Method for feasibility verification of simulated atmospheric updrafts and unmanned aircraft collecting wind energy

Abstract

The application discloses a kind of simulation atmospheric updraft and the feasibility verification method of unmanned aerial vehicle collection wind energy, this method first proposes an energy collection framework, first obtain the elevation data of the terrain of the region of interest, it is arranged and is drawn structured grid in pointwise software;Then import the grid information corresponding to real terrain in OpenFoam platform, the airflow velocity of whole flow field is calculated using large eddy simulation numerical calculation method, obtain corresponding unsteady wind field;Again construct fixed-wing unmanned aerial vehicle dynamics system mathematical equation;Meanwhile through simulation and simulation, the energy collection mechanism is discussed, from which the potential of collecting updraft from hillside can be found, and thus a feasible energy supply mode is provided for unmanned aerial vehicle.The application verifies the feasibility of collecting updraft from mountainous area, which is of great significance for improving the adaptability of unmanned aerial vehicle in mountainous environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of feasibility verification of unmanned aerial vehicle collecting wind energy, and particularly relates to a method for simulating atmospheric updraft and feasibility verification of unmanned aerial vehicle collecting wind energy. BACKGROUND

[0002] Inspired by the flight skills of intelligent animals in nature, researchers have gradually understood how birds obtain gradient wind fields. In order to utilize shear wind, large birds such as albatrosses have developed energy in various forms of atmospheric flow. They usually use updrafts, sea shear winds and mountain slopes to fly thousands of kilometers above the sea in the "powered soaring" strategy without flapping their wings. By tracking 16 albatrosses equipped with a wood device on the back feathers, it is verified that the energy acquisition behavior of albatrosses is divided into four stages. Compared with albatrosses, birds living in forests use thermal flow and wave updrafts caused by temperature difference and barriers to forage. Typical examples are goshawks and frigate birds, which are proven to be able to successfully use thermal energy for static soaring, thereby reducing energy consumption. Therefore, effective utilization of convection caused by heat exchange or natural obstacles will be a breakthrough in the research of long-haul fixed-wing micro unmanned aerial vehicles (FMUAU).

[0003] In order to imitate birds soaring with FMUAU in cities or mountainous areas, a large number of simulation and experimental researches have been carried out. In terms of controlling the energy collection of unmanned aerial vehicles, an adaptive LQ controller is used to automatically wander around the thermal center to utilize heat. The cooperative flight task of multiple gliders is also studied, and static soaring is adopted in the absence of thermal airflow 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 collection mainly focuses on thermal updrafts, and there is less research on wave updrafts. Although someone has successfully captured the updraft on the mountain, the topographic updraft generated by the wind hitting the obstacle is determined by potential flow calculation. However, the potential flow theory fails to capture the influence of non-constant vortices in simulation. In addition, in actual flight tests, the uncertainty of wind speed, the estimation error of aircraft state and the measurement noise of sensors hinder the realization of energy collection. Therefore, it is obviously crucial to explore the feasibility of extending the endurance by utilizing updrafts caused by complex terrain to some extent.

[0005] Fixed-wing micro unmanned aerial vehicles soar in the geographical environment, and play an irreplaceable role in civil and military fields. In order to efficiently and completely complete the task, it is essential to expand the range and endurance. However, their endurance has always been a challenge. Inspired by static and dynamic soaring, the present invention aims to explore the feasibility of energy utilization on the hillside. The present invention will utilize the longitudinal dynamics formula of the aircraft, consider the wind field simulation and energy collection process of the interaction of the airflow environment with the glider, and verify the feasibility of the unmanned aerial vehicle collecting wind energy in a specific area. SUMMARY

[0006] The present invention aims to overcome the shortcomings of the prior art and proposes a method for verifying the feasibility of simulating atmospheric updraft and unmanned aerial vehicle wind energy collection. The present invention verifies the feasibility of unmanned aerial vehicle wind energy collection in a specific area by simulating the terrain environment.

