in the inertial coordinate system to the airflow coordinate system. The two-dimensional conversion matrix of the two is as follows:

[0087]

[0088] In the formula, S γ represents the two-dimensional transformation matrix, and γ represents the track angle.

[0089] Then, the pitch angle θ is defined, and the relationship between the pitch angle θ and the track angle γ is obtained according to the two-dimensional transformation matrix, which is expressed as θ = α-γ, where α represents the angle of attack, and the pitch angle rate is set Substituting it into Newton's second law equation, the two-dimensional longitudinal dynamic equation under the action of wind field is derived, which is expressed as:

[0090]

[0091] In the formula, q represents the dynamic pressure, C T represents the thrust coefficient, w x Indicates the incoming wind speed, w z represents the updraft, Q represents the angular acceleration, Sc represents the chord length of the wing area, and C m Indicates the moment coefficient, I yy represents the moment of inertia. The lift coefficient C L and the drag coefficient C D Unsteady aerodynamic forces are not considered.

[0092] (4) Based on the velocity information of the atmospheric flow and the mathematical equations of the fixed-wing UAV dynamics system, simulation is performed to verify whether the UAV can collect wind energy by calculating the energy during the simulation process.

[0093] In general, the total energy of a drone is defined as the sum of its potential energy and kinetic energy, where kinetic energy usually refers to the kinetic energy in an inertial coordinate system, which is defined by the following formula:

[0094]

[0095] In the formula, E irepresents the energy of the drone, h represents the altitude, and x and z represent the vertical plane position coordinates.

[0096] The kinetic energy in the inertial coordinate system is very convenient for describing the aircraft in the stationary coordinate system. Bonnin et al. also attributed the mechanism of energy collection in dynamic soaring to the 'belly to the wind (breeze)' rule based on the total energy in the inertial coordinate system. However, due to the complex wind field environment in the environment where the aircraft is located, it is more convenient to analyze and explain the total energy of the aircraft in the airflow coordinate system. Although the kinetic energy of the airflow coordinate system from the perspective of the inertial coordinate system has no intuitive physical meaning, Liu explained through trajectory optimization that energy can be collected from the wind field during the dynamic soaring cycle in both coordinate systems. Therefore, in order to facilitate analysis, the following formula is used to define the total energy of the drone:

[0097]

[0098] In the formula, E a represents the total energy of the drone in the airflow coordinate system, subscript a represents the airflow coordinate system, h represents the height, V a Represents airspeed. Based on the total energy acquisition of the drone, the rate of change of specific energy over time is expressed as:

[0099]

[0100] In the formula, Represents the rate of change of energy. Based on the rate of change of specific energy over time and the mathematical equation of the fixed-wing UAV dynamics system, we can get:

[0101]

[0102] The first term on the right side of the equation represents the rate of energy change caused by the gravity field and the vertical wind field. z <0, then Indicates that the rate of change of the drone's energy is positive, that is, the drone can collect wind energy. In real flight applications, the drone is lifted by the vertical upward airflow, and the total energy of the drone increases. The first term in the brackets on the right side of the equal sign in the above formula indicates that the drone is subject to air resistance, causing the total energy of the drone to decrease. Define the specific energy consumption rate P c =DV a / m, P represents the drag power of the air on the drone. The rate of energy reduction is proportional to the specific energy dissipation power P c ,When there is no wind field in the flying environment of the UAV, the resistance of the UAV can be estimated based on the rate of energy change.

[0103] Embodiment 2:

[0104] The feasibility verification method of simulating atmospheric updrafts and collecting wind energy by unmanned aerial vehicles of the present invention specifically comprises the following steps:

[0105] S1. Obtain the three-dimensional real terrain from the open source national geographic information system, and further extract the two-dimensional real terrain map of the terrain of the area of ​​interest, that is, the corresponding elevation data, the accuracy of which is selected according to the best accuracy that can be obtained, and the accuracy in this embodiment is 12.5 meters.

[0106] S2. Select a suitable wind model. In this embodiment, the wind index model is used. Where V W Indicates the horizontal wind speed, which is determined by the height h; V R Indicates the reference height H R The wind speed determines the gradient within the wind profile and the size of the average wind speed. p is the power law exponent, and its value is affected by the surface characteristics.

