An aircraft optimal sizing parameter hierarchical optimization method considering performance constraints

Abstract

The application belongs to the field of aircraft overall design, and discloses a kind of aircraft optimal scale parameter layered optimization method considering performance constraint, comprising: constructing aerodynamic analysis model, propulsion analysis model and trajectory analysis model, for solving the performance parameter and scale parameter of aircraft;With shape configuration parameter, grain and nozzle parameter, angle of attack change rate sequence as design variable, with the minimum initial total mass of aircraft as objective function, build optimal scale optimization mathematical model, and decouple it into outer layer optimization problem and inner layer optimization problem for dimension reduction solution;In the inner layer optimization problem, the trajectory optimization of the angle of attack change rate sequence is carried out by using the Gauss pseudospectrum method;In the outer layer optimization problem, global optimization is carried out by combining the global evolution algorithm with the proxy model;Finally, the optimal proxy sample is determined from the proxy sample library, so that the design scheme of the optimal scale parameter of the aircraft is obtained.The application has the characteristics of low cost and high efficiency.

Description

Technical Field

[0001] This invention belongs to the field of aircraft overall design, and specifically relates to a hierarchical optimization method for the optimal scale parameters of an aircraft that takes performance constraints into account. Background Technology

[0002] The overall design of aircraft (such as commercial space launch vehicles) is a typical multidisciplinary, highly coupled problem, involving interdisciplinary analytical models of aerodynamic shape, propulsion systems, and flight trajectories. In traditional design processes, each discipline is often analyzed and designed independently by different professional teams. Aerodynamic design focuses on lift and drag characteristics, propulsion design emphasizes specific impulse and thrust, and trajectory design pursues range and landing velocity targets, with a lack of effective coordination mechanisms between disciplines. This independent disciplinary model makes it difficult to ensure optimal interdisciplinary coordination, often requiring multiple iterations to meet overall performance requirements. Furthermore, the high-fidelity analytical models of each discipline (such as computational fluid dynamics and internal ballistics solving) are computationally time-consuming, with a complete overall evaluation potentially taking hours or even days. This results in excessively long overall design cycles and high costs, making it difficult to meet the urgent needs of commercial spaceflight for rapid iteration and low-cost development.

[0003] In the traditional performance-oriented optimization framework, designers typically use mission indicators such as range, landing velocity, and landing angle as optimization targets, aiming to maximize performance given the spacecraft's size parameters. However, the core competitiveness of commercial spaceflight lies in launch cost, and the launch vehicle's weight and diameter directly determine the costs of materials, manufacturing, transportation, and launch services. Traditional methods, lacking targeted processing of size parameters such as launch vehicle weight and diameter, are prone to falling into the trap of over-performance and excessive cost.

[0004] Furthermore, the design variables for the overall design of commercial space launch vehicles and other aircraft span multiple disciplines, including aerodynamic parameters such as aspect ratio and tail area, propellant and nozzle parameters such as burn rate index and expansion ratio, and trajectory parameters such as the rate of change of angle of attack. These variables have vastly different value ranges and influence the final performance and scale through strong nonlinear coupling, leading to prominent problems in the optimization process such as the curse of dimensionality, convergence difficulties, and insufficient accuracy of surrogate models. Summary of the Invention

[0005] The purpose of this invention is to provide a hierarchical optimization method for the optimal scale parameters of an aircraft that takes performance constraints into account, in order to solve the problems of excessive performance and high cost in existing aircraft designs.

