Method for multi-objective aerodynamic optimization of wing-in-wing configuration wing based on doe stratified sampling

Abstract

The application discloses a method for winged layout wing multi-objective aerodynamic optimization based on DOE hierarchical sampling, and relates to the technical field of aircraft optimization. Firstly, multi-objective aerodynamic optimization design is carried out based on the straight and level flight state of the winged layout wing system. Secondly, the optimization target is evaluated based on the overall lift-drag ratio of the system, and a multi-objective optimization objective function and constraint condition are constructed. Then, the geometric model of the system is parameterized, the representation parameters of the cruise flight state are decomposed, and optimization variables are selected, so as to construct a multi-dimensional optimization space. Next, according to aerodynamic design experience, the optimization variables are layered and processed according to the significance of the influence on the objective function. Finally, the optimized result is obtained through cyclic iteration and evaluation. The method disclosed by the application solves the problems of high time cost and economic cost caused by too many optimization variables and large optimization space in the optimization design.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aircraft optimization technology, in particular to a wing-in-wing layout wing multi-objective aerodynamic optimization method based on DOE hierarchical sampling. BACKGROUND

[0002] The wing-in-wing layout aircraft has unique aerodynamic and structural characteristics such as light weight, high strength and rigidity, small induced drag, trim at positive lift, and good low-speed aerodynamic characteristics, thus having broad development prospects in the design of general aviation aircraft and the like. The aerodynamic efficiency of the wing-in-wing layout aircraft in the cruising state has always been one of the key concerns in the design of the type. The wing-in-wing layout concept has two pairs of wings, i.e., a front wing and a rear wing, and the wash between the two pairs of wings produces significant aerodynamic interference, which affects the overall aerodynamic efficiency of the wing system. Therefore, the wing system must be aerodynamically optimized. Compared with conventional layout aircraft, the wing-in-wing layout aircraft involves more geometric parameters of the two pairs of wings, and the aerodynamic state parameters of the optimal aerodynamic efficiency of the front wing and the rear wing also have significant differences due to the aerodynamic interference between the wings. Therefore, the optimization space formed by the various optimization parameters in the multi-objective aerodynamic optimization process of the wing system of the wing-in-wing layout aircraft is significantly larger than that of the aerodynamic optimization design of the wing of the conventional layout aircraft.

[0003] When aerodynamic optimization design is performed on the wing system of the wing-in-wing layout aircraft, the optimization variables involve not only the geometric model parameters of the wing system but also the flight state parameters. In a single iteration calculation, not only the geometric model and the grid file need to be modified, but also CFD simulation calculation needs to be performed. Due to the large optimization space, the number of calculation conditions increases in orders of magnitude with the increase in the dimension of the optimization variables, and a combinatorial optimization design method needs to be used to save calculation resources. The combinatorial optimization strategy generally reduces the optimization space through a sampling method and searches for the optimal solution through an optimization algorithm. In the process of reducing the optimization space through the sampling method, the changes of the various optimization variables have different effects on the aerodynamic efficiency of the wing-in-wing layout wing system, and some parameters have more obvious effects on the aerodynamic efficiency of the wing system. If an equal probability method is used for sampling, it is not conducive to improving the sampling resolution of the optimization parameters that have more obvious effects and is not conducive to reducing the optimization space of the wing-in-wing layout wing system and improving the optimization efficiency and saving the optimization cost. Therefore, there is an urgent need to develop a multi-objective aerodynamic optimization design method for the wing system of the wing-in-wing layout aircraft in the cruising state, which has the following two characteristics: first, it can reflect more information about the changes of the optimization variables that have greater effects on the aerodynamic efficiency target function of the wing-in-wing layout wing system in the same sampling sample capacity. Second, it can artificially intervene in the inclination direction of the sampling sample space according to the aerodynamic design experience of the wing-in-wing layout aircraft, and highlight the hierarchical relationship of the optimization variables that have significant differences in the effects on the target function. SUMMARY

[0004] The application aims to provide a wing-in-wing layout wing multi-objective aerodynamic optimization method based on DOE hierarchical sampling, and solve the problems of more optimization variables, larger optimization space, higher time cost and economic cost in the prior art.

[0005] To achieve the above-mentioned purpose, the application provides a wing-in-wing layout wing multi-objective aerodynamic optimization method based on DOE hierarchical sampling, comprising the following steps:

[0006] Step 1. According to the flight profile requirements, the multi-objective aerodynamic optimization design is carried out based on the straight and level flight state of the wing-in-wing layout wing system.

