Parameter design method and system of aircraft, medium and equipment

Abstract

The invention discloses an aircraft parameter design method and system, a medium and equipment, and relates to the technical field of aircraft design. Constructing an initial shape design space of the aircraft; performing iterative optimization on design parameters of the aircraft according to the initial shape design space, and determining an optimal solution of the initial shape design space; under the condition that the initial shape design space does not meet the convergence condition, the effectiveness index of each candidate point in the initial shape design space is calculated, and the initial shape design space is refined according to the candidate point with the maximum effectiveness index so as to update the initial shape design space; and taking the updated initial shape design space as a new initial shape design space, continuously performing iterative optimization on the design parameters of the aircraft until the shape design space of the aircraft meets a convergence condition, and determining an optimal solution of the shape design space meeting the convergence condition as a parameter design strategy of the aircraft. The method can improve the design precision of the aircraft.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft design, and in particular to an aircraft parameter design method, system, medium and equipment. Background Art

[0002] At present, aerodynamic shape optimization design is one of the core research topics in the aerospace field. Its purpose is to improve the performance of the aircraft, such as lift, drag and stability, by adjusting the parameters of the airfoil or other aerodynamic shapes.

[0003] With the continuous advancement of efficient computing hardware and advanced modeling technology, the accuracy and authenticity of aircraft design optimization have been significantly improved, and the enhancement of computing power has provided stronger support for optimized design. However, the difficulty of efficiently exploring complex design spaces and ensuring the global optimal solution still makes engineering design optimization face many challenges.

[0004] Therefore, there is an urgent need for a method that can improve the accuracy of aircraft design. Summary of the invention

[0005] Based on this, it is necessary to provide a method, system, medium and equipment for aircraft parameter design in response to the above technical problems, which method can improve the design accuracy of the aircraft.

[0006] The present invention adopts the following technical solutions: The present invention provides a method for designing parameters of an aircraft, comprising: According to the reference shape of the aircraft, an initial shape design space of the aircraft is constructed; the initial shape design space includes design parameters of multiple candidate points on the aircraft; According to the initial shape design space, the design parameters of the aircraft are iteratively optimized to determine the optimal solution of the initial shape design space; When the initial shape design space does not meet the convergence condition, the effectiveness index of each candidate point in the initial shape design space is calculated, and the initial shape design space is refined according to the candidate point with the largest effectiveness index to update the initial shape design space; the refinement of the initial shape design space means introducing new design parameters into the initial shape design space by inserting candidate points; The updated initial shape design space is used as the new initial shape design space, and the design parameters of the aircraft are continuously iterated and optimized until the shape design space of the aircraft meets the convergence conditions. The shape design space that meets the convergence conditions is determined as the parameter design strategy of the aircraft.

[0007] Optionally, calculate the effectiveness index of each candidate point in the initial shape design space, including: Obtaining the Spearman rank correlation coefficient between the optimization target of the aircraft and each candidate point in the initial shape design space, and the Spearman rank correlation coefficient between the design constraints of the aircraft and each candidate point; The effectiveness index of each candidate point is determined according to the Spearman rank correlation coefficient between the optimization target of the aircraft and each candidate point, and the Spearman rank correlation coefficient between the design constraints of the aircraft and each candidate point.

[0008] Optionally, the initial shape design space is refined according to the candidate point with the maximum effectiveness index to update the initial shape design space, including: According to the index and design parameters of the candidate point with the largest effectiveness index, the design parameters of two new candidate points are obtained; An updated initial shape design space is determined according to the design parameters of the two new candidate points and the design parameters of the candidate points other than the candidate point with the largest effectiveness index in the initial shape design space.

[0009] Optionally, the index of the candidate point with the largest validity index , the calculation formula of the design parameters of the updated initial shape design space is: ; Among them, the initial shape design space is , the updated initial shape design space is , represents the number of rounds of refinement, Indicates The design parameters of the first candidate point during round refinement, Indicates The design parameters of the first candidate point during round refinement, Indicates The design parameters of the second candidate point during round refinement, Indicates The design parameters of the second candidate point during round refinement, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, , The number of candidate points in the design space for the initial shape; and These are the design parameters of the two new candidate points; The index of the candidate point with the largest validity index satisfy , then the calculation formula of the design parameters of the updated initial shape design space is: ; in, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, , Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement Design parameters of candidate points; and These are the design parameters of the two new candidate points; The index of the candidate point with the largest validity index , then the calculation formula of the design parameters of the updated initial shape design space is: ; in, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, and These are the design parameters of the two new candidate points.

[0010] Optionally, a method of determining whether the initial shape design space satisfies a convergence condition includes: Obtaining an optimized shape corresponding to the optimal solution of the aircraft in the initial shape design space and an initial configuration of the initial shape design space; the initial configuration is a reference shape of the aircraft corresponding to the initial shape design space when no iterative optimization is performed; If the optimized shape is consistent with the initial configuration, it is determined that the initial shape design space meets the convergence condition; If the optimized shape is inconsistent with the initial configuration, it is determined that the initial shape design space does not meet the convergence condition.