[0007] The purpose of the present invention is achieved by the following technical scheme: a method for verifying the feasibility of simulating atmospheric updraft and unmanned aerial vehicle wind energy collection, comprising the following steps:

[0008] (1) Extract the elevation data of the terrain of the region of interest from the national geographic information system, organize it and draw a structured grid in the pointwise software;

[0009] (2) Import the grid information in step (1) into the OpenFoam platform, based on the grid information, use 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 atmospheric flow;

[0010] (3) Construct the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system;

[0011] (4) Based on the velocity information of the atmospheric flow and the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system, perform simulation, and verify whether the unmanned aerial vehicle can collect wind energy by calculating the energy during the simulation process.

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

[0013] In step (1), during the process of drawing a structured grid, it can also be replaced by an unstructured grid.

[0014] Further, the calculation of the airflow velocity of the entire flow field using the large eddy simulation numerical calculation method specifically includes:

[0015] The N-S equation in the physical space is filtered to describe the momentum conservation of viscous fluid, and the calculation formula is:

[0016]

[0017] where, p represents density, x represents physical quantity, p represents pressure, μ represents sub-grid turbulence viscosity, τ ij represents the sub-grid scale stress of the i-th row and the j-th column, u represents velocity vector, and the superscript ~ represents filtering operation;

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

[0019] Further, the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system comprises the following steps:

[0020] Firstly, the absolute motion of the fixed-wing unmanned aerial vehicle is determined by referring to the longitudinal two-dimensional dynamics formula, which is equal to the airflow coordinate system motion plus the relative motion, and is expressed as:

[0021]

[0022] where, represents the vector of the speed of the unmanned aerial vehicle in the inertial coordinate system, and the subscript I represents the inertial coordinate system in which the unmanned aerial vehicle is located; represents the vector of the speed of the unmanned aerial vehicle in the airflow coordinate system relative to the airspeed, and the subscript a represents the airflow coordinate system in which the unmanned aerial vehicle is located; represents the vector of the wind speed;

[0023] Then, the time derivative of both sides of the equation is obtained by moving the term, and is expressed as:

[0024]

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

[0026] Further, the following equation is set and the Newton's second law is used in the airflow coordinate system to obtain:

[0027]

[0028] where, T represents the thrust, the thrust of the unpowered glider T=0; m represents the mass, g represents the gravitational acceleration, k a represents the base vector in the airflow coordinate system, L represents the lift perpendicular to the airflow direction, and D represents the drag along the airflow direction, and their expressions are as follows:

[0029]

[0030] g=gk i

[0031] where ρ represents the density, S represents the wing area, k i represents the vertical base vector, C L represents 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, and the two-dimensional conversion matrix of the two is as follows:

[0033]

[0034] where S γ represents the two-dimensional conversion matrix, and γ represents the track angle;

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

[0036]

[0037] where q represents the dynamic pressure, C T represents the thrust coefficient, w x represents the incoming airflow speed, w z represents the ascending airflow, Q represents the angular acceleration, Sc represents the wing area chord length, C m represents the moment coefficient, I yy represents the moment of inertia.

[0038] Further, in the step (4), the total energy of the unmanned aerial vehicle is defined as:

[0039]

[0040] where E a represents the total energy of the unmanned aerial vehicle in the airflow coordinate system, the subscript a represents the airflow coordinate system, h represents the height, V a represents the airspeed; based on the total energy of the unmanned aerial vehicle, the rate of change of specific energy with time is obtained, and its expression is:

[0041]

[0042] where, represents the energy change rate; based on the rate of change of specific energy with time and the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system, the following is obtained:

[0043]

[0044] In the above formula, when the calculated At this time, it indicates that the unmanned aerial vehicle can collect wind energy.

[0045] Compared with the prior art, the present application has the beneficial effects that:

[0046] (1) In numerical simulation, the present application proposes an energy collection framework to simulate the rising air current caused by complex terrain for unmanned aerial vehicles, and uses 12.5m resolution hillside elevation data as physical information for simulating flight test sites.

[0047] (2) The present application uses large eddy simulation to calculate the turbulent wind field around the hillside environment in order to make more realistic simulation under specific terrain conditions. It has been proved in practice that gliders can use the hillside rising air current to improve the total energy without the support of the power system and fuel consumption.

