Design index optimization method for wing body fusion layout unmanned aerial vehicle

Abstract

The invention provides a wing body fusion layout unmanned aerial vehicle design index optimization method, which comprises the following steps: firstly, making an initial design scheme for initial design indexes; then selecting optimization variables and respective optimization intervals according to the initial design scheme; finally, the design scheme is adjusted according to the selected optimization variables, performance parameters are obtained through the simulation model, and comprehensive competitiveness is calculated based on the performance parameters. And finally, taking the performance parameter corresponding to the design scheme with the maximum comprehensive competitiveness as an optimized design index. According to the wing body fusion layout unmanned aerial vehicle design index optimization method, the subjectivity of determining the overall demand by virtue of experience in the prior art can be improved, so that the design indexes are formulated more reasonably and comprehensively, and the design scheme is guided to advance in a more competitive direction.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a method for optimizing design indicators of an unmanned aerial vehicle with a wing-body fusion layout. Background Art

[0002] The Blended Wing Body (BWB) layout is a layout form that highly integrates the fuselage and wings of the aircraft. The aircraft with this layout has a small wetted area and has the advantages of high lift-to-drag ratio, light structure weight, low noise and low emissions. Therefore, the BWB layout is an important research direction in aircraft layout design.

[0003] When developing BWB drones, the initial design indicators are usually used as a guide to complete the iteration and optimization of the design scheme. In other words, the rationality of the design indicators determines the direction and success of the project. In the traditional design process, the design indicators mainly rely on the expectations of users and the experience of designers, and the rationality of the indicators lacks a strict theoretical basis. If the indicator requirements are too loose, the design scheme will be too simple and lose competitiveness; on the contrary, if the indicator is too strict, it is likely that the design requirements cannot be met after multiple rounds of iterations. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for optimizing design indicators of a wing-body fusion layout UAV, so as to solve the optimization problem of setting design indicators in the early stage of a UAV development project.

[0005] The present invention achieves the above technical objectives through the following technical means.

[0006] A method for optimizing design indicators of wing-body fusion layout UAV: ​​optimizing design indicators with the goal of maximizing competitiveness;

[0007] The competitiveness is calculated by the following formula:

[0008]

[0009] Among them G m represents the competitiveness on the mth criterion, G cp Represents the comprehensive competitiveness in all criteria, m∈[1,M], n∈[1,N], M is the number of criteria, N is the number of design indicators; is the normalized value of the nth design indicator; w n The weight of the design indicator for the nth item.

[0010] Furthermore, the nonlinear S-shaped differentiable function normalization method is used to normalize each design index:

[0011]

[0012] Where V n represents the actual value of the nth design indicator, is the positive ideal solution value of the nth design index, is the negative ideal solution value of the nth design index, and A and B are the curve shape control parameters.

[0013] Furthermore, the indicator weight w n Calculated by the following formula:

[0014]

[0015] where ω m is the weight of the mth criterion, R mn is the relationship between the mth criterion and the nth design indicator.

[0016] Furthermore, R mn The value of is {0, 1, 3, 5, 7, 9}, and the value from small to large represents the degree of correlation from zero to strong.

[0017] Furthermore, the criterion weight ω m Calculated by the AHP judgment matrix.

[0018] Further:

[0019] Formulate initial design plan based on initial design indicators;

[0020] According to the initial design plan, select the optimization variables and their respective optimization intervals;

[0021] Adjust the design scheme with the selected optimization variables, obtain the performance parameters through the simulation model, and then calculate the comprehensive competitiveness G based on the performance parameters cp ; ultimately the comprehensive competitiveness G cp The performance parameters corresponding to the largest design solution are used as the optimized design indicators.

[0022] Furthermore, in the simulation model, the geometric model of the drone is described according to the following characteristic parameters:

[0023] The geometric shape of the wing-body fusion layout UAV is divided into five parts: fuselage, inner section of inner wing, outer section of inner wing, outer wing, and winglet according to the bending point of the edge contour;

[0024] The characteristic parameters of each segment include: span length, leading edge sweep angle, tip-to-root ratio, dihedral angle, and torsion angle; for the fuselage, they also include root chord length and installation angle.