[0011] Optionally, according to the initial shape design space, iteratively optimizing the design parameters of the aircraft to determine the optimal solution of the initial shape design space includes: A Latin hypercube sampling method is used to extract a geometric sample set in the initial shape design space; Calculate the aerodynamic characteristics of the geometric sample set to obtain a geometric aerodynamic sample library; Import the geometric aerodynamic sample library into the Kriging model to obtain a proxy model; Based on the geometric and aerodynamic constraints of the aircraft, a genetic algorithm is used according to the surrogate model to iteratively optimize the design parameters of the aircraft until the optimal solution of the initial shape design space is found.

[0012] Optionally, based on the reference shape of the aircraft, an initial shape design space of the aircraft is constructed, including: The Hicks-Henne parameterization method is used to characterize the reference shape of the aircraft and construct the initial shape design space.

[0013] The present invention provides a parameter design system for an aircraft, comprising: An initialization module is used to construct an initial shape design space of the aircraft according to a reference shape of the aircraft; the initial shape design space includes design parameters of multiple candidate points on the aircraft; An optimization module is used to iteratively optimize the design parameters of the aircraft according to the initial shape design space to determine the optimal solution of the initial shape design space; A refinement module is used to calculate the effectiveness index of each candidate point in the initial shape design space when the initial shape design space does not meet the convergence condition, and refine the initial shape design space according to the candidate point with the largest effectiveness index to update the initial shape design space; the refinement of the initial shape design space means introducing new design parameters into the initial shape design space by inserting candidate points; The iterative module is used to use the updated initial shape design space as the new initial shape design space, continue to iteratively optimize the design parameters of the aircraft until the shape design space of the aircraft meets the convergence conditions, and determine the shape design space that meets the convergence conditions as the parameter design strategy of the aircraft.

[0014] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned aircraft parameter design method is implemented.

[0015] The present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned aircraft parameter design method when executing the program.

[0016] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects: In the present invention, during the optimization process, the effectiveness index of each candidate point is calculated, and the design space is refined according to the candidate point with the largest effectiveness index. In this way, the design space is gradually refined according to the continuous insertion of candidate points, which effectively reduces the dependence of the optimization result on the initial shape design space, so that the optimization design time of the present invention is basically the same as that of the traditional fixed parameterization method, but the optimization result is significantly improved, thereby improving the design accuracy of the aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic diagram of a flow chart of a parameter design method for an aircraft provided by the present invention; Figure 2 A schematic flow chart of another method for designing aircraft parameters provided by the present invention; Figure 3 A schematic diagram of the changing relationship of an improved fifth-order Hicks-Henne type function provided by the present invention; Figure 4 A schematic diagram of a process for determining an initial shape design space in an optimizer provided by the present invention; Figure 5 A schematic diagram of the position distribution of multiple candidate points on the geometric shape of an aircraft in an initial shape design space provided by the present invention; Figure 6 A schematic diagram of a RAE 2822 airfoil grid provided by the present invention; Figure 7A schematic diagram for comparing the resultant pressure coefficients of three groups of grids provided by the present invention; Figure 8 A schematic diagram of the effectiveness index of each candidate point provided by the present invention; Fig. 9 A schematic diagram of the effect of inserting candidate points in four rounds of refinement provided by the present invention; Fig.10 A schematic diagram of an optimization iteration curve for the next stage caused by triggering space refinement at different times provided by the present invention; Fig.11 A schematic diagram of the convergence history of a fixed and adaptive parameterized optimization process provided by the present invention; Fig.12 A schematic diagram showing the comparison of geometry and pressure distribution of optimization results of a fixed and adaptive parameterization method provided by the present invention; Fig.13 A schematic diagram of the optimization results and refinement process of each round of an adaptive parameterization method provided by the present invention; Fig.14 A schematic diagram of the optimization results and refinement process of each round of an adaptive parameterization method starting from NACA 0012 provided by the present invention; Fig.15 A schematic diagram comparing the geometric shapes and pressure distribution of two initial shape optimization results provided by the present invention; Fig.16 A schematic diagram of a parameter design system for an aircraft provided by the present invention; Fig.17 A schematic diagram of a computer device for implementing a parameter design method for an aircraft provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] At present, aerodynamic optimization design is divided into two technical routes, depending on whether gradient information is needed in the design process of the aircraft: optimization design dominated by adjoint methods and optimization design dominated by evolutionary methods. The adjoint method can handle large-scale design variables. However, since gradient algorithms are weak in dealing with multi-extreme value problems, they often face the dilemma of local optimal solutions in practical applications and cannot fully explore the potential of the design space. In order to achieve global optimization effects, heuristic algorithms that imitate biological evolution or group behavior phenomena in nature to improve global optimization characteristics have attracted people's attention, such as common genetic algorithms and particle swarm optimization algorithms. Further combining heuristic algorithms with agent models to form an efficient optimization system can quickly find the global optimal solution.