[0048] (3) The present application finds through experiments that the main sources of energy change include vertical direction, wind speed and resistance; compared with the rising air current and resistance, the time-varying wind has limited effect on energy; the results show that they offset each other, even though their characteristics are uncertainty due to the randomness of turbulent fluctuations.

[0049] (4) In the simulation, the method of the present application obtains three-dimensional real terrain from an open data set, imports a two-dimensional topographic map in the OpenFOAM platform, and calculates the unsteady wind field around the terrain by using the large eddy simulation method; then the motion of the glider is simulated by using the motion equation of the unmanned aerial vehicle; through the analysis of the collected thermal rising air current, the overall energy collection framework is obtained to develop new ways to use the energy on the hillside. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is a schematic diagram of three wind forms of current wind energy utilization;

[0051] Figure 2 is a schematic diagram of constructing the motion equation of the unmanned aerial vehicle; the airflow and the fuselage coordinate system are involved, and the NED coordinate system is shown on the left side, and all the directions are positive;

[0052] Figure 3 is an energy collection framework diagram in simulation;

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

[0054] Figure 5 is a velocity time history diagram of four feature points under different grids;

[0055] Figure 6 is a sample spectrum and power spectrum plot;

[0056] Figure 7 is a pressure field cloud plot around a hill under exponential wind conditions;

[0057] Figure 8 is a streamer distribution at a maximum updraft value of 7.27 meters in front of a hill;

[0058] Figure 9 is a time history plot of flight parameters during energy acquisition on a windward slope for a sailplane; wherein, Figure 9 (a) is a time history plot of airspeed, Figure 9 (b) is a time history plot of track angle, Figure 9 (c) is a time history plot of vertical air current, Figure 9 (d) is a time history plot of altitude;

[0059] Figure 10 is a plot of specific power and energy harvest components for a longitudinal wind profile for unpowered flight; wherein, Figure 10 (a) is a plot of variation of drag specific power, Figure 10 (b) is a plot of variation of vertical time varying wind specific power, Figure 10 (c) is a plot of variation of horizontal time varying wind specific power, Figure 10 (d) is a plot of variation of updraft specific power, Figure 10 (e) is a plot of variation of total energy. DETAILED DESCRIPTION

[0060] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The description below concerns the drawings, unless otherwise indicated, in which like numbers refer to like elements throughout. The embodiments described in the following exemplary embodiments are not meant to represent all implementations consistent with the present application. Rather, they are simply an example of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims. It is understood that the foregoing General Description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

[0061] The terminology used in this application, and by those of ordinary skill in the art, is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in this application, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0062] It should be understood that, although the terms first, second, third, etc. can be employed in this application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one piece of information from another. For example, without departing from the scope of the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "in response to determining" as in this manner. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0063] The present application will be described in detail below with reference to the accompanying drawings. The features in the following examples and embodiments can be combined with each other without conflict.

[0064] Example 1:

[0065] The method for verifying the feasibility of simulating atmospheric updraft and unmanned aerial vehicle collecting wind energy of the present application specifically comprises the following steps:

[0066] (1) Extract the elevation data of the terrain of the region of interest from the national geographic information system (GIS), sort it and draw a structured grid in the pointwise software.

[0067] Further, the precision of the elevation data is 12.5 meters, and different geographic 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 by an unstructured grid due to complexity.

[0068] (2) Import the grid information in step (1) into OpenFoam, based on the grid information, use the large eddy simulation (LES) numerical calculation method in OpenFoam to calculate the air flow velocity of the whole flow field, and collect the air flow velocity information.

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

[0070] Further, the LES numerical calculation method is used to calculate the airflow velocity of the whole flow field, specifically including: the momentum conservation of viscous fluid is described by filtering the N-S equation in the physical space, and the calculation formula is:

[0071]

[0072] In the formula, ρ represents the density, x represents the physical quantity, p represents the pressure, μ represents the sub-grid turbulence viscosity, τ ij represents the sub-grid scale stress of the i-th row and the 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 whole flow field is calculated.

[0073] (3) Construct the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system.

