Vertical take-off and landing aircraft configuration optimization method

Abstract

The invention relates to a vertical take-off and landing aircraft configuration optimization method, and belongs to the technical field of aircraft design, and the method comprises the steps: constructing an aircraft power system product library; acquiring a flight mission index of the target aircraft; establishing a multi-fidelity neural network to predict a second parameter, and determining the type selection of a power system product of the target aircraft in the power system product library based on a flight mission index; constructing a target function of the target aircraft based on the parameters of the model selection of the power system product of the target aircraft, wherein the target function comprises an area ratio, a thrust-weight ratio, a loading ratio and maximum hovering time; solving under the constraint condition of the structural parameters of the target aircraft by taking the maximum area ratio, the maximum thrust-weight ratio, the maximum loading ratio and the maximum hovering time in the target function as targets to obtain the structural parameters of the target aircraft. The mutual influence of pneumatic, structural and power systems is comprehensively considered, and the performance of the vertical take-off and landing aircraft is comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of aircraft design technology, and in particular to a method for optimizing the configuration of a vertical takeoff and landing aircraft. Background Technology

[0002] Vertical takeoff and landing (VTOL) aircraft are widely used in various scenarios, including military, civilian, and emergency rescue, because they can take off and land without relying on a runway.

[0003] However, existing vertical takeoff and landing (VTOL) aircraft face numerous challenges in design and operation, with the complexity of configuration design, limitations in aerodynamic efficiency, and the complexity of flight control systems being the main difficulties. Aircraft performance heavily depends on its configuration optimization, including rotor layout, propulsion system, and airframe structure. Furthermore, while distributed electric propulsion technology has improved the performance of electric multi-rotor UAVs, it has also introduced more complex aerodynamic-structural coupling and structural-dynamic system coupling problems, increasing the complexity of analysis. The emergence of numerous new configurations of electric multi-rotor UAVs has rendered the traditional "sketch-build-fly-iterate" design method inadequate in both efficiency and results. This necessitates the introduction of multidisciplinary optimization design to consider the impact of aerodynamic characteristics on the overall parameters of the UAV.

[0004] Limited by current battery technology, battery energy density is far lower than that of aviation fuel, which restricts the endurance of electric multi-rotor drones. Optimizing the power system of electric multi-rotor drones has become another key challenge. The diverse configurations of electric multi-rotor drones also increase the difficulty of flight control system design, especially given the complexity of the aerodynamic characteristics and operational response of new configurations and the high coupling between their channels. Addressing these issues, designing and optimizing the flight control system for new configurations of electric multi-rotor drones is also crucial.

[0005] In summary, existing technologies do not fully consider the interaction between the aerodynamics, structure, and propulsion system of the configuration, resulting in poor optimization of the overall layout and structural parameters of the UAV, thereby reducing the flight performance of the vertical take-off and landing aircraft. Summary of the Invention

[0006] In view of this, it is necessary to provide a configuration optimization method for vertical take-off and landing (VTOL) aircraft to solve the problem of low flight performance of VTOL aircraft.

[0007] To address the aforementioned problems, in a first aspect, the present invention provides a method for optimizing the configuration of a vertical takeoff and landing (VTOL) aircraft, comprising: Construct an aircraft propulsion system product library, which includes an electric motor product library, an electronic speed controller product library, and a battery product library; Acquire the flight mission indicators of the target aircraft, including: maximum takeoff weight and hovering time at maximum takeoff weight; The first parameter is input into the fully trained multi-fidelity neural network to obtain the second parameter. The first parameter includes the rotor diameter of the target aircraft, the longitudinal height between the rotors, the lateral stagger ratio, and the total lift of the two rotors. The second parameter includes the upper rotor speed, the lower rotor speed, the rotor torque, the upper motor power, and the lower motor power of the target aircraft. Based on the second parameter and the flight mission indicators, the selection of the power system products for the target aircraft is determined from the power system product library. The selection of the power system products for the target aircraft includes: motor selection, ESC selection, and battery selection. Based on the selection parameters of the target aircraft's power system products, the objective function of the target aircraft is constructed. The objective function includes: area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time. The constraints for obtaining the structural parameters of the target aircraft include: the number of rotors, the number of blades, the vertical distance between the upper and lower rotors, the distance from the central axis of the upper rotor to the central axis of the casing, the distance from the central axis of the lower rotor to the central axis of the casing, and the rotor radius. Under constraints, the optimal structural parameters of the target aircraft are obtained by solving the objective function with the objectives of maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time.

[0008] In one possible implementation, the motor with the smallest rated voltage among the motors that meet the speed constraint, torque constraint, and first current constraint in the motor product library is determined as the first motor selection, and the speed constraint, torque constraint, and first current constraint are all obtained based on the second parameter. The difference between the nominal KV value and the ideal KV value of the first motor selection is sorted, and the motor with the smallest difference is determined as the second motor selection; The motor with the smallest nominal maximum operating current in the second motor selection is selected as the third motor; The motor with the lowest nominal internal resistance in the third motor selection is selected as the fourth motor. The motor with the lowest nominal no-load current among the fourth motor selections is selected as the fifth motor; The motor selection for the target aircraft is determined based on the fifth motor selection. In one possible implementation, the step of determining the ESC selection includes: In the ESC product library, the ESC selection that meets the second current constraint condition and the rated voltage constraint condition is determined as the first ESC selection; In the first ESC selection, the ESC with the lowest rated operating voltage is selected as the second ESC; In the second ESC selection, the ESC with the highest operating current is selected as the third ESC; The motor selection for the target aircraft is determined based on the third ESC selection.

[0009] In one possible implementation, the steps for determining the battery selection include: In the battery product library, the battery selection that meets the capacitance constraint, series voltage constraint, and nominal discharge rate constraint is determined as the first battery selection; In the first battery selection, the battery pack with the smallest nominal maximum discharge rate is selected as the second battery selection; In the second battery selection, the battery pack with the smallest number of batteries is selected as the third battery selection; In the third battery selection, the battery pack with the lowest price is selected as the fourth battery selection; The battery selection for the target aircraft is determined based on the fourth battery selection.

