Resolution method and system for flight path simulation of solar unmanned aerial vehicle

Abstract

The invention discloses a resolving method and system for flight path simulation of a solar unmanned aerial vehicle, and belongs to the technical field of flight mechanics simulation. The method comprises the following steps: firstly, deducing a three-degree-of-freedom mass point motion equation, and constructing a core calculation equation which only takes a flight speed as an independent variable; then, solving the equation by applying a Newton iteration method to obtain numerical solutions of the flight speed and the trajectory angle; and finally, in combination with a kinematics equation, continuously solving at different flight heights, and simulating a complete flight trajectory. The method can effectively replace traditional trajectory integration algorithms such as a fourth-order Runge-Kutta integration method, and is suitable for full-stage trajectory calculation of climbing, level flight, gliding and the like of the solar unmanned aerial vehicle. According to the method, an iteration initial value does not need to be set, the calculation process is stable and efficient, the deterministic convergence characteristic is achieved, and the method can be widely applied to flight path simulation and performance analysis of various fixed-wing aircrafts with electric propellers as power.

Description

Technical Field

[0001] This invention belongs to the field of flight mechanics simulation technology, specifically relating to a solution method and system for simulating the flight trajectory of solar-powered unmanned aerial vehicles (and can also be applied to all fixed-wing aircraft powered by electric propellers). Background Technology

[0002] With the rapid development of technology, drone technology has been widely applied in many fields, and its market demand continues to grow. Traditional drones mostly rely on fuel or batteries for power, resulting in limited flight time and environmental pollution. Solar energy, as a clean and renewable energy source, shows great potential in drone power supply. Solar-powered drones use solar panels to convert solar energy into electricity, providing power for their flight. Theoretically, this allows for long-duration or even unlimited flight, giving them unique advantages in areas such as environmental monitoring, communication relay, disaster relief, and border patrol.

[0003] Currently, the main factor hindering the widespread adoption of solar-powered drones is the low energy density of their storage batteries. This limitation often prevents them from sustaining continuous nighttime flight; even when overnight flight is possible, insufficient payload capacity significantly reduces mission efficiency, thus hindering large-scale promotion and application. However, it is anticipated that driven by market demand in sectors such as civilian electric passenger vehicles and technological innovation, a breakthrough in battery energy density could lead to rapid application of these advancements in solar-powered drones. At that time, solar-powered drones are expected to experience explosive growth in their application scenarios.

[0004] Given the long endurance of solar-powered UAVs, flight trajectory simulation, as the final step in the design process, plays a crucial role in accurately assessing whether various design parameters meet mission requirements. Currently, engineering practice typically employs a three-degree-of-freedom point mass kinematic model, simplifying the target into a single point mass for simulation studies.

[0005] When solving the ordinary differential equations corresponding to the kinematic model of this three-degree-of-freedom particle, the fourth-order Runge-Kutta integration method (RK4) is generally used for numerical integration. This method achieves a good balance between computational accuracy and computational cost, and has therefore become the "standard choice" in many engineering simulations. However, this method is quite sensitive to the selection of initial values. If the initial values ​​are not set properly, it will cause violent oscillations and large fluctuations in various parameters in the initial stage of the simulation, thus preventing the simulation from progressing smoothly. Summary of the Invention

[0006] In view of this, the present invention provides a solution method and system for simulating the flight trajectory of solar-powered unmanned aerial vehicles (UAVs), which can effectively replace traditional trajectory integration algorithms such as RK4, and is applicable to the trajectory solution of solar-powered UAVs in all stages, including climb, level flight, and descent. The method of the present invention does not require setting initial values ​​for iteration, and the calculation process is stable, efficient, and has deterministic convergence characteristics.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a solution method for simulating the flight trajectory of a solar-powered unmanned aerial vehicle (UAV), comprising:

[0009] Step 1: Set the initial parameters for the simulation conditions; the initial parameters include: simulation start altitude and simulation end altitude, simulation time step, angle of attack, propeller shaft installation angle, motor input power, aircraft mass, and wing reference area;

[0010] Step 2: Calculate the available propulsion power of the propeller based on the current flight altitude and the input power of the motor, combined with the propulsion system efficiency model; obtain the corresponding lift coefficient and drag coefficient by interpolating through the aerodynamic database according to the set angle of attack; calculate the current atmospheric density using the atmospheric model based on the current flight altitude.