[0006] To achieve the above objectives, the present invention employs the following technical solution: A hierarchical optimization method for optimal scale parameters of an aircraft considering performance constraints includes: The aerodynamic analysis model is characterized using shape configuration parameters, the propulsion analysis model is characterized using propellant grain and nozzle parameters, and the trajectory analysis model is characterized using an angle-of-attack rate of change sequence; the aerodynamic analysis model, propulsion analysis model, and trajectory analysis model are used to solve for the performance parameters and scale parameters of the aircraft. Using the shape configuration parameters, propellant and nozzle parameters, and angle of attack change rate sequence as design variables, and taking minimizing the initial total mass of the aircraft as the objective function, a best-scale optimization mathematical model considering performance constraints is constructed in combination with the constraints, and it is decoupled into an outer-layer optimization problem and an inner-layer optimization problem for dimensionality reduction solution. In the inner-layer optimization problem, initial samples are first constructed based on the design variables of the outer-layer optimization problem, and the trajectory optimization of the angle of attack change rate sequence is performed using the Gaussian pseudospectral method. The optimization results and initial samples are used to construct surrogate samples to form a surrogate sample library. In the outer-layer optimization problem, a surrogate model combined with a global evolutionary algorithm is used to globally optimize the shape configuration parameters, propellant grain and nozzle parameters, and the optimization results are used to enrich the surrogate sample library. Finally, the optimal surrogate sample is determined from the surrogate sample library, thereby obtaining the design scheme of the best scale parameters of the aircraft.

[0007] Furthermore, the external shape parameters As input, the trim aerodynamic function of the aircraft is established using Datcom software and the instantaneous equilibrium assumption. At a given angle of attack ,Mach number Location of the center of mass and external configuration parameters Under the condition of balancing aerodynamic functions, Obtain the balance aerodynamic drag coefficient With the balance lift coefficient : ; The above equation is the established aerodynamic analysis model.

[0008] Furthermore, the parameters of the propellant and the nozzle are... As input, the propulsion function of the aircraft is established using geometric modeling software, zero-dimensional internal ballistic equations, and steady isentropic flow equations of the nozzle. ; During a given working time and parameters of the propellant and nozzle Under the condition of, based on the propulsion function Get the thrust of the aircraft ,pressure Mass flow rate and changes in total mass : ; In the formula, The maximum operating time of the propulsion system is given by the formula above, which represents the established propulsion analysis model.

[0009] Furthermore, the angle of attack change rate sequence Using the longitudinal plane motion equations as input, the trajectory function of the aircraft is established. ; Given the initial displacement of the spacecraft in the X direction Initial height Initial velocity Initial velocity angle Initial angle of attack Angle of attack change rate sequence Under these conditions, combined with the balancing aerodynamic function Propulsion function of the aircraft Using the trajectory function of the aircraft The displacement change of the aircraft in the X direction was calculated. Altitude change speed change Velocity tilt angle change Angle of attack change for: ; The above formula is the established trajectory analysis model.

[0010] Furthermore, the external shape parameters Parameters of propellant and nozzle As design variables for the outer optimization problem To minimize the initial total mass of the aircraft As the objective function, a method is constructed based on the maximum operating time of the propulsion system. 5Ma maximum lift-to-drag ratio Ballistic deviation Given the constraints, the outer optimization problem is constructed as follows: ; in, To push forward the system's maximum operating time constraint value, This is the maximum boost-to-drag ratio constraint value at 5mA.

[0011] Furthermore, the angle of attack change rate sequence As design variables for inner-level optimization problems To maximize the maximum range As the objective function; construct based on the falling velocity Corner Given the constraints, the inner optimization problem is as follows: ; in, This is the drop velocity constraint value. This is the landing angle constraint value; The maximum range in the inner optimization problem Decline speed Corner Ballistic violation in the constraints of the outer optimization problem : ; in, This is the maximum range constraint value.

[0012] Furthermore, regarding the design variables of the outer optimization problem Set design variables The value space is determined, and then the Latin hypercube sampling method is used to generate the initial samples of the outer optimization problem, thus obtaining the initial sample library; For the Using an initial sample, the aerodynamic analysis model is called to obtain the trim aerodynamic drag coefficient. With the balance lift coefficient The maximum lift-to-drag ratio at 5Ma is then obtained and denoted as . ; Use the propulsion analysis model to obtain the thrust of the aircraft. ,pressure Mass flow rate and changes in total mass Thus, the first The maximum operating time of the propulsion system for the initial sample is denoted as The initial total mass is denoted as ; Based on the calculation results of the aerodynamic analysis model and the propulsion analysis model, the trajectory analysis model is further invoked to obtain the displacement change of the aircraft in the X direction under different angle-of-attack rate sequences as input conditions. Altitude change speed change Velocity tilt angle change Angle of attack change ; Based on the results of the trajectory analysis model, the Gaussian pseudospectral method is used to solve the inner-layer optimization problem, which yields the optimal angle-of-attack rate sequence. Then calculate the first... Ballistic deviation of an initial sample ; Each initial sample and the corresponding , , , As a proxy sample, a proxy sample library is constructed.