[0074] Specifically, first, in the manner of referring to the longitudinal two-dimensional dynamics formula, it is determined that the absolute motion of the fixed-wing unmanned aerial vehicle is equal to the airflow coordinate system motion plus the relative motion, which is represented as:

[0075]

[0076] In the formula, represents the vector of the speed of the unmanned aerial vehicle in the inertial coordinate system, and the subscript I represents the inertial coordinate system in which the unmanned aerial vehicle is located; represents the vector of the speed of the unmanned aerial vehicle in the airflow coordinate system relative to the airspeed, and the subscript a represents the airflow coordinate system in which the unmanned aerial vehicle is located; represents the vector of the wind speed.

[0077] Then, the time derivative is taken on both sides of the equation and the terms are moved, to obtain:

[0078]

[0079] In the formula, ω represents the rotation angular velocity of the airflow coordinate system along the j a 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] Again, set and use Newton's second law in the airflow coordinate system to obtain:

[0081]

[0082] In the formula, T represents the thrust, since the unpowered glider is considered, the thrust T=0; m represents the mass, g represents the gravitational acceleration, k a represents the basis vector in the airflow coordinate system, L represents the lift perpendicular to the airflow direction, and D represents the drag along the airflow direction, and their expressions are respectively:

[0083]

[0084] g = gk i

[0085] where ρ represents the density, S represents the wing area, k i represents the vertical base vector, C L represents the lift coefficient, C D represents the drag coefficient.

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

[0087]

[0088] where S γ represents the two-dimensional conversion 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 conversion matrix, which is represented as θ = α - γ, where α represents the angle of attack, and the pitch angle rate Substituting it into the Newton's second law equation derives the two-dimensional longitudinal dynamics equation under the action of the wind field, which is represented as:

[0090]

[0091] where q represents the dynamic pressure, C T represents the thrust coefficient, w x represents the incoming flow speed, w z represents the ascending airflow, Q represents the angular acceleration, Sc represents the wing area chord length, C m represents the moment coefficient, I yy represents the moment of inertia. Among them, the lift coefficient C L and the drag coefficient C D The unsteady aerodynamic force is not considered.

[0092] (4) Based on the speed information of the atmospheric flow and the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system, the simulation is carried out, and whether the unmanned aerial vehicle can collect wind energy is verified through the calculation of the energy in the simulation process.

[0093] Generally, the total energy of the unmanned aerial vehicle is defined as the sum of the potential energy and the kinetic energy of the unmanned aerial vehicle, wherein the kinetic energy usually refers to the kinetic energy in the inertial coordinate system, which is defined by the following formula:

[0094]

[0095] where E iEa = h + x + z represents the energy of the UAV, h represents the altitude, and x, z represent the longitudinal plane position coordinates.

[0096] The kinetic energy in the inertial coordinate system is convenient for describing the aircraft in the stationary coordinate system. Bonnin et al. also derived the mechanism of collecting energy in dynamic soaring from the total energy in the inertial coordinate system, which is attributed to the 'belly to the wind (breeze)' rule. However, due to the complex wind field environment of the aircraft, it is more convenient to analyze and explain the total energy of the aircraft in the airflow coordinate system. Although the airflow coordinate system kinetic energy in the inertial coordinate system has no intuitive physical meaning, Liu explained that in both coordinate systems, energy can be collected from the wind field during the dynamic soaring period. Therefore, in order to facilitate analysis, the total energy of the UAV is defined as follows:

[0097]

[0098] In the formula, E a represents the total energy of the UAV in the airflow coordinate system, subscript a represents the airflow coordinate system, h represents the altitude, V a represents the airspeed. Based on the change rate of specific energy with time, the expression is as follows:

[0099]

[0100] In the formula, represents the energy change rate. Based on the change rate of specific energy with time and the mathematical equation of the fixed-wing UAV dynamic system, the following is obtained:

[0101]

[0102] The first term on the right side of the above formula represents the energy change rate caused by the gravitational field and the vertical wind field. When w z <0, then represents the energy change rate of the UAV, which is positive, that is, the UAV can collect wind energy. In real flight applications, the UAV is lifted by the vertical airflow, and the total energy of the UAV increases. The first term in the parentheses on the right side of the above formula represents that the UAV is subjected to air resistance, which causes the total energy of the UAV to decrease. The specific energy consumption rate P c = DV a / m, P represents the resistance power of the air to the UAV. The energy reduction rate is proportional to the specific energy dissipation power P c When there is no wind field in the flight environment of the UAV, the resistance suffered by the UAV can be estimated according to the energy change rate.