[0025] Furthermore, in the simulation model, the semi-empirical method proposed by Howe is used to calculate the body structure mass of the UAV; the panel method is used to analyze the lift and induced drag of the UAV; and the PO method is used to calculate the RCS value;

[0026] The maximum level flight speed is calculated using the following UAV level flight state balance equation:

[0027]

[0028] Among them, T is thrust, which is affected by engine speed n, flight altitude h, and flight speed v; D is drag, which is affected by flight altitude h, flight speed v, and angle of attack α; L is lift, which is affected by flight altitude h, flight speed v, and angle of attack α; W is the total weight of the UAV; when solving, the engine uses the maximum available speed, and within the available angle of attack range, the maximum speed is continuously increased. The maximum speed that can satisfy the above balance equations is the maximum level flight speed;

[0029] The maximum range is calculated by segmentation with time Δt as the interval:

[0030]

[0031] Among them, the UAV cruises at a fixed height h0 with an initial speed v0 and a constant speed in a straight line, and the initial mass is m0; is the fuel consumed in the i-th period, m i is the mass of the drone at the beginning of the i-th period, R i is the distance flown at the beginning of the i-th period, SFC i is the engine fuel consumption rate during the i-th period, T i is the engine thrust during the i-th period; by calculating in sections until the fuel is completely consumed, the maximum range is obtained;

[0032] The duration of the maneuvering flight is calculated by analyzing the flight process in segments with time intervals of Δt:

[0033]

[0034] in, is the amount of fuel consumed in the i-th period, SFC i is the engine fuel consumption rate during the i-th period, T i is the engine thrust during the i-th period, m i is the mass of the drone at the beginning of the i-th period; a i is the acceleration during the i-th period, obtained from the following dynamic equation during overload maneuvering flight:

[0035]

[0036] n g is overload; the flight time is calculated in segments until the flight speed is reduced to the minimum value, and the flight time is the maneuvering flight time.

[0037] Furthermore, a single-objective subset simulation optimization algorithm is used for optimization.

[0038] Furthermore, the criteria include: economy, loading capacity, flight performance, and stealth capability;

[0039] The design indicators include: maximum load mass, maximum take-off mass, fuel mass, maximum level flight speed, maximum range, maneuvering flight time, and RCS.

[0040] The beneficial effects of the present invention are:

[0041] (1) The present invention provides a method for optimizing the design indicators of a wing-body fusion layout UAV, which can improve the subjectivity of the original determination of overall requirements based on experience, make the formulation of design indicators more reasonable and comprehensive, and further guide the design scheme to move in a more competitive direction.

[0042] (2) The present invention establishes a simulation model by reasonably segmenting the UAV and selecting a few but representative geometric parameters to define and describe the geometric characteristics of the UAV, which can reduce the workload, improve the work efficiency in the early indicator setting stage, and avoid a lot of duplication of work with subsequent formal design and development work.

[0043] (3) The simulation model of the present invention simulates and calculates various UAV performance parameters by using the PO method to calculate the RCS value of the BWB layout UAV, the semi-empirical method proposed by Howe to calculate the body structure weight, and the panel method to analyze the lift and induced drag of the model. Under the premise of ensuring reasonable calculation accuracy, it is possible to quickly analyze various performance data and improve computer operation efficiency.

[0044] (4) The normalization method adopted by the present invention can highlight the saturation characteristics of each design indicator. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of the method for optimizing the design index of the unmanned aerial vehicle of the present invention;

[0046] Figure 2 This is a schematic diagram of a sectional view of a wing-body fusion layout UAV in an embodiment of the present application;

[0047] Figure 3 This is a diagram showing the changes in competitiveness of the optimization process in the embodiment of the present application;

[0048] Figure 4This is a schematic diagram comparing the appearance and structure of the drone before and after optimization in the embodiment of the present application. DETAILED DESCRIPTION

[0049] Embodiments of the present invention are described in detail below, examples of the illustrated embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0050] 1. Technical solution

[0051] 1. Simulation Model

[0052] A simulation model of a wing-body fusion layout UAV is established, and the performance parameters of the UAV are obtained based on simulation analysis of the established model, including ① maximum payload mass, ② maximum take-off mass, ③ fuel mass, ④ maximum level flight speed, ⑤ maximum range, ⑥ maneuvering flight time, and ⑦ radar cross-section (RCS) related to the design indicators.

