Distributed power layout optimization method for hydrogen energy unmanned aerial vehicle

Abstract

The invention discloses a distributed power layout optimization method for a hydrogen energy unmanned aerial vehicle, relates to the technical field of hydrogen energy unmanned aerial vehicles, and aims to solve the problem of poor presentation effect of a power layout scheme. By calling the historical distributed power layout scheme and combining the current analysis result, repeated errors can be avoided, the design efficiency and the success rate can be improved by referring to previous successful experience, layout optimization is conducted on the initial position of the power assembly through the particle swarm optimization algorithm, the optimal solution can be efficiently searched through the intelligent algorithm, and the design efficiency and the success rate are improved. The overall performance of a layout scheme is improved, the assembly position and the installation angle are optimized, resistance is reduced, lift force is improved, pitching moment balance is improved, flight stability and controllability are enhanced, the method is particularly suitable for high-speed or large-attack-angle flight scenes, simulation analysis covers energy management, aerodynamic layout, structural strength, heat management and other aspects, and the method is suitable for popularization and application. And comprehensive evaluation and optimization of the unmanned aerial vehicle on a plurality of key performances are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen energy drones, and specifically to an optimization method for the distributed power layout of hydrogen energy drones. Background Technique

[0002] Hydrogen energy drones refer to drones that use hydrogen as energy, and they usually use hydrogen fuel cells as the power source.

[0003] Chinese Patent with Publication Number CN111695203B discloses a method for the aerodynamic layout design and performance evaluation of anti-swarm drones. By taking the flight altitude, level flight speed, take-off weight, and lift coefficient as the constraints for aerodynamic layout design, based on the given constraints for aerodynamic layout design, the geometric dimensions, center of gravity, tail wing, and installation angles of the wing and tail wing of the anti-swarm drone are determined. Considering the actual application situation, the aerodynamic efficiency of the drone during actual application is effectively improved, and the lift-to-drag ratio and stability of the drone are increased. By establishing a first model with a propeller and a second model without a propeller, the take-off flow field and level flight flow field of the drone in both cases are predicted and analyzed, and the two calculation results are evaluated and compared, making the evaluation results more in line with the actual situation. Although the above-mentioned Chinese patent solves the problem of aerodynamic layout design, there are still the following problems in actual operation:

[0004] 1. The actual physical model of the drone and the standard teaching model of the drone are not effectively established, resulting in the inability to further understand the detailed component composition.

[0005] 2. Targeted performance analysis is not carried out based on the actual model of the drone, resulting in the inability to perform an accurate power layout.

[0006] 3. The completed layout scheme is not managed and analyzed more carefully, resulting in poor integrity of the power layout scheme. Summary of the Invention

[0007] The object of the present invention is to provide an optimization method for the distributed power layout of a hydrogen - energy unmanned aerial vehicle (UAV). By retrieving historical distributed power layout schemes and combining with current analysis results, it is possible to draw on past successful experiences, avoid repeating mistakes, improve the design efficiency and success rate. The particle swarm optimization algorithm is used to optimize the initial positions of the power components. This intelligent algorithm can efficiently search for the optimal solution, improve the overall performance of the layout scheme, optimize the component positions and installation angles, reduce drag, increase lift, improve the pitch - moment balance, enhance flight stability and controllability, especially suitable for high - speed or large - angle - of - attack flight scenarios. The simulation analysis covers multiple aspects such as energy management, aerodynamic layout, structural strength, and thermal management, ensuring a comprehensive evaluation and optimization of the UAV in multiple key performances, and can solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An optimization method for the distributed power layout of a hydrogen - energy UAV, comprising:

[0010] Establish a parametric model according to the geometric structure and power system of the UAV, establish a teaching model of the hydrogen - energy power system of the UAV according to the established parametric model, perform a distributed power layout according to the established parametric model, and define the constraint conditions;

[0011] Conduct global management according to the distributed power layout scheme. The global management includes energy management, aerodynamic layout optimization, structural strength analysis, and thermal management optimization. Perform global collaborative optimization according to the global management results, and conduct testing and verification after the collaborative optimization is completed;

[0012] Before performing the distributed power layout, first determine the design objectives of the UAV, and determine the constraint conditions for the layout design after the design objectives are determined;

[0013] After the design objectives and constraint conditions are defined, analyze the parametric model of the UAV.

[0014] Preferably, establishing a parametric model according to the geometric structure and power system of the UAV includes:

[0015] First, establish a parametric model of the geometric structure of the UAV;

[0016] Among them, first collect the geometric dimensions, shape characteristics, and relative positions of its various components of the UAV, including the fuselage, airfoil, and tail. Use computer - aided tools to generate a three - dimensional model of the UAV with the collected data of the geometric dimensions, shape characteristics, and relative positions of its various components of the UAV;

[0017] Perform parametric processing on the generated three - dimensional model of the UAV. The parametric processing is to define geometric features by controlling key dimensions and shapes;

[0018] Discretize the continuous geometric features in the parameterized three-dimensional model of the drone into a grid model suitable for numerical calculation, and finally obtain the geometric model of the drone;

[0019] Then establish a parameterized model for the power system of the drone;

[0020] Among them, first collect the relevant parameters of the drone power system, including the technical parameters, power characteristics and energy density of the battery, hydrogen storage tank, fuel cell and motor, and determine the interaction relationship between each power component. The power components include the battery, hydrogen storage tank, fuel cell and motor;

[0021] According to the collected geometric dimensions, shape characteristics and relative position data of each component of the drone, establish a mathematical model of the hydrogen energy power system. The mathematical model of the hydrogen energy power system includes energy conversion and power output. Use the state equation and the principle of energy conservation to establish a dynamic model for the mathematical model of the hydrogen energy power system, and finally obtain the drone power model;

[0022] Couple the drone geometric model and the drone power model. After the coupling is completed, obtain the drone parameterized model.