[0010] In one possible implementation, the expression for the constraint condition of the structural parameters is:

[0011]

[0012] If N=6, then:

[0013]

[0014] If N=8, then:

[0015] In the formula, This indicates the vertical distance between the upper and lower rotors. This indicates the distance from the center axis of the upper rotor to the center axis of the housing. This indicates the distance from the center axis of the lower rotor to the center axis of the housing. Indicates the rotor radius. Indicates the number of rotors. Indicates the number of blades.

[0016] In one possible implementation, the formula for the area ratio is: ( <1)

[0017]

[0018]

[0019]

[0020] In the formula, Indicates the area ratio. This indicates the area of ​​the fuselage box panel. This represents the vertical projection area of ​​the display unit. This represents the radius of the circle that houses the box panel. Indicates the side length of the fuselage box. This represents the longest side of the battery pack. This indicates the shortest side length of the battery pack. R represents the number of rotors, R represents the rotor radius, and L represents the distance from the center axis of the lower rotor to the center axis of the housing.

[0021] In one possible implementation, the thrust-to-weight ratio is expressed as:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] In the formula, Indicates the thrust-to-weight ratio. This indicates the maximum lift of the target aircraft. This indicates the total weight of the target aircraft. This indicates the maximum takeoff weight of the target aircraft. express, express, Indicates the weight of the power system. This indicates the total weight of the carbon fiber plates. This indicates the total weight of the aluminum column. This indicates the total weight of the arm. This indicates the total weight of all components except the designable components. Indicates the weight of the motor. Indicates the weight of the ESC. Indicates the number of rotors. This indicates the number of batteries in the battery pack. Indicates battery weight. Indicates the volume of plate 1. Let the volume of plate 2 be... For the volume of plate 3, For the volume of plate 4, the units of the four variables are all... , This indicates the density of carbon fiber. This indicates the density of aluminum. This indicates the volume of the aluminum column. This indicates the total weight of the aluminum column.

[0030] In one possible implementation, the load ratio is expressed as:

[0031] In the formula, Indicates the load ratio. This indicates the maximum takeoff weight of the target aircraft. This indicates the total weight of the target aircraft.

[0032] In one possible implementation, the expression for the maximum hovering time is:

[0033] In the formula, Indicates the maximum hovering time. This indicates the total capacitance of the battery pack. This indicates the current output by the battery pack when the target aircraft is hovering after taking off at its maximum takeoff weight.

[0034] In one possible implementation, the constraints are solved by maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time in the objective function to obtain the optimal structural parameters of the target aircraft, including: Based on the multi-objective optimization algorithm, the optimal structural parameters of the target aircraft are obtained by solving the objective function with the constraints of maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time.

[0035] The beneficial effects of this invention are as follows: This invention provides a method for optimizing the configuration of a vertical takeoff and landing (VTOL) aircraft. First, a product library for the aircraft's power system is constructed, including a motor product library, an ESC product library, and a battery product library. Flight mission indicators for the target aircraft are obtained, including maximum takeoff weight and hovering time at maximum takeoff weight. A first parameter is input into a fully trained multi-fidelity neural network to obtain a second parameter. Based on the second parameter and the flight mission indicators, the selection of power system products for the target aircraft is determined from the power system product library. The selection of power system products for the target aircraft includes motor selection, ESC selection, and battery selection. The optimal power system selection is determined through flight missions, thereby enabling the aircraft to achieve its optimal configuration. Based on the parameters of the selected power system products for the target aircraft, an objective function is constructed for the target aircraft, including the area ratio, thrust-to-weight ratio, payload ratio, and maximum hovering time. This objective function fully considers the aircraft's flight performance and payload capacity under hovering conditions, and obtains the constraints on the structural parameters of the target aircraft. These structural parameters include the number of rotors, the number of blades, the vertical distance between the upper and lower rotors, the distance from the central axis of the upper rotor to the central axis of the casing, the distance from the central axis of the lower rotor to the central axis of the casing, and the rotor radius. Under these constraints, the optimal structural parameters of the target aircraft are obtained by maximizing the area ratio, thrust-to-weight ratio, payload ratio, and maximum hovering time within the objective function. This invention comprehensively considers the interaction between aerodynamics, structure, and power systems to achieve a comprehensive improvement in the performance of vertical takeoff and landing (VTOL) aircraft. Attached Figure Description

[0036] Figure 1 A flowchart illustrating an embodiment of the vertical takeoff and landing aircraft configuration optimization method provided by the present invention; Figure 2 This is a flowchart illustrating the motor selection process for an embodiment of a vertical takeoff and landing aircraft configuration optimization method provided by the present invention. Figure 3 This is a flowchart illustrating the electronic control unit (ECS) selection process for an embodiment of a vertical takeoff and landing (VTOL) aircraft configuration optimization method provided by the present invention. Figure 4 This is a schematic diagram of the battery pack assembly as an embodiment of the vertical takeoff and landing aircraft configuration optimization method provided by the present invention; Figure 5 A flowchart illustrating the battery selection process for an embodiment of the vertical takeoff and landing aircraft configuration optimization method provided by this invention; Figure 6 A schematic diagram of a dual-layer staggered six-rotor unmanned vehicle configuration, representing an embodiment of the vertical takeoff and landing aircraft configuration optimization method provided by the present invention; Figure 7A schematic diagram of a dual-layer staggered six-rotor UAV fuselage box plate, representing an embodiment of a vertical takeoff and landing aircraft configuration optimization method provided by the present invention; Figure 8 A schematic diagram of a dual-layer staggered six-rotor UAV fuselage strut model, which is an embodiment of the vertical takeoff and landing aircraft configuration optimization method provided by the present invention; Figure 9 A schematic diagram of a dual-layer staggered six-rotor UAV arm model, which is an embodiment of the vertical takeoff and landing aircraft configuration optimization method provided by the present invention; Figure 10 This is a logical diagram of a multi-objective intelligent optimization selection program, which is an embodiment of a vertical takeoff and landing aircraft configuration optimization method provided by the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] Before demonstrating the embodiments, the following terms will be explained.