[0053] 1) Geometry model

[0054] In the simulation model, the UAV geometric model is the basis for multidisciplinary analysis such as mass analysis, aerodynamic analysis, and stealth analysis. The geometric model to be established must be as close to the real model as possible, but it must also be able to quickly generate and extract the characteristic parameters required for subsequent calculations; therefore, the key issue in establishing the geometric model is how to describe the complete geometric shape with a small set of parameters.

[0055] like Figure 2 As shown, in this embodiment, according to the geometric features of the BWB layout, the geometric shape of the BWB UAV is divided into five parts, namely, the fuselage (central wing), the inner section of the inner wing, the outer section of the inner wing, the outer wing, and the winglet, according to the bending points of the wing-body edge contour. For the above five parts, the characteristic parameters commonly used for airfoil parts are used for description, including span length, leading edge sweep angle, tip-root ratio (or trapezoidal ratio), dihedral angle, and torsion angle; in addition, for the fuselage, the root chord length and installation angle are additionally used for description. The shapes of the inner and outer end sections of the five parts are defined by the airfoil file.

[0056] 2) Quality analysis

[0057] The total mass of the UAV mainly includes the mass of the airframe structure, the mass of the power system, the mass of various systems (flight control system, electrical system, instruments, etc.), the mass of the mission payload, and the mass of the fuel. Since the main difference between the mass calculation of the BWB UAV and the conventional configuration UAV is the mass of the airframe structure, the mass of the power system, flight control system, electrical system, etc. is estimated using the conventional UAV mass estimation method. As for the mass of the airframe structure, this embodiment uses the semi-empirical method proposed by Howe for calculation.

[0058] 3) Pneumatic analysis

[0059] Aerodynamic analysis is mainly used to calculate the lift and drag characteristics of the BWB UAV. In this embodiment, the panel method is used to analyze the lift and induced drag of the UAV, while conventional engineering calculation methods are used for other drags such as friction drag, form drag, compressibility drag (wave drag), trim drag, and secondary drag.

[0060] 4) Stealth analysis

[0061] The stealth characteristics of drones mainly refer to the ability to avoid detection by radar, infrared and other optical, electrical, acoustic and other detection systems. The radar cross-section (RCS) of drones is an important indicator of their electromagnetic stealth capability. The smaller the RCS value, the stronger the stealth capability. The calculation of RCS can choose the method of moments (MOM), multi-layer fast multipole method (MLFMM), physical optics method (PO), finite difference time domain method (FDTD), finite element method (FEM) and various hybrid algorithms. Considering that the BWB layout drone is an electrically large-sized target and the conceptual design stage has high requirements for computing efficiency, the PO method is used in this embodiment to calculate the RCS value of the BWB layout drone. If there are higher requirements for calculation accuracy and sufficient computing resources are available, other higher-precision calculation methods can also be used.

[0062] 5) Flight performance analysis

[0063] It is used to calculate the maximum and minimum level flight speed of BWB UAV, the maximum range at a specific speed, and the maneuvering flight time at a specific altitude and overload.

[0064] ①Maximum level flight speed

[0065] The maximum level flight speed is calculated using the following UAV level flight state balance equation:

[0066]

[0067] Among them, T is the thrust, and the thrust is affected by the engine speed n, flight altitude h, and flight speed v; D is the drag, and the drag is affected by the flight altitude h, flight speed v, and angle of attack α; L is the lift, and the lift is affected by the flight altitude h, flight speed v, and angle of attack α; W is the total weight of the UAV.