[0007] Step 2. The optimization target is evaluated based on the overall lift-drag ratio of the wing-in-wing layout wing system.

[0008] Step 3. The multi-objective optimization objective function is constructed according to the evaluation method determined in step 2.

[0009] Step 4. The constraint conditions are added to the multi-objective optimization objective function constructed in step 3 based on the overall mission design target and geometric constraints.

[0010] Step 5. The geometric model of the wing-in-wing layout wing system is parameterized, and the representation parameters of the cruise flight state are decomposed and the optimization variables are selected.

[0011] Step 6. The multi-dimensional optimization space composed of the multi-objective optimization variables of the wing-in-wing layout wing system is determined according to the definition range of each optimization variable.

[0012] Step 7. According to the aerodynamic design experience, all optimization variables of the wing-in-wing layout wing system aerodynamic multi-objective optimization design are divided into two layers according to the significance of the influence on the objective function, which are the first layer optimization variable and the second layer optimization variable.

[0013] Step 8. The DOE (Design of experience) optimization Latin square sampling method is used to sample the parameters of the first layer optimization variable and modify the geometric model parameters.

[0014] Step 9. The DOE optimization Latin square sampling method is used to sample the parameters of the second layer optimization variable, modify the geometric model parameters and update the CFD (Computational Fluid Dynamic) numerical simulation calculation boundary condition parameters, modify the CFD numerical simulation calculation script, perform automatic cyclic iteration calculation sampling, and save the DOE sampling objective function data results.

[0015] Step 10, after the DOE stratified sampling calculation is completed, the variation range of each variable is narrowed according to the saved target function data results, so as to narrow the multi-dimensional optimization space;

[0016] Step 11, according to the optimization space narrowed by the DOE stratified sampling, a multi-objective optimization algorithm is selected to start the global search genetic algorithm based on NSGA-II (second generation non-inferior sorting genetic algorithm) for optimization iteration calculation in the narrowed optimization variable range;

[0017] Step 12, according to the initial parameters of the wing system of the tandem wing layout, the geometric model and the grid model are updated, the flight boundary condition parameters are solved, and the CFD aerodynamic numerical simulation calculation script file is written;

[0018] Step 13, after the CFD aerodynamic numerical simulation calculation, the front wing and the rear wing of the tandem wing layout wing system are post-processed to obtain the aerodynamic force and moment of the tandem wing layout wing system under the current state, the target function is calculated according to the overall lift-drag ratio calculation method of the tandem wing layout wing system, the multi-objective optimization calculation process is iterated and calculated, and the calculation results are stored;

[0019] Step 14, evaluate the tandem wing layout wing system multi-objective aerodynamic optimization design according to the calculation results, and output the optimization results, wherein the evaluation indexes include lift-drag ratio, minimum drag coefficient of the front wing and minimum drag coefficient of the rear wing.

[0020] Preferably, the multi-objective optimization target function constructed in step 3 is as follows:

[0021] ;

[0022] In the formula, is the multi-objective optimization target function, is the front wing drag coefficient, is the rear wing drag coefficient, is the overall lift-drag ratio of the wing system.

[0023] Preferably, the expression of the multi-objective optimization target function constructed in step 3 after adding constraint conditions based on the task overall design target and geometric constraint configuration in step 4 is as follows:

[0024] ;

[0025] In the formula, is the half wing span of the front wing, is the half wing span of the rear wing, is the chord length of the middle section of the front wing, is the chord length of the middle section of the rear wing, is the chord length of the tip of the front wing, is the chord length of the tip of the rear wing, is a forward wing leading edge sweep angle, is a rear wing trailing edge sweep angle, is an angle of attack, is a Mach number.

[0026] Preferably, the characteristic parameters in step 5 include a forward wing half-span length, a rear wing half-span length, a forward wing mid-section chord length, a rear wing mid-section chord length, a forward wing tip chord length, a rear wing tip chord length, a forward wing leading edge sweep angle, a rear wing trailing edge sweep angle, an angle of attack, and a Mach number.

[0027] Preferably, the aerodynamic design experience in step 7 is that an influence of the angle of attack and the Mach number on the optimization objective function is not less than an influence of the geometric model parameters on the optimization objective function.