[0011] Step 3: Based on the angle of attack, the propeller shaft installation angle, the aircraft mass, the wing reference area, the available propulsion power, the lift coefficient, the drag coefficient, and the current atmospheric density, the core calculation equation is obtained; the core calculation equation is used to solve for the flight speed.

[0012] Step 4: Apply Newton's iteration method to iteratively solve the core calculation equation to obtain the flight speed at the current flight altitude; based on the flight speed at the current flight altitude, obtain the current trajectory angle;

[0013] Step 5: Based on the flight speed at the current flight altitude, the current trajectory angle, and the simulation time step, and in conjunction with the kinematic equations, calculate and update the time, horizontal flight distance, and flight altitude at the next time point;

[0014] Step 6: Repeat steps 2-5 until the calculated flight altitude reaches or exceeds the simulation end altitude, thus completing the simulation calculation of the entire flight trajectory; wherein, the flight altitude at the time of the first simulation calculation is the simulation start altitude;

[0015] Step 7: Based on the horizontal flight distance and flight altitude calculated at each time point, obtain the flight trajectory curve that reflects the complete simulation process.

[0016] Secondly, the present invention provides a solution system for simulating the flight trajectory of a solar-powered unmanned aerial vehicle, comprising:

[0017] The parameter setting module is used to set the initial parameters of the simulation conditions. The initial parameters include: simulation start altitude and simulation end altitude, simulation time step, angle of attack, propeller shaft installation angle, motor input power, aircraft mass, and wing reference area.

[0018] The first calculation module is used to: calculate the available propulsion power of the propeller based on the current flight altitude and the input power of the motor, combined with the propulsion system efficiency model; obtain the corresponding lift coefficient and drag coefficient by interpolating through the aerodynamic database according to the set angle of attack; and calculate the current atmospheric density using the atmospheric model based on the current flight altitude.

[0019] The second calculation module is used to: obtain the core calculation equation based on the angle of attack, the propeller shaft installation angle, the aircraft mass, the wing reference area, the available propulsion power, the lift coefficient, the drag coefficient, and the current atmospheric density; the core calculation equation is used to solve for the flight speed;

[0020] The third calculation module is used to: apply Newton's iteration method to iteratively solve the core calculation equation to obtain the flight speed at the current flight altitude; and obtain the current trajectory angle based on the flight speed at the current flight altitude.

[0021] The update module is used to: calculate and update the time, horizontal flight distance, and flight altitude at the next time point based on the flight speed at the current flight altitude, the current trajectory angle, and the simulation time step, combined with the kinematic equations;

[0022] The flight trajectory plotting module is used to: after completing the simulation calculation of the entire flight trajectory, obtain the flight trajectory curve reflecting the complete simulation process based on the horizontal flight distance and flight altitude calculated at each time point;

[0023] The parameter setting module, the first calculation module, the second calculation module, the third calculation module, and the update module are executed repeatedly until the calculated flight altitude reaches or exceeds the simulation end altitude, thus completing the simulation calculation of the entire flight trajectory; the flight altitude at the time of the first simulation calculation is the simulation start altitude.

[0024] Thirdly, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method.

[0025] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method.

[0026] The beneficial effects of this invention are as follows:

[0027] The flight trajectory simulation method for solar-powered unmanned aerial vehicles (UAVs) provided by this invention eliminates the need for initial iteration values, fundamentally preventing computational oscillations and ensuring process stability. Furthermore, while maintaining the same computational accuracy as RK4, the computation time required by this method is only one-third that of RK4. In addition, this method is highly versatile and applicable to the flight trajectory simulation of various fixed-wing aircraft powered by electric propellers. Attached Figure Description

[0028] Figure 1 This is a force analysis diagram of a solar-powered drone.

[0029] Figure 2 This is a flowchart of the flight trajectory simulation calculation method for solar-powered unmanned aerial vehicles (UAVs) according to the present invention.

[0030] Figure 3 This is a flowchart of a flight trajectory simulation calculation method for a solar-powered unmanned aerial vehicle according to an embodiment of the present invention.

[0031] Figure 4 This is a curve showing the change in motor efficiency with flight altitude.

[0032] Figure 5 This is a curve showing the change in propeller efficiency with flight altitude.

