Multi-stage variable hybrid optimization method for radial force thermal wing structure of aircraft

Abstract

The invention belongs to the field of structural design of high-speed aircrafts, and discloses a multi-stage variable hybrid optimization method for a radial force thermal wing structure of an aircraft, which comprises the following steps: acquiring an original configuration of the structure to obtain an original base structure; establishing a multi-stage variable hybrid optimization model, performing multi-stage variable hybrid optimization on the original base structure by using a genetic algorithm, and obtaining an optimal structure when a target function of the multi-stage variable hybrid optimization model tends to be stable; and judging whether the obtained optimal structure meets a design target or not, if the optimal structure meets the design target, outputting a final structure, otherwise, changing constraint conditions to reconstruct the multi-stage variable hybrid optimization model to continue optimization, and outputting the final structure meeting the design target. According to the method, a bearing / heat transport integrated path is considered at the same time, optimization variables of the beam section shape and the heat transport structure size are increased, multi-stage variable hybrid optimization is completed, the bearing and heat transport capacities of an aircraft structure are improved, and the performance of the radiation beam type wing structure under typical force heat loads is improved.

Description

Technical Field

[0001] The invention belongs to the field of high-speed aircraft structure design and relates to a multi-level variable hybrid optimization method for an aircraft radial thermal wing structure. Background Art

[0002] In the field of high-speed aircraft design, low aspect ratio wing structures are widely used due to their advantages in aerodynamic performance and attitude control. Such structures not only play a key role in lift and aerodynamic attitude control, but also have to withstand severe aerodynamic and thermal loads under extreme flight conditions. In aircraft structural design, achieving structural lightweighting is an important goal to improve payload and mission performance, as it is directly related to the fuel efficiency and endurance of the aircraft. However, this lightweight strategy will inevitably lead to a reduction in structural stiffness, which in turn exacerbates the aeroelastic effect, that is, the deformation and vibration of the structure under the action of aerodynamic loads. Therefore, in the design of wing structures, aeroelastic issues must be comprehensively considered to ensure the stability and safety of the structure during high-speed flight.

[0003] Facing the extreme thermal environment during high-speed flight, traditional thermal insulation and thermal protection systems face huge challenges. Although these systems can protect aircraft from high temperature damage to a certain extent, their performance is often limited when facing complex and changing thermal environments.

[0004] Traditional aircraft structural optimization design usually adopts a hierarchical optimization strategy, which separates layout optimization and size optimization, first performing layout optimization and then size optimization. However, this method ignores the correlation between optimization variables, limits the optimization design space, and reduces the optimization effect. Especially for aircraft with complex radial structures, the traditional base structure method requires a large number of preset base structures, which not only increases the number of variables in the optimization process, but also prolongs the optimization time and reduces the design efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-level variable hybrid optimization method for an aircraft radial thermal wing structure to solve the problem of low optimization effect and efficiency of existing aircraft structures.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A multi-level variable hybrid optimization method for a radial thermal wing structure of an aircraft, comprising: Obtain the original configuration of the structure and obtain the original base structure; Establish a multi-level variable hybrid optimization model, use genetic algorithm to perform multi-level variable hybrid optimization on the original base structure, and obtain the optimal structure when the objective function of the multi-level variable hybrid optimization model tends to be stable; Determine whether the optimal structure obtained meets the design goal. If it does, output the final structure. Otherwise, change the constraints and reconstruct the multi-level variable hybrid optimization model to continue the optimization and output the final structure that meets the design goal.

[0007] Furthermore, the objective function of the multi-level variable hybrid optimization model is:

[0008] Among them, X T is the topological variable of the structural component, X A is the angle variable of the structural component, X IS is the size variable of the structural component, X IH is the shape variable, X is the height factor, IW is the width factor, X t LB is the width of the web of the I-beam; IS is the size variable X of the I-beam flange IS Lower limit, UB IS is the upper limit of the value; LB IW is the dimension variable X of the web of the I-beam IW Lower limit, UB IW is the upper limit of the value; LB IH is the height variable X of the web of the I-beam IH Lower limit, UB IH is the upper limit of the value; Ms is the total mass constraint of the wing surface load-bearing structure, heat transfer structure, skin structure, thermal insulation structure and wing root joint.

[0009] Furthermore, the design variables of the multi-level variable hybrid optimization model include the number, size, angle, cross-sectional shape and heat transport structure size of the wing spars.