[0020] Existing studies have shown that in aerodynamic optimization, commonly used parameterization methods such as airfoil parameterization CST (Class Shape Transformation), Hicks-Henne type functions, and B-splines, although they have good versatility and flexible shape representation capabilities, also have some defects. On the one hand, existing parameterization methods rely heavily on the selection of initial shapes and the construction of design space, which requires high experience and preliminary settings from designers. Especially in unconventional configurations with limited design experience, the predetermined geometric parameterization is likely to limit the optimizer's ability to find the optimal geometric shape, resulting in unstable optimization results. On the other hand, these methods usually require a large number of design variables to accurately describe the airfoil shape, which significantly increases the computational complexity and time cost of the optimization process.

[0021] Based on this, the present invention provides a parameter design method, system, medium and equipment for an aircraft. By constructing an initial shape design space and introducing candidate point insertion technology, the design space is continuously refined, and candidate points are automatically inserted during the optimization process, which greatly reduces the dependence of the optimization results on the initial shape design space and effectively improves the efficiency and engineering practicality of the aircraft optimization design.

[0022] In the present invention, a server can be used as the execution subject. The server mentioned in the present invention can be a server set up on a business platform, or a device such as a desktop computer, a laptop computer, etc. that can execute the solution of the present invention.

[0023] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0024] Figure 1 The following is a flow chart of a method for designing parameters of an aircraft in the present invention, which specifically includes the following steps: S101, constructing an initial shape design space of the aircraft according to a reference shape of the aircraft; the initial shape design space includes design parameters of a plurality of candidate points on the aircraft.

[0025] In one embodiment, an initial shape design space of the aircraft is constructed based on a reference shape of the aircraft, including: using a Hicks-Henne parameterization method to characterize the reference shape of the aircraft and constructing the initial shape design space.

[0026] The reference shape can be designed independently or adopt an existing aircraft model; the Hicks-Henne parameterization method is used to characterize the reference shape, and the expression of the characterization function is as follows: (1); in, and Represent the upper and lower surface functions of the airfoil respectively, and Respectively represent the upper and lower surface functions of the reference airfoil, is the chord length of the airfoil, and its value range is generally . is a type function, is the weight coefficient of the shape function (i.e., candidate point), is the number of shape functions (i.e., candidate points), which is determined according to the design requirements. The candidate points are sorted from the leading edge to the trailing edge. is the index of the candidate point. It is used to control the slope change of the type function, and its value range is generally , which can control the change of airfoil geometry at the trailing edge, is the control function attenuation rate coefficient, the additional term is used to control the trailing edge attenuation in the airfoil geometry. Assume =2, 3, 4, 5, 6, 7, They are 0.15, 0.30, 0.45, 0.60, 0.75 and 0.90 respectively.

[0027] The Hicks-Henne parameterization method achieves the deformation of the aircraft's baseline shape by selecting appropriate candidate point locations and adjusting the weight coefficients of the shape function. That is, the design parameters of the candidate points are the locations of the candidate points.

[0028] S102, iteratively optimizing the design parameters of the aircraft according to the initial shape design space to determine the optimal solution of the initial shape design space.

[0029] In one embodiment, the design parameters of the aircraft are iteratively optimized according to the initial shape design space to determine the optimal solution of the initial shape design space, including: extracting a geometric sample set in the initial shape design space using Latin hypercube sampling; calculating the aerodynamic characteristics of the geometric sample set to obtain a geometric aerodynamic sample library; importing the geometric aerodynamic sample library into a Kriging model to obtain a proxy model; and iteratively optimizing the design parameters of the aircraft using a genetic algorithm according to the proxy model until the optimal solution of the initial shape design space is found.

[0030] The construction process of the proxy model includes: pre-processing each sample in the geometric sample set to form a watertight geometric model, using the pre-processing software (The Integrated Computer Engineering and Manufacturing code for Computational Fluid Dynamics, ICEMCFD) script to realize the generation of the geometric model to the computational grid; using Fluent commercial software to perform precise numerical simulation of computational fluid dynamics (CFD) to obtain high-fidelity aerodynamic characteristics; importing the geometric samples and aerodynamic characteristics (geometric aerodynamic sample library) into the Kriging model to construct the initial proxy model.

[0031] Among them, the proxy model can be updated by improving the expected point addition criterion to improve the global accuracy of the proxy model.

[0032] Through the selection, crossover and mutation of the genetic algorithm, the design parameters of the aircraft are iteratively optimized, and the geometric and aerodynamic characteristics of the design parameters are evaluated to check whether the optimization tends to converge. The design parameters of the last 30 iterations can be left unchanged as an evaluation method. If the optimization has not converged, the design sample is filled into the existing sample set to correct the proxy model. If the optimization converges, the optimal design parameters are output as the optimal solution.

[0033] S103, when the initial shape design space does not meet the convergence condition, calculate the effectiveness index of each candidate point in the initial shape design space, and refine the initial shape design space according to the candidate point with the largest effectiveness index to update the initial shape design space; the refinement of the initial shape design space means introducing new design parameters into the initial shape design space by inserting candidate points.