[0013] Furthermore, the proxy model is trained using all proxy samples in the proxy sample library; the input to the proxy model is the initial samples contained in the proxy samples. The output is an approximate value of the initial total mass. Approximate maximum operating time of the propulsion system Approximate maximum lift-to-drag ratio at 5mA and ballistic deviation approximation ; Using a surrogate model, the outer optimization problem is transformed into the following new optimization problem: ; in, These represent the values ​​of the external configuration parameters, the values ​​of the propellant grain and the nozzle parameters, respectively. These are the transformed design variables; A global evolutionary algorithm is used to solve the new optimization problem, and the optimal solution obtained is used as the new sample. ; For new samples Solve for the ballistic violation degree 5Ma maximum lift-to-drag ratio Maximum operating time of the propulsion system Initial total mass ; New samples and the corresponding , , , Add it to the proxy sample library as a new proxy sample; When the iteration process reaches the maximum number of iterations Then, select samples from the surrogate sample library that satisfy the constraints of the outer optimization problem and have an initial total mass. The smallest surrogate sample is taken as the optimal surrogate sample, and the shape configuration parameters, propellant grain and nozzle parameters contained therein are the design scheme of the best size parameters of the aircraft.

[0014] A terminal device includes a processor, a memory, and a computer program stored in the memory; when the processor executes the computer program, it implements the hierarchical optimization method for optimal aircraft size parameters considering performance constraints.

[0015] A computer-readable storage medium storing a computer program; when executed by a processor, the computer program implements the hierarchical optimization method for optimal aircraft size parameters considering performance constraints.

[0016] Compared with the prior art, the present invention has the following technical features: This invention uses the external configuration parameters, propellant and nozzle parameters, and the angle-of-attack rate of change sequence as design variables for aerodynamics, propulsion, and trajectory disciplines, respectively. With the goal of minimizing the initial mass and with range, velocity, and angle of attack as performance constraints, an optimal scale optimization mathematical model is constructed. Based on this, a 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, propellant and nozzle parameters. The surrogate model significantly reduces the number of calls to the high-fidelity discipline analysis model, thus significantly reducing computational costs. This method can efficiently explore design schemes that satisfy performance constraints and automatically find the design with the minimum initial total mass. It features high computational efficiency and strong engineering applicability. Using this invention, the optimal scale parameters of the spacecraft can be quickly solved under given overall performance requirements, providing strong support for the overall design of low-cost launch vehicles for commercial aerospace. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of the method of the present invention; Figure 2 To optimize the aerodynamic drag coefficient before and after the balance With the balance lift coefficient The comparison figures show that (a) represents the initial result of manual design and (b) represents the optimized result of the method of the present invention. Figure 3 The image shows a comparison of thrust before and after optimization, where (a) is the initial result designed manually, and (b) is the optimization result of the method of the present invention. Figure 4 The graph shows a speed comparison before and after optimization, where (a) is the initial result designed manually, and (b) is the optimization result of the method of the present invention. Detailed Implementation

