A method for range extension optimization of a passive stage of a vehicle considering aerodynamic trajectory coupling

Abstract

The application discloses a method for range optimization of a passive stage of a vehicle considering aerodynamic trajectory coupling, which establishes an aerodynamic analysis model and a trajectory analysis model, and takes an aerodynamic configuration parameter and an angle of attack change rate sequence as input respectively; a maximum range optimization model of the passive stage is constructed, which comprises an outer layer and an inner layer optimization mathematical model, the outer layer takes maximum lift-drag ratio maximization at 5Ma as an objective, and the inner layer takes maximum range maximization as an objective, and takes a falling speed and a falling angle as constraints. A layered optimization strategy is adopted, the inner layer uses a Gauss pseudospectrum method to perform trajectory optimization on the angle of attack change rate sequence, the outer layer uses a proxy model assisted evolution algorithm to perform global optimization on the aerodynamic configuration parameter, and a sample library is dynamically updated in iteration until termination. The method efficiently obtains optimal aerodynamic configuration parameters under the satisfaction of performance constraints, reduces the number of high-fidelity model calls, and is suitable for overall design of a carrier rocket.

Description

Technical Field

[0001] This application belongs to the field of multidisciplinary design optimization technology for aircraft, specifically involving a passive range extension optimization method for aircraft that considers aerodynamic trajectory coupling. Background Technology

[0002] The overall design of commercial space launch vehicles involves deep coupling between multiple disciplines, including aerodynamics and flight trajectory. The analytical models of each discipline are interdependent and highly nonlinear. In traditional design processes, aerodynamicists independently design the shape configuration parameters and evaluate lift and drag characteristics, while trajectory specialists independently perform ballistic planning and range analysis based on given aerodynamic data. Data exchange and coordination between these two disciplines rely on the experience and judgment of designers, lacking an integrated, coupled optimization mechanism. Due to the lack of interdisciplinary coordination strategies, the overall solution often requires multiple rounds of iterative adjustments to approximate the overall performance requirements. During these iterations, each discipline independently modifies its own variables, making it difficult to guarantee optimal interdisciplinary coordination within the global design space.

[0003] The application of high-fidelity analytical tools has further exacerbated the conflict between design cycle and cost. Aerodynamic analysis relies on engineering estimation software or computational fluid dynamics solutions, while trajectory analysis requires solving the longitudinal plane motion equations. The computation time for a single complete evaluation can range from several hours to several days. Under the traditional enumeration or gradient optimization framework, designers typically fix aerodynamic shape parameters and only use the angle-of-attack rate of change sequence as a variable to mine the maximum range of the passive segment. This single-discipline local optimization method cannot reflect the feedback impact of shape parameter changes on trajectory performance, nor does it incorporate trajectory constraints into the early decision-making of shape design. At the same time, the large range of shape configuration parameters and the high dimensionality of the angle-of-attack rate of change sequence, along with the nonlinear aerodynamic-trajectory coupling relationship, jointly determine the final range, landing velocity, and landing angle, making joint optimization suffer from the curse of dimensionality and convergence difficulties.

[0004] While surrogate modeling techniques have been introduced to reduce the number of calls to high-fidelity models, existing methods often treat aerodynamic and trajectory variables uniformly, resulting in excessively high input dimensionality of the surrogate model. This leads to insufficient approximation accuracy and low reliability of optimization results. Therefore, there is an urgent need for an extended-range optimization method that can effectively decouple aerodynamic and trajectory variables, reduce the input dimensionality of the surrogate model, and ensure global optimization capabilities. This would address the overall design inefficiency caused by the fragmented treatment of disciplinary design variables and the excessively high computational cost of joint optimization in existing technologies. Summary of the Invention

[0005] To address the problems of fragmented design of aerodynamic shape parameters and angle of attack change rate sequences, excessive computational costs due to frequent calls to high-fidelity disciplinary analysis models, and the curse of dimensionality and insufficient approximate accuracy of surrogate models in existing passive range extension optimization methods for aircraft, this application provides a passive range extension optimization method for aircraft that considers aerodynamic trajectory coupling. By constructing an inner and outer decoupled optimization architecture and introducing a surrogate model to assist in global evolutionary optimization, this method achieves collaborative optimization of aerodynamic shape and trajectory parameters and reduces the number of calls to high-fidelity models.