[0034] Optionally, a method for determining whether the initial shape design space satisfies the convergence condition includes: obtaining an optimized shape corresponding to the optimal solution of the aircraft in the initial shape design space and an initial configuration of the initial shape design space; the initial configuration is a reference shape of the aircraft corresponding to the initial shape design space when no iterative optimization is performed; if the optimized shape is consistent with the initial configuration, it is determined that the initial shape design space satisfies the convergence condition; if the optimized shape is inconsistent with the initial configuration, it is determined that the initial shape design space does not meet the convergence condition.

[0035] Specifically, according to the reference shape of the aircraft, an initial shape design space is designed. The initial shape design space includes the design parameters of 6 candidate points. The optimal solution of the initial shape design space when there are 6 candidate points is obtained through iterative optimization. The optimized shape corresponding to the 6 candidate points is determined to determine whether the convergence condition is met (whether the optimized shape corresponding to the initial shape design space when there are 6 candidate points is consistent with the reference shape. If it is consistent, it is determined that the convergence condition is met). If the convergence condition is not met, the initial shape design space is refined and updated. At this time, the initial shape design space has 7 candidate points. After finding the optimal solution of the initial shape design space, it is determined whether the convergence condition is met. At this time The method for judging whether the convergence condition is met is as follows: whether the optimized shape corresponding to the optimal solution of the initial shape design space when there are 7 candidate points is consistent with the initial configuration of the initial shape design space when there are 7 candidate points; the initial configuration is the reference shape of the aircraft corresponding to the initial shape design space when there are 7 candidate points before iterative optimization, that is, the optimized shape corresponding to the optimal solution of the initial shape design space when there are 6 candidate points, and so on. When judging whether the initial shape design space meets the convergence condition, the optimized shape corresponding to the optimal solution of the current initial shape design space is compared with the reference shape of the aircraft before the current initial shape design space is iteratively optimized to judge whether they are consistent.

[0036] If the initial shape design space meets the convergence condition, the optimal solution of the initial shape design space is used as the global optimal solution, and the optimal solution of the initial shape design space that meets the convergence condition is determined as the parameter design strategy of the aircraft.

[0037] When the initial shape design space does not meet the convergence conditions, the refinement of the initial shape design space is triggered, that is, the effectiveness index considering the constraints is calculated for each candidate point, and a node is inserted at the position of the best performing candidate point to achieve adaptive shape design space refinement, thereby ensuring that the optimization efficiency is greatly improved while introducing a small number of design variables.

[0038] That is, the specific time that triggers the refinement of the initial shape design space is to find the optimal solution in the current shape design space. There are two main reasons: first, it is necessary to add additional candidate points only when the existing shape design space can no longer improve the geometry and aerodynamic characteristics of the aircraft. Second, the effectiveness index composed of the Spearman rank correlation coefficient is accurate only when the optimization reaches full convergence.

[0039] Among them, calculating the effectiveness index of each candidate point in the initial shape design space includes: obtaining the Spearman rank correlation coefficient between the optimization target of the aircraft and each candidate point in the initial shape design space, and the Spearman rank correlation coefficient between the design constraints of the aircraft and each candidate point; determining the effectiveness index of each candidate point according to the Spearman rank correlation coefficient between the optimization target of the aircraft and each candidate point, and the Spearman rank correlation coefficient between the design constraints of the aircraft and each candidate point.

[0040] Specifically, the Spearman rank correlation coefficient is used to represent the correlation between two variables. The calculation formula of the Spearman rank correlation coefficient is as follows: (2); in, For variables and The Spearman rank correlation coefficient between , , is the number of samples, , , , , for rank, for rank.

[0041] Therefore, formula (2) can be used to calculate the Spearman rank correlation coefficient between the optimization target of the aircraft and each candidate point in the initial shape design space, as well as the Spearman rank correlation coefficient between the design constraints of the aircraft and each candidate point. For example, when calculating the Spearman rank correlation coefficient between the optimization target of the aircraft and any candidate point in the initial shape design space, As the design parameters of the candidate points, is the number of samples in the initial shape design space, is the optimization target. The optimization target can be the lift coefficient , drag coefficient etc. The design constraint can be area , torque constraint wait.

[0042] The calculation formula of the effectiveness index of the candidate point is: (3); in, Indicates Candidate points The effectiveness index, Optimization targets and candidate points for the aircraft The Spearman rank correlation coefficient between Design constraints and candidate points for the aircraft The Spearman rank correlation coefficient between is the number of design constraints, For the The weight coefficient of the design constraint, that is, if there are multiple design constraints, the Spearman rank correlation coefficient between any design constraint and each candidate point is calculated. , upper boundary design constraint .

[0043] In one embodiment, the initial shape design space is refined according to the candidate point with the largest validity index to update the initial shape design space, including: obtaining the design parameters of two new candidate points according to the index and design parameters of the candidate point with the largest validity index; and determining the updated initial shape design space according to the design parameters of the two new candidate points and the design parameters of the candidate points in the initial shape design space except the candidate point with the largest validity index.