[0023] Preferably, establish a teaching model for the drone hydrogen energy power system according to the established parameterized model, including:

[0024] Retrieve the key technical parameters in the drone parameterized model. The key technical parameters include the hydrogen storage tank, fuel cell, electric motor and battery. Among them, the parameters of the hydrogen storage tank are the capacity, pressure, volume and material for hydrogen storage; the parameters of the fuel cell are the power output, efficiency, energy density and reaction kinetics of the fuel cell; the parameters of the electric motor are the power, speed, efficiency and torque curve of the electric motor; the parameters of the battery are the charging and discharging characteristics, capacity, durability and charging time of the battery;

[0025] After the key technical parameters are confirmed and retrieved, determine the interaction relationship between the components;

[0026] After the interaction relationship is confirmed, establish a teaching model;

[0027] The teaching model is to establish a data model for each power system component, including the static and dynamic behavior models of the component;

[0028] Integrate each component model into a complete power system model, and define the energy flow and signal interaction between the components;

[0029] Finally, use the teaching model development tool to construct a teaching model of the UAV hydrogen energy power system for the power system model, and design the user interface.

[0030] Preferably, perform a distributed power layout according to the established parametric model, and define the constraint conditions, including:

[0031] The design goals include maximum flight time, load capacity, energy efficiency, and flight stability;

[0032] The constraint conditions include the position of the center of gravity, the position of the center of mass, structural strength, heat dissipation requirements, and the space occupied by the power system;

[0033] Finally, complete the definition of the design goals and constraint conditions.

[0034] Preferably, perform a distributed power layout according to the established parametric model, and define the constraint conditions, further including:

[0035] Among them, analyze the key parameters and performance characteristics of the power components in the parametric model. The power components include hydrogen storage tanks, fuel cells, electric motors, and batteries. After the analysis, confirm the interaction relationship between the power components;

[0036] Retrieve the historical distributed power layout scheme from the database, combine the historical distributed power layout scheme with the analysis results of the key parameters and performance characteristics and the interaction relationship between the power components, and obtain the preliminary positions of the power components after the combination;

[0037] Use the particle swarm optimization algorithm to optimize the layout of the preliminary positions of the power components. During the optimization process, adjust the key parameters of the power components in the layout scheme, including changing the installation position and angle of the power components, and adjusting the connection method between the components;

[0038] After the adjustment, obtain the distributed power layout scheme of the parametric model.

[0039] Preferably, use the particle swarm optimization algorithm to optimize the layout of the preliminary positions of the power components, including:

[0040] Based on the overall characteristics of the UAV and the characteristics of the power components of the hydrogen energy UAV, determine the constraint conditions for the layout between the power components;

[0041] Based on the current flight mission of the hydrogen energy UAV, determine the index requirements for flight air resistance, flight balance center of gravity, and flight endurance time. Based on the index requirements, determine the weighted weights for flight air resistance, flight balance center of gravity, and flight endurance time;

[0042] Based on flight air resistance, flight balance center of gravity, flight endurance time, and the weighted weights, determine the target optimization index;

[0043] Based on the target optimization index and constraint conditions, the installation position of the power components is solved by the particle swarm optimization algorithm to obtain the optimal installation position;

[0044] Determine the feasible installation angles and the feasible connection modes of the power components at the optimal safety position;

[0045] Based on the optimal installation position, a mathematical model of the hydrogen energy drone is established with hydrogen energy consumption and battery energy consumption as prediction indexes;

[0046] Based on the mathematical model, determine the predicted hydrogen energy consumption and predicted battery energy consumption under the feasible installation angles and the feasible connection modes of the power components in the current flight mission, and select the feasible installation angles and the feasible connection modes of the power components with the minimum comprehensive energy consumption of the predicted hydrogen energy consumption and the predicted battery energy consumption as the target installation angles and the target connection modes.

[0047] Preferably, global management is carried out according to the distributed power layout scheme, including:

[0048] First, confirm the key data in the distributed power layout scheme, including layout scheme data, parametric model data, and external environment data;

[0049] Carry out energy management according to the distributed power layout scheme. The energy management is to analyze the power required in different stages according to the design goal of the drone and in combination with the flight mission profile, and then formulate an energy distribution strategy based on the performance characteristics of the power components; at the same time, establish an energy monitoring system to obtain the energy state and power output of each power component in real time, and dynamically adjust the energy distribution strategy according to the monitoring results;

[0050] Carry out aerodynamic layout optimization according to the distributed power layout scheme. The aerodynamic layout optimization is to use computational fluid dynamics tools to numerically simulate the aerodynamic performance of the drone under the distributed power layout, analyze the influence of the positions of different power components on the airflow, evaluate the lift, drag, and pitching moment, evaluate the aerodynamic performance, and adjust the position and installation angle parameters of the power components according to the evaluation results.

[0051] Preferably, global management carried out according to the distributed power layout scheme further includes:

[0052] Conduct structural strength analysis according to the distributed power layout scheme. The structural strength analysis is to calculate the dynamic loads generated by the power components on the UAV structure based on their working states, determine the aerodynamic loads borne by the UAV under different flight conditions in combination with the results of aerodynamic performance evaluation, establish a structural finite element model of the UAV using finite element analysis software, apply the dynamic loads and aerodynamic loads to the model, analyze the stress and strain distribution of the UAV structure under various loads, and finally select appropriate materials and adjust the structural layout according to the strength analysis results;

[0053] Conduct thermal management optimization according to the distributed power layout scheme. The thermal management optimization is to determine the heat generation of the power components during operation, analyze the distribution of heat sources and the heat transfer path, establish a thermal management model using the physical principles of heat conduction, convection and radiation, simulate the temperature field distribution of the UAV under different flight conditions, confirm the heat dissipation methods according to the temperature field simulation results, and the heat dissipation methods include air cooling, liquid cooling or heat pipe cooling. Install temperature sensors to monitor the temperature of the power components and key parts in real time, and dynamically adjust the working parameters of the heat dissipation system according to the temperature monitoring results;

[0054] Finally, obtain the distributed power layout scheme completed by global management.

