Edge encryption experimental design method for aircraft multidisciplinary optimization design

Abstract

The invention discloses an edge encryption experimental design method for aircraft multidisciplinary optimization design, and belongs to the technical field of aircraft design and optimization. The problem of obtaining prediction data with higher precision under the condition that the total amount of modeling data is not changed is solved. According to experimental design requirements, the number of sample points in a to-be-generated evaluation scheme, the dimension of each sample point and the specified number of numerical values needing to be generated are obtained; creating an initial assignment group for each dimension in the to-be-generated evaluation scheme; designing an edge encryption method; updating the initial assignment group by using an edge encryption method to obtain an updated assignment group; generating an initial experiment scheme with a uniform space by using a uniform design or Latin hypercube experiment method, and setting the value range of all dimensions to be 0.0-1.0; and on the basis of the updated assignment group, endowing each dimension of each sample in the initial experiment scheme with uniform space with a specified numerical value, and obtaining an edge encryption experiment scheme for multidisciplinary optimization design.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of aircraft design and optimization, and particularly relates to an edge encryption experimental design method for aircraft multidisciplinary optimization design. BACKGROUND

[0002] With the rapid development of aerospace technology, the traditional aircraft design method of separate design of each discipline and reciprocal iteration cannot meet the increasingly stringent performance requirements of various aircraft. Multidisciplinary coupled optimization design technology emerges as the times require, systematically considers the comprehensive performance of multiple disciplines such as aerodynamics, structure and control in the aircraft design process, and uniformly plans to solve the performance conflicts between disciplines, thereby significantly improving the comprehensive performance level of the aircraft and effectively avoiding the reciprocal iteration process between disciplines, and improving the aircraft design efficiency. The core of affecting the efficiency and effect of aircraft multidisciplinary coupled optimization design is the efficient optimization algorithm of each discipline performance evaluation, multidisciplinary coupled model and parameters between disciplines.

[0003] In view of the different data requirements of each discipline performance evaluation in aircraft multidisciplinary coupled optimization design, a large amount of aerodynamic data (thousands to tens of thousands) is required for performance evaluation of some disciplines, such as control and flight mechanics, which will restrict the comprehensive design of such disciplines in aircraft multidisciplinary coupled optimization design. A small amount of aerodynamic data can be used to predict full-state aerodynamic data, thereby reducing the data requirements of disciplines such as control and flight mechanics. For example, when designing a control system, 20% of the required aerodynamic data is used in combination with a machine learning algorithm to predict full-state aerodynamic data, which can reduce the aerodynamic data by 80% and significantly improve the efficiency of control system design. However, in order to ensure the engineering usability of the predicted data, the precision of the predicted data needs to meet the design requirements of each discipline. The selection of modeling data (a small amount of data) is an important step in obtaining high-precision predicted data. Current technologies use modern experimental design methods such as Latin hypercube and uniform design to select modeling data. Such experimental design methods emphasize spatial uniformity, and the experimental scheme data is continuous, which is not suitable for cases where the number of generated experimental schemes needs to be specified, such as aerodynamic data that requires Mach number to be 0.3, 0.6, 0.8 and 0.9, and angle of attack to be 0° to 20° with an interval of 2°. At the same time, in order to improve the prediction accuracy of aerodynamic modeling, the data edges need to be encrypted while ensuring the total amount of modeling data remains unchanged. Modern experimental design methods cannot achieve separate encryption of data edges. SUMMARY

[0004] The problem to be solved by the present application is to obtain higher precision predicted data while ensuring the total amount of modeling data remains unchanged, and an edge encryption experimental design method for aircraft multidisciplinary optimization design is proposed.