[0006] To achieve the above technical objectives, this application specifically employs the following technical solution: In one aspect of this application, a passive-segment range extension optimization method for an aircraft considering aerodynamic trajectory coupling is provided, comprising the following steps: Step 1: Establish an aerodynamic analysis model and a trajectory analysis model; the input of the aerodynamic analysis model is the aircraft's external configuration parameters, and the output is the aircraft's trim aerodynamic drag coefficient and trim lift coefficient; the input of the trajectory analysis model is the aircraft's angle of attack rate of change sequence and the output of the aerodynamic analysis model, and the output is the aircraft's maximum range, landing speed, and landing angle; Step 2: Establish a mathematical model for optimizing the maximum range of the passive section considering aerodynamic trajectory coupling; the mathematical model for optimizing the maximum range of the passive section considering aerodynamic trajectory coupling includes an outer optimization mathematical model and an inner optimization mathematical model. The outer optimization mathematical model uses the external configuration parameters as design variables, the ballistic violation converted from the inner optimization results as constraints, and maximizing the maximum lift-to-drag ratio of the aircraft at Mach 5 as the optimization objective; the inner optimization mathematical model uses the angle of attack change rate sequence as design variables, maximizes the maximum range as the optimization objective, and uses the landing speed and landing angle as constraints. Step 3: Initialize the outer layer optimization mathematical model and the inner layer optimization mathematical model with hierarchical optimization samples to generate an initial sample library; Step 4: Train a proxy model based on the initial sample library, and use the proxy model to assist in iterative optimization of the outer optimization mathematical model and the inner optimization mathematical model. Update the sample library dynamically according to the optimization results of each iteration until the iteration termination condition is met, and output the sample that meets the constraint conditions and has the largest maximum boost-to-drag ratio at 5mA as the optimization result.

[0007] In one implementation, the aerodynamic analysis model is established in step 1 as follows: The aforementioned shape configuration parameters are denoted as aerodynamic design variables. The external configuration parameters include the projectile length. Bullet diameter The ratio of all cone segments to projectile length Tail rudder span to missile outer diameter ratio Aspect ratio at the root of the tail rudder Tail rudder slightly root ratio Tail sweep angle The ratio of the convergence segment length to the projectile length The ratio of the diameter of the tail section of the convergent phase to the projectile diameter The ratio of the top arc radius to the projectile diameter The ratio of the length of the front cone section to the length of the bullet. The ratio of the diameter of the front cone section to the projectile diameter Minimum distance between the tail rudder root and the convergence section Maximum rudder deflection Circumferential position of the rudder Tail rudder airfoil ; Using the aforementioned aerodynamic design variables as input, and employing Datcom software and instantaneous balance assumptions, the trim aerodynamic function of the aircraft is established. ; At a given angle of attack ,Mach number Location of the center of mass and the aforementioned aerodynamic design variables Under these conditions, the aerodynamic drag coefficient of the balance With the balance lift coefficient satisfy: .

[0008] In one implementation, the trajectory analysis model is established in step 1 as follows: The total number is Angle of attack rate of change sequence As a trajectory design variable, denoted as ; Using the trajectory design variables as input, the trajectory function of the aircraft is established using the longitudinal plane motion equation. ; Given an initial X-direction displacement Initial height Initial velocity Initial velocity angle Initial angle of attack Under these conditions, the X-direction displacement of the aircraft changes Altitude change speed change Velocity tilt angle change Angle of attack change satisfy: .

[0009] In the formula, For the trim aerodynamic function of the aircraft, This is the propulsion system function of the aircraft.

[0010] In one implementation, the outer layer optimization mathematical model is established in step 2 as follows: The external shape parameters are used as design variables for the outer layer optimization mathematical model; The maximum lift-to-drag ratio of the aircraft at Mach 5 As the objective function of the outer optimization mathematical model; ballistic deviation As constraints on the outer layer optimization mathematical model; The outer layer optimization mathematical model is expressed as follows:

[0011] In the formula, For aerodynamic design variables.