[0044] The refinement of the initial shape design space refers to introducing new candidate points by inserting nodes to refine the initial shape design space, that is, increasing the dimension of the design space, aiming to introduce a small number of candidate points while greatly improving the optimization efficiency.

[0045] Optionally, the index of the candidate point with the largest validity index , the calculation formula of the design parameters of the updated initial shape design space is: (4); Among them, the initial shape design space is , the updated initial shape design space is , represents the number of rounds of refinement, Indicates The design parameters of the first candidate point during round refinement, Indicates The design parameters of the first candidate point during round refinement, Indicates The design parameters of the second candidate point during round refinement, Indicates The design parameters of the second candidate point during round refinement, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, , The number of candidate points in the design space for the initial shape; and These are the design parameters of the two new candidate points.

[0046] The index of the candidate point with the largest validity index satisfy , then the calculation formula of the design parameters of the updated initial shape design space is: (5); in, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, , Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement Design parameters of candidate points; and These are the design parameters of the two new candidate points.

[0047] The index of the candidate point with the largest validity index , then the calculation formula of the design parameters of the updated initial shape design space is: (6); in, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, and These are the design parameters of the two new candidate points.

[0048] S104, using the updated initial shape design space as a new initial shape design space, continuing to iteratively optimize the design parameters of the aircraft until the shape design space of the aircraft meets the convergence conditions, and determining the shape design space that meets the convergence conditions as the parameter design strategy of the aircraft.

[0049] The updated initial shape design space is used as the new initial shape design space, and the design parameters of the aircraft are continuously iteratively optimized to re-determine the optimal solution of the initial shape design space. When the initial shape design space does not meet the convergence conditions, the effectiveness index of each candidate point in the initial shape design space is calculated, and the initial shape design space is refined according to the candidate point with the largest effectiveness index to update the initial shape design space. The updated initial shape design space is then used as the new initial shape design space, and the design parameters of the aircraft are continuously iteratively optimized until the shape design space of the aircraft meets the convergence conditions. The shape design space that meets the convergence conditions is determined as the parameter design strategy of the aircraft.

[0050] In one embodiment, the present invention also provides a method for designing aircraft parameters. Figure 2 As shown, this embodiment includes the following steps: S201, obtaining a reference shape, and determining a low-dimensional initial shape design space according to the reference shape.

[0051] Among them, the Hicks-Henne parameterization method can be used to characterize the reference shape. The Hicks-Henne parameterization method realizes the deformation of the geometric shape by selecting appropriate candidate point positions and adjusting the weight coefficient of the shape function. In this scheme, the slope change coefficient is taken as , the decay rate coefficient is . See Figure 3, showing the schematic diagram of the change relationship of the improved 5th-order Hicks-Henne type function, the candidate point position , it can be seen from the distribution law of the function curve that Mainly affects the airfoil leading edge line shape, Mainly affects the middle line type, Mainly affects the trailing edge line shape.

[0052] S202, inputting the initial shape design space into the optimizer.

[0053] S203, iteratively optimizing the design parameters of the aircraft through the optimizer to obtain the optimal solution of the initial shape design space output by the optimizer.

[0054] The process of iteratively optimizing the design parameters of the aircraft through the optimizer to obtain the initial shape design space output by the optimizer, such as Figure 4 As shown, the process specifically includes: S401, Latin hypercube sampling is used to extract a geometric sample set in the initial shape design space.

[0055] like Figure 5 As shown, Figure 5 Schematic diagram of the distribution of multiple candidate points on the geometric shape of the aircraft in the initial shape design space, and the candidate points on the upper and lower surfaces of the airfoil , a total of 6 candidate points.

[0056] S402, performing CFD numerical simulation on the geometric sample set to calculate its aerodynamic characteristics, and forming a geometric aerodynamic sample library.

[0057] Among them, the process of extracting a geometric aerodynamic sample library in the initial shape design space can be called a DOE process.

[0058] S403, importing the geometric aerodynamic sample library into the Kriging model to obtain a proxy model.

[0059] The construction process of the proxy model includes: S501, pre-processing each sample in the geometric sample set to form a watertight geometric model.

[0060] S502, using ICEM script to realize the generation of geometric model to computational mesh.

[0061] In order to verify the sensitivity of the calculation results to the change of grid density, the grid independence verification is carried out under the design conditions. Three sets of grids are designed, with the number of grids being 32000, 62000, and 120000 respectively. Figure 6 , Figure 6This is a schematic diagram of a RAE 2822 airfoil grid. The lift and drag coefficients calculated by three sets of grids and the pressure distribution of the symmetric section are compared to verify the grid independence.

[0062] S503 uses Fluent software to perform precise CFD numerical simulation to obtain high-fidelity aerodynamic characteristics.