[0010] Furthermore, the constraints of the multi-level variable hybrid optimization model include extreme stress, maximum wingtip displacement, structural mass, maximum temperature and average temperature considering the thermal environment, angle range of the beam, size range of the beam flange and the heat transfer structure, height range of the beam web and size range of the beam web.

[0011] Furthermore, the genetic algorithm comprises: Based on the base structure, the optimization variables are chromosomally encoded and binary strings are randomly generated as the initial population; Conduct structural analysis on the initial population to generate the offspring population; Decode the individuals in the offspring population, reduce the population size through the fitness evaluation of the RBF neural network proxy model, and obtain the reduced offspring population; Decode the reduced offspring population, construct the corresponding structural geometric model, generate a numerical model according to the design goal, and obtain the structural analysis results corresponding to each individual in the population through numerical analysis; Evaluate the fitness of the corresponding structure of each individual in the population, and output the optimal structure according to the multi-level variable mixed optimization target.

[0012] Furthermore, the structural analysis method of the initial population is: The fitness of different individuals in the initial population is evaluated, and the individuals with good fitness performance are selected. The individuals are decomposed into strings corresponding to different levels of design variables through variable decomposition operations, and crossover and mutation operations are performed respectively to generate a new offspring population.

[0013] Furthermore, the method for reducing the population size is: Decode the individuals in the offspring population, convert the individual coding string into the corresponding structural design variables, input the design variables into the RBF neural network agent model, obtain the predicted fitness function of the individuals in the population, evaluate and sort the individuals in the population according to the predicted target fitness function, and remove the individuals with poor fitness performance in the population according to the target population size reduction value.

[0014] A multi-level variable hybrid optimization system for a radial thermal wing structure of an aircraft, comprising: An acquisition module, wherein the acquisition module is used to acquire the original configuration of the structure and obtain the original base structure; An optimization module, wherein the optimization module is used to establish a multi-level variable hybrid optimization model, and use a genetic algorithm to perform multi-level variable hybrid optimization on the original base structure, and when the objective function of the multi-level variable hybrid optimization model tends to be stable, an optimal structure is obtained; The judgment module is used to judge whether the obtained optimal structure meets the design goal. If it does, the final structure is output; otherwise, the constraint conditions are changed to reconstruct the multi-level variable hybrid optimization model to continue the optimization and output the final structure that meets the design goal.

[0015] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the method are implemented when the processor executes the computer program.

[0016] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method are implemented.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a multi-level variable hybrid optimization method for a radial mechanical and thermal wing structure of an aircraft. By establishing a multi-level variable hybrid optimization model, the original base structure is optimized based on a genetic algorithm, and the proxy model is used to predict the offspring population, and the predicted fitness function value is quickly obtained, and individuals with low fitness are eliminated, thereby reducing the amount of calculation of the precise model, reducing the optimization cost, and improving the optimization efficiency. Different from the existing structure quantity-size-angle hybrid optimization method considering aeroelasticity, the present invention simultaneously considers the load-bearing / heat transport integrated path, increases the optimization variables of the beam cross-section shape and the heat transport structure size, completes the multi-level variable hybrid optimization, reduces the aeroelasticity effect of high-speed aircraft, improves the load-bearing and heat transport capacity of the aircraft structure, and realizes the performance improvement of the radial beam wing structure under typical mechanical and thermal loads. It provides an important reference for the quality evaluation of the overall design stage of the aircraft and the preliminary design of the aircraft structure, and has significant academic value and application prospects.

[0018] Furthermore, the heat transport structure of the present invention transfers heat from the high temperature area to the low temperature area through the high thermal conductivity material, which can effectively reduce the maximum temperature of the structure and achieve uniform temperature distribution. Combining the heat transport structure with the heat insulation and thermal protection system can more effectively cope with complex thermal environments and meet the thermal protection requirements of high-speed aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flowchart of the multi-level variable hybrid optimization method for the aircraft radial thermal wing structure of the present invention.

[0021] Figure 2 This is a framework diagram of the multi-level variable hybrid optimization program of the present invention.

[0022] Figure 3 It is a schematic diagram of the detailed shape of the cross section of the I-beam of the present invention.

[0023] Figure 4 This is a diagram of the radial optimal individual structure type of the present invention.