[0005] To achieve the above-mentioned purpose, the technical scheme is as follows:

[0006] An edge encryption experimental design method for aircraft multidisciplinary optimization design, comprising the following steps:

[0007] S1. According to the experimental design requirements, obtain the number of sample points in the evaluation scheme to be generated , the dimension of each sample point and the number of values to be generated for each dimension are specified;

[0008] S2. For each dimension in the evaluation scheme to be generated obtained in step S1, create an initial assignment group;

[0009] S3. Design an edge encryption method;

[0010] S4. Update the initial assignment group obtained in step S2 using the edge encryption method designed in step S3 to obtain an updated assignment group;

[0011] S5. Generate a spatially uniform initial experimental scheme using a uniform design or Latin hypercube experimental method, and set the value range of all dimensions to 0.0~1.0;

[0012] S6. Based on the updated assignment group obtained in step S4, assign a specified value to each dimension of each sample in the spatially uniform initial experimental scheme obtained in step S5 to obtain an edge encryption experimental scheme for multidisciplinary optimization design.

[0013] Further, the number of values to be generated for each dimension in the evaluation scheme to be generated in step S1 is , wherein is the number of values to be generated for the nth dimension.

[0014] Further, step S2 creates an initial assignment group for each dimension, and the nth dimension is divided into segments from 0.0 to 1.0, containing group points, and the value of each group point is :

[0015]

[0016] wherein i is any one of , j is any one of , and is the number of segments of the nth dimension.

[0017] Further, the edge encryption method designed in step S3 has the following edge encryption form:

[0018] ​

[0019] wherein, is an encryption control factor; is a relaxation factor;

[0020] Each dimension corresponds to a relaxation factor, when is 0, the encryption method degenerates into a non-encryption method, when is an effective encryption.

[0021] Further, the specific implementation method of step S4 includes the following steps:

[0022] S4.1. Update each group point value to obtain the updated each group point value, the expression is:

[0023] ;

[0024] wherein, is the updated each group point value;

[0025] S4.2. Scale the updated each group point value to the range of 0.0~1.0, the expression is:

[0026]

[0027] wherein, is the scaled each group point value; is the last group point value before scaling, that is, the maximum value of the group point;

[0028] Then the updated assignment group is obtained as:

[0029] .

[0030] Further, the space-uniform initial experimental scheme generated by step S5 is:

[0031]

[0032] wherein, X is a space-uniform initial experimental scheme, is the initial value of the dimension of the th sample point.

[0033] Further, step S6 gives each dimension of each sample in the space-uniform initial experimental scheme obtained by step S5 a specified value, and obtains the edge encryption experimental scheme for multidisciplinary optimization design as follows:

[0034]

[0035]

[0036] wherein m is wherein n is wherein m is is the first dimensional value after being assigned a specified value, dimensional value after being assigned a specified value, is an edge encryption experimental design scheme for multidisciplinary optimization design, is the first dimensional specified value.

[0037] Advantages of the present application:

[0038] The edge encryption experimental design method for aircraft multidisciplinary optimization design provided by the present application can generate non-continuous data, and can design an experimental scheme according to a specified value; the present application considers the strong nonlinear characteristics of the edge of aerodynamic data, and encrypts the edge of aerodynamic data while ensuring the number of experimental design sample points unchanged, thereby effectively improving the precision of the modeling and prediction data of the aircraft. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of the edge encryption experimental design method for aircraft multidisciplinary optimization design provided by the present application;

[0040] Figure 2 is a data quantity statistical chart of different specified value data in the Ma dimension in the experimental scheme designed by the present application;

[0041] Figure 3 is a data quantity statistical chart of different specified value data in the AoA dimension in the experimental scheme designed by the present application;

[0042] Figure 4 is a data quantity statistical chart of different specified value data in the delta dimension in the experimental scheme designed by the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application, that is, the described specific embodiments are only a part of the embodiments of the present application, but not all the specific embodiments. The components of the specific embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations, and the present application can also have other embodiments.

[0044] Therefore, the following detailed description of the specific embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the application claimed, but merely represents selected specific embodiments of the application. Based on the specific embodiments of the application, all other specific embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0045] In order to further understand the invention content, characteristics and effects of the present application, the following specific embodiments are exemplified, and the accompanying drawings are Figure 1 -Appendix Figure 4 The detailed description is as follows:

[0046] Example 1:

[0047] An edge encryption experimental design method for aircraft multidisciplinary optimization design, comprising the following steps:

[0048] S1. According to the experimental design requirements, obtain the number of sample points in the evaluation scheme to be generated , the dimension of each sample point and the number of values to be generated for each dimension are specified;

[0049] Further, the sample is aircraft aerodynamic data;

[0050] Further, the number of values to be generated for each dimension in the evaluation scheme to be generated in step S1 is , wherein is the number of values to be generated for the nth dimension.