[0063] Turbulence model using Shear Stress Transport (SST) k The model uses the second-order precision advection upstream splitting method (AUSM+) format for convective flux, and the viscosity term is obtained by the central difference format, where the laminar viscosity coefficient is calculated using the Sutherland formula, and the time advancement uses implicit advancement. In terms of boundary condition setting, the pressure far field boundary is used in the far front, and the adiabatic no-slip wall boundary is used on the fuselage wall.

[0064] The incoming flow condition is: Mach number =0.734, Reynolds number =6.5×10 6 , lift coefficient =0.824. The angle of attack and drag coefficient calculated by the three groups of grids (coarse grid, medium grid and fine grid) are compared in Table 1. The angle of attack of the three groups of grids when the lift coefficient is satisfied is The error is less than 0.02°, and the corresponding resistance coefficient The error is less than 1 count. For a comparison of the pressure coefficients of the three grids, see Figure 7 , Figure 7 The horizontal axis is the position of the candidate point, and the vertical axis is the pressure coefficient , it can be seen that the consistency of the pressure coefficient is good. Therefore, in order to ensure the calculation accuracy and efficiency in the optimization process, we choose the medium grid for subsequent aerodynamic optimization design.

[0065] Table 1

[0066] S504, importing the geometric samples and aerodynamic characteristics into the Kriging model to construct an initial proxy model.

[0067] S404, based on the proxy model, using the point addition criterion, performing selection, crossover and mutation operations on the randomly sampled initial samples.

[0068] S405, evaluate the fitness of the samples and find the sample with the highest fitness.

[0069] S406, determining whether the sample with the highest fitness converges to the optimal solution of the current shape design space.

[0070] Among them, whether it converges can be determined by whether the sample with the highest fitness changes within 30-50 iterations. If not, it converges to the optimal solution; if it does not converge to the global optimal solution, execute step S407; if it converges to the global optimal solution, execute step S408.

[0071] S407, adding the sample with the highest fitness to the existing sample set to correct the proxy model.

[0072] S408, output the sample with the highest fitness.

[0073] Based on the agent model, a genetic algorithm is used to iteratively optimize the current shape design space until the sample with the highest fitness is the optimal solution of the current shape design space.

[0074] S204, obtaining an optimized shape of the aircraft according to the optimal solution of the initial shape design space.

[0075] S205, determining whether the optimized shape satisfies a convergence condition.

[0076] If the optimized shape does not meet the convergence condition, step S206 is executed; if the optimized shape meets the convergence condition, step S208 is executed.

[0077] S206, calculating the effectiveness index of each candidate point in the initial shape design space.

[0078] like Figure 8 As shown, Figure 8 A schematic diagram of the effectiveness indicators of each candidate point, showing the location of the candidate point The validity index of each candidate point can be used to find the front edge position of the upper surface. It is strongly negatively correlated with the target variable and weakly positively correlated with the area. To reduce the target variable, you need to increase The corresponding weight , the area will also increase, meeting the upper boundary constraint of the area. Therefore, when the lower boundary constraint is , such as area constraints, upper boundary constraints , such as resistance constraint. From the above calculation, we can know that is the best candidate point. Position refinement can maximize the design space.

[0079] S207, refining the initial shape design space according to the effectiveness index to update the initial shape design space, and inputting the updated initial shape design space into the optimizer as a new initial shape design space.

[0080] like Fig. 9 As shown, Fig. 9 A schematic diagram of the effect of inserting candidate points in four rounds of refinement. Specifically, the way candidate points are inserted at the leading and trailing edges is significantly different from the other positions. It is worth noting that in order to prevent unreasonable tight spacing of shape control, a minimum spacing is imposed between adjacent candidate points. If the best candidate point does not meet the spacing requirement, other candidate points need to be considered to maintain the effectiveness of adding points.

[0081] The trigger for the space refinement algorithm is a stopping condition that terminates the optimization in the current shape design space and starts parameter refinement. The trigger is critical to efficiency. Fig.10 , Fig.10 This is a schematic diagram of the next stage optimization iteration curve caused by triggering space refinement at different times. Fig.10 The horizontal axis is the number of iterations, and the vertical axis is the resistance coefficient ,For an airfoil drag reduction optimization problem, the two branches show the impact of triggering at different times on the performance, indicating that the ,refinement positions selected at different times are different and the ,optimization results in the next stage are also different. This is because the validity index of the ,candidate points is determined by the Spearman rank correlation coefficient, and the ,size of the database will affect the validity index and thus the ,position of the best candidate points.

[0082] The specific time to trigger the refinement of the shape design space is to find the optimal solution in the current shape design space. The parameter refinement is started when each cycle reaches full convergence. There are two main reasons: first, it is necessary to add additional candidate points only when the existing design parameters can no longer improve the objective function. Second, the effectiveness index composed of the Spearman rank correlation coefficient is accurate only when the optimization reaches full convergence.

[0083] S208, determining the shape design space corresponding to the optimized shape that meets the convergence condition as the optimal design space of the aircraft, and the optimal solution of the optimal design space is the global optimal solution.