[0103] Example 2:

[0104] The method for verifying the feasibility of simulating atmospheric updraft and unmanned aerial vehicle collecting wind energy of the application specifically comprises the following steps:

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

[0106] S2, select a suitable wind model, and in the embodiment, a wind index model is adopted wherein V W represents a horizontal wind speed determined by height h; V R represents a wind speed at a reference height H R , determines the gradient and average wind speed in the wind profile, and p is a power law index, the value of which is affected by the characteristics of the ground surface.

[0107] S3, import the elevation data into pointwise software to draw a grid, and in the embodiment, a structured grid is adopted, which has a high calculation speed, wherein the time term is discretized by implicit Euler format, the gradient term is discretized by Gauss linear format, the speed diffusion term is discretized by a second-order Gauss linear upwind format of cell limit, and the Laplace term is discretized by a modified Gauss linear format. In addition, in the embodiment, a PISO (Pressure-implicit with Splitting operator) algorithm is adopted to process the pressure-velocity coupling problem.

[0108] S4, perform numerical simulation on the unsteady wind field on the terrain, and in the simulation process, the time step is 0.001s. The LES numerical calculation method is embedded in OpenFoam to capture large-scale effects and quasi-ordered structures that occur in non-steady and non-equilibrium processes. Based on the measurement information of the aircraft on the same day, the wind field at the calculation inlet is set to be a logarithmic wind field. The terrain in the case is composed of two inclined ridges, the highest absolute elevation is 712 meters, and a valley is separated in the middle, the windward angle of the slope is 27.4°, which provides good conditions for the generation of terrain updraft. 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, simulate the domain according to the following parameters: the distances of the flight region center from the upper, front air duct and rear outlet are 1.7KM, 1.8KM and 2.2KM respectively; the inlet velocity is set to be an exponential wind model, and according to the measurement value of the wind indicator placed on the mountain, the height of 712m is 12.10m / s; the air density is 1.225kg / m 3, the kinematic viscosity coefficient is 1.84 kg / (m.s). Due to the obstruction of the hill terrain, the airflow is decelerated on the windward slope, forming a high pressure area and an upward and rightward velocity component. On the contrary, on the leeward side, the phenomenon of interleaved updraft and downdraft occurs, resulting in a large number of vortices, and the flight consumes a lot of energy.

[0110] S6, compare the sampling spectrum in the simulation process with the Von Karman spectrum, and calculate the error. Finally, it can be found that the error is more obvious in the high frequency band, and less obvious in the low frequency band and the medium frequency band, which is generally in line with the standard spectrum.

[0111] S7, verify the grid independence. Select the vertical direction velocity curve of the four characteristic points in front of the hill with time, and after 200s of simulation calculation, the velocity of the characteristic points starts to converge and does not change significantly. Whether the velocity value of the characteristic points changes according to different grid numbers is used to judge whether the grid drawn is appropriate.

[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, calculate the aerodynamic parameters by using the vortex lattice method, use the XFLR5 (X5) software to model the aerodynamic shape, extract the corresponding aerodynamic coefficients after calculating the aerodynamic parameters, and the inertia moment is estimated by using the suspension method.

[0114] S10, analyze the motion trajectory information of the unmanned aerial vehicle, and determine whether the unmanned aerial vehicle is in a balanced state and whether the unmanned aerial vehicle can fly stably according to the pitch angle and roll angle of the unmanned aerial vehicle.

[0115] S11, after the simulation ends, analyze the energy of the unmanned aerial vehicle. According to the simulation results provided, the rate of energy is affected by four different components (i.e. resistance ratio power, vertical time-varying wind ratio power, horizontal time-varying wind ratio power and updraft ratio power), and whether the selected area can support the energy-free flight is analyzed from three angles. ① Specific power: this is the instantaneous consumption of the glide surface caused by resistance. ② Energy change caused by vertical and horizontal time-varying wind field: relative to the updraft and resistance, these parts can be ignored. ③ Power generated by the updraft: the power change rule generated by the updraft is similar to the updraft. During the whole process, the gain of energy is much larger than the consumed power. In this embodiment, the energy increases continuously during the energy-free flight, which provides important support for the energy sustainability of the FMUAV in the mountain environment.