[0068] When solving the maximum level flight speed, the engine uses the maximum available speed and continuously increases the maximum speed within the available angle of attack range. The maximum speed that can satisfy the above-mentioned balance equations is the maximum level flight speed.

[0069] ②Maximum range

[0070] When calculating the maximum range, the take-off and landing phases are ignored. It is assumed that the UAV cruises at a fixed altitude h0 with an initial speed v0 and an initial mass of m0. The entire flight process is analyzed by a segmented calculation method with time intervals of Δt. Assume that at the beginning of the i-th time segment, the mass of the UAV is m i , the distance flown is R i At this time, the UAV is in a balanced state. According to the above UAV level flight state balance equation, the current engine thrust T can be obtained i The engine fuel consumption rate is SFC. i , assuming that the thrust change during flight can be ignored, then the next period after Δt:

[0071]

[0072] in is the fuel consumed in the i-th period. Through the above segmented calculation, until the fuel is consumed, the maximum range can be obtained.

[0073] ③Maneuvering flight time

[0074] When performing high-overload maneuvering flight, the resistance is large and the drone will decelerate and the overload is n. g When , the dynamic equation of the UAV is as follows:

[0075]

[0076] Where m is the mass of the drone, and a is the acceleration of the drone when it decelerates. As the speed of the drone continues to decrease, the large lift required to maintain maneuvering flight becomes more difficult to achieve. When the flight speed drops to a certain value, the lift condition cannot be achieved and the maneuvering flight cannot be maintained. The duration of the entire flight process reflects the maneuverability under the overload condition.

[0077] Similar to the calculation of the maximum range, this embodiment adopts segmented analysis of the entire flight process. At the beginning of a certain period of time, the mass of the drone is m iAccording to the above overload dynamic equation, the current engine thrust T can be obtained i , acceleration a i , and the engine fuel consumption rate SFC is also found i , assuming that the thrust change in the subdivided time period can be ignored, after Δt, the fuel consumed in this stage Flight speed i+1 、UAV mass m i+1 , can be calculated by the following formula:

[0078]

[0079] Through the above segmented calculation, the flight time until the flight speed drops to the minimum value is the maneuvering flight time.

[0080] 2. Competitiveness evaluation

[0081] In order to achieve quantitative balance of various design indicators, this embodiment determines the importance of each design indicator and converts it into a quantitative weight to form an intuitive judgment basis, thereby completing the overall demand formulation. The details are as follows:

[0082] 1) Calculation of criteria weights

[0083] When formulating the design requirements of a BWB UAV, it is usually based on the perspective of completing a specific task, and detailed indicator requirements are formulated based on criteria such as loading capacity, economy, flight performance, stealth capability, etc. For each criterion, this embodiment uses the judgment matrix in the hierarchical analysis method to calculate the respective weights:

[0084] Table 1: Judgment Matrix

[0085]

[0086] where h jk The value of (j∈[1,M], k∈[1,M]) is 1 to 9 or their reciprocals; h jk >1, indicating that criterion j is more important than criterion k, and h jk The larger the value, the higher the relative importance; conversely, h jk <1, indicating that the importance of criterion j is lower than that of criterion k, and h jk The smaller the value, the lower the relative importance. jk =1, indicating that the two are equally important. Finally, according to the above judgment matrix, the weight of each criterion can be calculated: m , m∈[1,M].

[0087] In actual operation, a multi-person evaluation mechanism can be further introduced, with multiple experts independently scoring to create multiple judgment matrices; finally, a composite judgment matrix is ​​established based on the consistency indicators of each judgment matrix to improve the objectivity of the judgment matrix.

[0088] 2) Calculation of indicator weights

[0089] For design indicators such as maximum load mass, maximum takeoff mass, and fuel mass, this embodiment uses the relationship matrix in Quality Function Deployment (QFD) to establish the relationship between the design indicators and various criteria:

[0090] Table 2: Relationship Matrix

[0091]

[0092] Where R mn (m∈[1,M], n∈[1,N]) is the relationship between the mth criterion and the nth design index. In this embodiment, R mn The value of is {0, 1, 3, 5, 7, 9}, and the value from small to large represents the degree of correlation from zero to strong.