[0012] In one implementation, the method for establishing the inner-layer optimization mathematical model in step 2 is as follows: The angle of attack change rate sequence As design variables for the inner-layer optimization mathematical model; Maximum range The objective function of the inner layer optimization mathematical model; Maximum range Decline speed Corner As constraints on the inner-layer optimization mathematical model; The inner-layer optimization mathematical model is expressed as follows:

[0013] In the formula, This is a sequence of rates of change of angle of attack. The velocity constraint value for the inner layer optimization mathematical model. The landing angle constraint value is the value of the inner layer optimization mathematical model.

[0014] In one implementation, in step 2, the output of the inner-layer optimization mathematical model is converted into the ballistic violation degree in the constraints of the outer-layer optimization mathematical model. The method is as follows:

[0015] In the formula, The function that takes the maximum value of each term. This represents the maximum range constraint value of the outer layer optimized mathematical model. The settling velocity constraint value for the outer layer optimization mathematical model. The landing angle constraint value of the outer layer optimization mathematical model. For maximum range, For the drop speed, It is the landing angle.

[0016] In one implementation, the specific method for generating the initial sample library in step 3 is as follows: Step 3.1: Use the Latin hypermethod experimental design method to generate initial sample points for the outer optimization mathematical model. The number of initial sample points is... , No. initial sample points Represented as ; Step 3.2: For the first The initial sample point calls the aerodynamic analysis model to obtain the first... Maximum lift-to-drag ratio at 5mA for initial sample points ; Step 3.3: For the first The trajectory analysis model is invoked using an initial sample point to obtain the maximum range, landing speed, and landing angle under different angle of attack rate of change sequence input conditions; Step 3.4: For the first The inner-layer optimization mathematical model is solved using the Gaussian pseudospectral method for each initial sample point to obtain the optimal angle of attack change rate sequence. and obtain the first Ballistic deviation at initial sample points ; Step 3.5: Repeat steps 3.2 to 3.4 until the maximum lift-to-drag ratio and ballistic deviation at 5Ma for all initial sample points are calculated. Utilize all initial sample points... and the corresponding , Build a sample library.

[0017] In one implementation, the specific method for using the proxy model to assist in iterative optimization and dynamically update the sample library in step 4 is as follows: Step 4.1: Train the surrogate model using all samples in the sample library. The input to the surrogate model is the external configuration parameters, and the output of the surrogate model is the approximate maximum lift-to-drag ratio at the initial 5Ma. Approximate value of ballistic deviation : ; In the formula, For proxy model functions, For aerodynamic design variables; Step 4.2: Use the surrogate model to transform the outer optimization mathematical model into: ; Step 4.3: Solve the transformed outer-layer optimization mathematical model using a global evolutionary algorithm, and use the optimal solution obtained by the global evolutionary algorithm as the new sample point. , represented as ; Step 4.4: Apply the aerodynamic analysis model to the newly added sample point to obtain the maximum lift-to-drag ratio at 5Ma for the newly added sample point. ; Step 4.5: Apply the trajectory analysis model to the newly added sample points to obtain the maximum range, landing velocity, and landing angle under different angle of attack change rate sequence input conditions; Step 4.6: Solve the inner-layer optimization mathematical model using the Gaussian pseudospectral method for the newly added sample points to obtain the optimal angle of attack change rate sequence. And obtain the ballistic violation degree of the newly added sample points. ; Step 4.7: Add the new sample points and the corresponding , Add to the sample library; Step 4.8: Repeat steps 4.1 to 4.7 until the maximum number of iterations is reached. .

[0018] In one implementation, in step 4.8, if the maximum number of iterations is reached... Then, the sample that satisfies the constraints and has the largest initial 5Ma maximum lift-to-drag ratio is selected from the sample library, which is the optimal result of the passive segment maximum range layer optimization considering aerodynamic trajectory coupling.

[0019] In another aspect of this application, a terminal device is provided, including a processor, a memory, and a computer program stored in the memory, characterized in that, when the processor executes the computer program, it implements the aircraft passive segment range extension optimization method considering aerodynamic trajectory coupling.