[0107] S3. Import the elevation data into the pointwise software to draw the grid. The method used in this embodiment is a structured grid with a high calculation speed. Among them, the time term discretization adopts the implicit Euler format, the gradient term discretization adopts the Gaussian linear format, the velocity diffusion term discretization adopts the unit-limited second-order Gaussian linear upwind format, and the Laplace term discretization adopts the modified Gaussian linear format. In addition, in this embodiment, the PISO (Pressure-implicit with Splitting operator) algorithm is used to deal with the pressure-velocity coupling problem.

[0108] S4. Numerical simulation of the unsteady wind field on the terrain is performed. During the simulation process, the time step is 0.001s. The LES numerical calculation method is embedded in OpenFoam to capture the large-scale effects and coherent structures that appear in non-steady-state and non-equilibrium processes. Based on the measurement information of the aircraft on the day, the wind field at the calculation entrance is set as a logarithmic wind field. The terrain in this case consists of two inclined ridges with the highest absolute altitude of 712 meters, separated by a valley, and the windward angle of the hillside is 27.4°, which provides good conditions for the generation of terrain updrafts. In addition, the real two-dimensional terrain extracted from the three-dimensional digital elevation model (DEM) will be used as a physical environment model.

[0109] S5. Set the simulation domain according to the following parameters: the distances from the center of the flight area to the upper, front air intakes and rear exits are 1.7 km, 1.8 km and 2.2 km respectively; the inlet velocity is set to an exponential wind model, which is 12.10 m / s at an altitude of 712 m according to the measurement value of the wind indicator placed on the mountain; the air density is 1.225 kg / m 3, the kinematic viscosity coefficient is 1.84kg / (ms). Due to the obstruction of the hillside terrain, the airflow slows down on the windward slope, forming a high-pressure area and a velocity component that moves upward and to the right. On the contrary, on the leeward side, there is a phenomenon of staggered updrafts and downdrafts, resulting in a large number of vortices, and the flight consumes a huge amount of energy.

[0110] S6. Compare the sampled spectrum in the simulation process with the spectrum of Von Karman and calculate the error. It can be found that the error is more obvious in the high frequency band, but not so obvious in the low and medium frequency bands, and it is generally consistent with the standard spectrum.

[0111] S7. Verify grid independence. Select the vertical velocity curve of the first four feature points on the hillside over time. After 200 seconds of simulation, the velocity of the feature points begins to converge and no longer changes significantly. Determine whether the drawn grid is appropriate based on whether different grid numbers change the velocity value of the feature points.

[0112] S8. Use three-dimensional scanning to obtain the shape parameters of the glider, simulate the energy collection process of the glider in the actual experiment, and use the reverse engineering method to reconstruct the geometric model of the glider.

[0113] S9. The aerodynamic parameters are calculated using the vortex grid method, and the aerodynamic shape is modeled using XFLR5 (X5) software. After calculating the aerodynamic parameters, the corresponding aerodynamic coefficients are extracted, and the moment of inertia is estimated using the suspension method.

[0114] S10, analyzing the motion trajectory information of the drone, and determining whether the drone is in a balanced state and whether the drone can fly stably according to the pitch angle and roll angle of the drone.

[0115] S11. After the simulation is completed, the energy of the UAV is analyzed. According to the simulation experimental results provided, the rate of energy is affected by four different components (i.e., drag specific work, vertical time-varying wind specific work, horizontal time-varying wind specific work, and updraft specific work). Whether the selected area can support energy-free flight is analyzed from three perspectives. ① Specific power: This is the instantaneous consumption of the sliding surface caused by resistance. ② Energy changes caused by vertical and horizontal time-varying wind fields: These parts can be ignored relative to updrafts and resistance. ③ Power generated by updrafts: The power change law generated by updrafts is similar to that of updrafts. Throughout the process, the energy gain is much greater than the power consumed. In this embodiment, the energy continues to increase during the unpowered flight, which provides important support for the sustainable use of energy of FMUAV in mountainous environments.

[0116] Embodiment 3:

[0117] This embodiment provides a feasibility verification method for simulating atmospheric updrafts and collecting wind energy by drones. Figure 1 As shown, drones typically take advantage of updrafts, sea shear winds, and hillside gradient wind fields.