[0028] Preferably, the first layer optimization variables include the angle of attack and the Mach number; and the second layer optimization variables include the forward wing half-span length, the rear wing half-span length, the forward wing mid-section chord length, the rear wing mid-section chord length, the forward wing tip chord length, the rear wing tip chord length, the forward wing leading edge sweep angle, and the rear wing trailing edge sweep angle.

[0029] Preferably, the geometric model parameters in step 9 are the forward wing half-span length, the rear wing half-span length, the forward wing mid-section chord length, the rear wing mid-section chord length, the forward wing tip chord length, the rear wing tip chord length, the forward wing leading edge sweep angle, and the rear wing trailing edge sweep angle; and the boundary condition parameters are the angle of attack and the Mach number.

[0030] Preferably, the solving of the flight boundary condition parameters in step 12 includes a true airspeed three-dimensional projection under a current angle of attack state, a forward wing and a rear wing effective projection area of the tandem wing layout wing system, and an average aerodynamic chord length.

[0031] Preferably, the expression of the target function calculated according to the overall lift-drag ratio calculation method of the tandem wing layout wing system in step 13 is as follows:

[0032] ;

[0033] In the formula, is a lift-drag ratio, is a total lift of the wing system, is a total drag of the wing system, is a forward wing lift, is a rear wing lift, is a forward wing drag, is a rear wing drag, is a local altitude atmospheric density, is a flow velocity, is a forward wing effective projection area, is a forward wing lift coefficient, is a forward wing drag coefficient, is a rear wing effective projection area, is the lift coefficient of the rear wing, is the drag coefficient of the rear wing.

[0034] Therefore, the present application adopts the above-mentioned winged layout wing multi-objective aerodynamic optimization method based on DOE hierarchical sampling, which has the following beneficial effects:

[0035] (1) When the multi-objective aerodynamic optimization design of the winged layout wing system in the cruise state is carried out, compared with the conventional combined optimization strategy, the combined optimization strategy based on DOE hierarchical sampling can reflect more information about the change of the optimization variables which have greater influence on the aerodynamic efficiency objective function of the winged layout wing system in the same sampling sample capacity, improve the optimization efficiency, and reduce the multi-objective optimization time and economic cost.

[0036] (2) The combined optimization strategy based on DOE hierarchical sampling can fully absorb the existing aerodynamic design experience and results of the winged layout wing system, and can adjust the tilt direction of the hierarchical sampling strategy intervention sample space through artificial intervention methods to highlight the hierarchical relationship of each level of optimization variables.

[0037] (3) The multi-objective aerodynamic optimization design method of the winged layout wing system can quickly capture the change relationship of each optimization variable on the overall objective function; and this optimization design method has good universality and is also applicable to the aerodynamic optimization design and verification of other layout type aircraft wing systems and other main components.

[0038] The technical solutions of the present application will be further described in detail below with the aid of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is the overall flow chart of the present application based on the DOE hierarchical sampling of the winged layout wing multi-objective aerodynamic optimization method;

[0040] Figure 2 is the DOE sampling distribution diagram of the winged layout wing system total lift-drag ratio, front wing lift-drag ratio and rear wing lift-drag ratio with Mach number and angle of attack, wherein (a) is a sampling data space view; (b) is a sampling data plan view; (c) is a sampling data front view; (d) is a sampling data side view;

[0041] Figure 3 is the NSGA-II multi-objective optimization result three views of the present application, wherein (a) is a multi-objective optimization result space view; (b) is a multi-objective optimization result plan view; (c) is a multi-objective optimization result front view; (d) is a multi-objective optimization result side view. DETAILED DESCRIPTION

[0042] The following detailed description of embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the application claimed, but merely represents selected embodiments of the application. Based upon the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0043] Referring to Figure 1 , the multi-objective aerodynamic optimization method for the wing of a tandem wing layout based on DOE stratified sampling includes the following steps:

[0044] Step 1. According to the flight profile requirements, the multi-objective aerodynamic optimization design is performed based on the straight and level flight state of the tandem wing layout wing system.

[0045] Step 2. The optimization target is evaluated based on the overall lift-drag ratio of the tandem wing layout wing system. For the aerodynamic efficiency of the straight and level cruise state, the overall lift-drag ratio (L / D) of the three-dimensional wing system or the product of the cruise Mach number (Ma) and the overall lift-drag ratio (Ma·L / D) is used for evaluation. In the present application, the lift-drag ratio (L / D) of the wing system needs to be calculated based on the aerodynamic parameters of the individual front wing and rear wing, and the cruise Mach number is determined according to the overall design task. Therefore, the optimization target uses the overall lift-drag ratio (L / D) of the wing system.