[0039] Unmanned Aerial Vehicles (UAVs) are a type of unmanned aerial vehicle controlled by radio remote control equipment and automated programs. UAVs can be categorized into three types based on their lift generation method: rotary-wing, fixed-wing, and hybrid-thrust UAVs. Rotary-wing UAVs generate lift through the high-speed rotation of their rotors, fixed-wing UAVs generate lift through the high-speed relative motion between their wings and the airflow, and hybrid-thrust UAVs generate lift through both their rotors and fixed wings. Compared to fixed-wing UAVs, rotary-wing UAVs possess vertical takeoff and landing (VTOL) capabilities and hovering capabilities; compared to hybrid-thrust UAVs, rotary-wing UAVs have a simpler structure and are easier to control. Although rotary-wing UAVs are slower and have less endurance than the other two types, their VTOL capabilities, hovering ability, simple structure, and flexible operation make them highly suitable for short-range, high-precision flight missions, demonstrating unique advantages in complex environments.

[0040] This invention provides a method for optimizing the configuration of a vertical takeoff and landing aircraft, which will be described in detail below.

[0041] Figure 1 A schematic flowchart of an embodiment of the vertical takeoff and landing aircraft configuration optimization method provided by the present invention is shown below. Figure 1 As shown, the configuration optimization method for vertical takeoff and landing aircraft includes: S101. Construct an aircraft power system product library, which includes a motor product library, an electronic speed controller product library, and a battery product library. S102. Obtain the flight mission indicators of the target aircraft, including: maximum takeoff weight and hovering time at maximum takeoff weight; S103. Input the first parameter into the trained multi-fidelity neural network to obtain the second parameter. The first parameter includes the rotor diameter of the target aircraft, the longitudinal height between the rotors, the lateral stagger ratio, and the total lift of the two rotors. The second parameter includes the upper rotor speed, the lower rotor speed, the rotor torque, the upper motor power, and the lower motor power of the target aircraft. S104. Based on the second parameter and the flight mission indicators, determine the selection of the power system product for the target aircraft from the power system product library. The selection of the power system product for the target aircraft includes: motor selection, ESC selection, and battery selection. S105. Construct the objective function of the target aircraft based on the selection parameters of the target aircraft's power system products. The objective function includes: area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time. S106. Obtain the constraints of the structural parameters of the target aircraft, wherein the structural parameters include: number of rotors, number of blades, vertical distance between upper and lower rotors, distance from the central axis of the upper rotor to the central axis of the housing, distance from the central axis of the lower rotor to the central axis of the housing, and rotor radius. S107. Under the constraints, the objective function is solved with the goal of maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time to obtain the optimal structural parameters of the target aircraft.

[0042] Compared with existing technologies, this embodiment provides a vertical takeoff and landing (VTOL) aircraft configuration optimization method. First, it constructs an aircraft power system product library, including a motor product library, an ESC product library, and a battery product library. It then obtains the target aircraft's flight mission indicators, including maximum takeoff weight and hovering time at maximum takeoff weight. A first parameter is input into a fully trained multi-fidelity neural network to obtain a second parameter. Based on the second parameter and the flight mission indicators, it determines the selection of the target aircraft's power system products from the power system product library. The selection of the target aircraft's power system products includes motor selection, ESC selection, and battery selection. Finally, the optimal power system selection is determined through flight missions, thereby enabling the aircraft to achieve its desired configuration. The objective function for the target aircraft is constructed based on the parameters of the selected power system products, considering the maximum takeoff weight and hovering time at that maximum takeoff weight. The objective function includes: area ratio, thrust-to-weight ratio, payload ratio, and maximum hovering time. It fully considers the aircraft's flight performance and payload capacity under hovering conditions, obtaining constraints on the structural parameters of the target aircraft. These structural parameters include: number of rotors, number of blades, vertical distance between the upper and lower rotors, distance from the central axis of the upper rotor to the central axis of the casing, distance from the central axis of the lower rotor to the central axis of the casing, and rotor radius. Under these constraints, the optimal structural parameters of the target aircraft are obtained by maximizing the area ratio, thrust-to-weight ratio, payload ratio, and maximum hovering time within the objective function. This invention comprehensively considers the interaction between aerodynamics, structure, and power systems to achieve a comprehensive improvement in the performance of vertical takeoff and landing aircraft.

[0043] It should be noted that the configuration optimization method of this embodiment is applicable to staggered rotor six-rotor UAVs, and also to staggered rotor eight-rotor UAVs. It can be understood that the staggered rotor UAV of the present invention is applicable to staggered rotor multi-rotor UAVs.

[0044] In some embodiments of the present invention, the motor with the smallest rated voltage among the motor selections that meet the speed constraint, torque constraint, and first current constraint in the motor product library is determined as the first motor selection, and the speed constraint, torque constraint, and first current constraint are all obtained based on the second parameter; The difference between the nominal KV value and the ideal KV value of the first motor selection is sorted, and the motor with the smallest difference is determined as the second motor selection; The motor with the smallest nominal maximum operating current in the second motor selection is selected as the third motor; The motor with the lowest nominal internal resistance in the third motor selection is selected as the fourth motor. The motor with the lowest nominal no-load current among the fourth motor selections is selected as the fifth motor; The motor selection for the target aircraft is determined based on the fifth motor selection. It should be noted that, considering the characteristics of dual-layer staggered rotor design variables with multiple dimensions and high sample acquisition costs, a nested Latin hypercube sampling method (Nested LHS) was adopted for differentiated experimental design. This method constructs a dual-fidelity dataset by using high-density coverage of the medium-fidelity computational region and low-density sampling of the high-fidelity subspace. The high-fidelity data is based on experimental acquisition; the high-fidelity data is calculated using a rapid aerodynamic analysis tool based on the vortex particle method (VPM).