[0118] like Figure 2 As shown in the figure, the mathematical equation of the fixed-wing UAV dynamics system is constructed. Based on the mathematical equation of the UAV motion, the process of UAV energy collection can be analyzed, where V i is the wind speed vector in the inertial coordinate system.

[0119] Unlike the stable atmospheric flow in the stratosphere, commercial aircraft cruise to ensure a stable and comfortable passenger experience, while small fixed drones usually fly in the atmospheric boundary layer. Among them, atmospheric turbulence is turbulence that occurs in the atmosphere and is studied through random process theory. The basic characteristic of turbulent motion is the irregularity of the velocity field along space and time distribution. Turbulence appears in the wind speed profile as continuous random pulsations superimposed on the average wind. Complex landforms such as land, ocean or desert are subject to uneven sunlight exposure (due to complex cloud structures) to form rising or horizontal airflow. Horizontal airflow forms a shear layer due to the frictional resistance of the ground or waves, or the influence of mountains. Another wind field that approximates the sea surface, ground or ridge is the exponential wind field, where an exponential function is used to represent the wind field. Where V W Indicates the horizontal wind speed, which is determined by the height h; V R Indicates the reference height H R The wind speed determines the gradient and average wind speed in the wind profile; the value of p is affected by the surface characteristics. Considering that the simulated environment is in a valley, most of its surface is covered by jungle, and p is set to 0.24 in areas with many trees.

[0120] Energy harvesting frameworks such as Figure 3 As shown in Figure 1, part A is the geographical environment modeling, that is, obtaining the three-dimensional real terrain from the open data set, extracting the two-dimensional real terrain map, and obtaining the corresponding grid information; part B numerically simulates the unsteady wind field on the terrain. Specifically, in order to simulate the energy collection process of the glider in the actual experiment, the shape parameters of the glider are obtained by three-dimensional scanning, and its geometric model is reconstructed by reverse engineering method. For simplicity, the winglets are not considered in the simulation and flight experiments, and the configurations are listed in Table 1. At the same time, the aerodynamic parameters are calculated by the vortex grid method, and the moment of inertia is estimated by the suspension method. In the flight simulation, since the glider flies in the updraft, the instantaneous aerodynamic coefficients will be updated according to the instantaneous flow conditions, which are determined by the position of the glider and its unsteadiness.

[0121] Table 1: Glider parameters

[0122] describe value The mass of the glider 1.435 kg Wingspan 2.45 m Root\26% of span\tip chord 0.17\0.13\0.08m body 0.1025m Tail rudder area 0.38 m2 Vertical tail area 0.033 m2 Vertical tail height 0.2m

[0123] The parameters of the glider are shown in Table 1. The wing mass of the glider is 1.435 kg, the wingspan is 2.45 meters, the wingspan root is 0.26 times the wingspan, the wingtip chord lengths are 0.17 meters, 0.13 meters and 0.08 meters respectively, the fuselage length is 0.1025 meters, the horizontal tail rudder area is 0.38 square meters, the vertical tail area is 0.033 square meters, and the height of the vertical tail is 0.2 meters.

[0124] Such a glider configuration is designed to be suitable for specific application scenarios. It is necessary to consider factors such as the weight balance of the fuselage and wings, and the stability of the vertical tail. These parameters will have an important impact on the flight performance and stability of the glider, so it is selected as the simulation object.

[0125] In the physical environment, the flow field modeling is carried out using the elevation data obtained from the National Geographic Information System (GIS). Among them, the 12.5m resolution mountain digital elevation model consisting of ridges and valleys is as follows: Figure 4 As shown, the yellow area is the area of ​​interest for simulation. The contour map is shown in Figure 4 As shown in the lower right corner of the figure, the contour map consists of two sloping ridges with the highest absolute elevation of 712 meters, separated by a valley.