[0093] w n is the weight of the nth design indicator, calculated by the following formula:

[0094]

[0095] 3) Index normalization

[0096] Since different design indicators have different meanings and dimensions, they cannot be directly compared. Therefore, this embodiment uses a nonlinear S-type differentiable function normalization method to normalize each design indicator. This method can highlight the saturation characteristics of each design indicator. The normalization process for the nth design indicator is as follows:

[0097]

[0098] Where V n represents the actual value of the nth design indicator (e.g. the maximum take-off mass design target is 160 kg), is the normalized value of the nth design indicator, is the positive ideal solution value of the nth design index, is the negative ideal solution value of the nth design index. If the expectation of an index is as large as possible, then is the maximum value of the indicator, otherwise is the minimum value of the indicator; A and B are curve shape control parameters, which can control the flatness of the S-shaped curve.

[0099] 4) Competitiveness evaluation calculation

[0100] The calculated indicator weight represents the importance of each design indicator to the design requirements. The larger the weight, the more important the design indicator. The normalized indicator value represents the strictness of the design requirements for the indicator. The closer the value is to 1, the stricter the requirement. Based on the weight of each design indicator and the normalized value, the competitiveness score and comprehensive competitiveness under each criterion are calculated as follows:

[0101]

[0102] Among them G m represents the competitiveness of the UAV design solution in the mth criterion, G cp Indicates the comprehensive competitiveness of the design solution in all criteria.

[0103] 3. Index optimization

[0104] Reference Figure 1 As shown:

[0105] 1) Develop an initial design plan (including initial geometry and engine, instrumentation selection, etc.) based on the given initial design indicators;

[0106] 2) According to the initial design scheme, select optimization variables (such as outer wingspan length, load mass, etc.), and give the optimization range of each optimization variable. The selection of specific optimization variables and their corresponding optimization ranges can be determined according to actual conditions or past experience, etc., and this embodiment does not make any requirements here.

[0107] 3) Comprehensive competitiveness cp The optimization algorithm is used to optimize the design indicators with the maximum value. The design scheme is adjusted with the selected optimization variables, and the corresponding performance parameters are obtained according to the simulation model. The comprehensive competitiveness G is then calculated based on the performance parameters. cp ; ultimately the comprehensive competitiveness G cp The performance parameters corresponding to the largest design solution are taken as the optimization result, that is, the optimized design index.

[0108] 2. Test Verification

[0109] 1. After market research and comparison with similar models, the following initial top-level design indicators are formulated:

[0110] Table 3: Initial design specifications

[0111]

[0112] 2. Using the group hierarchical analysis method, two experts establish judgment matrices separately, and then establish a composite judgment matrix based on the judgment weights of the two experts:

[0113] Table 4: Judgment matrix given by expert 1

[0114]

[0115] Table 5: Judgment matrix given by expert 2

[0116]

[0117] Table 6: Composite judgment matrix

[0118]

[0119]

[0120] 3. Analyze the correlation between design indicators and criteria, establish the following relationship matrix, and complete the weight calculation of each design indicator. On the other hand, by statistically analyzing the data of similar models, determine the reasonable value range of design indicators, and obtain the maximum and minimum values ​​of each design indicator in the table for normalization calculation.