[0045] Based on a limited amount of high-fidelity data and a large amount of medium-fidelity data, combined with a "multi-fidelity neural network architecture," a two-layer interleaved multirotor aerodynamic prediction surrogate model and a multi-fidelity neural network system are constructed for characteristic operating conditions such as hovering, forward flight, and vertical takeoff and landing. Common multi-fidelity modeling methods include: y H =ρ(x)y L +δ(x) y H and y L Let ρ(x) represent the high-fidelity and low-fidelity response values, respectively. x is the input variable, ρ(x) is the multiplicative scaling model, and δ(x) is the additive correction term. However, the relationship between low-fidelity and high-fidelity data in this study exhibits nonlinear characteristics, and this model cannot accurately characterize the complex relationships between multi-fidelity data. To capture the nonlinear correlation, the generalized autoregressive scheme proposed by Meng is adopted: y H =F(x,y L ) in, F=F l +F nl function F l and F nl These represent the linear and nonlinear modeling parts, respectively. To measure the degree of linear correlation, a hyperparameter β is introduced: y H =βF l (x,y L )+(1−β)F nl (x,y L ), β∈[0,1] The parameter β is used to adjust the contribution ratio of high-fidelity and low-fidelity models in the final prediction.

[0046] The neural network architecture employed is a two-layer cascaded structure. The first-level network trains the backbone network using low-fidelity samples to learn the overall trend of aerodynamic response. The second-level network is trained on high-fidelity data and incorporates the output of the first-level network as an auxiliary input variable. It models the coupling mapping relationship between high-fidelity and low-fidelity data by fusing linear and nonlinear functional relationships. In this structure, the high-fidelity network does not directly predict physical quantities such as aerodynamic forces. Instead, it accurately corrects the output of the first-level network through nonlinear transformation and fusion mechanisms, thereby achieving high-precision aerodynamic performance prediction under the condition of limited high-fidelity samples.

[0047] To evaluate the predictive capability of this multi-fidelity surrogate model, it was further compared and validated with classic single-precision data-driven regression models such as Kriging, RBF neural networks, and random forests, as well as the multi-precision surrogate model Co-Kriging. The accuracy level of the model under various operating conditions and different indicators was quantified using metrics such as test set error rate (MAPE), residual standard deviation (STD), and R² coefficient.

[0048] In a specific embodiment of the present invention, the maximum takeoff weight of the dual-layer staggered hexacopter UAV is obtained. That is, the maximum takeoff weight and six design variables in the flight mission specifications. The initial value will be the rotor diameter. Rotor spacing stagger ratio The sum of the lift forces from the upper and lower rotors is input into a fully trained multi-fidelity neural network model, and the corresponding upper rotor speed is output. Lower rotor speed Rotor torque Upper motor power and the power of the lower motor When the aircraft flies at 50% throttle, the aerodynamic forces of the rotor and the operating state of the motor can be calculated as shown in equation (1).

[0049]

[0050] (1) In the formula, This indicates the rotational speed of the upper motor at 50% throttle. This indicates the speed of the lower motor at 50% throttle. This indicates the rotor torque at 50% throttle. This indicates the electrical power of the upper motor at 50% throttle. This indicates that 50% is the electrical power of the lower-level motor. This represents a fully trained multi-fidelity neural network model.

[0051] Obtain the maximum takeoff weight of the dual-layer staggered hexacopter UAV That is, the maximum takeoff weight and six design variables in the flight mission specifications. The initial values ​​of the rotor aerodynamic force and motor operation state when the aircraft flies at 100% throttle can be calculated as shown in equation (1).

[0052]

[0053] In the formula, This indicates the rotational speed of the upper motor when the throttle is at 100%. This indicates the speed of the lower motor when the throttle is at 100%. This represents the rotor torque at 100% throttle. This indicates the electrical power of the upper motor at 100% throttle. This indicates that 100% represents the electrical power of the lower-level motor.

[0054] To ensure that the motor can meet the speed and torque requirements and operate at safe power, the lower limits of speed and torque should be appropriately increased. Therefore, when the motor operates at 50% throttle and 100% throttle, it should meet the constraints shown in equations (3) to (6). Equations (3) and (4) are speed constraints, and equations (5) and (6) are torque constraints.

[0055]

[0056]

[0057]

[0058]

[0059] In the formula, This indicates the actual speed of the motor product at 50% throttle. This indicates the actual speed of the motor product at 100% throttle. This indicates the torque specified by the actual motor product at 50% throttle. This indicates the torque specified by the actual motor product at 100% throttle.

[0060] The first current constraint condition is:

[0061] In the formula, This indicates the motor's nominal maximum current. This represents the rotor torque at 100% throttle. This refers to the motor's KV value. It is the nominal open-circuit voltage. It is the nominal no-load current. This is the equivalent armature internal resistance.

[0062] The KV value of a motor is also an important parameter for selecting motor products. The KV value refers to the increase in motor speed for every 1V increase in voltage. To avoid excessive redundancy in motor performance, an ideal motor, operating at its rated voltage, should have a full-throttle speed of exactly [missing value]. That is, it just meets the speed constraint. Therefore, the formula for calculating the ideal KV value of the motor is shown in equation (3.34), where This is the ideal KV value for the motor.

[0063]

[0064] In the formula, This is the ideal kV value for the motor. This indicates the electrical power of the upper motor at 100% throttle. This indicates the speed of the lower motor when the throttle is at 100%. This indicates the motor's operating voltage.

[0065] The final selected motor products should meet the constraints shown in equation (9). Motor products that do not meet the constraints of equation (9) will be removed from the candidate list. This indicates the rated kV value of the actual motor product. Additionally, it records... and The difference is As shown in equation (10).

[0066]

[0067]

[0068] Once the motor products that meet the constraints are selected and the ideal motor parameters are calculated, the product selection of the motor can be carried out.