[0126] Table 2: Three different grid information

[0127] Name No.cells Total Points Mesh1(M1) 327879 492574 Mesh2(M2) 528437 696956 Mesh3(M3) 727155 980070

[0128] To verify the feasibility of the current simulation settings, a grid-independent verification is performed. Specifically, three different grids are compared as shown in Table 2. The velocity time history of the four characteristic points in front of the mountain is shown in Figure 5 As shown. Figure 5 It can be seen that under the same flow pattern, these curves are basically consistent, meeting the numerical accuracy requirements of the collected energy. M2 represents the best trade-off between numerical accuracy and computational speed, so this grid will be used in the following study.

[0129] The sampled spectrum is compared with Figure 6 It is worth mentioning that the error is more obvious in the high frequency band, but not so obvious in the low and middle frequency bands, which are generally consistent with the standard spectrum.

[0130] For the simulation results, the calculated flow field pressure cloud diagram is as follows Figure 7 As shown in Figure 1. Due to the obstruction of the hillside terrain, the airflow slows down on the windward slope, forming a high-pressure area and a velocity component that is upward and to the right. On the contrary, on the leeward side, there is a phenomenon of staggered updrafts and downdrafts, resulting in a large number of vortices, and the flight consumes a lot of energy. The maximum vertical speed on the windward side of the terrain is about 7.27m / s, as shown in Figure 1. Figure 8As shown. The speed of thermal updrafts is usually in the range of 5-10m / s, which can extend its flight time to a greater extent. Therefore, the unpowered glider will definitely gain energy in the current updraft field.

[0131] In the flight simulation, the glider initially takes off from the ridge top in a balanced state. The flight lasts a total of 40 seconds until the updraft decreases significantly. Fig. 9 (a) is the airspeed time course. The airspeed is kept constant at about 10.2m / s during the entire soaring process. Due to the influence of the updraft and turbulence on the slope, the track angle is Fig. 9 In (b), there are slight fluctuations until 5 s, but the controller stabilizes the pitch back to the horizontal position. Fig. 9 (c) is the vertical airflow experienced by the glider. The negative sign indicates that the air is flowing upward from the ground. Fig. 9 (c) It can be seen that due to the influence of horizontal time-varying wind, the airspeed and track angle change slightly (22.5s-30s); as the glider rises and gradually moves away from the slope, the vertical airflow becomes weaker and decreases. When there is no wind and the track angle is negative, the glider continues to sink. Once the glider is lifted by the updraft, it can climb even with a negative track angle. As time goes by, the height of the glider gradually increases, such as Fig. 9 As shown in (d), at the same time, the distance between the glider and the slope peak is getting farther and farther, so the updraft felt by the glider is reduced. Fig. 9 In (c), after 17.5 seconds, the updraft is less than 2 m / s, so the glider's climbing speed slows down, and after 35 seconds, the glider returns to horizontal flight.

[0132] like Fig.10 As shown, the rate of energy is affected by four different components. Fig.10 The conditions of these components during unpowered flight are given. The instantaneous consumption of the sliding surface, that is, the specific power Pp, is as follows Fig.10 As shown in (a), this is caused by drag, where the green dashed line represents the inevitable average drag during the ascent, with a specific power of -10.73 J / kg / s and a total work done of -429.05 J / kg. Fig.10 (b) and Fig.10 The energy variations of the vertical and horizontal time-varying wind fields shown in (c) are also instructive. It is clear that these components are negligible compared to the drag of the updraft and the average specific power of 0.03 J / kg / s and 0.05 J / kg / s, respectively. The power Pup generated by the updraft is Fig.10 (d) is shown. It is worth mentioning that its variation pattern is similar to that of the updraft. In addition, it can be seen that the energy gain is much greater than the power consumed, so the energy increases continuously throughout the process. The result is as follows Fig.10As shown in (e), the net energy gain is about 397.63 J / kg.

[0133] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A feasibility verification method for simulating atmospheric updrafts and collecting wind energy by drones, characterized in that: The following steps are involved: (1) Extract elevation data of the terrain of the area of ​​interest from the National Geographic Information System, organize it and draw a structured grid in pointwise software; (2) importing the grid information in step (1) into the OpenFoam platform, and based on the grid information, using the large eddy simulation numerical calculation method in the OpenFoam platform to calculate the airflow velocity of the entire flow field, and collect the velocity information of the large airflow; (3) Construct mathematical equations for the fixed-wing UAV dynamics system; (4) Based on the velocity information of the atmospheric flow and the mathematical equations of the fixed-wing UAV dynamics system, simulation is performed to verify whether the UAV can collect wind energy by calculating the energy during the simulation process.