[0055] Preferably, conduct global collaborative optimization according to the global management results, and conduct testing and verification after the collaborative optimization is completed, including:

[0056] Integrate the teaching model of the UAV hydrogen energy power system into the collaborative optimization platform, and determine the optimization objectives and constraints;

[0057] Confirm the model data in the distributed power layout scheme obtained after global management, and conduct simulation analysis on each data;

[0058] The simulation analysis includes energy management simulation, aerodynamic layout simulation, structural strength simulation and thermal management simulation;

[0059] Compare and evaluate the simulation analysis results with the optimization objectives and constraints in the teaching model of the UAV hydrogen energy power system, and judge whether the simulation analysis results meet the optimization objectives and constraints according to the evaluation results;

[0060] Adjust the design variables using the genetic algorithm according to the evaluation results, where the genetic algorithm automatically iterates;

[0061] Conduct UAV simulated flight tests after the design variable adjustment is completed, and collect simulated flight test data in real time;

[0062] Compare the simulated analysis test data collected in real time with the simulation data, and optimize the scheme again according to the comparison results.

[0063] Preferably, based on the target optimization index and constraint conditions, the particle swarm optimization algorithm is used to solve the installation position of the power components to obtain the optimal installation position, including:

[0064] Based on the current flight mission, combined with the target optimization index and constraint conditions, determine the iteration coefficient of the particle swarm optimization algorithm;

[0065] Obtain the maximum number of iterations corresponding to the iteration coefficient from the preset data table;

[0066] Based on the maximum number of iterations, the initial inertia weight, and the end inertia weight, determine the dynamic decay weight determination value of the inertia weight;

[0067] Based on the maximum number of iterations and the dynamic decay weight determination value, design the particle swarm optimization algorithm, and solve the installation position of the power components based on the particle swarm optimization algorithm to obtain the optimal installation position.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] 1. A method for optimizing the distributed power layout of a hydrogen energy unmanned aerial vehicle provided by the present invention can enable students to clearly see how each component inside the power system works together and the processes of energy flow and signal interaction by confirming the interaction relationship between components and establishing a teaching model accordingly.

[0070] 2. A method for optimizing the distributed power layout of a hydrogen energy unmanned aerial vehicle provided by the present invention can draw on past successful experiences, avoid repeating mistakes, improve the design efficiency and success rate by retrieving historical distributed power layout schemes and combining with the current analysis results, and optimize the preliminary position of the power components using the particle swarm optimization algorithm. This intelligent algorithm can efficiently search for the optimal solution and improve the overall performance of the layout scheme.

[0071] 3. A method for optimizing the distributed power layout of a hydrogen energy unmanned aerial vehicle provided by the present invention optimizes the component position and installation angle, reduces resistance, increases lift, improves the pitching moment balance, enhances flight stability and controllability, and is especially suitable for high-speed or large angle-of-attack flight scenarios. The simulation analysis covers multiple aspects such as energy management, aerodynamic layout, structural strength, and thermal management, ensuring a comprehensive evaluation and optimization of the unmanned aerial vehicle in multiple key performances. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the optimization steps for the distributed power layout of the hydrogen energy unmanned aerial vehicle of the present invention;

[0073] Figure 2 It is a schematic diagram of the optimization process for the distributed power layout of the hydrogen energy unmanned aerial vehicle of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0074] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0075] To solve the problem in the prior art that there is no effective establishment of the actual physical model of the unmanned aerial vehicle (UAV) and the standard teaching model of the UAV, resulting in the inability to further understand the detailed component composition, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment:

[0076] An optimization method for the distributed power layout of a hydrogen energy UAV, including:

[0077] Establish a parametric model according to the geometric structure and power system of the UAV, establish a teaching model of the hydrogen energy power system of the UAV according to the established parametric model, perform a distributed power layout according to the established parametric model, and define the constraint conditions;

[0078] Perform global management according to the distributed power layout plan. The global management includes energy management, aerodynamic layout optimization, structural strength analysis, and thermal management optimization. Perform global collaborative optimization according to the global management results, and perform testing and verification after the collaborative optimization is completed.

[0079] Specifically, through the establishment of the parametric model, factors such as the geometric structure and power system of the UAV can be comprehensively considered, thereby performing global layout optimization. This global consideration helps to improve the overall performance of the UAV, including multiple aspects such as aerodynamic efficiency, structural strength, and thermal management. The global management part particularly emphasizes energy management, which means that the UAV can more effectively utilize the energy provided by the hydrogen energy power system. Through technologies such as intelligent algorithms and dynamic programming, the UAV can adjust the energy distribution in real time according to the flight mission and environmental conditions, thereby achieving the maximum utilization of energy. Aerodynamic layout optimization is the key to improving the flight performance of the UAV. By optimizing the aerodynamic layout of the UAV, the flight resistance can be reduced and the flight efficiency can be improved. At the same time, combined with the layout of the distributed propulsion system, flutter and gust response can be further suppressed, and the flight stability of the UAV can be improved. Structural strength analysis is considered to ensure that the UAV can withstand various mechanical loads during flight and ensure flight safety. This is of great significance for improving the reliability and service life of the UAV.

[0080] Establishing a parametric model according to the geometric structure and power system of the UAV includes:

[0081] First, establish a parametric model for the geometric structure of the unmanned aerial vehicle (UAV).

[0082] Among them, first collect the geometric dimensions, shape features of the UAV and the relative positions of its various components, including the fuselage, airfoil and tail. Use computer-aided tools to generate a three-dimensional model of the UAV based on the collected data of the geometric dimensions, shape features of the UAV and the relative positions of its various components.