[0018] This invention provides a hierarchical optimization method for the optimal scale parameters of an aircraft considering performance constraints, comprising: The aerodynamic analysis model is characterized using shape configuration parameters, the propulsion analysis model is characterized using propellant grain and nozzle parameters, and the trajectory analysis model is characterized using an angle-of-attack rate of change sequence; the aerodynamic analysis model, propulsion analysis model, and trajectory analysis model are used to solve for the performance parameters and scale parameters of the aircraft. Using the shape configuration parameters, propellant and nozzle parameters, and angle of attack change rate sequence as design variables, and taking minimizing the initial total mass of the aircraft as the objective function, a best-scale optimization mathematical model considering performance constraints is constructed in combination with the constraints, and it is decoupled into an outer-layer optimization problem and an inner-layer optimization problem for dimensionality reduction solution. In the inner-layer optimization problem, initial samples are constructed and the trajectory of the angle-of-attack change rate sequence is optimized using the Gaussian pseudospectral method. The optimization results and initial samples are used to construct surrogate samples. In the outer-layer optimization problem, the surrogate model is combined with a global evolutionary algorithm to perform global optimization of the shape configuration parameters, propellant grain and nozzle parameters. The optimization results are used to enrich the surrogate sample library. Finally, the optimal surrogate sample is determined from the surrogate sample library, thereby obtaining the design scheme of the optimal scale parameters of the aircraft.

[0019] The method of this invention can efficiently and automatically obtain the overall design of an aircraft with the minimum initial mass while satisfying multiple performance constraints. It features high computational efficiency and strong engineering applicability, providing strong support for the overall design of low-cost commercial aerospace aircraft. The specific steps of this invention are as follows: Step 1: Construct aerodynamic analysis model, propulsion analysis model, and trajectory analysis model.

[0020] Step 1.1, denote the external shape parameters as... ; Specifically, this includes: bullet length Bullet diameter The ratio of all cone segments to projectile length Tail rudder span to aircraft 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 .

[0021] The above external shape parameters As input, the trim aerodynamic function of the aircraft is established using Datcom software and the instantaneous equilibrium assumption. .

[0022] At a given angle of attack ,Mach number Location of the center of mass and external configuration parameters Under the condition of balancing aerodynamic functions, Obtain the balance aerodynamic drag coefficient With the balance lift coefficient : (1); Equation (1) is the established aerodynamic analysis model.

[0023] Step 1.2, record the parameters of the propellant and nozzle as follows: ; Specifically, this includes: combustion chamber length The ratio of the inner diameter of the head to the diameter of the bullet. The ratio of the head bore length to the combustion chamber length The ratio of transition section length to combustion chamber length The ratio of the diameter of the circumscribed circle of the star aperture to the diameter of the projectile. Star aperture angle fraction The ratio of the radius of the star tip to the diameter of the projectile. Star edge half angle The ratio of the diameter of the inscribed circle of the star aperture to the diameter of the bullet. The ratio of the radius of the transition arc of the star aperture to the diameter of the projectile. The proportion of low-burning-rate charge length , number of stars Combustion chamber head ellipsoid ratio The ratio of the combustion chamber inlet diameter to the projectile diameter The ratio of the rear opening diameter of the combustion chamber to the projectile diameter Maximum pressure in the combustion chamber Specific impulse efficiency Reference burning rate for low-burning-rate charges Reference burning rate of high-burning-rate charges The ratio of the outer diameter of the nozzle outer profile cylindrical section to the projectile diameter The ratio of the length of the nozzle outer profile column section to the projectile diameter Expansion ratio .

[0024] The above propellant and nozzle parameters As input, the propulsion function of the aircraft is established using geometric modeling software, zero-dimensional internal ballistic equations, and steady isentropic flow equations of the nozzle. .

[0025] During a given working time and parameters of the propellant and nozzle Under the condition of, based on the propulsion function Get the thrust of the aircraft ,pressure Mass flow rate and changes in total mass : (2); In the formula, The maximum working time of the propulsion system is given by equation (2), which is the established propulsion analysis model.

[0026] Step 1.3, the total number of angle-of-attack changes is The sequence of rates of change of angle of attack is denoted as , represented as ;in For the first Rate of change of angle of attack.

[0027] The angle of attack change rate sequence Using the longitudinal plane motion equations as input, the trajectory function of the aircraft is established. .