[0121] Table 7: Relationship Matrix

[0122]

[0123] 4. Complete the initial concept design according to the initial design indicators, and obtain the corresponding performance parameters and comprehensive competitiveness calculation based on simulation:

[0124] Table 8: Performance comparison of initial solutions

[0125]

[0126] 5. Comprehensive competitiveness cp The maximum is the goal, and the trade-off optimization of various design indicators is completed by optimizing the initial design scheme. The following optimization variables and their respective optimization intervals are selected:

[0127] Table 9: Optimization variables

[0128] Optimization variables Optimization interval Optimization variables Optimization interval Central wingspan length / 2 [0.35m, 0.55m] Outer wing tip root ratio [0.47，0.71] Center wing leading edge sweep angle [45°，65°] Outer wing twist angle [-5°，5°] External wingspan length / 2 [1.8m, 2.4m] Load mass [25kg, 45kg] Outer wing leading edge sweep angle [30°，40°] Fuel quality [28kg, 40kg]

[0129] The single-objective subset simulation optimization algorithm is used during optimization, with a maximum number of layers of 15 and 120 samples per layer. Multi-core parallelism is used during the optimization process. Figure 3 As shown, the final optimization took about 10 hours.

[0130] Figure 3The significant improvement in competitiveness during the entire optimization process is demonstrated. According to the comprehensive competitiveness evaluation system established previously through expert scoring, flight performance has the greatest impact on overall competitiveness. Therefore, a significant improvement in flight performance was observed during the optimization process. Economic efficiency ranks second in competitiveness impact, so this item has also been moderately improved. In contrast, the impact of payload capacity and stealth capability is relatively small, resulting in the smallest changes in these two. Ultimately, it can be seen that the optimization process is moving in the direction of enhancing flight performance and economy.

[0131] Finally, the comparison between the design indicators obtained through optimization results and the initial design indicators is shown in the following table:

[0132] Table 9: Comparison of design index optimization results

[0133]

[0134] The changes in design indicator requirements shown in the above table indicate that:

[0135] 1) Based on the initial design requirements, stricter weight limits effectively enhance the overall competitiveness of the design. With only minor changes in payload and fuel weight, the reduction in maximum take-off weight directly leads to a reduction in UAV manufacturing and operating costs, while also promoting further enhancement of flight performance.

[0136] 2) The duration of maneuvering flight required varies greatly before and after optimization. The initially formulated requirement was too loose, which limited the adaptability of the UAV. After optimization, the requirement became 266.8s, which gave the UAV enough time to complete the maneuver and improved the mission adaptability.

[0137] 3) The increase in maximum cruising speed and maximum range indicates that optimizing the aerodynamic layout can further enhance the flight performance of the UAV.

[0138] 4) The RCS value is further reduced, indicating that thanks to the clean layout of BWB, the overall solution still has certain potential in stealth.

[0139] Figure 4 The plane shape comparison between the optimized UAV geometry and the initial solution is shown; the comparison of the corresponding optimization variables is shown in Table 10. It is noted that the optimized UAV size has been reduced, which indicates that under the current competitiveness evaluation system, UAVs with higher payload density show greater competitiveness.

[0140] Table 10: Comparison of variables before and after optimization

[0141]

[0142] The final optimized UAV design is only used to prove the feasibility of the optimized design indicators. In the subsequent formal UAV design and development, you can choose to use the above optimized design as a reference and make further specific developments based on the optimized design according to the actual budget, cycle, and technical capabilities; or you can just follow the optimized design indicators and re-design the UAV's appearance and structure.

[0143] In the description of the present invention, it should be understood that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0144] The present invention is not limited to the above-mentioned embodiments. Any obvious improvement, substitution or deformation that can be made by those skilled in the art without departing from the essential content of the present invention belongs to the protection scope of the present invention.

Claims

1. A method for optimizing design indicators of a wing-body fusion layout UAV, characterized by: Optimize design indicators with the goal of maximizing competitiveness; The competitiveness is calculated by the following formula: Among them G m represents the competitiveness on the mth criterion, G cp Represents the comprehensive competitiveness in all criteria, m∈[1,M], n∈[1,N], M is the number of criteria, N is the number of design indicators; is the normalized value of the nth design indicator; w n The weight of the design indicator for the nth item.

2. The design index optimization method of the wing-body fusion layout UAV according to claim 1 is characterized by: The nonlinear S-shaped differentiable function normalization method is used to normalize each design index: Where V n represents the actual value of the nth design indicator, is the positive ideal solution value of the nth design index, is the negative ideal solution value of the nth design index, and A and R are the curve shape control parameters.