[0083] Perform parametric processing on the generated three-dimensional model of the UAV. The parametric processing is to define geometric features by controlling key dimensions and shapes.

[0084] Discretize the continuous geometric features in the three-dimensional model of the UAV after parametric processing into a grid model suitable for numerical calculation, and finally obtain the geometric model of the UAV.

[0085] Next, establish a parametric model for the power system of the UAV.

[0086] Among them, first collect the relevant parameters of the UAV power system, including the technical parameters, power characteristics and energy density of the battery, hydrogen storage tank, fuel cell and motor, and determine the interaction relationship between each power component. The power components include the battery, hydrogen storage tank, fuel cell and motor.

[0087] Based on the collected data of the geometric dimensions, shape features of the UAV and the relative positions of its various components, establish a mathematical model of the hydrogen energy power system. The mathematical model of the hydrogen energy power system includes energy conversion and power output. Use the state equation and the principle of energy conservation to establish a dynamic model of the mathematical model of the hydrogen energy power system, and finally obtain the power model of the UAV.

[0088] Couple the geometric model of the UAV and the power model of the UAV. After coupling, obtain the parametric model of the UAV.

[0089] Specifically, through parametric processing, it is convenient to define geometric features by controlling key dimensions and shapes, thus quickly generating a 3D model of the UAV. Compared with traditional manual modeling, this method greatly improves the modeling efficiency. The establishment of the parametric model of the power system also depends on the collection and collation of key parameters, making the modeling process more systematic and efficient. The parametric model allows the adjustment and optimization of the geometric structure and power system of the UAV to adapt to different application scenarios and requirements. For example, the flight performance of the UAV can be optimized by adjusting parameters such as the airfoil and fuselage size of the UAV. The parametric model of the power system also allows the adjustment of technical parameters of components such as batteries and motors to achieve better energy conversion and power output. Parametric modeling can reduce the dependence on physical UAVs, verify and improve the design through virtual simulation and testing, thus reducing the R & D and production costs. At the same time, the parametric model is also convenient for multi-scheme comparison and optimization, which helps to select the design scheme with the highest cost performance. The continuous geometric features in the 3D model of the UAV completed by parametric processing are discretized into a grid model suitable for numerical calculation, making the subsequent numerical calculation and simulation analysis more accurate and efficient. The establishment of the mathematical model of the power system also depends on mathematical tools such as state equations and the principle of energy conservation, making the simulation analysis of the UAV power system more accurate and reliable.

[0090] Based on the established parametric model, establish a teaching model of the UAV hydrogen energy power system, including:

[0091] Retrieve the key technical parameters in the UAV parametric model. The key technical parameters include hydrogen storage tanks, fuel cells, electric motors, and batteries. Among them, the parameters of the hydrogen storage tank are the capacity, pressure, volume, and material for hydrogen storage; the parameters of the fuel cell are the power output, efficiency, energy density, and reaction kinetics of the fuel cell; the parameters of the electric motor are the power, speed, efficiency, and torque curve of the electric motor; the parameters of the battery are the charging and discharging characteristics, capacity, durability, and charging time of the battery;

[0092] After the retrieval of the key technical parameters is completed, determine the interaction relationship between the components;

[0093] After the interaction relationship is confirmed, establish a teaching model;

[0094] The teaching model is to establish a data model for each power system component, including the static and dynamic behavior models of the component;

[0095] Integrate the models of each component into a complete power system model, and define the energy flow and signal interaction between the components;

[0096] Finally, use the teaching model development tool to construct a teaching model of the UAV hydrogen energy power system for the power system model, and design the user interface.

[0097] Specifically, through the parametric model, detailed data retrieval and model establishment are carried out for each key component (hydrogen storage tank, fuel cell, motor, battery) of the UAV hydrogen energy power system, enabling students to comprehensively and systematically understand the composition and working principle of the UAV hydrogen energy power system. The key technical parameters of each component are listed, such as the capacity and pressure of the hydrogen storage tank, which helps students deeply understand the performance characteristics of each component and the matching relationship between them. By confirming the interaction relationship between components and establishing a teaching model based on this, students can clearly see how each component inside the power system works together and the process of energy flow and signal interaction. The teaching model establishes static and dynamic behavior models for each power system component, which helps students understand the performance of the component under different working conditions and the dynamic response characteristics of the overall system. Integrating the component models into a complete power system model enables students to intuitively see the operating state and performance of the entire system, thereby deepening their understanding of the overall structure of the UAV hydrogen energy power system. Using the teaching model development tool to construct the power system model not only improves the accuracy and reliability of the model but also makes the teaching more intuitive and vivid. This helps students better understand and master relevant knowledge. By establishing a teaching model of the UAV hydrogen energy power system, it helps to promote the development and popularization of hydrogen energy technology in the education field, cultivate more talents with hydrogen energy technology knowledge and skills, and lay a solid foundation for the wide application of hydrogen energy technology.

[0098] To solve the problem in the prior art that there is no targeted performance analysis based on the actual model of the UAV, resulting in the inability to perform accurate power layout, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0099] Conduct distributed power layout according to the established parametric model, and define the constraint conditions, including:

[0100] Before conducting the distributed power layout, first determine the design objectives of the UAV, and the design objectives include the maximum flight time, load capacity, energy efficiency, and flight stability;

[0101] After the design objectives are determined, determine the constraint conditions for the layout design, and the constraint conditions include the weight center position, center of gravity position, structural strength, heat dissipation requirements, and space occupancy of the power system;

[0102] Finally, complete the definition of the design objectives and constraint conditions.