[0033] Figure 6 The flight trajectory curve is obtained by simulating the flight trajectory calculation method for solar-powered drones according to the present invention.

[0034] Figure 7 The results show a comparison of time-elevation curves obtained from simulations using the Runge-Kutta integral method and the method of this invention.

[0035] Figure 8 The results show a comparison of time-velocity curves obtained from simulations using the Runge-Kutta integral method and the method of this invention.

[0036] Figure 9 The comparison results show the time-height difference curves obtained by simulation using the Runge-Kutta integral method and the method of this invention. Detailed Implementation

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0038] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0039] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0040] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0041] Furthermore, to better illustrate the present invention, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0042] The method used in this invention arose from the process of finding a reasonable set of initial values ​​for RK4. To better understand the implementation steps of this invention, the derivation process of the core calculation equation is first briefly described.

[0043] When the aircraft is in equilibrium, the net force is 0. The force analysis of the solar-powered drone is as follows: Figure 1 As shown. Projecting each force into the directions of lift and drag respectively, we obtain the following equilibrium equations:

[0044] (1)

[0045] (2)

[0046] in, For gravity; The trajectory angle; For propeller thrust; The angle between the line of force and the direction of velocity. It equals the sum of the angle of attack and the propeller shaft installation angle; For lift; The resistance is calculated by squaring both sides of the two equations and adding them together to eliminate the variable. ,get:

[0047] (3)

[0048] The parameters in the above formula can be calculated using the following formula:

[0049] (4)

[0050] (5)

[0051] (6)

[0052] (7)

[0053] (8)

[0054] in, The available propulsion power of the propeller; The speed of the aircraft; Atmospheric density; This refers to the wing reference area. The lift coefficient; This is the drag coefficient; For aircraft mass; The acceleration due to gravity is taken as 9.8. ; Angle of attack; The installation angle for the propeller shaft.

[0055] Substituting the calculation formulas for the above parameters into formula (3) and rearranging, we obtain the information regarding flight speed. The quartic equation is shown in formula (9):

[0056] (9)

[0057] make The coefficients of each item are as follows: , , , ,Right now:

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] Formula (10) can be obtained:

[0063] (10)

[0064] Thus, we have obtained a result containing only one independent variable (i.e., flight speed). The equation (10) can be solved numerically using Newton's iteration method. Equation (10) is called the core computational equation. , , , It is called the core coefficient.

[0065] The following describes the solution method for simulating the flight trajectory of a solar-powered unmanned aerial vehicle (UAV) according to the present invention. Figure 2 A flowchart of the method of the present invention is shown, such as Figure 2 As shown, the method includes the following steps:

[0066] Step 1: Set the initial parameters for the simulation conditions; the initial parameters include: simulation start altitude and simulation end altitude, simulation time step, angle of attack, propeller shaft installation angle, motor input power, aircraft mass, and wing reference area.

[0067] Step 2: Calculate the available propulsion power of the propeller based on the current flight altitude and the input power of the motor, combined with the propulsion system efficiency model; obtain the corresponding lift coefficient and drag coefficient by interpolating through the aerodynamic database according to the set angle of attack; and calculate the current atmospheric density using the atmospheric model based on the current flight altitude.

[0068] For example, the propulsion system efficiency model is as follows:

[0069] (11)

[0070] in, The usable propulsion power of the propeller is the power that the propeller actually uses to push the air to generate thrust, while the effective power output by the propeller is the actual power of the propeller. Input power to the motor; The efficiency of an electric motor in converting electrical energy into mechanical energy (i.e., motor efficiency). The efficiency by which a propeller converts the mechanical shaft power of an electric motor into effective thrust (i.e., propeller efficiency). and This is calculated by interpolation based on the current flight altitude.

[0071] For example, the atmospheric model used to calculate the current atmospheric density is the 1976 US Standard Atmospheric Model (USSA76). Given that any electrically driven propeller-driven aircraft will not fly at an altitude exceeding 30 km, in practical applications, only data segments from the USSA76 model within this altitude range are needed. Based on the variation of atmospheric density with flight altitude, a segmented calculation method can be used as follows:

[0072] (1) 0km≤ ≤11.0191km:

[0073] (12)

[0074] In formula (12), .

[0075] (2) 11.0191km≤ ≤20.0631km:

[0076] (13)

[0077] In formula (13), .