[0020] In another aspect of this application, a computer-readable storage medium is provided, wherein a computer program is stored therein, characterized in that, when the computer program is executed by a processor, it implements the aircraft passive segment range extension optimization method considering aerodynamic trajectory coupling.

[0021] The beneficial effects of this application are as follows: This application uses the external configuration parameters and the angle-of-attack rate of change sequence as design variables for aerodynamics and trajectory, respectively. With the goal of maximizing the maximum lift-to-drag ratio at Mach 5 and using range, landing velocity, and landing angle as performance constraints, a maximum range optimization mathematical model is constructed. Based on this, an inner-outer layered optimization architecture is invented: the inner layer uses the Gaussian pseudospectral method to optimize the trajectory of the angle-of-attack rate of change sequence, while the outer layer uses a surrogate model-assisted evolutionary algorithm to globally optimize the external configuration parameters. The surrogate model significantly reduces the number of calls to the high-fidelity disciplinary analysis model, thereby significantly reducing computational costs. This method can efficiently explore design schemes that satisfy performance constraints and automatically find the design with the minimum mass, featuring high computational efficiency and strong engineering practicality, providing strong support for the overall design of low-cost launch vehicles for commercial aerospace. Attached Figure Description

[0022] Figure 1 A schematic diagram of the passive-segment range extension optimization process for an aircraft considering aerodynamic trajectory coupling for this application; Figure 2 The diagram shows a comparison of the aircraft configuration before and after optimization in the embodiments of this application; where a is the initial result and b is the optimized result. Figure 3 This is a comparison chart of the aerodynamic lift-to-drag ratio before and after optimization in the embodiments of this application; where a is the initial result and b is the optimized result; Figure 4 This is a comparison diagram of flight trajectories before and after optimization in an embodiment of this application; where a is the initial result and b is the optimized result; Figure 5 This is a comparison chart of flight speed before and after optimization in the embodiments of this application; where a is the initial result and b is the optimized result. Detailed Implementation

[0023] The technical solution of this application will be clearly and completely described below with reference to specific embodiments. However, those skilled in the art will understand that the embodiments described below are only some embodiments of this application, not all embodiments, and are only used to illustrate this application, and should not be regarded as limiting the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] This application provides a passive range extension optimization method for aircraft considering aerodynamic trajectory coupling. The aerodynamic analysis model is established as a function of the aircraft's shape configuration parameters, and the trajectory analysis model is established as a function of the angle-of-attack rate of change sequence. The shape configuration parameters and the angle-of-attack rate of change sequence are used as design variables. The performance parameters of the aircraft are solved using the aerodynamic and trajectory analysis models. A maximum range optimization mathematical model is constructed with the objective function of maximizing the maximum lift-to-drag ratio at Mach 5, and with range constraints, landing speed constraints, and landing angle constraints as constraints. In the inner-layer optimization, the Gaussian pseudospectral method is used to optimize the trajectory of the angle-of-attack rate of change sequence. In the outer-layer optimization, a surrogate model-assisted evolutionary algorithm is used to globally optimize the shape configuration parameters. The surrogate model reduces the number of calls to the high-fidelity disciplinary analysis model, thereby reducing computational costs. Sample points that satisfy the constraints and have the largest maximum lift-to-drag ratio at Mach 5 are selected from the sample database as the design scheme for the maximum range parameters of the aircraft's passive range. The method proposed in this application automatically obtains the overall design of a launch vehicle with optimal aerodynamic shape parameters under multiple performance constraints. It has high computational efficiency and strong engineering applicability, providing support for the overall design of low-cost launch vehicles for commercial aerospace.

[0025] Specifically, this application proposes a passive-segment range extension optimization method for aircraft that considers aerodynamic trajectory coupling, referring to... Figure 1 As shown, this can be achieved through the following steps: Step 1: Establish aerodynamic analysis model and trajectory analysis model Step 1.1: Treat the external configuration parameters as aerodynamic design variables, denoted as... Aerodynamic design variables include: missile length. Bullet diameter The ratio of all cone segments to projectile length Tail rudder span to missile outer diameter ratio Aspect ratio at the root of the tail rudder Tail rudder slightly root ratio Tail sweep angle The ratio of the convergence segment length to the projectile length The ratio of the diameter of the tail section of the convergent phase to the projectile diameter The ratio of the top arc radius to the projectile diameter The ratio of the length of the front cone section to the length of the bullet. The ratio of the diameter of the front cone section to the projectile diameter Minimum distance between the tail rudder root and the convergence section Maximum rudder deflection Circumferential position of the rudder Tail rudder airfoil Using the aforementioned aerodynamic design variables as input, and employing Datcom software and instantaneous equilibrium assumptions, the trim aerodynamic function of the aircraft is established. .