[0051] S2. For each dimension in the evaluation scheme to be generated obtained in step S1, create an initial assignment group;

[0052] Further, step S2 creates an initial assignment group for each dimension, and the dimension is divided into segments from 0.0 to 1.0, containing group points, then the value of each group point is :

[0053]

[0054] wherein i is any one of , j is any one of , and is the number of segments of the nth dimension.

[0055] S3. Design edge encryption method;

[0056] Further, the edge encryption method designed in step S3 is in the form of:

[0057]

[0058] wherein, is an encryption control factor; is a relaxation factor;

[0059] Each dimension corresponds to a relaxation factor, when is 0, the encryption method degenerates into a non-encryption method, when is effective encryption.

[0060] S4. Using the edge encryption method designed in step S3 to update the initial assignment group obtained in step S2 to obtain an updated assignment group;

[0061] Further, the specific implementation method of step S4 includes the following steps:

[0062] S4.1. Update each group point value to obtain an updated each group point value, the expression is:

[0063]

[0064] wherein, is an updated each group point value;

[0065] S4.2. Scale the updated each group point value to the range of 0.0~1.0, the expression is:

[0066]

[0067] wherein, is a scaled each group point value; is the last group point value before scaling, that is, the maximum value of the group point;

[0068] Then the updated assignment group is obtained as:

[0069]

[0070] S5. Using the uniform design or Latin hypercube experimental method to generate a spatially uniform initial experimental scheme, setting the value range of all dimensions to be 0.0~1.0;

[0071] Further, the spatially uniform initial experimental scheme generated in step S5 is:

[0072]

[0073] wherein, X is a spatially uniform initial experimental scheme, is the first sample point​​ initial values of the dimensions.

[0074] S6. Based on the updated assignment group obtained in step S4, a specified value is assigned to each dimension of each sample in the spatially uniform initial experimental scheme obtained in step S5, to obtain an edge-encrypted experimental scheme for multidisciplinary optimization design.

[0075] Further, step S6 assigns a specified value to each dimension of each sample in the spatially uniform initial experimental scheme obtained in step S5, to obtain an edge-encrypted experimental scheme for multidisciplinary optimization design as follows:

[0076]

[0077]

[0078] wherein m is any one of the above, n is any one of the above, the specified value of the mth dimension of the nth sample point, the edge-encrypted experimental scheme for multidisciplinary optimization design, the value of the mth dimension of the nth sample point after the specified value is assigned, the edge-encrypted experimental scheme for multidisciplinary optimization design, the specified value of the mth dimension. The following implementation case is given taking the design of an experimental scheme for modeling data required for full-state prediction of aerodynamic characteristics of an aircraft as an example, to further illustrate the technical scheme of the present application. In this case, the number of required modeling data samples is 100, each sample has 3 dimensions, namely Mach number Ma, angle of attack AoA, and rudder deflection angle δ. The specified values of Ma are 0.3, 0.6, 0.8, 0.9, 1.2, 1.5, a total of 6, with left-end encryption required; the specified values of AoA are -4°~16°, with an interval of 2°, a total of 11, with left and right end encryption required; the specified values of δ are -30°~30°, with an interval of 10°, a total of 7, with right-end encryption required.

[0079] An initial assignment group is created for each dimension, and the results are shown in Table 1:

[0080] Table 1

[0081]

[0082] The group density is changed, Ma, AoA, and δ are left-end encryption, left and right end encryption, and right-end encryption respectively, and the relaxation factor is all taken as 0.5, and the results are shown in Table 2:

[0083] Table 2

[0084]

[0085] ​

[0086] Update the assignment group, update each point to , and obtain the assignment group as shown in Table 3:

[0087] Table 3

[0088]

[0089] An initial experiment scheme with uniform space is generated based on the uniform design method, as shown in Table 4:

[0090] Table 4

[0091]

[0092]

[0093] Each dimension of each sample in the initial experiment scheme is assigned a value according to the assignment group, and an evaluation scheme for aerodynamic edge encryption generated by the method of the present application is obtained, as shown in Table 5:

[0094] Table 5

[0095]

[0096] Figures 2-4 The number of data of different values of Ma, AoA and δ in the experiment scheme designed by the present application is respectively given, and it can be seen that the present application can design an experiment scheme according to a specified value, and can also consider the strong nonlinear edge characteristics of aerodynamic data to encrypt the aerodynamic data experiment scheme.