[0078] (3) 20.0631km≤ ≤30km:

[0079] (14)

[0080] In formula (14), .

[0081] in, Atmospheric density; Flight altitude; For intermediate calculation variables; ; It is a natural exponential function.

[0082] Step 3: Based on the angle of attack, the propeller shaft installation angle, the aircraft mass, the wing reference area, the available propulsion power, the lift coefficient, the drag coefficient, and the current atmospheric density, the core calculation equation is obtained; the core calculation equation is used to solve for the flight speed.

[0083] The core calculation equation in step 3 is shown in formula (10), based on the above. , , , The calculation formula can be used to calculate the four core coefficients of the core calculation equation.

[0084] Step 4: Apply Newton's iteration method to iteratively solve the core calculation equation to obtain the flight speed at the current flight altitude; based on the flight speed at the current flight altitude, obtain the current trajectory angle.

[0085] For example, in the initial calculation, the flight speed The initial value for the iteration can be set to a relatively large value (such as 1000 m / s); subsequent steps use the values ​​calculated at the previous flight altitude. The value is used as the initial value for this iteration to accelerate convergence. Based on formulas (2), (4), (6), (7), and (8) and the calculated value at the current flight altitude... The value can be used to calculate the current trajectory angle.

[0086] Step 5: Based on the flight speed at the current flight altitude, the current trajectory angle, and the simulation time step, and in conjunction with the kinematic equations, calculate and update the time, horizontal flight distance, and flight altitude at the next time point.

[0087] For example, the kinematic equations are:

[0088] (15)

[0089] in, This refers to the horizontal flight distance. Flight altitude; For time; For flight speed; The trajectory angle; The rate of change of horizontal flight distance per unit time; This represents the rate of change of flight altitude per unit time. It represents the current flight altitude. Value and current Substituting the values ​​into formula (15), we can obtain the rate of change of horizontal flight distance. and rate of change of flight altitude The horizontal flight distance at the next time point can be obtained based on the current horizontal flight distance, the rate of change of horizontal flight distance, and the simulation time step. Similarly, the flight altitude at the next time point can be obtained based on the current flight altitude, the rate of change of flight altitude, and the simulation time step. The time of the next time point is determined based on the simulation time step.

[0090] Step 6: Repeat steps 2-5 until the calculated flight altitude reaches or exceeds the simulation end altitude, thus completing the simulation calculation of the entire flight trajectory; wherein, the flight altitude at the time of the first simulation calculation is the simulation start altitude.

[0091] Step 7: Based on the horizontal flight distance and flight altitude calculated at each time point, obtain the flight trajectory curve that reflects the complete simulation process.

[0092] For example, the horizontal flight distance and flight altitude data calculated at each time point (i.e., equilibrium point) can be plotted on a two-dimensional plane to form a flight trajectory curve that reflects the complete simulation process.

[0093] The proposed solution method for simulating the flight trajectory of solar-powered unmanned aerial vehicles (UAVs) can be named the "equilibrium point connection method." First, the motion equations of a three-degree-of-freedom particle are derived, constructing a core calculation equation with flight velocity as the only independent variable. Then, Newton's iteration method is applied to solve this equation, obtaining numerical solutions for flight velocity and trajectory angles. Finally, combining the kinematic equations, the solution is continuously applied at different flight altitudes to simulate the complete flight trajectory. This method can effectively replace traditional trajectory integration algorithms such as RK4 and is suitable for solving the trajectory of solar-powered UAVs in all stages, including climb, level flight, and descent. Its unique advantage lies in the fact that it does not require setting initial iteration values, and the calculation process is stable, efficient, and has deterministic convergence characteristics. It can be widely applied to the flight trajectory simulation and performance analysis of various electrically driven propeller-driven aircraft (fixed-wing).

[0094] The following describes in detail the solution method for simulating the flight trajectory of a solar-powered unmanned aerial vehicle (UAV) according to the present invention, with reference to the accompanying drawings and embodiments.

[0095] Figure 3 A flowchart illustrating a solution method for simulating the flight trajectory of a solar-powered unmanned aerial vehicle (UAV) according to an embodiment of the present invention is shown, as follows: Figure 3 As shown, the method includes the following steps:

[0096] Step 1: Initialize parameter settings:

[0097] Set the initial parameters for the simulation conditions, including: simulation start altitude and simulation end altitude, simulation time step, angle of attack, propeller shaft installation angle, motor input power, aircraft mass, and wing reference area.