[0028] Given the initial displacement of the spacecraft in the X direction Initial height Initial velocity Initial velocity inclination angle Initial angle of attack Angle of attack change rate sequence Under these conditions, combined with the balancing aerodynamic function , advancement function Using trajectory functions The displacement change of the aircraft in the X direction was calculated. Altitude change speed change Velocity tilt angle change Angle of attack change for: (3); Equation (3) is the established trajectory analysis model; in the launch coordinate system of the aircraft, the X direction points to the launch direction (shooting direction), the Y direction is perpendicular to the local horizontal plane and points upward, and the Z direction, together with the X and Y directions, forms a right-hand rectangular coordinate system.

[0029] By using aerodynamic analysis models, propulsion analysis models, and trajectory analysis models to solve for the shape configuration parameters, propellant and nozzle parameters, and angle-of-attack change rate sequence, the performance and scale parameters of the aircraft can be obtained. These parameters serve as the basis for solving the Gaussian pseudospectral method in step 3 when solving the inner-layer optimization problem. The performance parameters include the trim aerodynamic drag coefficient. Balance lift coefficient Thrust of the aircraft ,pressure Mass flow rate X-direction displacement change Altitude change speed change Velocity tilt angle change Angle of attack change Scale parameters include: total mass change .

[0030] Step 2: Construct the optimal size optimization mathematical model that takes into account performance constraints.

[0031] Step 2.1: Use the external configuration parameters, propellant grain and nozzle parameters, and angle of attack change rate sequence as design variables to minimize the initial total mass of the aircraft. As the objective function, construct a function based on maximum range. Maximum operating time of the propulsion system 5Ma maximum lift-to-drag ratio Decline speed Corner The constraints are then used to obtain the optimal scale optimization mathematical model, as shown in equation (4).

[0032] (4); In the formula, This is the maximum range constraint value. To push forward the system's maximum operating time constraint value, This is the maximum boost-to-drag ratio constraint value at 5Ma. This is the drop velocity constraint value. This is the landing angle constraint value.

[0033] Due to the angle of attack change rate sequence The angle of attack change rate is numerous and coupled with other design variables. Direct solution would face the curse of dimensionality. Therefore, the optimal scale optimization mathematical model is decoupled into an outer optimization problem and an inner optimization problem for dimensionality reduction solution.

[0034] Step 2.2, establish the outer layer optimization problem.

[0035] external configuration parameters Parameters of propellant and nozzle As design variables for the outer optimization problem To minimize the initial total mass of the aircraft As the objective function; construct a function based on the maximum operating time of the propulsion system. 5Ma maximum lift-to-drag ratio Ballistic deviation Given the constraints, the outer optimization problem is constructed as follows: (5); Step 2.3: Establish the inner-layer optimization problem.

[0036] The angle of attack change rate sequence As design variables for inner-level optimization problems To maximize the maximum range As the objective function; construct based on the falling velocity Corner Given the constraints, the inner optimization problem is as follows: (6); According to equation (7), the maximum range in the inner-layer optimization problem is... Decline speed Corner Ballistic violation in the constraints of the outer optimization problem : (7); In the formula above, To maximize the function, This is the function to be minimized.

[0037] Step 3: Optimal Scale Stratification Sample Initialization.

[0038] Step 3.1, design variables for the outer optimization problem Set design variables The value space of the problem is determined, and then the Latin hypercube sampling method is used to generate initial samples for the outer optimization problem, resulting in an initial sample library; where the number of initial samples is... ;No. initial samples It can be represented as ;in These represent the values ​​of the external shape parameters and the values ​​of the propellant and nozzle parameters, respectively.

[0039] Step 3.2, for the first Using an initial sample, the aerodynamic analysis model is called to obtain the trim aerodynamic drag coefficient. With the balance lift coefficient The maximum lift-to-drag ratio at 5Ma is then obtained and denoted as . ; Use the propulsion analysis model to obtain the thrust of the aircraft. ,pressure Mass flow rate and changes in total mass Thus, the first The maximum operating time of the propulsion system for the initial sample is denoted as The initial total mass is denoted as .