[0024] Figure 5 It is a schematic diagram of the structure of a multi-level variable hybrid optimization system for an aircraft radial thermal wing structure according to a preferred embodiment of the present invention.

[0025] Figure 6It is a schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0029] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0030] It should be noted that the terminals involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers, personal computers (PCs), MP3 players, MP4 players, wearable devices (for example, smart glasses, smart watches, smart bracelets, etc.), smart home devices and other smart devices.

[0031] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0032] The present invention is further described in detail below in conjunction with the accompanying drawings: See also Figure 1 The present invention provides a multi-level variable hybrid optimization method for a radial thermal wing structure of an aircraft, which specifically includes the following steps: Step 1: Structural configuration: Determine the original structural configuration, including structural size, shape, and initial layout of the base structure.

[0033] Step 2: Structural optimization: Establish a multi-level variable mixed optimization model.

[0034] For the mixed optimization of structural topology-size-angle-beam cross-section shape-heat transport structure size based on static aerodynamics, the optimization model is established as follows:

[0035] Where, X T is the topological variable of the structural component, including 19 topological variables X T (0 or 1) topology, the value is 0 or 1; X A is the angle variable of the structural component, and its dimension is the same as X T Same, the value is 0°~180°; X IS is the size variable of the structural component, and its dimension is related to X T The same, the value is 0.5~20mm; the shape variable is the height factor X IH and width factor X IW , where X IH and X IW The value range is between 0 and 1; Figure 3 As shown in the figure, the height h of the web of the I-beam is determined by the beam section height H and the height factor X IH The web width IW is obtained by multiplying the beam section width X IS and width factor X IW The heat transport structure is described by three variables: length, height, and width. The length is equal to the beam length, which is determined by the aerodynamic shape of the wing, the height is equal to the height h of the I-beam web, and the width X t is the main design variable. The heat transport structure is evenly distributed on both sides of the web, and the single side width is X t / 2 LB IS is the size variable X of the I-beam flange IS Lower limit, UB IS is the upper limit of the value; LB IW is the dimension variable X of the web of the I-beam IW Lower limit, UB IW is the upper limit of the value; LB IH is the height variable X of the web of the I-beam IH Lower limit, UB IH is the upper limit of the value. sIt is the total mass constraint of the wing surface load-bearing structure, heat transfer structure, skin structure, thermal insulation structure and wing root joint.

[0036] Objective function: Minimize the aeroelastic effect of the wing; Design variables: number, size, angle, cross-sectional shape of wing spars and dimensions of heat transfer structures; Constraints: including extreme stress, maximum wingtip displacement, structural mass, maximum and average temperatures under thermal conditions, beam angle range, beam flange and heat transfer structure size range, beam web height range, and beam web size range.

[0037] Based on the above constructed objective function, a structural optimization process is established, such as Figure 2 As shown. The number, size, angle, beam cross-section shape and heat transport structure size parameters of the base structure configuration are modified to generate a new model. Its aeroelastic performance is analyzed, and the aeroelastic effect under given constraints is minimized as the optimization goal. The variable parameters are modified multiple times until the optimal structure is obtained.

[0038] Initialization: Based on the base structure, the optimization variables are encoded into chromosomes. The chromosomes of the mixed optimization of structure topology-size-angle-beam cross-section shape-heat transport structure size include topological variables, size variables, angle variables, beam cross-section shape variables and heat transport structure size variables. Topological variables are variables with values ​​of 0 and 1: 1 means the structure exists; 0 means the structure does not exist. Size variables, angle variables, beam cross-section shape variables and heat transport structure size variables are continuous variables within a given range. According to the predetermined base structure form and encoding method, a set of binary strings are randomly generated as the initial population.

[0039] Population operation: Since the first generation of initial population does not obtain the fitness function value corresponding to the individual, it skips this module and directly enters the "Structural Analysis". For the population with fitness value obtained after "convergence judgment", fitness evaluation is performed according to the corresponding different individuals in the population, and individuals with better fitness performance are selected; the string is decomposed into strings corresponding to different levels of design variables through variable decomposition operation; crossover and mutation operations are performed separately, and different design variables are operated independently and do not affect each other; a new offspring population is generated, in which individuals are new individuals generated by crossover and mutation after inheriting the "high fitness" genes of the initial population.