[0098] In this embodiment, the simulation start height is set to 0m, the simulation end height is set to 20000m, the simulation time step is set to 0.1s, the angle of attack is set to a constant 3°, the propeller shaft installation angle is set to -3°, the motor input power is set to 1600W, the aircraft mass is set to 75kg, and the wing reference area is set to 23.6m².

[0099] Step 2, Calculation of dynamic, aerodynamic and environmental parameters:

[0100] Based on the current flight altitude and motor input power, the available propulsion power of the propeller is calculated using the propulsion system efficiency model. The lift and drag coefficients are obtained by interpolation using an aerodynamic database based on the set angle of attack. The current atmospheric density is calculated using an atmospheric model based on the current flight altitude. The initial flight altitude for the first calculation is 0m, the simulation's starting altitude.

[0101] The efficiency model for the propulsion system is shown in Equation (11). Wherein, the motor input power... In this embodiment, the value is a constant 1600W; motor efficiency and propeller efficiency The curves showing the change with flight altitude are as follows: Figure 4 and Figure 5 As shown, and It can be calculated by interpolation based on the current flight altitude.

[0102] In this embodiment, the angle of attack corresponding to the maximum lift-to-drag ratio (a fixed value of 3°) is selected. By interpolating through the aerodynamic database, the corresponding lift coefficient is found to be 0.93 and the drag coefficient is 0.028.

[0103] The current atmospheric density can be obtained using a piecewise calculation method. For details on the calculation method, please refer to the above text, which will not be repeated here.

[0104] Step 3: Calculate the core coefficients:

[0105] According to the above , , , The calculation formula yields the four core coefficients of the core calculation equation, thus obtaining the result based solely on flight speed. The core calculation equation for the independent variable is shown in formula (10).

[0106] Step 4: Solve for flight speed and trajectory angle:

[0107] Formula (10) in (0, It is continuous and differentiable everywhere on the [aspect], and its derivative function is [function]. for:

[0108] ;

[0109] Set flight speed Convergence threshold Given a velocity of 0.0001 m / s, Newton's iteration method is applied to iteratively solve the problem, yielding the current flight altitude. The numerical solution. In the initial calculation, The initial value for the iteration was set to 1000 m / s, and subsequent steps used the value calculated at the previous flight altitude. The value is used as the initial value for this iteration to accelerate convergence. The value calculated at the current flight altitude... Substituting the values ​​into formulas (2), (4), (6), (7), and (8), we obtain the current trajectory angle. .

[0110] Step 5: Update flight status:

[0111] Using the kinematic equations shown in formula (15), calculate and update the time, horizontal flight distance, and flight altitude at the next time point. Increment the current time by the simulation time step (0.1s) to obtain the time at the next time point; increase the current horizontal flight distance by... Increase the current flight altitude This allows us to obtain the horizontal flight distance and flight altitude at the next time point.

[0112] Step 6: Iterate through the loop.

[0113] Repeat steps 2-5 until the calculated flight altitude reaches or exceeds the simulation end altitude, thus completing the simulation calculation of the entire flight trajectory.

[0114] The flight altitude value obtained in this calculation will be used as the flight altitude for the next round of simulation calculations, and the process will start again from step 2 to step 5. When the altitude calculated in step 5 is equal to or greater than 20,000m, the simulation ends and proceeds to step 7.

[0115] Step 7: Trajectory Generation (Connecting Equilibrium Points):

[0116] The horizontal flight distance and altitude data calculated at each time point (equilibrium point) are plotted on a two-dimensional plane to form a flight trajectory curve reflecting the complete simulation process, such as... Figure 6 As shown.

[0117] The final simulation results showed that the aircraft climbed from 0m to 20000m at an angle of attack of 3°, with a total flight time of 5.945h, a range (i.e., horizontal flight distance) of 329.132km, and a power consumption of 9.512 kWh.

[0118] In addition to the method of this invention, simulations were also performed using RK4 for comparison. The computation time required by RK4 was 0.259s, while the computation time required by the "equilibrium point connection method" of this invention was 0.089s, requiring only 34% of the time required by RK4, which is approximately a two-thirds reduction in computation time.