[0040] Step 3.3, for the first Based on the initial sample and the calculation results of the aerodynamic analysis model and propulsion analysis model, the trajectory analysis model is further invoked to obtain the displacement change of the aircraft in the X direction under different angle-of-attack rate sequences as input conditions. Altitude change speed change Velocity tilt angle change Angle of attack change .

[0041] Step 3.4, for the first Based on the results of the trajectory analysis model, the inner optimization problem of equation (6) can be solved using the Gaussian pseudospectral method with an initial sample, thereby obtaining the optimal angle of attack rate sequence. Then, according to equation (7), calculate the first... Ballistic deviation of an initial sample .

[0042] Step 3.5: Repeat steps 3.2 to 3.4 until the maximum lift-to-drag ratio at 5mA is achieved for all initial samples. Maximum operating time of the propulsion system Initial total mass and ballistic deviation All calculations are complete; using each initial sample and the corresponding , , , As a proxy sample, a proxy sample library is constructed using all proxy samples.

[0043] Step 4: Optimize the agent sample library by layering and dynamically adjusting the optimal size.

[0044] Step 4.1: Train the proxy model using all proxy samples in the proxy sample library. (e.g., Kriging model, support vector regression model, or radial basis function network); the input to the surrogate model is the initial sample contained in the surrogate sample. The output is an approximate value of the initial total mass. Approximate maximum operating time of the propulsion system Approximate maximum lift-to-drag ratio at 5mA and ballistic deviation approximation , see formula (8) for details: (8); Step 4.2, using the surrogate model (8), transform the outer optimization problem (5) into the new optimization problem form (9): (9); Step 4.3: Solve equation (9) using a global evolutionary algorithm (e.g., differential evolution algorithm), and use the obtained optimal solution as the new sample. , can be represented as ;in This represents the solved values ​​of the external configuration parameters, as well as the solved values ​​of the propellant grain and nozzle parameters.

[0045] Step 4.4, for the new samples Using the same method as steps 3.3 to 3.4 (that is, treating the newly added proxy samples as the initial samples in 3.3), the ballistic violation degree is obtained. 5Ma maximum lift-to-drag ratio Maximum operating time of the propulsion system Initial total mass .

[0046] Step 4.5, add new samples and the corresponding , , , Added to the proxy sample library as a new proxy sample.

[0047] Step 4.6: Repeat steps 4.1 to 4.5 until the maximum number of iterations is reached. Then, constraints satisfying the outer optimization problem are selected from the surrogate sample library, and the initial total mass is... The smallest surrogate sample is taken as the optimal surrogate sample. The shape configuration parameters, propellant grain and nozzle parameters contained in it are the design scheme of the best size parameters of the aircraft considering performance constraints.

[0048] In practical engineering applications, the maximum operating time of the propulsion system is... Includes soft constraints for the thrust tail period Since the thrust trailing section does not affect the engine heat shield, the constraint condition in equation (5) can be rewritten as follows: .

[0049] Example

[0050] In one embodiment of the present invention, the initial aerodynamic and design variable values ​​of a certain launch vehicle are shown in Table 1.

[0051] Table 1: Initial results of the artificial design of the aircraft, including aerodynamic and design variable values.

[0052]

[0053] The lower bound of the values ​​of the shape configuration parameters, propellant grain and nozzle parameters is taken as 0.7 times the corresponding values ​​of the initial results, and the upper bound is taken as 1.3 times the corresponding values ​​of the initial results; the upper bound of the specific impulse efficiency is taken as 0.95 considering the actual engineering constraints; the angle of attack change rate sequence can be adaptively adjusted in the inner layer optimization problem by the pseudo-spectral method, and the lower bound of the angle of attack change rate sequence is -3° / s, and the upper bound is 3° / s.

[0054] In this embodiment, the initial sample size Set to 150, maximum number of iterations The number of iterations is set to 250; the population of the differential evolution algorithm is 200, the number of generations is 500, and the scaling factor and crossover probability are both set to 0.5. The values ​​of each constraint are shown in Table 2.

[0055] Table 2: Constraint values.