[0069] The specific operation of motor product selection is as follows: (1) Sort the rated working voltage of the motor products that meet the constraints and select the motor with the smallest rated voltage; (2) Sort the difference between the nominal KV value and the ideal KV value of the motor selected in the previous step and select the motor with the smallest difference; (3) Sort the nominal maximum working current of the motor selected in the previous step and select the motor with the smallest nominal maximum working current; (4) Sort the nominal internal resistance of the motor selected in the previous step and select the motor with the smallest nominal internal resistance; (5) Sort the nominal no-load current of the motor selected in the previous step and select the motor with the smallest nominal no-load current.

[0070] In summary, the process of optimizing motor selection can be summarized as follows: Figure 2The flowchart shown is shown. The order in which the parameters are selected should conform to the order shown in equation (11).

[0071]

[0072] The diagram shows the motor information to be collected, including motor model name, compatible blade size, nominal operating voltage, nominal kV value, nominal maximum current, nominal internal resistance, nominal no-load current, nominal 50% throttle speed, nominal 50% throttle torque, nominal 100% throttle speed, nominal 100% throttle torque, and weight. The speed and torque filters in the diagram indicate that the motor should meet the speed and torque constraints at 50% and 100% throttle. The maximum current filter indicates that the motor should meet the maximum current constraint. This diagram illustrates the selection process and method; following the steps shown in the diagram will allow for the completion of product-optimized motor selection.

[0073] In some embodiments of the present invention, the step of determining the ESC selection includes: In the ESC product library, the ESC selection that meets the second current constraint condition and the rated voltage constraint condition is determined as the first ESC selection; In the first ESC selection, the ESC with the lowest rated operating voltage is selected as the second ESC; In the second ESC selection, the ESC with the highest operating current is selected as the third ESC; The motor selection for the target aircraft is determined based on the third ESC selection.

[0074] It should be noted that the main function of an ESC is to regulate the voltage and current input to the motor from the battery in order to control the motor speed. In a specific embodiment of the present invention, building an ESC product library requires collecting the model name, nominal operating voltage, nominal maximum current, and weight of the ESCs in advance.

[0075] For safety reasons, the actual maximum current of the ESC must not exceed the nominal maximum current. The nominal maximum current of the ESC should be greater than the maximum current of the motor, as shown in the following formula: Furthermore, the rated operating voltage of the ESC must not be less than the rated operating voltage of the motor, and the rated operating voltage of the ESC should meet the constraint shown in equation (13). Where, This is the rated operating voltage of the electronic speed controller.

[0076]

[0077] The selection order of the ESC parameters is shown in Equation (14), from left to right.

[0078]

[0079] The specific operation of ESC product selection is as follows: (1) Sort the rated working voltage of the ESC products that meet the constraints and select the ESC with the smallest rated voltage; (2) Sort the nominal maximum working current of the ESC selected in the previous step and select the ESC with the smallest nominal maximum working current.

[0080] In summary, the process for optimizing and selecting an ESC can be summarized as follows: Figure 3 The flowchart shown illustrates the required ESC information, including ESC model name, nominal operating voltage, nominal maximum current, and weight. The maximum current filter indicates that the ESC product must meet the maximum current constraint, and the rated voltage filter indicates that the ESC product must meet the rated operating voltage constraint. This diagram explains the selection process and method; following the flowchart allows for the optimization and selection of ESC products.

[0081] In some embodiments of the present invention, the battery selection determination step includes: In the battery product library, the battery selection that meets the capacitance constraint, series voltage constraint, and nominal discharge rate constraint is determined as the first battery selection; In the first battery selection, the battery pack with the smallest nominal maximum discharge rate is selected as the second battery selection; In the second battery selection, the battery pack with the smallest number of batteries is selected as the third battery selection; In the third battery selection, the battery pack with the lowest price is selected as the fourth battery selection; The battery selection for the target aircraft is determined based on the fourth battery selection.

[0082] It's important to note that the battery is the energy source for the entire electric multirotor aircraft system, directly impacting its flight time and payload capacity. Larger battery capacity results in greater flight endurance, but since increased capacity also increases the aircraft's weight, excessively large batteries may actually reduce its range. Furthermore, excessive battery weight reduces the aircraft's additional payload capacity. Therefore, selecting the appropriate battery based on the flight mission is crucial for improving flight performance. Commonly used battery types for multirotor aircraft include lithium polymer batteries, lithium-ion batteries, and lithium-sulfur batteries. This study focuses on battery optimization based on lithium polymer batteries. Lithium polymer batteries are currently the most common type of battery for multirotor aircraft, characterized by high energy density and high discharge rate, making them suitable for applications requiring high power output and long flight endurance.

[0083] In a specific embodiment of the present invention, the optimized object is a dual-layer staggered hexacopter UAV, therefore the battery needs to supply current to 6 motors. The battery current when the dual-layer staggered hexacopter UAV takes off at its maximum takeoff weight and remains hovering can be calculated using equation (15).

[0084]

[0085] This indicates the battery current of a dual-layer staggered hexacopter drone when it takes off at its maximum takeoff weight and hovers. This represents the current used by other airborne equipment (such as flight control), denoted as in this embodiment. The value is 0.5A.

[0086] Similarly, the maximum current of the battery can be calculated using equation (16). This indicates the maximum current value that the battery needs to output.

[0087]

[0088] When the desired maximum takeoff weight and hovering time of the aircraft are clearly defined, it can be based on The expected capacitance of the battery is calculated using formula (17) as follows:

[0089] In the formula, This represents the desired capacitance of the battery pack. This indicates the hovering time at the aircraft's expected maximum takeoff weight. This represents the percentage of the battery's dischargeable capacitance.

[0090] To ensure the battery discharges safely without burning out, the battery's nominal maximum discharge rate should be greater than the battery's actual maximum discharge rate during use. The battery's nominal maximum discharge rate should satisfy the constraint shown in equation (18).

[0091]

[0092] In the formula, This indicates the battery's nominal maximum discharge rate. This indicates the actual capacitance of the battery pack.

[0093] To ensure the motor operates normally at its rated voltage, the battery voltage should meet the constraint shown in equation (19). Where, This indicates the battery's nominal voltage.

[0094]

[0095]

[0096] In the formula, This represents the desired capacitance of the battery pack. This indicates the actual capacitance of the battery pack.