[0116] Embodiment 3:

[0117] This embodiment provides a method for verifying the feasibility of simulating atmospheric updraft and energy collection of unmanned aerial vehicles. As shown inFigure 1 As shown, drones typically utilize updrafts, sea shear winds, and hillside gradient wind fields.

[0118] like Figure 2 As shown, the mathematical equations of the dynamic system of the fixed-wing UAV are constructed. Based on the mathematical equations of motion of the UAV, the energy harvesting process of the UAV can be analyzed, where V i This 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 typically fly in the atmospheric boundary layer. Atmospheric turbulence, or turbulent flow, occurs in the atmosphere and is studied through stochastic process theory. The fundamental characteristic of turbulent motion is the irregularity of the velocity field distribution along space and time; turbulence manifests in wind speed profiles as continuous random fluctuations superimposed on the mean wind. Complex terrains such as land, ocean, or desert are subject to uneven sunlight exposure (due to complex cloud structures), creating rising or horizontal airflows. Horizontal airflows can form shear layers due to frictional resistance from the ground or waves, or the influence of mountains. Another type of wind field that approximates the sea surface, ground, or mountain ridge is the exponential wind field, represented here by an exponential function. Where V W Horizontal wind speed is determined by altitude h; V R Indicates reference height H R The wind speed determines the gradient and average wind speed within the wind profile; the value of p is affected by surface characteristics. Considering that the simulated environment is in a valley, where most of the surface is covered by forest, p is taken as 0.24 in areas with many trees.

[0120] Energy harvesting framework such as Figure 3 As shown, Part A involves geographic environment modeling, which involves acquiring 3D real terrain from an open dataset, extracting 2D real terrain maps, and obtaining the corresponding grid information. Part B involves numerical simulation of unsteady wind fields on the terrain. Specifically, to simulate the energy harvesting process of the glider in the actual experiment, 3D scanning was used to obtain the glider's shape parameters, and its geometric model was reconstructed using reverse engineering methods. For simplicity, winglets were not considered in the simulation and flight experiment; the configurations are listed in Table 1. Meanwhile, aerodynamic parameters were calculated using the vortex lattice method, and the moment of inertia was estimated using the suspension method. In the flight simulation, since the glider flies in an updraft, the instantaneous aerodynamic coefficients are updated according to the instantaneous flow conditions, which are determined by the glider's position and its unsteadiness.

[0121] Table 1: Glider Parameters

[0122] Description Value Mass of the glider 1.435 kg Wing span 2.45 m Root / 26% span / tip chord 0.17 / 0.13 / 0.08 m Fuselage 0.1025 m Area of the flat tail surface 0.38 m2 Area of the vertical tail 0.033 m2 Height of the vertical tail 0.2 m

[0123] The parameters of the glider are shown in Table 1. The wing mass of the glider is 1.435 kg, the wing span is 2.45 m, the root chord is 0.26 times the wing span, the tip chord is 0.17 m, 0.13 m and 0.08 m respectively, the fuselage length is 0.1025 m, the area of the horizontal tail is 0.38 m2, the area of the vertical tail is 0.033 m2, and the height of the vertical tail is 0.2 m.

[0124] Such a glider configuration is suitable for specific application scenarios in design, and factors such as weight balance of the fuselage and wing, stability of the vertical tail, etc. need to be considered, which will have an important influence 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 is modeled using elevation data obtained from the national geographic information system (GIS). Among them, the 12.5 m resolution digital elevation model of the mountain composed of ridges and valleys is shown in Figure 4 , and the yellow area is the area of interest for building the simulation, and the contour map is shown in the lower right corner of Figure 4 . The contour map is composed of two inclined ridges, with the highest absolute elevation of 712 m, and a valley in between.