2. The feasibility verification method for simulating atmospheric updrafts and collecting wind energy by unmanned aerial vehicles according to claim 1 is characterized in that: In the step (1), the accuracy of the elevation data is 12.5 meters; In the step (1), during the process of drawing the structured grid, it can also replace the unstructured grid.

3. The feasibility verification method for simulating atmospheric updrafts and collecting wind energy by unmanned aerial vehicles according to claim 1 is characterized in that: The large eddy simulation numerical calculation method is used to calculate the airflow velocity of the entire flow field, specifically including: The momentum conservation of viscous fluid is described by filtering the NS equation in the physical space. The calculation formula is: In the formula, ρ represents density, x represents physical quantity, p represents pressure, μ represents subgrid turbulent viscosity, τ ij represents the subgrid-scale stress at the i-th row and j-th column, u represents the velocity vector, and the superscript ~ indicates a filtering operation; The above calculation formula is modified to calculate the airflow velocity of the entire flow field.

4. The feasibility verification method for simulating atmospheric updrafts and collecting wind energy by unmanned aerial vehicles according to claim 1 is characterized in that: The construction of the mathematical equation of the fixed-wing UAV dynamics system specifically includes: First, refer to the longitudinal two-dimensional dynamics formula to determine that the absolute motion of the fixed-wing drone is equal to the motion of the airflow coordinate system plus the relative motion, expressed as: In the formula, The vector representing the velocity of the UAV in the inertial coordinate system, and the subscript I represents the inertial coordinate system where the UAV is located; The vector representing the velocity of the UAV relative to the airspeed in the airflow coordinate system, and the subscript a represents the airflow coordinate system in which the UAV is located; A vector representing wind speed; Then, taking the derivative with respect to time on both sides of the equation and shifting the terms, we get: Where ω represents the airflow coordinate system along j a The angular velocity of the axis, × represents the cross product of the vector, v a represents the airspeed in the airflow coordinate system, and the superscript · represents the derivative; Reset And using Newton's second law in the airflow coordinate system, we get: In the formula, T represents thrust, and the thrust of an unpowered glider is T=0; m represents mass, g represents gravitational acceleration, and k a represents the basis vector in the airflow coordinate system, L represents the lift in the direction perpendicular to the airflow, and D represents the drag along the incoming flow direction. Their expressions are: g=gk i In the formula, ρ represents density, S represents wing area, k i represents the vertical basis vector, C L is the lift coefficient, C D represents the drag coefficient; Secondly, the wind field in the inertial coordinate system is converted to the airflow coordinate system. The two-dimensional conversion matrix between the two is as follows: In the formula, S γ represents the two-dimensional transformation matrix, γ represents the track angle; Then, the pitch angle θ is defined, and the relationship between the pitch angle θ and the track angle γ is obtained according to the two-dimensional transformation matrix, which is expressed as θ = α-γ, where α represents the angle of attack, and the pitch angle rate is set Substituting it into Newton's second law equation, the two-dimensional longitudinal dynamic equation under the action of wind field is derived, which is expressed as: In the formula, q represents the dynamic pressure, C T represents the thrust coefficient, w x Indicates the incoming wind speed, w z represents the updraft, Q represents the angular acceleration, Sc represents the chord length of the wing area, and C m Indicates the moment coefficient, I yy Represents the moment of inertia.

5. The feasibility verification method for simulating atmospheric updrafts and collecting wind energy by unmanned aerial vehicles according to claim 1 is characterized in that: In step (4), the total energy of the drone is defined as: In the formula, E a represents the total energy of the drone in the airflow coordinate system, subscript a represents the airflow coordinate system, h represents the height, V a represents the airspeed; based on the total energy acquisition rate of the drone relative to the energy change rate over time, its expression is: In the formula, Represents the rate of change of energy; based on the rate of change of specific energy over time and the mathematical equation of the fixed-wing UAV dynamics system, we can get: In the above formula, when the calculated , it means that the drone can collect wind energy.

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