[0084] In one embodiment, the design constraints include moment constraints and area constraints, and a transonic drag reduction aerodynamic optimization design under area constraints and moment constraints is carried out for RAE 2822. The optimized mathematical model is: (7); in, is the drag coefficient, is the lift coefficient, is the torque coefficient, To optimize the area of ​​the configuration, is the area of ​​the initial configuration.

[0085] like Fig.11 As shown, Fig.11 is a schematic diagram of the convergence history of the optimization process with a fixed number of candidate points and an adaptive parameterization. Table 2 shows the comparison of the optimization results of the fixed number of candidate points and the adaptive parameterization method. The fixed parameterization method converges in 72 steps. is 145.79 kuang; The fixed parameterization method converges at step 146. 143.62 square meters; initial shape design space dimensions The adaptive Hicks-Henne parameterization method converged in 80 steps. The optimization process went through three rounds of refinement. is 137.28 K. Overall, the optimization time of the adaptive parameterization method is The fixed parameterization method is basically the same, and the drag coefficient of the optimization result is reduced by 8.51%.

[0086] Table 2

[0087] Schematic diagram of the comparison of geometry and pressure distribution of the optimization results of fixed and adaptive parameterization methods. Fig.12 The optimized geometry shows that the curvature of the front half of the upper surface is reduced, the maximum thickness position is moved backward, and the trailing edge is reversed. Compared with the reference configuration, the shock wave intensity of the three parameterization methods is weakened, and the shock wave drag is significantly reduced. Among them, the shock wave of the adaptive parameterization method is basically eliminated, so the drag coefficient is the lowest. Fig.13 The optimization results and refinement process of each round of the adaptive parameterization method are shown. The first refinement is at the leading edge of the upper surface. This is because the formation of the shock wave is mainly affected by the curvature of the leading edge of the upper surface, making the effectiveness index at this position The largest is at the leading edge of the lower surface, and two refinements are performed. The subsequent refinements have no gain on the optimization results. Therefore, three rounds of refinement are sufficient to find the optimal airfoil. The adaptive parameterization method better solves the problem that the traditional parameterization method cannot take into account both low-dimensional design variables and the optimal deformation space.

[0088] The initial airfoil was replaced with NACA 0012, and the transonic drag reduction aerodynamic optimization design was carried out under the same operating conditions and constraints as RAE 2822. Fig.14 , showing the optimization results and refinement process of each round of the adaptive parameterization method starting from NACA 0012. The main refinement locations are the leading edge of the upper surface and the trailing edge of the lower surface, and after 8 refinements, it converges to the optimal configuration.

[0089] Table 3 shows the aerodynamic performance of the baseline airfoil (RAE 2822) and the optimization results from two initial shapes, RAE 2822 and NACA 0012. Fig.15 , showing a schematic diagram comparing the geometric shapes and pressure distribution of the benchmark and the two initial shape optimization results. It is not difficult to find that starting from the two initial shapes of RAE 2822 and NACA 0012 for optimization has little effect on the final optimization results. The optimized geometric shapes have a slight deviation at the leading edge, while the other positions are basically the same. Therefore, the adaptive parameterization method has better solved the problem that the optimization results of the traditional parameterization method depend on the initial configuration.

[0090] Table 3

[0091] When applying the aircraft parameter design method provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0092] The above is a method for designing aircraft parameters provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding aircraft parameter design system, such as Fig.16 shown.

[0093] Fig.16 A schematic diagram of a parameter design system for an aircraft provided by the present invention, the system 1600 includes: The initialization module 1601 is used to construct an initial shape design space of the aircraft according to the reference shape of the aircraft; the initial shape design space includes design parameters of multiple candidate points on the aircraft.

[0094] The optimization module 1602 is used to iteratively optimize the design parameters of the aircraft according to the initial shape design space to determine the initial shape design space.

[0095] The refinement module 1603 is used to calculate the effectiveness index of each candidate point in the initial shape design space when the initial shape design space does not meet the convergence condition, and refine the initial shape design space according to the candidate point with the largest effectiveness index to update the initial shape design space; the refinement of the initial shape design space means introducing new design parameters into the initial shape design space by inserting candidate points.

[0096] Iteration module 1604 is used to use the updated initial shape design space as the initial shape design space, continue to iteratively optimize the design parameters of the aircraft until the initial shape design space of the aircraft meets the convergence condition, and determine the initial shape design space that meets the convergence condition as the parameter design strategy of the aircraft.

[0097] The specific definition of the aircraft parameter design system can be found in the definition of the aircraft parameter design method above, which will not be repeated here. Each module in the above-mentioned aircraft parameter design system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0098] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A parameter design method for an aircraft is provided.