[0126] Table 2: Three different grid information

[0127] Name No. cells Total Points Mesh 1 (M1) 327879 492574 Mesh 2 (M2) 528437 696956 Mesh 3 (M3) 727155 980070

[0128] Verify the feasibility of the current simulation settings and perform grid-independent verification. Specifically, three different grids as shown in Table 2 are compared, and the velocity time history of the four characteristic points on the mountain front is shown in Figure 5 . As can be seen from Figure 5 , 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 calculation speed, so this kind of grid will be used in the following research.

[0129] Compare the sampling spectrum with the Von Karman spectrum shown in Figure 6 . It is worth mentioning that the error is more obvious in the high frequency band, and less obvious in the low frequency band and the medium frequency band, which is generally in line with the standard spectrum.

[0130] For the simulation results, the calculated flow field pressure cloud map is shown in Figure 7 . Due to the obstruction of the mountain slope terrain, the airflow slows down on the windward slope, forming a high pressure area and a upward and rightward velocity component. On the other hand, on the leeward side, there is a phenomenon of alternating updraft and downdraft, resulting in a large number of vortices, and the flight consumes a lot of energy. The maximum vertical velocity on the windward side of the terrain is about 7.27 m / s, as shown in Figure 8The speed of the thermal updrafts is usually in the order of 5-10 m / s, which can be increased to extend its duration. Therefore, the unpowered glider will definitely gain energy in the current updraft field.

[0131] In the flight simulation, the glider was initially launched from the ridge top in a balanced state. The flight lasted for 40 seconds until the updrafts were significantly weakened. Figure 9 (a) is the airspeed profile, which remained constant at around 10.2 m / s throughout the entire soar. The track angle fluctuated slightly in Figure 9 (b) due to the influence of the updrafts and turbulence on the slope, but the controller re-stabilized the pitch to the horizontal position. Figure 9 (c) is the vertical air flow experienced by the glider, with the negative sign indicating that the air flows upward from the ground, from Figure 9 (c) it can be seen that the airspeed and track angle changed slightly due to the influence of the horizontal time-varying wind (22.5s-30s); as the glider rises and gradually moves away from the slope, the vertical air flow tends to be weak and decreases. When there is no wind and the track angle is negative, the glider continues to sink, and once the glider is lifted by the updraft, it can climb even if the track angle is negative. Over time, the height of the glider gradually increases, as shown in Figure 9 (d), while the distance between the glider and the peak of the slope also increases, so the updrafts perceived by the glider decrease. Figure 9 (c) is less than 2 m / s after 17.5s, so the glider's climbing speed slows down, and after 35s the glider returns to horizontal flight.

[0132] As shown in Figure 10 , the rate of energy is affected by four different components. Figure 10 The situation of these components during unpowered flight is given. The instantaneous consumption of the glide surface, i.e. the specific power Pp, is shown in Figure 10 (a), which is caused by the resistance, where the green dashed line indicates that the average resistance specific power is -10.73 J / kg / s during the ascent, and the total work done is -429.05 J / kg. Figure 10 (b) and Figure 10 (c) show the energy changes caused by the vertical and horizontal time-varying wind fields, which are instructive, and it is clear that these parts can be ignored compared to the updrafts and the average specific power of the resistance, which are 0.03 J / kg / s and 0.05 J / kg / s, respectively. The power Pup generated by the updrafts is shown in Figure 10 (d), it is worth mentioning that its variation is similar to the updrafts. In addition, it can be seen that the gain in energy is much greater than the power consumed, so the energy continues to increase throughout the process, and the result is shown in Figure 10(e) As shown, the net energy gain is about 397.63 J / kg.