[0040] Population reduction: Based on the constructed database, train the RBF neural network proxy model. Decode the individuals in the generated population and convert the individual's code string into the corresponding structural design variables; input the design variables into the constructed RBF neural network proxy model to obtain the predicted fitness function of the individuals in the population; evaluate and sort the individuals in the population according to the predicted objective function; remove a certain number of individuals with poor fitness performance in the population according to the set population size reduction value, and enter the individuals with better fitness into the "Structural Analysis" module for precise model analysis. In addition, the structural performance data obtained by the "Structural Analysis" is also added to the sample database after the fitness evaluation, and the proxy model is updated to further improve the accuracy of the proxy model.

[0041] Structural analysis: Decode the offspring population, convert the individual character string into the corresponding design variables with physical meaning, and construct the corresponding structural geometric model; apply external loads and boundary conditions according to the given flight environment to generate a numerical model; conduct numerical analysis; and obtain the structural analysis results corresponding to each individual in the population.

[0042] Convergence judgment: The present invention chooses to use the evolutionary generation of the population as the convergence judgment criterion of the optimization framework, or when it is observed that the objective function tends to be stable, the optimization program can also be manually ended.

[0043] Step 3: Structure output.

[0044] like Figure 4 As shown in the figure, check whether the optimization result meets the design requirements and output the optimal structure of step 2. If the design goal is met, output the final structure. Otherwise, change the constraints to reconstruct the optimization model and repeat step 2 until the result that meets the design goal is obtained and output the final result.

[0045] Another object of the present invention is to provide a multi-level variable hybrid optimization system for the aircraft radial thermal wing structure of the present invention, such as Figure 5 As shown, the embodiment of the system includes: an acquisition module, an optimization module and a judgment module.

[0046] An acquisition module, wherein the acquisition module is used to acquire the original configuration of the structure and obtain the original base structure; An optimization module, wherein the optimization module is used to establish a multi-level variable hybrid optimization model, and use a genetic algorithm to perform multi-level variable hybrid optimization on the original base structure, and when the objective function of the multi-level variable hybrid optimization model tends to be stable, an optimal structure is obtained; The judgment module is used to judge whether the obtained optimal structure meets the design goal. If it does, the final structure is output; otherwise, the constraint conditions are changed to reconstruct the multi-level variable hybrid optimization model to continue the optimization and output the final structure that meets the design goal.

[0047] It can be understood that the multi-level variable hybrid optimization system of the aircraft radial thermal wing structure provided by the present invention corresponds to the multi-level variable hybrid optimization method of the aircraft radial thermal wing structure provided by the aforementioned embodiments. The relevant technical features of the multi-level variable hybrid optimization system of the aircraft radial thermal wing structure can refer to the relevant technical features of the multi-level variable hybrid optimization method of the aircraft radial thermal wing structure, which will not be repeated here.

[0048] The third object of the present invention is to provide an electronic device, such as Figure 6 As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the steps of the multi-level variable hybrid optimization method of the aircraft radial thermal wing structure.

[0049] The multi-level variable hybrid optimization method for the aircraft radial thermal wing structure comprises the following steps: Obtain the original configuration of the structure and obtain the original base structure; Establish a multi-level variable hybrid optimization model, use genetic algorithm to perform multi-level variable hybrid optimization on the original base structure, and obtain the optimal structure when the objective function of the multi-level variable hybrid optimization model tends to be stable; Determine whether the optimal structure obtained meets the design goal. If it does, output the final structure. Otherwise, change the constraints and reconstruct the multi-level variable hybrid optimization model to continue the optimization and output the final structure that meets the design goal.

[0050] The fourth object of the present invention is to provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the steps of the multi-level variable hybrid optimization method of the aircraft radial thermal wing structure.

[0051] The multi-level variable hybrid optimization method for the aircraft radial thermal wing structure comprises the following steps: Obtain the original configuration of the structure and obtain the original base structure; Establish a multi-level variable hybrid optimization model, use genetic algorithm to perform multi-level variable hybrid optimization on the original base structure, and obtain the optimal structure when the objective function of the multi-level variable hybrid optimization model tends to be stable; Determine whether the optimal structure obtained meets the design goal. If it does, output the final structure. Otherwise, change the constraints and reconstruct the multi-level variable hybrid optimization model to continue the optimization and output the final structure that meets the design goal.