[0103] After the design objectives and constraints are defined, analyze the parametric model of the UAV;

[0104] Among them, analyze the key parameters and performance characteristics of the power components in the parametric model. The power components include hydrogen storage tanks, fuel cells, electric motors, and batteries. After the analysis, confirm the interaction relationship between the power components;

[0105] Retrieve the historical distributed power layout schemes from the database, and combine the historical distributed power layout schemes with the analysis results of the key parameters and performance characteristics and the interaction relationship between the power components. After the combination, obtain the preliminary positions of the power components;

[0106] Use the particle swarm optimization algorithm to optimize the layout of the preliminary positions of the power components. During the optimization process, adjust the key parameters of the power components in the layout scheme, including changing the installation positions and angles of the power components, and adjusting the connection methods between the components;

[0107] After the adjustment, obtain the distributed power layout scheme of the parametric model.

[0108] In one embodiment, using the particle swarm optimization algorithm to optimize the layout of the preliminary positions of the power components includes:

[0109] Based on the overall characteristics of the UAV and the characteristics of the power components of the hydrogen energy UAV, determine the layout constraints between the power components;

[0110] Based on the current flight mission of the hydrogen energy UAV, determine the index requirements for flight air resistance, flight balance center of gravity, and flight endurance time. Based on the index requirements, determine the weighted weights for flight air resistance, flight balance center of gravity, and flight endurance time;

[0111] Based on flight air resistance, flight balance center of gravity, flight endurance time, and weighted weights, determine the target optimization index;

[0112] Based on the target optimization index and constraints, solve for the installation positions of the power components based on the particle swarm optimization algorithm to obtain the optimal installation positions;

[0113] Determine the feasible installation angles and the feasible connection methods of the power components at the optimal safety positions;

[0114] Based on the optimal installation positions, establish a mathematical model of the hydrogen energy UAV with hydrogen energy consumption and battery energy consumption as prediction indicators;

[0115] Based on the mathematical model, determine the predicted hydrogen energy consumption and predicted battery energy consumption under the feasible installation angles and feasible connection methods of the power components in the current flight mission, and select the feasible installation angles and feasible connection methods of the power components with the minimum comprehensive energy consumption of the predicted hydrogen energy consumption and predicted battery energy consumption as the target installation angles and target connection methods.

[0116] In this embodiment, the weighting weights are determined based on the current flight mission. If there are requirements for flight speed, a larger weighting weight is set for the flight air resistance. If there are requirements for flight duration, a larger weighting weight is set for the flight endurance time.

[0117] In this embodiment, the target optimization is a target group, including the final targets for flight air resistance, flight balance center of gravity, and flight endurance time.

[0118] In this embodiment, there are multiple feasible installation angles and feasible connection methods of the components.

[0119] The beneficial effects of the above design scheme are as follows: By determining the constraint conditions for the layout between the power components based on the overall characteristics of the hydrogen energy unmanned aerial vehicle and the characteristics of the power components, determining the target optimization indicators based on the flight air resistance, flight balance center of gravity, flight endurance time, and weighting weights, and solving for the installation positions of the power components based on the target optimization indicators and constraint conditions using the particle swarm optimization algorithm to obtain the optimal installation positions, the determination of the optimal installation positions based on the particle swarm optimization algorithm is realized, ensuring that the optimal installation positions meet the requirements of the current flight mission. At the same time, using the hydrogen energy consumption and battery energy consumption as the prediction indicators, the target installation angles and target connection methods are determined at the optimal installation positions to ensure the minimum energy consumption, guaranteeing the optimality of the distributed power layout of the obtained unmanned aerial vehicle from both the aspects of requirements and costs.

[0120] In one embodiment, based on the target optimization indicators and constraint conditions, solving for the installation positions of the power components using the particle swarm optimization algorithm to obtain the optimal installation positions includes:

[0121] Based on the current flight mission, combining the target optimization indicators and constraint conditions, determine the iteration coefficient of the particle swarm optimization algorithm;

[0122]

[0123] where K represents the iteration coefficient of the particle swarm optimization algorithm, represents the complexity of the current flight mission, with a value range of (0, 1), represents the number of target optimization indicators, represents the preset maximum number of indicators, represents the natural constant, with a value of 2.72, The maximum lateral movement standard value representing the optimal position of the constraint condition, with a value range of (0, 1). The maximum lateral standard movement value representing the optimal position of the constraint condition, with a value range of (0, 1); Represents the comprehensive correlation degree of the target optimization index, with a value range of 0 to 1.

[0124] Obtain the maximum number of iterations corresponding to the iteration coefficient from the preset data table;

[0125] Based on the maximum number of iterations, the initial inertia weight, and the ending inertia weight, determine the dynamic decay weight determination value for the inertia weight;

[0126]

[0127] Among them, Represents the dynamic decay weight determination value, Is the reference movement range value, Represents the initial inertia weight, Represents the ending inertia weight, Represents the maximum number of iterations, Represents the current number of iterations;

[0128] Based on the maximum number of iterations and the dynamic decay weight determination value, design a particle swarm optimization algorithm, and solve the installation position of the power component based on the particle swarm optimization algorithm to obtain the optimal installation position.

[0129] In this embodiment, the initial inertia weight is usually 0.9, and the ending inertia weight is usually 0.4.

[0130] In this embodiment, the reference movement range value is preset, The larger it is, the larger the dynamic decay weight determination value is.

[0131] In this embodiment, the larger the iteration coefficient is, the larger the corresponding maximum number of iterations is.

[0132] In this embodiment, the smaller the comprehensive correlation degree of the target optimization index is, the more complex the particle swarm is, and the larger the corresponding iteration coefficient is

[0133] The beneficial effect of the above design scheme is that by designing the specific parameters of the particle swarm optimization algorithm based on the current flight mission, combining the target optimization index and the constraint conditions, it ensures the accuracy and practicality of the optimal installation position obtained based on the particle swarm optimization algorithm, providing a basis for power layout optimization.