[0097] In practical applications, it is difficult for non-customized battery products to simultaneously meet both capacitance and voltage constraints. Therefore, batteries for multi-rotor aircraft are often battery packs composed of multiple batteries. For series-connected battery packs, since series connection cannot change the characteristic of battery capacitance, the capacitance of individual batteries in the candidate list must first be screened. Batteries that do not meet the capacitance constraints shown in equation (20) are removed from the candidate list. Subsequently, a series-connected battery pack is constructed for all batteries that meet the capacitance constraints, and the battery pack should meet the constraints shown in equation (21).

[0098]

[0099] In the formula, This indicates the number of cells connected in series in the battery pack, and The value must be a positive integer. Batteries that do not meet the constraints of equation (21) are removed from the candidate list. Finally, the discharge rate of each battery pack that meets the constraints is calculated, and battery packs that do not meet the constraints shown in equation (18) are removed from the candidate list. The battery packs that finally meet all the above requirements are the selectable series battery packs.

[0100] For parallel battery packs, given that parallel connection cannot change the battery voltage, the voltage of individual batteries in the candidate list must first be screened. Batteries that do not meet the voltage constraints shown in equation (19) are removed from the candidate list. Subsequently, parallel battery packs are constructed for all batteries that meet the voltage constraints, and the battery packs should meet the constraints shown in equation (22).

[0101]

[0102] In the formula, This indicates the battery's nominal capacitance. Indicates the number of batteries connected in parallel in the battery pack. It must be a positive integer and exactly make equation (22) true, that is Must meet .right The constraint on the value is to ensure that there is no waste of the battery pack's capacitance and that the size of the battery pack's capacitance fits the requirements of the preset flight mission. Batteries that do not meet the constraint of equation (22) are removed from the candidate list. Finally, the discharge rate of each battery pack that meets the constraint is calculated, and battery packs that do not meet the constraint shown in equation (18) will be removed from the candidate list. The series and parallel connection of batteries does not affect the calculation of the battery pack's discharge rate, so both series and parallel battery packs can use equation (18) to calculate the discharge rate. Finally, the battery pack that meets all the above requirements is the selectable parallel battery pack.

[0103] After obtaining both series and parallel battery pack candidate libraries, the two libraries are merged into a single overall candidate battery pack library, followed by the final product selection process. The series or parallel connection of the batteries does not affect the parameters involved in the final product selection. Furthermore, regardless of whether the batteries are connected in series or parallel, the batteries in the battery pack are arranged closely together, with the sides of the batteries having the largest surface area overlapping and touching tightly. Figure 4 This is a schematic diagram of the battery pack assembly.

[0104] Based on the conclusions drawn from experiments and statistical data, a nominal maximum discharge rate far exceeding the actual maximum discharge rate during battery use will significantly increase the weight of the battery pack without improving the flight performance of the aircraft. Therefore, the nominal maximum discharge rate of the battery pack should be as small as possible while meeting constraints. In addition, from the perspective of safety and assembly, an increase in the number of batteries in the battery pack will lead to an increased probability of battery pack failure, an increased time cost for battery maintenance, and an increased space and equipment required for assembly. Therefore, the number of batteries in the battery pack should be as small as possible. Finally, for economic reasons, the total price of the battery pack should also be one of the factors to consider when selecting it. Finally, the battery pack parameter selection order shown in equation (23) is determined, with the selection order from left to right. Where, This indicates the number of batteries in the battery pack. This indicates the total price of the battery pack.

[0105]

[0106] The specific steps for battery product selection are as follows: (1) Sort all selectable battery packs by their nominal maximum discharge rate and select the battery pack with the lowest nominal maximum discharge rate; (2) Sort the battery packs selected in the previous step by the number of batteries and select the battery pack with the fewest batteries; (3) Sort the battery packs selected in the previous step by the total price and select the battery pack with the lowest total price. Figure 5 The diagram below illustrates the battery selection process.

[0107] In summary, once the selection of the power system for the dual-layer staggered six-rotor UAV is determined, the parameters for the power system selection in the objective function can be determined.

[0108] In some embodiments of the present invention, the expressions for the constraints of the structural parameters are as follows:

[0109]

[0110] If N=6, then:

[0111]

[0112] If N=8, then:

[0113] In the formula, This indicates the vertical distance between the upper and lower rotors. This indicates the distance from the center axis of the upper rotor to the center axis of the housing. This indicates the distance from the center axis of the lower rotor to the center axis of the housing. Indicates the rotor radius. Indicates the number of rotors. Indicates the number of blades.

[0114] Before introducing the area ratio, thrust-to-weight ratio, and payload ratio, it is necessary to introduce the fuselage box model of the double-layer staggered six-drone UAV, such as... Figure 6 The diagram shown is a schematic of a double-layered, staggered six-rotor unmanned aerial vehicle (UAV). Figure 7 The diagram shows a schematic of the fuselage panels of a dual-layer staggered hexagonal drone. There are four panels in total, all of which are either regular hexagons or regular octagons. The design principle of the panels is to minimize their area while ensuring sufficient space within the fuselage to accommodate the battery pack, thereby reducing the drone's weight. Therefore, the panel dimensions will be designed based on the battery pack's size. Let the longest side of the battery pack be... The second longest side is The shortest side is Therefore, the box panel must be able to accommodate a radius of [missing information]. The circle, equation (1-1) is The calculation formula.

[0115]

[0116] In addition to ensuring sufficient space for the battery pack, space must also be reserved for the installation of the chassis support columns. The radius of the circle accommodating the chassis panel should be [missing information]. Equation (1-2) is The calculation formula,

[0117] In the formula, This refers to the diameter of the chassis support column; its specific value will be introduced in a later section. It is the distance between the chassis support column and the edge of the plate. The value is 10mm. The side length of the box panel is... The calculation formula is as follows:

[0118] Due to considerations for fuselage assembly, weight reduction, and onboard equipment wiring, holes or grooves need to be drilled in all four panels. The thickness of the four panels... All are 2.5mm thick and made of carbon fiber. The final formulas for calculating the box panel volume are shown in equations (1-4) to (1-7).