[0052] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0057] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-level variable hybrid optimization method for a radial thermal wing structure of an aircraft, characterized in that: include: Obtain the original configuration of the structure and obtain the original base structure; Establish a multi-level variable hybrid optimization model, use genetic algorithm to perform multi-level variable hybrid optimization on the original base structure, and obtain the optimal structure when the objective function of the multi-level variable hybrid optimization model tends to be stable; Determine whether the optimal structure obtained meets the design goal. If it does, output the final structure. Otherwise, change the constraints and reconstruct the multi-level variable hybrid optimization model to continue the optimization and output the final structure that meets the design goal.

2. The multi-level variable hybrid optimization method for the radial thermal wing structure of an aircraft according to claim 1, characterized in that: The objective function of the multi-level variable hybrid optimization model is: Among them, X T is the topological variable of the structural component, X A is the angle variable of the structural component, X IS is the size variable of the structural component, X IH is the shape variable, X is the height factor, IW is the width factor, X t LB is the width of the web of the I-beam; IS is the size variable X of the I-beam flange IS Lower limit, UB IS is the upper limit of the value; LB IW is the size variable X of the web of the I-beam IW Lower limit, UB IW is the upper limit of the value; LB IH is the height variable X of the web of the I-beam IH Lower limit, UB IH is the upper limit of the value; Ms is the total mass constraint of the wing surface load-bearing structure, heat transfer structure, skin structure, thermal insulation structure and wing root joint.

3. The multi-level variable hybrid optimization method for an aircraft radial thermal wing structure according to claim 1, characterized in that: The design variables of the multi-level variable hybrid optimization model include the number, size, angle, cross-sectional shape and heat transport structure size of the wing spars.

4. The multi-level variable hybrid optimization method for an aircraft radial thermal wing structure according to claim 1, characterized in that: The constraints of the multi-level variable hybrid optimization model include extreme stress, maximum displacement of the wing tip, structural mass, maximum temperature and average temperature under thermal environment, angle range of beam, size range of beam flange and heat transfer structure, height range of beam web and size range of beam web.

5. The multi-level variable hybrid optimization method for a radial thermal wing structure of an aircraft according to claim 1, characterized in that: The genetic algorithm comprises: Based on the base structure, the optimization variables are chromosomally encoded and binary strings are randomly generated as the initial population; Conduct structural analysis on the initial population to generate the offspring population; Decode the individuals in the offspring population, reduce the population size through the fitness evaluation of the RBF neural network proxy model, and obtain the reduced offspring population; Decode the reduced offspring population, construct the corresponding structural geometric model, generate a numerical model according to the design goal, and obtain the structural analysis results corresponding to each individual in the population through numerical analysis; Evaluate the fitness of the corresponding structure of each individual in the population, and output the optimal structure according to the multi-level variable mixed optimization target.

6. The multi-level variable hybrid optimization method for the radial thermal wing structure of an aircraft according to claim 5, characterized in that: The structural analysis method of the initial population is: The fitness of different individuals in the initial population is evaluated, and the individuals with good fitness performance are selected. The individuals are decomposed into strings corresponding to different levels of design variables through variable decomposition operations, and crossover and mutation operations are performed respectively to generate a new offspring population.

7. The multi-level variable hybrid optimization method for an aircraft radial thermal wing structure according to claim 5, characterized in that: The method of reducing the population size is: Decode the individuals in the offspring population, convert the individual coding string into the corresponding structural design variables, input the design variables into the RBF neural network agent model, obtain the predicted fitness function of the individuals in the population, evaluate and sort the individuals in the population according to the predicted target fitness function, and remove the individuals with poor fitness performance in the population according to the target population size reduction value.

8. A multi-level variable hybrid optimization system for an aircraft radial thermal wing structure, characterized in that: include: An acquisition module, wherein the acquisition module is used to acquire the original configuration of the structure and obtain the original base structure; An optimization module, wherein the optimization module is used to establish a multi-level variable hybrid optimization model, and use a genetic algorithm to perform multi-level variable hybrid optimization on the original base structure, and when the objective function of the multi-level variable hybrid optimization model tends to be stable, an optimal structure is obtained; The judgment module is used to judge whether the obtained optimal structure meets the design goal. If it does, the final structure is output; otherwise, the constraint conditions are changed to reconstruct the multi-level variable hybrid optimization model to continue the optimization and output the final structure that meets the design goal.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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