[0097] It should be noted that the relational terms, such as "first" and "second", and the like are used only to distinguish one entity or action from another, and do not necessarily require or imply any such actual relationship or order between or among the entities or actions. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not expressly listed, or other elements inherent in such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0098] Although the present application has been described with reference to the specific embodiments thereof, it should be understood by those skilled in the art that various changes can be made and equivalents can be substituted for elements thereof without departing from the scope of the present application. In particular, various features and aspects of the present application can be used individually or in any combination depending on the specific application and implementation. Therefore, it is expressly intended that the specific embodiments of the present application both as set forth and including any equivalents thereof should not limit the present application or scope of the claims herein, but rather the overall scope of pertaining solely to the methods and the articles of manufacture specifically recited in the following claims.

Claims

1. A method for edge-encrypted experimental design for multidisciplinary optimization design of aircraft, characterized in that, Includes the following steps: S1. According to the experimental design requirements, obtain the number of sample points in the evaluation scheme to be generated. Dimension of each sample point And specify the number of values ​​to be generated for each dimension; S2. For each dimension in the evaluation scheme to be generated obtained in step S1, create an initial assignment group; S3. Design an edge encryption method; S4. Use the edge encryption method designed in step S3 to update the initial assignment group obtained in step S2 to obtain the updated assignment group; S5. Generate a spatially uniform initial experimental scheme using a uniform design or Latin hypercube experimental method, setting all dimensions to a range of 0.0~1.0; S6. Based on the updated assignment grouping obtained in step S4, assign a specified value to each dimension of each sample in the spatially uniform initial experimental scheme obtained in step S5 to obtain an edge encryption experimental scheme for multidisciplinary optimization design.

2. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 1, characterized in that, In step S1, the number of values ​​to be generated for each dimension of the evaluation scheme to be generated is specified as follows: ,in Specify the number of values ​​to be generated for the nv-th dimension.

3. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 2, characterized in that, Step S2 creates initial assignment groups for each dimension, and assigns the first group to each dimension. Dimensions range from 0.0 to 1.

0. Segment, containing If there are 1 grouping points, then the value of each grouping point is... for: Where i is any one of nv, where j is any one of nv. For the first The number of segments in a dimension.

4. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 3, characterized in that, The edge encryption form of the edge encryption method designed in step S3 is as follows: in, For encryption control factors; It is a relaxation factor; Each dimension corresponds to a relaxation factor, when When the value is 0, the encryption method degenerates into a non-encryption method. This is a valid encryption.

5. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 4, characterized in that, The specific implementation method of step S4 includes the following steps: S4.

1. Update the value of each group point to obtain the updated value of each group point, expressed as: ； in, This is the updated value for each group of points; S4.

2. Scale the updated value of each group point to the range of 0.0~1.0, using the following expression: in, The value of each group of points after scaling; This is the value of the last group point before scaling, i.e., the maximum value of the group point; Then the updated assignment group is obtained as follows: 。 6. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 5, characterized in that, The initial experimental scheme with spatial uniformity generated in step S5 is as follows: Where X represents the initial experimental scheme with uniform spatial distribution. For the first The sample point of the th sample point The initial value of the dimension.

7. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 6, characterized in that, Step S6 assigns a specified value to each dimension of each sample in the spatially uniform initial experimental scheme obtained in step S5, resulting in the following edge encryption experimental scheme for multidisciplinary optimization design: Where m is For any one of them, n is Any one of them, For the first The sample point of the th sample point The value of a dimension after assigning a specified numerical value. For edge encryption experimental schemes used in multidisciplinary optimized design, For the first The dimension specifies a numerical value.

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