[0134] Specifically, the design objectives of the drone are first clarified, including the maximum flight time, payload capacity, energy efficiency, and flight stability, which provide a clear direction and benchmark for subsequent design and optimization. By defining constraints such as the position of the center of weight, the center of gravity, structural strength, heat dissipation requirements, and the space occupancy of the power system, the feasibility and safety of the layout design in practical applications are ensured. In-depth analysis of the key parameters and performance characteristics of the power components in the parametric model helps to understand the performance characteristics and interaction relationships of each component, providing a scientific basis for the layout design. By retrieving historical distributed power layout schemes and combining with the current analysis results, successful past experiences can be learned from, avoiding repeated mistakes, and improving the design efficiency and success rate. The particle swarm optimization algorithm is used to optimize the initial positions of the power components. This intelligent algorithm can efficiently search for the optimal solution and improve the overall performance of the layout scheme. During the optimization process, adjustments are allowed to the key parameters of the power components in the layout scheme, including changing the installation position and angle of the power components and adjusting the connection methods between components, which increases the flexibility and adaptability of the layout. The application of the parametric model and the optimization algorithm makes the layout design more standardized and modular, which helps to simplify the production, assembly, and maintenance processes of the drone.

[0135] To solve the problem in the prior art that there is no more meticulous management and analysis of the completed layout scheme, resulting in poor integrity of the power layout scheme, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0136] Perform global management according to the distributed power layout scheme, including:

[0137] First, confirm the key data in the distributed power layout scheme, including layout scheme data, parametric model data, and external environment data;

[0138] Perform energy management according to the distributed power layout scheme. Energy management is to analyze the power required in different stages based on the design objectives of the drone and in combination with the flight mission profile, and then formulate an energy allocation strategy based on the performance characteristics of the power components. At the same time, establish an energy monitoring system to obtain the energy status and power output of each power component in real time, and dynamically adjust the energy allocation strategy according to the monitoring results;

[0139] Perform aerodynamic layout optimization according to the distributed power layout scheme. Aerodynamic layout optimization is to use computational fluid dynamics tools to numerically simulate the aerodynamic performance of the drone under the distributed power layout, analyze the influence of the positions of different power components on the airflow, evaluate the lift, drag, and pitching moment, evaluate the aerodynamic performance, and adjust the position and installation angle parameters of the power components according to the evaluation results.

[0140] Conduct structural strength analysis according to the distributed power layout scheme. The structural strength analysis is to calculate the dynamic loads generated by the power components on the UAV structure based on their working states, determine the aerodynamic loads borne by the UAV under different flight conditions by combining the results of aerodynamic performance evaluation, establish a structural finite element model of the UAV using finite element analysis software, apply the dynamic loads and aerodynamic loads to the model, analyze the stress and strain distribution of the UAV structure under various loads, and finally select appropriate materials and adjust the structural layout according to the strength analysis results;

[0141] Conduct thermal management optimization according to the distributed power layout scheme. The thermal management optimization is to determine the heat generation of the power components during operation, analyze the distribution of heat sources and the heat transfer path, establish a thermal management model using the physical principles of heat conduction, convection and radiation, simulate the temperature field distribution of the UAV under different flight conditions, confirm the heat dissipation methods according to the temperature field simulation results, the heat dissipation methods include air cooling, liquid cooling or heat pipe cooling, install temperature sensors to monitor the temperature of the power components and key parts in real time, and dynamically adjust the working parameters of the heat dissipation system according to the temperature monitoring results;

[0142] Finally, obtain the distributed power layout scheme with global management completed.

[0143] Specifically, by integrating modules such as energy management, aerodynamic optimization, structural strength, and thermal management, the overall system collaborative optimization of the power layout with aerodynamic, structural, and thermal characteristics is achieved, avoiding the limitations of single-discipline optimization. The unified confirmation and iterative update of key data (layout scheme, parametric model, environmental data) ensure consistent design basis for each link, reduce design conflicts, match the power demand in real time according to the flight mission profile (such as climb, cruise, landing), combine the performance of power components (such as motor efficiency, battery discharge characteristics), optimize the energy distribution strategy, extend the endurance of the UAV, monitor the energy states (remaining power, power output) of each power component (such as motor, battery) in real time, dynamically adjust the load, avoid energy waste or overload, analyze the interference of distributed power components (such as multi-rotors, ducted fans) on the air flow (such as wing vortex, engine jet impact) through CFD simulation, optimize the component position and installation angle, reduce drag, increase lift, improve the pitch moment balance, enhance flight stability and controllability, especially suitable for high-speed or high angle-of-attack flight scenarios, combine the dynamic loads (such as motor vibration, thrust reaction force) with the aerodynamic loads (such as lift, drag), accurately evaluate the structural stress and strain through finite element analysis, avoid the redundancy or weakness problems of traditional "empirical design", optimize the material selection (such as carbon fiber composite material) and structural layout (such as the position of stiffeners), increase the strength while reducing the weight, extend the life of the UAV, establish heat conduction, convection, and radiation models, simulate the temperature field distribution under different working conditions (such as hovering, high-speed flight), design the heat dissipation scheme (air cooling, liquid cooling, heat pipe) specifically, avoid overheating failure of power components, monitor the temperature in real time and dynamically adjust the heat dissipation system (such as fan speed, coolant flow rate) to adapt to complex environments (high temperature, low temperature, high altitude), and the parameters such as the position, angle, and heat dissipation method of power components are highly adjustable to facilitate adaptation to different mission requirements (such as load, range, speed) or model iteration (such as from multi-rotor to compound wing).