[0026] At a given angle of attack ,Mach number Location of the center of mass and aerodynamic design variables Under the condition of balance, the aerodynamic drag coefficient With the balance lift coefficient characteristic: (1) Equation (1) is the established aerodynamic analysis model of the aircraft.

[0027] Step 1.2: The total number is Angle of attack rate of change sequence As a trajectory design variable, denoted as Using the aforementioned trajectory design variables as input, and employing the longitudinal plane motion equations, the trajectory function of the aircraft is established. .

[0028] Given an initial displacement in the X direction Initial height Initial velocity Initial velocity angle Initial angle of attack Under these conditions, the X-direction displacement of the aircraft changes Altitude change speed change Velocity tilt angle change Angle of attack change for: (2) Equation (2) is the established aircraft trajectory analysis model.

[0029] Step 2: Establish a mathematical model for optimizing the maximum range of the passive segment considering aerodynamic trajectory coupling. Step 2.1: Use the external configuration parameters and angle-of-attack change rate sequence as design variables for the mathematical model of the maximum range optimization of the aircraft's passive section; use the aircraft's maximum lift-to-drag ratio at Mach 5 as the design variable. As the objective function of the maximum range optimization mathematical model; the maximum range Decline speed Corner As a constraint condition for the maximum range optimization mathematical model, the maximum range optimization mathematical model for the passive segment considering aerodynamic trajectory coupling is established as equation (3): (3) In the formula, This is the maximum range constraint value. This is the drop velocity constraint value. This is the landing angle constraint value.

[0030] Due to the angle of attack change rate sequence Due to the large number of items, optimization will be carried out in two stages: outer layer optimization and inner layer optimization.

[0031] Step 2.2: Establish an outer-layer optimization mathematical model for the aircraft's maximum range. The external configuration parameters are used as design variables for the outer-layer optimization mathematical model; the maximum lift-to-drag ratio of the aircraft at Mach 5 is set as... As the objective function of the outer optimization mathematical model; ballistic deviation is used. As a constraint condition for the outer-layer optimization mathematical model, the outer-layer optimization mathematical model for the maximum range of the aircraft is established as equation (4): (4) Step 2.3: Establish the optimal inner-layer mathematical model for the aircraft. Use the angle-of-attack rate of change sequence as the design variable of the inner-layer mathematical model; use the maximum range as the objective function of the inner-layer mathematical model; and use the maximum range... Decline speed Corner As constraints of the inner-layer optimization mathematical model, the optimal size inner-layer optimization mathematical model for the aircraft is established as equation (5): (5) According to equation (6), the maximum range of the inner layer is optimized. Decline speed Corner Transformed into ballistic violation of constraints in the outer optimization mathematical model : (6) In equation (6), This is a function that takes the maximum value of each term.

[0032] Step 3: Maximum Range Stratified Optimization Sample Initialization Step 3.1: Generate the initial sample points for the outer-layer optimization mathematical model using the Latin hypersquare experimental design method, where the number of initial sample points is... . No. initial sample points It can be represented as .

[0033] Step 3.2: For the first Using an initial sample point, the aerodynamic analysis model is invoked to obtain the trim aerodynamic drag coefficient and trim lift coefficient characteristics. The maximum lift-to-drag ratio at the initial sample points is denoted as 5Ma. The initial 5Ma maximum lift-to-drag ratio is denoted as .

[0034] Step 3.3: For the first Based on the characteristics of the trimmed aerodynamic drag coefficient and trimmed lift coefficient, the aircraft trajectory analysis model can be further invoked to obtain the maximum range, landing speed, and landing angle under different angle-of-attack rate sequences as input conditions.