[0099] The present invention also provides Fig.17 The structural diagram of the computer device shown in FIG. Fig.17 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 A parameter design method for an aircraft is provided.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0101] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for designing aircraft parameters, characterized in that: include: According to the reference shape of the aircraft, construct the initial shape design space of the aircraft; The initial shape design space includes design parameters of a plurality of candidate points on the aircraft; According to the initial shape design space, the design parameters of the aircraft are iteratively optimized to determine the optimal solution of the initial shape design space; When the initial shape design space does not meet the convergence condition, calculating the effectiveness index of each candidate point in the initial shape design space, and refining the initial shape design space according to the candidate point with the largest effectiveness index to update the initial shape design space; The refinement of the initial shape design space means introducing new design parameters into the initial shape design space by inserting candidate points; The updated initial shape design space is used as the new initial shape design space, and the design parameters of the aircraft are continuously iterated and optimized until the shape design space of the aircraft meets the convergence condition, and the optimal solution of the shape design space that meets the convergence condition is determined as the parameter design strategy of the aircraft.

2. The method according to claim 1, characterized in that The calculating the effectiveness index of each candidate point in the initial shape design space includes: Obtaining the Spearman rank correlation coefficient between the optimization target of the aircraft and each candidate point in the initial shape design space, and the Spearman rank correlation coefficient between the design constraint of the aircraft and each candidate point; The effectiveness index of each candidate point is determined according to the Spearman rank correlation coefficient between the optimization target of the aircraft and each candidate point, and the Spearman rank correlation coefficient between the design constraints of the aircraft and each candidate point.

3. The method according to claim 2, characterized in that The refining of the initial shape design space according to the candidate point with the largest effectiveness index to update the initial shape design space includes: According to the index and design parameters of the candidate point with the largest effectiveness index, the design parameters of two new candidate points are obtained; An updated initial shape design space is determined according to the design parameters of the two new candidate points and the design parameters of the candidate points in the initial shape design space except the candidate point with the largest effectiveness index.

4. The method according to claim 3, characterized in that The index of the candidate point with the largest validity index , the calculation formula of the design parameters of the updated initial shape design space is: ； Among them, the initial shape design space is , the updated initial shape design space is , represents the number of rounds of refinement, Indicates The design parameters of the first candidate point during round refinement, Indicates The design parameters of the first candidate point during round refinement, Indicates The design parameters of the second candidate point during round refinement, Indicates The design parameters of the second candidate point during round refinement, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, , The number of candidate points in the design space for the initial shape; and These are the design parameters of the two new candidate points; The index of the candidate point with the largest validity index satisfy , then the calculation formula of the design parameters of the updated initial shape design space is: ； in, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, , Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points are Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement Design parameters of candidate points; and These are the design parameters of the two new candidate points; The index of the candidate point with the largest validity index , then the calculation formula of the design parameters of the updated initial shape design space is: ； in, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, Indicates Round refinement The design parameters of candidate points, and These are the design parameters of the two new candidate points.

5. The method according to claim 1, characterized in that Methods for determining whether the initial shape design space meets the convergence conditions include: Obtaining an optimized shape corresponding to an optimal solution of the aircraft in the initial shape design space and an initial configuration of the initial shape design space; the initial configuration is a reference shape of the aircraft corresponding to the initial shape design space when no iterative optimization is performed; If the optimized shape is consistent with the initial configuration, it is determined that the initial shape design space meets the convergence condition; If the optimized shape is inconsistent with the initial configuration, it is determined that the initial shape design space does not meet the convergence condition.

6. The method according to claim 1, characterized in that The iterative optimization of the design parameters of the aircraft according to the initial shape design space to determine the optimal solution of the initial shape design space includes: A Latin hypercube sampling method is used to extract a geometric sample set in the initial shape design space; Calculating the aerodynamic characteristics of the geometric sample set to obtain a geometric aerodynamic sample library; Import the geometric aerodynamic sample library into the Kriging model to obtain a proxy model; Based on the geometric and aerodynamic constraints of the aircraft, a genetic algorithm is used according to the surrogate model to iteratively optimize the design parameters of the aircraft until the optimal solution of the initial shape design space is found.

7. The method according to claim 1, characterized in that The step of constructing an initial shape design space of the aircraft according to the reference shape of the aircraft includes: The Hicks-Henne parameterization method is used to characterize the reference shape of the aircraft and construct the initial shape design space.

8. A parameter design system for an aircraft, characterized in that: The system comprises: An initialization module, used to construct an initial shape design space of the aircraft according to a reference shape of the aircraft; the initial shape design space includes design parameters of the aircraft at multiple candidate points; An optimization module is used to iteratively optimize the design parameters of the aircraft according to the initial shape design space to determine the optimal solution of the initial shape design space; A refinement module, for calculating the effectiveness index of each candidate point in the initial shape design space when the initial shape design space does not meet the convergence condition, and refining the initial shape design space according to the candidate point with the largest effectiveness index to update the initial shape design space; the refinement of the initial shape design space means introducing new design parameters into the initial shape design space by inserting candidate points; The iterative module is used to use the updated initial shape design space as a new initial shape design space, continue to iteratively optimize the design parameters of the aircraft until the shape design space of the aircraft meets the convergence condition, and determine the optimal solution of the shape design space that meets the convergence condition as the parameter design strategy of the aircraft.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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