[0144] Conduct global collaborative optimization based on the global management results. After the collaborative optimization is completed, conduct tests and verifications, including:

[0145] Integrate the teaching model of the UAV hydrogen energy power system into the collaborative optimization platform and determine the optimization objectives and constraints;

[0146] Confirm the model data in the distributed power layout scheme obtained after global management, and conduct simulation analysis on each data;

[0147] The simulation analysis includes energy management simulation, aerodynamic layout simulation, structural strength simulation, and thermal management simulation;

[0148] Compare and evaluate the simulation analysis results with the optimization objectives and constraints in the teaching model of the UAV hydrogen energy power system, and judge whether the simulation analysis results meet the optimization objectives and constraints according to the evaluation results;

[0149] Adjust the design variables using a genetic algorithm according to the evaluation results, where the genetic algorithm iterates automatically;

[0150] After the design variable adjustment is completed, conduct a simulated flight test of the drone and collect the simulated flight test data in real time;

[0151] Compare the simulated analysis test data collected in real time with the simulation data, and optimize the scheme again according to the comparison results.

[0152] Specifically, through the global management results for collaborative optimization, it ensures the optimal performance of each component of the drone hydrogen energy power system (such as energy management, aerodynamic layout, structural strength, thermal management, etc.) as a whole. This systematic optimization method can improve the overall performance of the drone more than optimizing each part separately. Integrating the teaching model of the drone hydrogen energy power system into the collaborative optimization platform realizes the unified management and optimization of the model. At the same time, using the genetic algorithm to automatically iterate and adjust the design variables improves the automation degree of the optimization process, reduces manual intervention, and improves efficiency. The simulation analysis covers multiple aspects such as energy management, aerodynamic layout, structural strength, and thermal management, ensuring a comprehensive evaluation and optimization of the drone in multiple key performances. This comprehensive simulation analysis helps to discover potential problems and make targeted improvements. Collect data in real time during the simulated flight test of the drone and compare it with the simulation data. This real-time data collection and analysis helps to verify the accuracy of the simulation results and optimize the scheme further according to the actual situation. The adjustment and optimization according to the evaluation results mentioned show a high degree of flexibility and iterability. This means that during the optimization process, the scheme can be continuously adjusted and improved according to the actual situation to achieve the best performance.

[0153] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0154] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made in these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A method for optimizing the distributed power layout of a hydrogen-powered UAV, characterized in that: include: According to the geometric structure and power system of the UAV, a parametric model is established. According to the established parametric model, a teaching model of the hydrogen power system of the UAV is established. According to the established parametric model, a distributed power layout is carried out, and the constraint conditions are defined; Carry out global management according to the distributed power layout plan, including energy management, aerodynamic layout optimization, structural strength analysis and thermal management optimization. Carry out global collaborative optimization according to the global management results, and conduct testing and verification after the collaborative optimization is completed. Before conducting a distributed power layout, the design goals of the UAV should be determined first, and after the design goals are determined, the constraints of the layout design should be determined; After the design goals and constraints are defined, the parametric model of the UAV is analyzed.

2. A method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 1, characterized in that: Parameterized modeling is performed based on the geometry and power system of the drone, including: First, the geometric structure of the UAV is parametrically modeled; Among them, the geometric dimensions, shape characteristics and relative positions of the UAV's components, including the fuselage, wing profile and tail, are first collected, and the collected geometric dimensions, shape characteristics and relative positions of the UAV's components are used to generate a three-dimensional model of the UAV using computer-aided tools; The generated UAV 3D model is parameterized, and the parameterization is to define geometric features by controlling key dimensions and shapes; Discretize the continuous geometric features in the parametrically processed three-dimensional model of the UAV into a mesh model suitable for numerical calculation, and finally obtain the UAV geometric model; Then, a parameterized model of the UAV's power system is established; First, the relevant parameters of the UAV power system are collected, including the technical parameters, power characteristics and energy density of the battery, hydrogen storage tank, fuel cell and motor, and the interaction relationship between each power component is determined. The power components include batteries, hydrogen storage tanks, fuel cells and motors; According to the collected data of the geometric size, shape characteristics and relative position of each component of the UAV, a mathematical model of the hydrogen power system is established. The mathematical model of the hydrogen power system includes energy conversion and power output. The mathematical model of the hydrogen power system is dynamically established using the state equation and the principle of energy conservation, and finally the UAV power model is obtained; The UAV geometric model and the UAV dynamic model are coupled, and after the coupling is completed, the UAV parameterized model is obtained.

3. The method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 1 is characterized in that: According to the established parameterized model, the teaching model of the UAV hydrogen power system is established, including: The key technical parameters in the parametric model of the drone are retrieved. The key technical parameters include hydrogen storage tanks, fuel cells, motors and batteries. The parameters of the hydrogen storage tank are the capacity, pressure, volume and material of hydrogen storage; the parameters of the fuel cell are the power output, efficiency, energy density and reaction kinetics of the fuel cell; the parameters of the motor are the power, speed, efficiency and torque curve of the motor; the parameters of the battery are the charging and discharging characteristics, capacity, durability and charging time of the battery; After the key technical parameters are confirmed and retrieved, the interaction relationship between components is determined; After the interaction relationship is confirmed, a teaching model is established; The teaching model is to establish a data model for each power system component, including static and dynamic behavior models of the components; Integrate the component models into a complete power system model and define the energy flow and signal interaction between components; Finally, the teaching model development tool is used to build a UAV hydrogen power system teaching model of the power system model and design the user interface.

4. The method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 1, characterized in that: Distributed power layout is carried out according to the established parameterized model, and constraints are defined, including: Design goals include maximum flight time, payload capacity, energy efficiency, and flight stability; Constraints include center of mass location, center of gravity location, structural strength, heat dissipation requirements, and space occupation by the power system; Finally, complete the definition of design goals and constraints.