[0119] Figure 7 , Figure 8 The calculation accuracy of the "equilibrium point connection method" of this invention and RK4 is compared. Figure 7 The curves showing the flight altitude versus time obtained using two simulation methods are presented. Figure 8 The graphs showing the flight speed versus time obtained using two different methods are presented. As can be seen from the graph, the simulation curves obtained by the two methods almost completely overlap, proving that the computational accuracy of the method described in this invention is comparable to that of RK4. Figure 9The graph shows the difference in altitude between the two methods of calculating flight altitude over time. As can be seen from the graph, even when climbing to the final altitude of 20,000m, the difference in flight altitude calculated by the two methods is only 23.5 meters, and the flight altitudes calculated by the two methods are very close.

[0120] This invention also provides a solution system for simulating the flight trajectory of a solar-powered unmanned aerial vehicle (UAV), used to implement the aforementioned solution method for simulating the flight trajectory of a solar-powered UAV. The system includes:

[0121] The parameter setting module is used to set the initial parameters of the simulation conditions. The initial parameters include: simulation start altitude and simulation end altitude, simulation time step, angle of attack, propeller shaft installation angle, motor input power, aircraft mass, and wing reference area.

[0122] The first calculation module is used to: calculate the available propulsion power of the propeller based on the current flight altitude and the input power of the motor, combined with the propulsion system efficiency model; obtain the corresponding lift coefficient and drag coefficient by interpolating through the aerodynamic database according to the set angle of attack; and calculate the current atmospheric density using the atmospheric model based on the current flight altitude.

[0123] The second calculation module is used to: obtain the core calculation equation based on the angle of attack, the propeller shaft installation angle, the aircraft mass, the wing reference area, the available propulsion power, the lift coefficient, the drag coefficient, and the current atmospheric density; the core calculation equation is used to solve for the flight speed;

[0124] The third calculation module is used to: apply Newton's iteration method to iteratively solve the core calculation equation to obtain the flight speed at the current flight altitude; and obtain the current trajectory angle based on the flight speed at the current flight altitude.

[0125] The update module is used to: calculate and update the time, horizontal flight distance, and flight altitude at the next time point based on the flight speed at the current flight altitude, the current trajectory angle, and the simulation time step, combined with the kinematic equations;

[0126] The flight trajectory plotting module is used to: after completing the simulation calculation of the entire flight trajectory, obtain the flight trajectory curve reflecting the complete simulation process based on the horizontal flight distance and flight altitude calculated at each time point;

[0127] The parameter setting module, the first calculation module, the second calculation module, the third calculation module, and the update module are executed repeatedly until the calculated flight altitude reaches or exceeds the simulation end altitude, thus completing the simulation calculation of the entire flight trajectory; the flight altitude at the time of the first simulation calculation is the simulation start altitude.

[0128] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes and related descriptions of each module of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0129] The present invention also provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described solution method for simulating the flight trajectory of a solar-powered unmanned aerial vehicle.

[0130] The present invention also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the above-described solution method for simulating the flight trajectory of a solar-powered unmanned aerial vehicle.

[0131] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as object-oriented programming languages ​​C++, C#, etc.

[0132] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0136] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. The above descriptions are exemplary and not exhaustive. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A solution method for simulating the flight trajectory of a solar-powered unmanned aerial vehicle (UAV), characterized in that, include: Step 1: Set the initial parameters for the simulation conditions; The initial parameters include: simulation start altitude and simulation end altitude, simulation time step, angle of attack, propeller shaft installation angle, motor input power, aircraft mass and wing reference area; Step 2: Calculate the available propulsion power of the propeller based on the current flight altitude and the input power of the motor, combined with the propulsion system efficiency model; obtain the corresponding lift coefficient and drag coefficient by interpolating through the aerodynamic database according to the set angle of attack; calculate the current atmospheric density using the atmospheric model based on the current flight altitude. Step 3: Based on the angle of attack, the propeller shaft installation angle, the aircraft mass, the wing reference area, the available propulsion power, the lift coefficient, the drag coefficient, and the current atmospheric density, the core calculation equation is obtained; the core calculation equation is used to solve for the flight speed. Step 4: Apply Newton's iteration method to iteratively solve the core calculation equation to obtain the flight speed at the current flight altitude; based on the flight speed at the current flight altitude, obtain the current trajectory angle; Step 5: Based on the flight speed at the current flight altitude, the current trajectory angle, and the simulation time step, and in conjunction with the kinematic equations, calculate and update the time, horizontal flight distance, and flight altitude at the next time point; Step 6: Repeat steps 2-5 until the calculated flight altitude reaches or exceeds the simulation end altitude, thus completing the simulation calculation of the entire flight trajectory; wherein, the flight altitude at the time of the first simulation calculation is the simulation start altitude; Step 7: Based on the horizontal flight distance and flight altitude calculated at each time point, obtain the flight trajectory curve that reflects the complete simulation process.