[0046] Step 3. The multi-objective optimization objective function is constructed according to the evaluation method determined in Step 2, which is as follows:

[0047] ;

[0048] In the formula, is the multi-objective optimization objective function, is the front wing drag coefficient, is the rear wing drag coefficient, is the overall lift-drag ratio of the wing system.

[0049] Step 4. The constraints are added to the multi-objective optimization objective function constructed in Step 3 based on the overall design target of the task and the geometric constraints, and the specific expression is as follows:

[0050] ;

[0051] In the formula, is the half-span length of the front wing, is the half-span length of the rear wing, is the chord length of the middle section of the front wing, is the chord length of the middle section of the rear wing, is the tip chord length of the front wing, is the tip chord length of the rear wing, is the front-swept angle of the front wing, is the rear-swept angle of the rear wing, is an angle of attack, is a Mach number;

[0052] Step 5, parameterize the geometric model of the tandem wing wing system, and decompose the representation parameters of the cruise flight state and select the optimization variables; wherein the representation parameters include the half wing span of the front wing, the half wing span of the rear wing, the chord length of the middle section of the front wing, the chord length of the middle section of the rear wing, the tip chord length of the front wing, the tip chord length of the rear wing, the rear-swept angle of the leading edge of the front wing, the forward-swept angle of the trailing edge of the rear wing, the angle of attack, and the Mach number;

[0053] Step 6, determine the multi-dimensional optimization space constituted by the multi-objective optimization variables of the tandem wing wing system according to the definition range of each optimization variable;

[0054] Step 7, according to the aerodynamic design experience, all optimization variables of the aerodynamic multi-objective optimization design of the tandem wing wing system are layered according to the significance of the influence on the objective function, a total of two layers of optimization variables, respectively, the first layer of optimization variables and the second layer of optimization variables, the first layer of optimization variables includes the angle of attack and the Mach number; the second layer of optimization variables includes the half wing span of the front wing, the half wing span of the rear wing, the chord length of the middle section of the front wing, the chord length of the middle section of the rear wing, the tip chord length of the front wing, the tip chord length of the rear wing, the rear-swept angle of the leading edge of the front wing, and the forward-swept angle of the trailing edge of the rear wing; wherein the aerodynamic design experience is that the influence of the angle of attack and the Mach number on the optimization objective function is not less than the influence of the geometric model parameters on the optimization objective function;

[0055] Step 8, using the DOE optimization Latin square sampling method, parameter sampling is performed on the first layer of optimization variables and the geometric model parameters are modified;

[0056] Step 9, using the DOE optimization Latin square sampling method, parameter sampling is performed on the second layer of optimization variables, and the geometric model parameters and the boundary condition parameters of the CFD numerical simulation calculation are updated, the CFD numerical simulation calculation script is modified, automatic cyclic iteration calculation sampling is performed, and the DOE sampling objective function data results are saved; wherein the geometric model parameters are the half wing span of the front wing, the half wing span of the rear wing, the chord length of the middle section of the front wing, the chord length of the middle section of the rear wing, the tip chord length of the front wing, the tip chord length of the rear wing, the rear-swept angle of the leading edge of the front wing, and the forward-swept angle of the trailing edge of the rear wing; the boundary condition parameters are the angle of attack and the Mach number;

[0057] Step 10, after the DOE layered sampling calculation is completed, according to the saved objective function data results, the variation range of each variable is reduced, thereby reducing the multi-dimensional optimization space;

[0058] Step 11, according to the optimization space reduced by the DOE layered sampling, select a multi-objective optimization algorithm, and start the optimization iteration calculation based on the NSGA-II global search genetic algorithm in the reduced optimization variable range;

[0059] Step 12, initialize parameters of multi-objective aerodynamic optimization design of the wing system in the tandem wing layout, update the geometric model and the grid model, solve the flight boundary condition parameters, and write into the CFD aerodynamic numerical simulation calculation script file; wherein, solving the flight boundary condition parameters includes the true airspeed three-dimensional projection under the current angle of attack state, the effective projection area and the average aerodynamic chord length of the front wing and the rear wing of the wing system in the tandem wing layout;