[0035] Step 3.4: For the first Using an initial sample point, the inner optimization problem of equation (5) is solved using the Gaussian pseudospectral method. The optimal angle-of-attack rate sequence can then be obtained. According to equation (6), the first... Ballistic deviation at initial sample points .

[0036] Step 3.5: Repeat steps 3.2 to 3.4 until the maximum lift-to-drag ratio and ballistic deviation at 5Ma for all initial sample points are calculated. Utilize all initial sample points... and the corresponding , Build a sample library.

[0037] Step 4: Dynamically adjust the sample library for maximum range stratification optimization Step 4.1: Train the surrogate model using all samples in the sample library. The input to the surrogate model is the aerodynamic design variables. The output of the surrogate model is the initial maximum lift-to-drag ratio of the aircraft at Mach 5, denoted as... and ballistic deviation approximation , see formula (7) for details: (7) Step 4.2: Using equation (7), transform the optimal outer layer optimization mathematical model of the aircraft in equation (4) into equation (8): (8) Step 4.3: Solve equation (8) using a global evolutionary algorithm, and use the optimal solution obtained by the global evolutionary algorithm as a new sample point in the sample library. , can be represented as .

[0038] Step 4.4: Add new sample points The aerodynamic analysis model is called to obtain the trim aerodynamic drag coefficient and trim lift coefficient characteristics, and new sample points are added. The maximum boost-to-drag ratio of 5Ma is denoted as The initial 5Ma maximum lift-to-drag ratio is denoted as .

[0039] Step 4.5: Add new sample points Based on the characteristics of the trim aerodynamic drag coefficient and trim lift coefficient, the aircraft trajectory analysis model can be further invoked to obtain the maximum range, landing speed, and landing angle under different angle of attack change rate sequences as input conditions.

[0040] Step 4.6: Add new sample points The inner optimization problem of equation (5) is solved using the Gaussian pseudospectral method. The optimal angle-of-attack rate sequence can then be obtained. According to equation (6), the newly added sample points can be obtained. ballistic violation .

[0041] Step 4.7: Add new sample points and the corresponding , Added to the sample library.

[0042] Step 4.8: Repeat steps 4.1 to 4.8 until the maximum number of iterations is reached. If the maximum number of iterations is reached, the sample that satisfies the constraints and has the largest initial 5Ma maximum lift-to-drag ratio is selected from the sample library. This sample is the optimal result of the hierarchical optimization of the maximum range parameter of the passive segment considering aerodynamic trajectory coupling.

[0043] Example In this embodiment, the values ​​of the aerodynamic design variables for the initial manually designed scheme of the aircraft are shown in Table 1.

[0044] Table 1: Values ​​of aerodynamic and dynamic design variables in the initial design scheme of the aircraft

[0045] The lower bound of the aerodynamic design variables is set at 0.75 times the initial value, and the upper bound is set at 1.25 times the initial value. The trajectory design variables can be adaptively adjusted in the inner layer optimization using the pseudospectral method. The lower bound of the angle-of-attack change rate sequence is -3° / s, and the upper bound is 3° / s.

[0046] In this implementation, the initial number of sample points Set to 150, maximum number of iterations The number of iterations is set to 200. The differential evolution algorithm uses a population of 300, with 400 generations, and both the scaling factor and crossover probability are set to 0.5. The values ​​of each constraint are shown in Table 2.

[0047] Table 2: Constraint Values

[0048] Table 3 shows a comparison between the optimized results obtained by the present invention and the initial results of manual design. It can be seen that the optimized results of the present invention meet the constraints for maximum range, maximum lift-to-drag ratio at Mach 5, landing speed, and landing angle. Furthermore, the maximum lift-to-drag ratio of the aircraft at Mach 5 can be increased from 3.16 to 3.22. The initial results of manual design require 4 people and 2 days of manpower and computational cost; the method of the present invention requires 1 person and 8 hours of manpower and computational cost. The configuration comparison diagrams, trim aerodynamic lift-to-drag ratio comparison diagrams, trajectory comparison diagrams, and velocity comparison diagrams before and after optimization are shown below. Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown.