[0056]

[0057] Table 3 shows a comparison between the final design obtained in this embodiment and the initial results of manual design. It can be seen that the optimized results of this invention meet the constraints for maximum range, maximum operating time of the propulsion system, maximum lift-to-drag ratio at Mach 5, landing speed, and landing angle. Furthermore, the initial total mass of the aircraft can be reduced from 986.81 kg to 737.22 kg. The initial results of manual design required 5 people and 3 days of manpower and computation; the method of this invention requires 1 person and 6 hours. The comparison charts of trim aerodynamic lift-to-drag ratio, thrust, and velocity before and after optimization are shown below. Figure 2 , Figure 3 , Figure 4 As shown, it can be seen that compared with the initial result of manual design, the final design scheme of the present invention has been significantly optimized in terms of initial total mass and other performance parameters, design efficiency, etc., while meeting various performance constraints.

[0058] Table 3: Comparison of the optimization results and the initial results of this invention.

[0059]

[0060] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A hierarchical optimization method for optimal scale parameters of an aircraft considering performance constraints, characterized in that, include: The aerodynamic analysis model is characterized using shape configuration parameters, the propulsion analysis model is characterized using propellant grain and nozzle parameters, and the trajectory analysis model is characterized using an angle-of-attack rate of change sequence; the aerodynamic analysis model, propulsion analysis model, and trajectory analysis model are used to solve for the performance parameters and scale parameters of the aircraft. Using the shape configuration parameters, propellant and nozzle parameters, and angle of attack change rate sequence as design variables, and taking minimizing the initial total mass of the aircraft as the objective function, a best-scale optimization mathematical model considering performance constraints is constructed in combination with the constraints, and it is decoupled into an outer-layer optimization problem and an inner-layer optimization problem for dimensionality reduction solution. In the inner-layer optimization problem, initial samples are first constructed based on the design variables of the outer-layer optimization problem, and the trajectory optimization of the angle of attack change rate sequence is performed using the Gaussian pseudospectral method. The optimization results and initial samples are used to construct surrogate samples to form a surrogate sample library. In the outer-layer optimization problem, a surrogate model combined with a global evolutionary algorithm is used to globally optimize the shape configuration parameters, propellant grain and nozzle parameters, and the optimization results are used to enrich the surrogate sample library. Finally, the optimal surrogate sample is determined from the surrogate sample library, thereby obtaining the design scheme of the best scale parameters of the aircraft.

2. The hierarchical optimization method for optimal aircraft size parameters considering performance constraints according to claim 1, characterized in that, external configuration parameters As input, the trim aerodynamic function of the aircraft is established using Datcom software and the instantaneous equilibrium assumption. At a given angle of attack ,Mach number Location of the center of mass and external configuration parameters Under the condition of balancing aerodynamic functions, Obtain the balance aerodynamic drag coefficient With the balance lift coefficient : ； The above equation is the established aerodynamic analysis model.

3. The hierarchical optimization method for optimal aircraft size parameters considering performance constraints according to claim 2, characterized in that, Parameters of the propellant and nozzle As input, the propulsion function of the aircraft is established using geometric modeling software, zero-dimensional internal ballistic equations, and steady isentropic flow equations of the nozzle. ; During a given working time and parameters of the propellant and nozzle Under the condition of, based on the propulsion function Get the thrust of the aircraft ,pressure Mass flow rate and changes in total mass : ； In the formula, The maximum operating time of the propulsion system is given by the formula above, which represents the established propulsion analysis model.

4. The hierarchical optimization method for optimal aircraft size parameters considering performance constraints according to claim 3, characterized in that, The angle of attack change rate sequence Using the longitudinal plane motion equations as input, the trajectory function of the aircraft is established. ; Given the initial displacement of the spacecraft in the X direction Initial height Initial velocity Initial velocity inclination angle Initial angle of attack Angle of attack change rate sequence Under these conditions, combined with the balancing aerodynamic function Propulsion function of the aircraft Using the trajectory function of the aircraft The displacement change of the aircraft in the X direction was calculated. Altitude change speed change Velocity tilt angle change Angle of attack change for: ； The above formula is the established trajectory analysis model.