[0133] The above examples are only used to illustrate the technical solutions of the present application, but not to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for simulating atmospheric updrafts and validating the feasibility of UAVs harvesting wind energy, characterized in that, The method comprises the following steps: (1) extracting elevation data of a region of interest from a national geographic information system, collating the data, and drawing a structured grid in pointwise software; (2) importing the grid information in step (1) into an OpenFoam platform, using a large eddy simulation numerical calculation method to calculate the airflow velocity of the entire flow field based on the grid information, and collecting the velocity information of the atmospheric airflow; wherein the calculation of the airflow velocity of the entire flow field using the large eddy simulation numerical calculation method specifically comprises: filtering the N-S equation in the physical space to describe the momentum conservation of a viscous fluid, and the calculation formula is: where p denotes density, x denotes a physical quantity, p denotes pressure, μ denotes a sub-grid turbulent viscosity, τ ij denotes the sub-grid scale stress at the i-th row and j-th column, u denotes the velocity vector, and the superscript ~ denotes a filtering operation; based on the above calculation formula, the airflow velocity of the entire flow field is calculated; (3) constructing a mathematical equation of a fixed-wing unmanned aerial vehicle dynamics system, and the two-dimensional longitudinal dynamics equation under the action of a wind field is: where q represents dynamic pressure, C T represents a thrust coefficient, w x represents an incoming flow speed, w z represents an ascending airflow, Q represents an angular acceleration, Sc represents a wing area chord length, C m represents a moment coefficient, I yy represents a moment of inertia; (4) based on the velocity information of the atmospheric airflow and the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system, simulating and simulating, and verifying whether the unmanned aerial vehicle can collect wind energy by calculating the energy in the simulation process.

2. The method for simulating atmospheric updrafts and verifying the feasibility of collecting wind energy by UAVs according to claim 1, characterized in that, In step (1), the precision of the elevation data is 12.5 meters; In step (1), the structured grid can also be replaced by an unstructured grid during the drawing process.

3. The method for simulating atmospheric updrafts and verifying the feasibility of collecting wind energy by UAVs according to claim 1, wherein, The construction of the mathematical equation of the fixed-wing unmanned aerial vehicle dynamics system specifically comprises: first, determine the absolute motion of the fixed-wing unmanned aerial vehicle equal to the airflow coordinate system motion plus the relative motion by referring to the two-dimensional longitudinal dynamics formula, expressed as: In the formula, represents the vector of the speed of the UAV in the inertial coordinate system, and the subscript I represents the inertial coordinate system in which the UAV is located; represents the vector of the speed of the UAV in the airflow coordinate system relative to the airspeed, and the subscript a represents the airflow coordinate system in which the UAV is located; represents the vector of the wind speed; then, derive with respect to time on both sides of the equation and move the term to obtain: where ω denotes the rotational angular velocity of the airflow coordinate system along the j a axis, x denotes the cross product of vectors, and v a denotes the airspeed in the airflow coordinate system, and the superscript · denotes the derivative. re-arranging And using Newton's second law in the airflow coordinate system, we get: where T represents the thrust, m represents the mass, g represents the gravitational acceleration, k a represents the base vector in the airflow coordinate system, L represents the lift perpendicular to the airflow direction, and D represents the drag along the airflow direction, and their expressions are as follows: g = gk i where p represents the density, S represents the wing area, k i represents the perpendicular base vector, C L represents the lift coefficient, C D represents the drag coefficient; secondly, convert the wind field in the inertial coordinate system to the airflow coordinate system, and the two-dimensional conversion matrix is as follows: In the formula, S γ denotes a two-dimensional conversion matrix, and γ denotes a 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 conversion matrix, expressed as θ = a - γ, wherein a represents the angle of attack, and the pitch angle rate is set Substituting it into the Newton second law equation, the two-dimensional longitudinal dynamics equation under the action of the wind field is derived, expressed as: where q represents dynamic pressure, C T represents a thrust coefficient, w x represents an incoming flow speed, w z represents an ascending airflow, Q represents an angular acceleration, Sc represents a wing area chord length, C m represents a moment coefficient, I yy represents a moment of inertia.

4. The method for simulating atmospheric updrafts and verifying the feasibility of collecting wind energy by UAVs according to claim 1, wherein, in step (4), the total energy of the unmanned aerial vehicle is defined as: In the formula, E a represents the total energy of the UAV in the airflow coordinate system, the subscript a represents the airflow coordinate system, h represents the height, V a represents the airspeed; the specific energy change rate with time is obtained based on the total energy of the UAV, and the expression is as follows: In the formula, represents the rate of energy change; based on the rate of change of specific energy with time and the fixed-wing UAV dynamics system mathematical equation after sorting: In the above equation, when the calculated indicates that the UAV is able to collect wind energy.

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