[0119]

[0120]

[0121]

[0122]

[0123] In the formula, Let the volume of plate 1 be... Let the volume of plate 2 be... For the volume of plate 3, For the volume of plate 4, the units of the four variables are all... .

[0124] like Figure 8 The diagram shows a model of a dual-layered, staggered six-rotor drone's fuselage support structure. Three identical cylindrical parts are used for support in the center of the casing. For torsional strength and lightweight design, the three supports are made of aluminum. (Variables shown in the diagram...) This represents the length of the column excluding the base, and formula (1-8) is its calculation formula.

[0125]

[0126] In the formula, Indicates the distance between the upper and lower rotor layers, 2 2 indicates the thickness of the base. This indicates the thickness of the arm base and the arm itself.

[0127] The formula for calculating the volume of a support column is:

[0128] like Figure 9 The diagram shows a model of a dual-layer staggered six-rotor drone arm. For considerations of material bending resistance and lightweight fuselage, the six arms are made of carbon fiber. Indicates the length of the upper boom tube. The length of the lower boom tube is represented by the formula ( ). Equation (10) and equation (1-11) are the calculation formulas for these two variables.

[0129]

[0130]

[0131] In the formula, IThis indicates the distance from the center axis of the upper rotor to the center axis of the housing. L This indicates the distance from the center axis of the lower rotor to the center axis of the housing; 80 mm is the extra length of the tube extending into the housing for assembly.

[0132] Finally, the formulas for calculating the volumes of the upper and lower booms can be obtained, as shown in formula (). ) and formula ( As shown in the figure.

[0133]

[0134]

[0135] In the formula, This indicates the volume of the upper arm. This indicates the volume of the lower arm.

[0136] In some embodiments of the present invention, the formula for the area ratio is: ( <1)

[0137]

[0138]

[0139]

[0140] In the formula, Indicates the area ratio. This indicates the area of ​​the fuselage box panel. This represents the vertical projection area of ​​the display unit. This represents the radius of the circle that houses the box panel. Indicates the side length of the fuselage box. This represents the longest side of the battery pack. This indicates the shortest side length of the battery pack. R represents the number of rotors, R represents the rotor radius, and L represents the distance from the center axis of the lower rotor to the center axis of the housing.

[0141] In some embodiments of the present invention, the expression for the thrust-to-weight ratio is:

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] In the formula, Indicates the thrust-to-weight ratio. This indicates the maximum lift of the target aircraft. This indicates the total weight of the target aircraft. This indicates the maximum takeoff weight of the target aircraft. express, express, Indicates the weight of the power system. This indicates the total weight of the carbon fiber plates. This indicates the total weight of the aluminum column. This indicates the total weight of the arm. This indicates the total weight of all components except the designable components. Indicates the weight of the motor. Indicates the weight of the ESC. Indicates the number of rotors. This indicates the number of batteries in the battery pack. Indicates battery weight. Indicates the volume of plate 1. Let the volume of plate 2 be... For the volume of plate 3, For the volume of plate 4, the units of the four variables are all... , This indicates the density of carbon fiber. This indicates the density of aluminum. This indicates the volume of the aluminum column. This indicates the total weight of the aluminum column.

[0150] In some embodiments of the present invention, the expression for the loading ratio is:

[0151] In the formula, Indicates the load ratio. This indicates the maximum takeoff weight of the target aircraft. This indicates the total weight of the target aircraft.

[0152] In some embodiments of the present invention, the expression for the maximum hovering time is:

[0153] In the formula, Indicates the maximum hovering time. This indicates the total capacitance of the battery pack. This represents the current output by the battery pack when the target aircraft is hovering after taking off at its maximum takeoff weight. It is understandable that... and The value can be determined after the aircraft's power type is selected. and The value of .

[0154] In some embodiments of the present invention, the optimal structural parameters of the target aircraft are obtained by solving the constraints with the objective function of maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time, including: Based on the multi-objective optimization algorithm, the optimal structural parameters of the target aircraft are obtained by solving the constraints with the objective function of maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time.

[0155] In specific embodiments of the present invention, such as Figure 10 The diagram shown is a logical representation of a multi-objective intelligent optimization selection procedure. The specific steps are as follows: (1) User-defined flight mission parameters: maximum takeoff weight And the expected hovering time at maximum takeoff weight .

[0156] (2) Randomly generate the initial population. All design points in the initial population are four-dimensional vectors, with each dimension corresponding to a design variable. The design variables are as follows: The population size is set to 200, which can be modified according to the actual optimization task. Generally, a larger population results in a better distribution of the optimized solution but takes more time; conversely, a smaller population results in a poorer distribution of the optimized solution but takes less time.

[0157] (3) Based on the existing population and the preset flight mission indicators, select the power system for each design point in the population. Calculate the optimization objectives for each design point in the population based on the power system product information, the preset flight mission indicators, and the design variables of the design point. The optimization objectives are: maximum area ratio, maximum thrust-to-weight ratio, maximum payload ratio, and maximum hovering time. Record the four optimization objective values ​​corresponding to each design point.

[0158] (4) Sort all design points in a non-dominated order according to the optimization objective corresponding to each design point. Then, determine whether the number of iterations meets the set number. If not, proceed to step (6). If it does, end the iteration process and proceed to step (7). The number of iterations set in this study is 15,000. The setting of the number of iterations needs to be analyzed in conjunction with the specific problem. Too many iterations will waste computing power and time costs, while too few iterations will make it impossible to find the optimal solution to the problem.

[0159] (5) Generate reference points for each target based on the current population, select design points that are highly correlated with the reference points and have a high non-dominant ranking, pair them up, and perform crossover and mutation on their design variables to obtain the next generation population. Return to step (3) and perform the optimization selection operation again.

[0160] (6) Output the non-dominated solution of the current population, i.e., the Pareto front of the current generation. The multi-objective intelligent optimization selection program ends.