[0049] Table 3: Comparison of Optimization Results and Initial Results of the Invention

[0050] Although the embodiments of this application have been described above in conjunction with the accompanying drawings, this application is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of this application, and these are all within the scope of protection of this application.

Claims

1. A passive-segment range extension optimization method for aircraft considering aerodynamic trajectory coupling, characterized in that, include: Step 1: Establish an aerodynamic analysis model and a trajectory analysis model; the input of the aerodynamic analysis model is the aircraft's external configuration parameters, and the output is the aircraft's trim aerodynamic drag coefficient and trim lift coefficient; the input of the trajectory analysis model is the aircraft's angle of attack rate of change sequence and the output of the aerodynamic analysis model, and the output is the aircraft's maximum range, landing speed, and landing angle; Step 2: Establish a mathematical model for optimizing the maximum range of the passive section considering aerodynamic trajectory coupling; the mathematical model for optimizing the maximum range of the passive section considering aerodynamic trajectory coupling includes an outer optimization mathematical model and an inner optimization mathematical model. The outer optimization mathematical model uses the external configuration parameters as design variables, the ballistic violation converted from the inner optimization results as constraints, and maximizing the maximum lift-to-drag ratio of the aircraft at Mach 5 as the optimization objective; the inner optimization mathematical model uses the angle of attack change rate sequence as design variables, maximizes the maximum range as the optimization objective, and uses the landing speed and landing angle as constraints. Step 3: Initialize the outer layer optimization mathematical model and the inner layer optimization mathematical model with hierarchical optimization samples to generate an initial sample library; Step 4: Train a proxy model based on the initial sample library, and use the proxy model to assist in iterative optimization of the outer optimization mathematical model and the inner optimization mathematical model. Update the sample library dynamically according to the optimization results of each iteration until the iteration termination condition is met, and output the sample that meets the constraint conditions and has the largest maximum boost-to-drag ratio at 5mA as the optimization result.

2. The passive-segment range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 1, characterized in that, The method for establishing the aerodynamic analysis model in step 1 is as follows: The aforementioned shape configuration parameters are denoted as aerodynamic design variables. The external configuration parameters include the projectile length. Bullet diameter The ratio of all cone segments to projectile length Tail rudder span to missile outer diameter ratio Aspect ratio at the root of the tail rudder Tail rudder slightly root ratio Tail sweep angle The ratio of the convergence segment length to the projectile length The ratio of the diameter of the tail section of the convergent phase to the projectile diameter The ratio of the top arc radius to the projectile diameter The ratio of the length of the front cone section to the length of the bullet. The ratio of the diameter of the front cone section to the projectile diameter Minimum distance between the tail rudder root and the convergence section Maximum rudder deflection Circumferential position of the rudder Tail rudder airfoil ; Using the aforementioned aerodynamic design variables as input, and employing Datcom software and instantaneous balance assumptions, the trim aerodynamic function of the aircraft is established. ; At a given angle of attack ,Mach number Location of the center of mass and the aforementioned aerodynamic design variables Under these conditions, the aerodynamic drag coefficient of the balance With the balance lift coefficient satisfy: 。 3. The passive range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 1, characterized in that, The method for establishing the trajectory analysis model in step 1 is as follows: The total number is Angle of attack rate of change sequence As a trajectory design variable, denoted as ; Using the trajectory design variables as input, the trajectory function of the aircraft is established using the longitudinal plane motion equation. ; Given an initial X-direction displacement Initial height Initial velocity Initial velocity inclination angle Initial angle of attack Under these conditions, the X-direction displacement of the aircraft changes Altitude change speed change Velocity tilt angle change Angle of attack change satisfy: 。 In the formula, For the trim aerodynamic function of the aircraft, This is the propulsion system function of the aircraft.

4. The passive-segment range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 1, characterized in that, The method for establishing the outer layer optimization mathematical model in step 2 is as follows: The external shape parameters are used as design variables for the outer layer optimization mathematical model; The maximum lift-to-drag ratio of the aircraft at Mach 5 As the objective function of the outer optimization mathematical model; ballistic deviation As constraints on the outer layer optimization mathematical model; The outer layer optimization mathematical model is expressed as follows: In the formula, For aerodynamic design variables.