2. The method according to claim 1, characterized in that, In step 2, the efficiency model of the propulsion system is as follows: ； in, The available propulsion power of the propeller; Input power to the motor; The efficiency of an electric motor in converting electrical energy into mechanical energy; The efficiency by which the propeller converts the mechanical shaft power of the motor into effective thrust; and This is calculated by interpolation based on the current flight altitude.

3. The method according to claim 1, characterized in that, In step 2, the atmospheric model is the USSA76 model.

4. The method according to claim 3, characterized in that, In step 2, the current atmospheric density is calculated using an atmospheric model based on the current flight altitude, including: Based on the variation of atmospheric density with flight altitude, the following piecewise calculation method is adopted: (1)0km≤ ≤11.0191km: ； ； (2)11.0191km≤ ≤20.0631km: ； ； (3)20.0631km≤ ≤30km: ； ； in, Atmospheric density; Flight altitude; For intermediate calculation variables; ; It is a natural exponential function.

5. The method according to claim 1, characterized in that, In step 3, the core calculation equation is: ； in, For flight speed; , , , The four core coefficients of the core computational equation are calculated using the following formula: ； ； ； ； in, Atmospheric density; This refers to the wing reference area. The lift coefficient; This is the drag coefficient; The available propulsion power of the propeller; The angle between the line of force and the direction of velocity. , For the angle of attack, The installation angle for the propeller shaft; For gravity, , For aircraft quality, The acceleration due to gravity is taken as 9.

8. .

6. The method according to claim 1, characterized in that, In step 5, the kinematic equations are: ； in, This refers to the horizontal flight distance. Flight altitude; For flight speed; The trajectory angle; For time.

7. The method according to claim 1, characterized in that, In step 7, the flight trajectory curve is obtained by plotting the horizontal flight distance and flight altitude data calculated at each time point on a two-dimensional plane.

8. A solution system for simulating the flight trajectory of a solar-powered unmanned aerial vehicle (UAV), characterized in that, include: The parameter setting module is used to set the initial parameters of the simulation conditions. The initial parameters include: simulation start altitude and simulation end altitude, simulation time step, angle of attack, propeller shaft installation angle, motor input power, aircraft mass and wing reference area; The first calculation module is used to: calculate the available propulsion power of the propeller based on the current flight altitude and the input power of the motor, combined with the propulsion system efficiency model; obtain the corresponding lift coefficient and drag coefficient by interpolating through the aerodynamic database according to the set angle of attack; and calculate the current atmospheric density using the atmospheric model based on the current flight altitude. The second calculation module is used to: obtain the core calculation equation based on the angle of attack, the propeller shaft installation angle, the aircraft mass, the wing reference area, the available propulsion power, the lift coefficient, the drag coefficient, and the current atmospheric density; the core calculation equation is used to solve for the flight speed; The third calculation module is used to: apply Newton's iteration method to iteratively solve the core calculation equation to obtain the flight speed at the current flight altitude; and obtain the current trajectory angle based on the flight speed at the current flight altitude. The update module is used to: calculate and update the time, horizontal flight distance, and flight altitude at the next time point based on the flight speed at the current flight altitude, the current trajectory angle, and the simulation time step, combined with the kinematic equations; The flight trajectory plotting module is used to: after completing the simulation calculation of the entire flight trajectory, obtain the flight trajectory curve that reflects the complete simulation process based on the horizontal flight distance and flight altitude calculated at each time point; The parameter setting module, the first calculation module, the second calculation module, the third calculation module, and the update module are executed repeatedly until the calculated flight altitude reaches or exceeds the simulation end altitude, thus completing the simulation calculation of the entire flight trajectory; the flight altitude at the time of the first simulation calculation is the simulation start altitude.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; Wherein, when one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to perform the method described in any one of claims 1-7.

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