[0060] Step 13, post-processing the front wing and the rear wing of the wing system in the tandem wing layout after the CFD aerodynamic numerical simulation calculation, obtaining the aerodynamic force and the moment of the front wing and the rear wing of the wing system in the tandem wing layout under the current state, calculating the objective function according to the overall lift-drag ratio calculation method of the wing system in the tandem wing layout, performing cyclic iteration calculation on the multi-objective optimization calculation process, and storing the calculation results; wherein, the expression of calculating the objective function according to the overall lift-drag ratio calculation method of the wing system in the tandem wing layout is as follows:

[0061] ;

[0062] In the formula, is the lift-drag ratio, is the total lift of the wing system, is the total drag of the wing system, is the lift of the front wing, is the lift of the rear wing, is the drag of the front wing, is the drag of the rear wing, is the local altitude atmospheric density, is the incoming flow speed, is the effective projection area of the front wing, is the lift coefficient of the front wing, is the drag coefficient of the front wing, is the effective projection area of the rear wing, is the lift coefficient of the rear wing, is the drag coefficient of the rear wing;

[0063] Step 14, evaluating the multi-objective aerodynamic optimization design of the wing system in the tandem wing layout according to the calculation results, and outputting the optimization results, wherein, the evaluation indexes include the lift-drag ratio, the minimum drag coefficient of the front wing, and the minimum drag coefficient of the rear wing.

[0064] Figure 2 is the DOE sampling distribution of the overall lift-drag ratio of the wing system in the tandem wing layout, the lift-drag ratio of the front wing, and the lift-drag ratio of the rear wing with Mach number and angle of attack, it can be found from the drawings that the first layer sampling sample distribution of Mach number and angle of attack based on DOE hierarchical sampling is relatively uniform, and the sampling results roughly cover the feasible region of angle of attack and Mach number, which can be used as the initial conditions of the genetic algorithm multi-objective aerodynamic optimization.

[0065] Figure 3For the three views of the NSGA-II multi-objective optimization results, it can be found from the drawings that the NSGA-II optimization algorithm obtains the optimized search Pareto solution set and the optimization results, and the multi-objective optimization achieves good optimization effect.

[0066] Therefore, the present application adopts the above-mentioned wing layout wing multi-objective aerodynamic optimization method based on DOE hierarchical sampling, carries out multi-objective optimization aiming at the cruise state aerodynamic efficiency and flight state parameters as the objective function, and solves the problems of high time cost and economic cost caused by more optimization variables and larger optimization space in the optimization design through the combination optimization strategy based on DOE hierarchical sampling.

[0067] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit them, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: the technical solutions of the present application can still be modified or replaced by the equivalent, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for multi-objective aerodynamic optimization of a wing in a wing-in- wing configuration based on stratified sampling of a DOE, characterized in that, The method comprises the following steps: Step 1, according to the flight profile requirements, based on the wing system of the tandem wing layout in the straight and level flight state, multi-objective aerodynamic optimization design is carried out; Step 2, based on the overall lift-drag ratio of the tandem wing layout wing system, the optimization target is evaluated; Step 3, according to the evaluation method determined in step 2, a multi-objective optimization objective function is constructed; Step 4, based on the overall mission design target and geometric constraints, constraint conditions are added to the multi-objective optimization objective function constructed in step 3; Step 5, the geometric model of the tandem wing layout wing system is parameterized, and the representation parameters of the cruise flight state are decomposed and optimization variables are selected; Step 6, according to the definition range of each optimization variable, a multi-dimensional optimization space composed of multi-objective optimization variables of the tandem wing layout wing system is determined; Step 7, according to aerodynamic design experience, all optimization variables of the tandem wing layout wing system aerodynamic multi-objective optimization design are layered according to the significance of the influence on the objective function, and a total of two layers of optimization variables are included, which are first layer optimization variables and second layer optimization variables; Step 8, using the DOE optimization Latin square sampling method, parameter sampling is carried out for the first layer optimization variables, and the geometric model parameters are modified; Step 9, using the DOE optimization Latin square sampling method, parameter sampling is carried out for the second layer optimization variables, and the geometric model parameters and the CFD numerical simulation calculation boundary condition parameters are updated, the CFD numerical simulation calculation script is modified, automatic cyclic iteration calculation sampling is carried out, and the DOE sampling objective function data results are saved; Step 10, after the DOE hierarchical sampling calculation is completed, according to the saved objective function data results, the variation range of each variable is reduced, so as to reduce the multi-dimensional optimization space; Step 11, according to the optimization space reduced by the DOE hierarchical sampling, a multi-objective optimization algorithm is selected, and the NSGA-II global search genetic algorithm is used to carry out optimization iteration calculation in the reduced optimization variable range; Step 12, according to the multi-objective aerodynamic optimization design initialization parameters of the tandem wing layout wing system, the geometric model and the grid model are updated, the flight boundary condition parameters are solved, and the CFD aerodynamic numerical simulation calculation script file is written; Step 13, after the CFD aerodynamic numerical simulation calculation, the front wing and the rear wing of the tandem wing layout wing system are post-processed, the aerodynamic force and the moment of the tandem wing layout wing system front wing and rear wing under the current state are obtained, the objective function is calculated according to the overall lift-drag ratio calculation method of the tandem wing layout wing system, the multi-objective optimization calculation process is cyclically iterated and calculated, and the calculation results are stored; Step 14, according to the calculation results, the multi-objective aerodynamic optimization design of the tandem wing layout wing system is evaluated, and the optimization results are output, wherein the evaluation indexes include the lift-drag ratio, the minimum drag coefficient of the front wing and the minimum drag coefficient of the rear wing.

2. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 1, wherein, The multi-objective optimization objective function constructed in step 3 is as follows: ； wherein is a multi-objective optimization objective function, is a front wing drag coefficient, is a rear wing drag coefficient, is a total lift-drag ratio of the wing system, and T denotes a transpose.

3. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 2, wherein, The expression after adding constraint conditions to the multi-objective optimization objective function constructed in step 3 based on the overall mission design target and geometric constraints is as follows: ； wherein is the half wing span length of the front wing, is the half wing span length of the rear wing, is the mid-section chord length of the front wing, is the mid-section chord length of the rear wing, is the tip chord length of the front wing, is the tip chord length of the rear wing, is the forward sweep angle of the front wing, is the rearward sweep angle of the rear wing, is the angle of attack, is the Mach number.

4. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 3, characterized in that: The characterization parameters in step 5 include a front wing half wingspan length, a rear wing half wingspan length, a front wing midsection cross-sectional chord length, a rear wing midsection cross-sectional chord length, a front wing tip chord length, a rear wing tip chord length, a front wing leading edge sweep angle, a rear wing trailing edge sweep angle, an angle of attack and a Mach number.

5. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 4, wherein, The aerodynamic design experience in step 7 is specifically that the influence of the angle of attack and the Mach number on the optimization objective function is not less than the influence of the geometric model parameters on the optimization objective function.

6. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 5, characterized in that: The first layer optimization variables include the angle of attack and the Mach number; and the second layer optimization variables include the front wing half wingspan length, the rear wing half wingspan length, the front wing midsection cross-sectional chord length, the rear wing midsection cross-sectional chord length, the front wing tip chord length, the rear wing tip chord length, the front wing leading edge sweep angle and the rear wing trailing edge sweep angle.

7. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 6, characterized in that: The geometric model parameters in step 9 are the front wing half wingspan length, the rear wing half wingspan length, the front wing midsection cross-sectional chord length, the rear wing midsection cross-sectional chord length, the front wing tip chord length, the rear wing tip chord length, the front wing leading edge sweep angle and the rear wing trailing edge sweep angle; and the boundary condition parameters are the angle of attack and the Mach number.

8. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 7, characterized in that: The solving of the flight boundary condition parameters in step 12 includes a true airspeed three-dimensional projection in a current angle of attack state, a front wing and a rear wing effective projection area and an average aerodynamic chord length of the tandem wing layout wing system.

9. The DOE-based stratified sampling layout wing multi-objective aerodynamic optimization method according to claim 8, wherein, The expression of the target function calculated according to the overall lift-drag ratio calculation method of the tandem wing layout wing system in step 13 is as follows: ； wherein is the lift-to-drag ratio, is the total lift of the wing system, is the total drag of the wing system, is the lift of the front wing, is the lift of the rear wing, is the drag of the front wing, is the drag of the rear wing, is the local altitude atmospheric density, is the incoming flow velocity, is the effective projected area of the front wing, is the lift coefficient of the front wing, is the drag coefficient of the front wing, is the effective projected area of the rear wing, is the lift coefficient of the rear wing, is the drag coefficient of the rear wing.

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