5. The passive range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 1, characterized in that, The method for establishing the inner-layer optimization mathematical model in step 2 is as follows: The angle of attack change rate sequence As design variables for the inner-layer optimization mathematical model; Maximum range The objective function of the inner layer optimization mathematical model; Maximum range Decline speed Corner As constraints on the inner-layer optimization mathematical model; The inner-layer optimization mathematical model is expressed as follows: In the formula, For the sequence of rates of change of angle of attack, The velocity constraint value for the inner layer optimization mathematical model. The landing angle constraint value is the value of the inner layer optimization mathematical model.

6. The passive-segment range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 1, characterized in that, In step 2, the output of the inner-layer optimization mathematical model is converted into the ballistic violation degree in the constraints of the outer-layer optimization mathematical model. The method is as follows: In the formula, The function that takes the maximum value of each term. This represents the maximum range constraint value of the outer layer optimized mathematical model. The settling velocity constraint value for the outer layer optimization mathematical model. The landing angle constraint value of the outer layer optimization mathematical model. For maximum range, For the drop speed, It is the landing angle.

7. The passive-segment range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 1, characterized in that, The specific method for generating the initial sample library in step 3 is as follows: Step 3.1: Use the Latin hypermethod experimental design method to generate initial sample points for the outer optimization mathematical model. The number of initial sample points is... , No. initial sample points Represented as ; Step 3.2: For the first The initial sample point calls the aerodynamic analysis model to obtain the first... Maximum lift-to-drag ratio at 5mA for initial sample points ; Step 3.3: For the first The trajectory analysis model is invoked using an initial sample point to obtain the maximum range, landing speed, and landing angle under different angle of attack rate of change sequence input conditions; Step 3.4: For the first The inner-layer optimization mathematical model is solved using the Gaussian pseudospectral method for each initial sample point to obtain the optimal angle of attack change rate sequence. and obtain the first Ballistic deviation at initial sample points ; Step 3.5: Repeat steps 3.2 to 3.4 until the maximum lift-to-drag ratio and ballistic deviation at 5Ma for all initial sample points are calculated. Utilize all initial sample points... and the corresponding , Build a sample library.

8. The passive-segment range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 1, characterized in that, The specific method for using the surrogate model to assist in iterative optimization and dynamically update the sample database in step 4 is as follows: Step 4.1: Train the surrogate model using all samples in the sample library. The input to the surrogate model is the external configuration parameters, and the output of the surrogate model is the approximate maximum lift-to-drag ratio at the initial 5Ma. Approximate value of ballistic deviation : ； In the formula, For proxy model functions, For aerodynamic design variables; Step 4.2: Use the surrogate model to transform the outer optimization mathematical model into: ； Step 4.3: Solve the transformed outer-layer optimization mathematical model using a global evolutionary algorithm, and use the optimal solution obtained by the global evolutionary algorithm as the new sample point. , represented as ; Step 4.4: Apply the aerodynamic analysis model to the newly added sample point to obtain the maximum lift-to-drag ratio at 5Ma for the newly added sample point. ; Step 4.5: Apply the trajectory analysis model to the newly added sample points to obtain the maximum range, landing velocity, and landing angle under different angle of attack change rate sequence input conditions; Step 4.6: Solve the inner-layer optimization mathematical model using the Gaussian pseudospectral method for the newly added sample points to obtain the optimal angle of attack change rate sequence. And obtain the ballistic violation degree of the newly added sample points. ; Step 4.7: Add the new sample points and the corresponding , Add to the sample library; Step 4.8: Repeat steps 4.1 to 4.7 until the maximum number of iterations is reached. .

9. The passive-segment range extension optimization method for aircraft considering aerodynamic trajectory coupling according to claim 8, characterized in that, In step 4.8, if the maximum number of iterations is reached... Then, the sample that satisfies the constraints and has the largest initial 5Ma maximum lift-to-drag ratio is selected from the sample library, which is the optimal result of the passive segment maximum range layer optimization considering aerodynamic trajectory coupling.

10. A terminal device, comprising a processor, a memory, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the passive range extension optimization method for aircraft that considers aerodynamic trajectory coupling as described in any one of claims 1 to 9.

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