5. The hierarchical optimization method for optimal aircraft size parameters considering performance constraints according to claim 4, characterized in that, external configuration parameters Parameters of propellant and nozzle As design variables for the outer optimization problem To minimize the initial total mass of the aircraft As the objective function, a method is constructed based on the maximum operating time of the propulsion system. 5Ma maximum lift-to-drag ratio Ballistic deviation Given the constraints, the outer optimization problem is constructed as follows: ； in, To push forward the system's maximum operating time constraint value, This is the maximum boost-to-drag ratio constraint value at 5mA.

6. The hierarchical optimization method for optimal aircraft size parameters considering performance constraints according to claim 5, characterized in that, The angle of attack change rate sequence As design variables for inner-level optimization problems To maximize the maximum range As the objective function; Construction based on fall velocity Corner Given the constraints, the inner optimization problem is as follows: ； in, This is the drop velocity constraint value. This is the landing angle constraint value; The maximum range in the inner optimization problem Decline speed Corner Ballistic violation in the constraints of the outer optimization problem : ； in, This is the maximum range constraint value.

7. The hierarchical optimization method for optimal aircraft size parameters considering performance constraints according to claim 6, characterized in that, Design variables for outer layer optimization problems Set design variables The value space is determined, and then the Latin hypercube sampling method is used to generate the initial samples of the outer optimization problem, thus obtaining the initial sample library; For the Using an initial sample, the aerodynamic analysis model is called to obtain the trim aerodynamic drag coefficient. With the balance lift coefficient The maximum lift-to-drag ratio at 5Ma is then obtained and denoted as . ; Use the propulsion analysis model to obtain the thrust of the aircraft ,pressure Mass flow rate and changes in total mass Thus, the first The maximum operating time of the propulsion system for the initial sample is denoted as The initial total mass is denoted as ; Based on the calculation results of the aerodynamic analysis model and the propulsion analysis model, the trajectory analysis model is further invoked to obtain the displacement change of the aircraft in the X direction under different angle-of-attack rate sequences as input conditions. Altitude change speed change Velocity tilt angle change Angle of attack change ; Based on the results of the trajectory analysis model, the inner-layer optimization problem can be solved using the Gaussian pseudospectral method to obtain the optimal angle-of-attack rate sequence. Then calculate the first... Ballistic deviation of an initial sample ; Each initial sample and the corresponding , , , As a proxy sample, a proxy sample library is constructed.

8. The hierarchical optimization method for optimal size parameters of an aircraft considering performance constraints according to claim 7, characterized in that, Execute the following iterative process: The proxy model is trained using all proxy samples in the proxy sample library; the input to the proxy model is the initial sample contained in the proxy sample library. The output is an approximate value of the initial total mass. Approximate maximum operating time of the propulsion system Approximate maximum lift-to-drag ratio at 5mA and ballistic deviation approximation ; Using a surrogate model, the outer optimization problem is transformed into the following new optimization problem: ； in, These represent the values ​​of the external configuration parameters, the values ​​of the propellant grain and the nozzle parameters, respectively. These are the transformed design variables; A global evolutionary algorithm is used to solve the new optimization problem, and the optimal solution obtained is used as the new sample. ; For new samples Solve for the ballistic violation degree 5Ma maximum lift-to-drag ratio Maximum operating time of the propulsion system Initial total mass ; New samples and the corresponding , , , Add it to the proxy sample library as a new proxy sample; When the iteration process reaches the maximum number of iterations Then, select samples from the surrogate sample library that satisfy the constraints of the outer optimization problem and have an initial total mass. The smallest surrogate sample is taken as the optimal surrogate sample, and the shape configuration parameters, propellant grain and nozzle parameters contained therein are the design scheme of the best size parameters of the aircraft.

9. 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 hierarchical optimization method for the optimal size parameters of an aircraft that takes into account performance constraints, as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program; characterized in that, When the computer program is executed by the processor, it implements the hierarchical optimization method for optimal aircraft size parameters considering performance constraints as described in any one of claims 1-8.

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