3. The design index optimization method of the wing-body fusion layout UAV according to claim 1 is characterized by: Index weight w n Calculated by the following formula: where ω m is the weight of the mth criterion, R mn is the relationship between the mth criterion and the nth design indicator.

4. The design index optimization method of the wing-body fusion layout UAV according to claim 3 is characterized by: R mn The value of is {0, 1, 3, 5, 7, 9}, and the value from small to large represents the degree of correlation from zero to strong.

5. The design index optimization method of the wing-body fusion layout UAV according to claim 3 is characterized by: Criterion weight ω m Calculated by the AHP judgment matrix.

6. The design index optimization method of the wing-body fusion layout UAV according to claim 1 is characterized by: Formulate initial design plan based on initial design indicators; According to the initial design plan, select the optimization variables and their respective optimization intervals; Adjust the design scheme with the selected optimization variables, obtain the performance parameters through the simulation model, and then calculate the comprehensive competitiveness G based on the performance parameters cp ; ultimately the comprehensive competitiveness G cp The performance parameters corresponding to the largest design solution are used as the optimized design indicators.

7. The design index optimization method of the wing-body fusion layout UAV according to claim 1 is characterized by: In the simulation model, the geometric model of the drone is described according to the following characteristic parameters: The geometric shape of the wing-body fusion layout UAV is divided into five parts: fuselage, inner section of inner wing, outer section of inner wing, outer wing, and winglet according to the bending point of the edge contour; The characteristic parameters of each segment include: span length, leading edge sweep angle, tip-to-root ratio, dihedral angle, and twist angle; For the fuselage, the root chord length and mounting angle are additionally included.

8. The design index optimization method of the wing-body fusion layout UAV according to claim 1 is characterized by: In the simulation model, the semi-empirical method proposed by Howe is used to calculate the structural mass of the UAV; the panel method is used to analyze the lift and induced drag of the UAV; and the PO method is used to calculate the RCS value; The maximum level flight speed is calculated using the following UAV level flight state balance equation: Among them, T is thrust, which is affected by engine speed n, flight altitude h, and flight speed v; D is drag, which is affected by flight altitude h, flight speed v, and angle of attack α; L is lift, which is affected by flight altitude h, flight speed v, and angle of attack α; W is the total weight of the drone; when solving, the engine uses the maximum available speed, and within the available angle of attack range, the maximum speed is continuously increased. The maximum speed that can satisfy the above balance equations is the maximum level flight speed; The maximum range is calculated by segmentation with time Δt as the interval: Among them, the UAV cruises at a fixed height h0 with an initial speed v0 and a constant speed in a straight line, and the initial mass is m0; is the fuel consumed in the i-th period, m i is the mass of the drone at the beginning of the i-th period, R i is the distance flown at the beginning of the i-th period, SFC i is the engine fuel consumption rate during the i-th period, T i is the engine thrust during the i-th period; by calculating in sections until the fuel is completely consumed, the maximum range is obtained; The duration of the maneuvering flight is calculated by analyzing the flight process in segments with time intervals of Δt: in, is the amount of fuel consumed in the i-th period, SFC i is the engine fuel consumption rate during the i-th period, T i is the engine thrust during the i-th period, m i is the mass of the drone at the beginning of the i-th period; a i is the acceleration during the i-th period, obtained from the following dynamic equation during overload maneuvering flight: n g is overload; the flight time is calculated in segments until the flight speed is reduced to the minimum value, and the flight time is the maneuvering flight time.

9. The design index optimization method of the wing-body fusion layout UAV according to claim 1 is characterized by: The single objective subset simulation optimization algorithm is used for optimization.

10. The design index optimization method of the wing-body fusion layout UAV according to claim 1 is characterized by: The criteria include: economy, loading capacity, flight performance, stealth capability; The design indicators include: maximum load mass, maximum take-off mass, fuel mass, maximum level flight speed, maximum range, maneuvering flight time, and RCS.

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