5. A method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 4, characterized in that: Distributed power layout is carried out according to the established parameterized model, and constraints are defined. include: Among them, the key parameters and performance characteristics of the power components in the parameterized model are analyzed. The power components include hydrogen storage tanks, fuel cells, electric motors and batteries. After the analysis is completed, the interaction relationship between the power components is confirmed; Retrieving historical distributed power layout plans from a database, combining the historical distributed power layout plans with the analysis results of key parameters and performance characteristics and the interaction relationship between power components, and obtaining preliminary positions of the power components after the combination is completed; The particle swarm optimization algorithm is used to optimize the layout of the preliminary position of the power component. During the optimization process, the key parameters of the power component in the layout plan are adjusted, including changing the installation position and angle of the power component and adjusting the connection method between the components. After the adjustment is completed, the distributed power layout plan of the parameterized model is obtained.

6. A method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 5, characterized in that: The particle swarm optimization algorithm is used to optimize the layout of the preliminary position of the power components, including: Based on the overall characteristics of the hydrogen-powered drone and the characteristics of its power components, determine the constraints on the layout of the power components; Based on the current flight mission of the hydrogen-powered UAV, determine the index requirements for flight air resistance, flight balance center of gravity and flight endurance time, and based on the index requirements, determine the weighted weights of flight air resistance, flight balance center of gravity and flight endurance time; Determine the target optimization index based on flight air resistance, flight balance center of gravity, flight endurance, and weighted weights; Based on the target optimization index and constraint conditions, the installation position of the power component is solved based on the particle swarm optimization algorithm to obtain the optimal installation position; Determine the feasible installation angle and the feasible connection mode of the power component in the optimal safety position; Based on the optimal installation position, a mathematical model of hydrogen-powered UAV is established with hydrogen energy consumption and battery energy consumption as prediction indicators; Based on the mathematical model, the predicted hydrogen energy consumption and the predicted battery energy consumption under the feasible installation angles and the feasible connection modes of the power components in the current flight mission are determined, and the feasible installation angle and the feasible connection mode of the power components with the minimum combined energy consumption of the predicted hydrogen energy consumption and the predicted battery energy consumption are selected as the target installation angle and the target connection mode.

7. The method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 1, characterized in that: Global management based on the distributed power layout plan, including: First, confirm the key data in the distributed power layout plan, including layout plan data, parameterized model data and external environment data; Energy management is carried out according to the distributed power layout plan. Energy management is to analyze the power required at different stages according to the design goals of the UAV and the flight mission profile, and then formulate energy allocation strategies based on the performance characteristics of the power components. At the same time, an energy monitoring system is established to obtain the energy status and power output of each power component in real time, and dynamically adjust the energy allocation strategy according to the monitoring results. The aerodynamic layout is optimized according to the distributed power layout scheme. The aerodynamic layout optimization uses computational fluid dynamics tools to numerically simulate the aerodynamic performance of the UAV under the distributed power layout, analyze the influence of different power component positions on the airflow, evaluate the lift, drag and pitch moment, evaluate the aerodynamic performance, and adjust the position and installation angle parameters of the power components according to the evaluation results.

8. The method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 7, characterized in that: Global management based on the distributed power layout plan also includes: According to the distributed power layout scheme, the structural strength analysis is carried out. The structural strength analysis is to calculate the dynamic load generated by the power component on the UAV structure according to the working state of the power component, and determine the aerodynamic load borne by the UAV under different flight conditions in combination with the aerodynamic performance evaluation results. The structural finite element model of the UAV is established using finite element analysis software, and the dynamic load and aerodynamic load are applied to the model to analyze the stress and strain distribution of the UAV structure under various loads. Finally, according to the strength analysis results, the appropriate material is selected and the structural layout is adjusted; Thermal management optimization is performed according to the distributed power layout scheme. Thermal management optimization is to determine the heat generation of the power components during operation, analyze the distribution of heat sources and the heat flow transfer path, use the physical principles of heat conduction, convection and radiation, establish a thermal management model, simulate the temperature field distribution of the UAV under different flight conditions, and confirm the heat dissipation method based on the temperature field simulation results. The heat dissipation method includes air cooling, liquid cooling or heat pipe cooling. Temperature sensors are installed to monitor the temperature of the power components and key parts in real time. According to the temperature monitoring results, the working parameters of the heat dissipation system are dynamically adjusted; Finally, a distributed power layout plan with global management is obtained.

9. A method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 8, characterized in that: Perform global collaborative optimization based on the global management results. After the collaborative optimization is completed, test and verify it, including: Integrate the teaching model of the UAV hydrogen power system into the collaborative optimization platform and determine the optimization objectives and constraints; Confirm the model data in the distributed power layout scheme obtained after global management, and perform simulation analysis on each data; Simulation analysis includes energy management simulation, aerodynamic layout simulation, structural strength simulation and thermal management simulation; Compare and evaluate the simulation analysis results with the optimization objectives and constraints in the teaching model of the UAV hydrogen power system, and judge whether the simulation analysis results meet the optimization objectives and constraints based on the evaluation results; According to the evaluation results, the design variables are adjusted by using a genetic algorithm, wherein the genetic algorithm automatically iterates; After the design variables are adjusted, simulated flight tests of UAVs are conducted to collect simulated flight test data in real time; Compare the simulation analysis test data collected in real time with the simulation data, and optimize the solution again based on the comparison results.

10. The method for optimizing the distributed power layout of a hydrogen-powered UAV according to claim 6, characterized in that: Based on the target optimization index and constraint conditions, the installation position of the power component is solved based on the particle swarm optimization algorithm to obtain the optimal installation position, including: Based on the current flight mission, combined with the target optimization index and constraints, determine the iteration coefficient of the particle swarm optimization algorithm; From the preset data table, obtain the maximum number of iterations corresponding to the iteration coefficient; Determine a dynamic attenuation weight determination value for the inertia weight based on the maximum number of iterations, the initial inertia weight, and the final inertia weight; Based on the maximum number of iterations and the determined value of the dynamic attenuation weight, a particle swarm optimization algorithm is designed, and the installation position of the power component is solved based on the particle swarm optimization algorithm to obtain the optimal installation position.

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