[0161] Following the above procedure, users can obtain a design scheme for a dual-layer staggered hexacopter drone that meets preset flight mission requirements. The scheme provides specific product model names for available motors, ESCs, and batteries, as well as design dimensions for custom structural components such as chassis panels, struts, arms, and propeller radii. Users can quickly purchase all components for the dual-layer staggered hexacopter drone based on this design scheme.

[0162] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of a vertical takeoff and landing aircraft, characterized in that, include: Construct an aircraft propulsion system product library, which includes an electric motor product library, an electronic speed controller product library, and a battery product library; Acquire the flight mission indicators of the target aircraft, including: maximum takeoff weight and hovering time at maximum takeoff weight; The first parameter is input into the fully trained multi-fidelity neural network to obtain the second parameter. The first parameter includes the rotor diameter of the target aircraft, the longitudinal height between the rotors, the lateral stagger ratio, and the total lift of the two rotors. The second parameter includes the upper rotor speed, the lower rotor speed, the rotor torque, the upper motor power, and the lower motor power of the target aircraft. Based on the second parameter and the flight mission indicators, the selection of the power system products for the target aircraft is determined from the power system product library. The selection of the power system products for the target aircraft includes: motor selection, ESC selection, and battery selection. Based on the selection parameters of the target aircraft's power system products, the objective function of the target aircraft is constructed. The objective function includes: area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time. The constraints for obtaining the structural parameters of the target aircraft include: the number of rotors, the number of blades, the vertical distance between the upper and lower rotors, the distance from the central axis of the upper rotor to the central axis of the casing, the distance from the central axis of the lower rotor to the central axis of the casing, and the rotor radius. Under constraints, the optimal structural parameters of the target aircraft are obtained by solving the objective function with the objectives of maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time.

2. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The steps for determining the motor selection include: In the motor product library, the motor with the smallest rated voltage among the motors that meet the speed constraint, torque constraint, and first current constraint is selected as the first motor. The speed constraint, torque constraint, and first current constraint are all obtained based on the second parameter. The difference between the nominal KV value and the ideal KV value of the first motor selection is sorted, and the motor with the smallest difference is determined as the second motor selection; The motor with the smallest nominal maximum operating current in the second motor selection is selected as the third motor; The motor with the lowest nominal internal resistance in the third motor selection is selected as the fourth motor. The motor with the lowest nominal no-load current among the fourth motor selections is selected as the fifth motor; The motor selection for the target aircraft is determined based on the fifth motor selection.

3. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The steps for determining the ESC selection include: In the ESC product library, the ESC selection that meets the second current constraint condition and the rated voltage constraint condition is determined as the first ESC selection; In the first ESC selection, the ESC with the lowest rated operating voltage is selected as the second ESC; In the second ESC selection, the ESC with the highest operating current is selected as the third ESC; The motor selection for the target aircraft is determined based on the third ESC selection.

4. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The steps for determining battery selection include: In the battery product library, the battery selection that meets the capacitance constraint, series voltage constraint, and nominal discharge rate constraint is determined as the first battery selection; In the first battery selection, the battery pack with the smallest nominal maximum discharge rate is selected as the second battery selection; In the second battery selection, the battery pack with the smallest number of batteries is selected as the third battery selection; In the third battery selection, the battery pack with the lowest price is selected as the fourth battery selection; The battery selection for the target aircraft is determined based on the fourth battery selection.

5. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The expression for the constraint conditions of the structural parameters is: If N=6, then: If N=8, then: In the formula, This indicates the vertical distance between the upper and lower rotors. This indicates the distance from the center axis of the upper rotor to the center axis of the housing. This indicates the distance from the center axis of the lower rotor to the center axis of the housing. Indicates the rotor radius. Indicates the number of rotors. Indicates the number of blades.

6. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The formula for the area ratio is: ( <1) In the formula, Indicates the area ratio. This indicates the area of ​​the fuselage box panel. This represents the vertical projection area of ​​the display unit. This represents the radius of the circle that houses the box panel. Indicates the side length of the fuselage box. This represents the longest side of the battery pack. This indicates the shortest side length of the battery pack. R represents the number of rotors, R represents the rotor radius, and L represents the distance from the center axis of the lower rotor to the center axis of the housing.

7. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The expression for the thrust-to-weight ratio is: In the formula, Indicates the thrust-to-weight ratio. This indicates the maximum lift of the target aircraft. This indicates the total weight of the target aircraft. This indicates the maximum takeoff weight of the target aircraft. express, express, Indicates the weight of the power system. This indicates the total weight of the carbon fiber plates. This indicates the total weight of the aluminum column. This indicates the total weight of the arm. This indicates the total weight of all components except the designable components. Indicates the weight of the motor. Indicates the weight of the ESC. Indicates the number of rotors. This indicates the number of batteries in the battery pack. Indicates battery weight. Indicates the volume of plate 1. Let the volume of plate 2 be... For the volume of plate 3, For the volume of plate 4, the units of the four variables are all... , This indicates the density of carbon fiber. This indicates the density of aluminum. Indicates the volume of the aluminum column. This indicates the total weight of the aluminum column.

8. The vertical takeoff and landing aircraft configuration optimization method according to claim 7, characterized in that, The expression for the loading ratio is: In the formula, Indicates the load ratio. This indicates the maximum takeoff weight of the target aircraft. This indicates the total weight of the target aircraft.

9. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The expression for the maximum hovering time is: In the formula, Indicates the maximum hovering time. This indicates the total capacitance of the battery pack. This indicates the current output by the battery pack when the target aircraft is hovering after taking off at its maximum takeoff weight.

10. The vertical takeoff and landing aircraft configuration optimization method according to claim 1, characterized in that, The optimal structural parameters of the target aircraft are obtained by solving the objective function under constraints, focusing on maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time. These parameters include: Based on the multi-objective optimization algorithm, the optimal structural parameters of the target aircraft are obtained by solving the objective function with the constraints of maximizing the area ratio, thrust-to-weight ratio, load ratio, and maximum hovering time.

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