An edge-encrypted experimental design method for aircraft multidisciplinary optimization design

Abstract

An edge encryption experimental design method for aircraft multidisciplinary optimization design belongs to the technical field of aircraft design and optimization. In order to solve the problem of obtaining higher precision prediction data under the condition that the total amount of modeling data is unchanged, the number of sample points, the dimension of each sample point and the number of specified values to be generated in the generated evaluation scheme are obtained according to the experimental design requirements; for each dimension in the generated evaluation scheme, an initial assignment group is created; an edge encryption method is designed; the initial assignment group is updated by using the edge encryption method to obtain an updated assignment group; a space-uniform initial experimental scheme is generated by using the uniform design or Latin hypercube experimental method, and the value range of all dimensions is set to 0.0~1.0; based on the updated assignment group, each dimension of each sample in the space-uniform initial experimental scheme is assigned with a specified value, and an edge encryption experimental scheme for multidisciplinary optimization design is obtained.

Description

Technical Field

[0001] This invention belongs to the field of aircraft design and optimization technology, specifically relating to an edge-encrypted experimental design method for multidisciplinary optimization design of aircraft. Background Technology

[0002] With the rapid development of aerospace technology, traditional aircraft design methods involving individual disciplines and iterative iterations are no longer sufficient to meet the increasingly stringent performance requirements of various aircraft. Multidisciplinary coupled optimization design technology has emerged to address this need. This technology systematically considers the comprehensive performance of multiple disciplines, including aerodynamics, structure, and control, during the aircraft design process. It unifies planning to resolve performance conflicts between disciplines, thereby significantly improving the overall performance level of the aircraft and effectively avoiding iterative processes between disciplines, thus increasing design efficiency. The core factors influencing the efficiency and effectiveness of multidisciplinary coupled optimization design for aircraft lie in the performance evaluation of each discipline, the multidisciplinary coupling model, and efficient optimization algorithms for interdisciplinary parameters.

[0003] In multidisciplinary coupled optimization design of aircraft, the performance evaluation of each discipline has different data requirements. Some disciplines, such as control and flight mechanics, require large amounts of aerodynamic data (in the thousands to tens of thousands), which restricts the integrated design of these disciplines during multidisciplinary coupled optimization design. A small amount of aerodynamic data can be used to predict full-state aerodynamic data, thereby reducing the data requirements for control, flight mechanics, and other disciplines. For example, when designing a control system, using 20% ​​of the required aerodynamic data combined with machine learning algorithms to predict full-state aerodynamic data can reduce aerodynamic data by 80%, significantly improving the efficiency of control system design. However, to ensure the engineering usability of the predicted data, the accuracy of the predicted data must meet the design requirements of each discipline. The selection of modeling data (a small amount of data) is a crucial step in obtaining high-precision predicted data. Current technologies employ modern experimental design methods for modeling data selection, such as Latin hypercube and uniform design. This type of experimental design emphasizes spatial uniformity and continuous experimental data, making it unsuitable for aerodynamic data, which requires specifying numerical values ​​for generating experimental schemes. For example, aerodynamic data may require Mach numbers of 0.3, 0.6, 0.8, and 0.9, with angles of attack ranging from 0° to 20° in 2° increments. Furthermore, to improve the accuracy of aerodynamic modeling and prediction, it is necessary to densify the data edges while maintaining a constant total amount of modeling data. Modern experimental design methods cannot achieve this independent densification of data edges. Summary of the Invention

[0004] The problem this invention aims to solve is to obtain more accurate prediction data while keeping the total amount of data used for modeling constant. It proposes an edge-encrypted experimental design method for multidisciplinary optimization design of aircraft.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An edge-encrypted experimental design method for multidisciplinary optimization design of aircraft includes the following steps:

[0007] 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;

[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. 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;

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

[0012] 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.

[0013] Furthermore, 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.

[0014] Furthermore, step S2 creates initial assignment groups for each dimension, and assigns the first... 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:

[0015]

[0016] Where i is any one of nv, where j is any one of nv. For the first The number of segments in a dimension.

[0017] Furthermore, the edge encryption form of the edge encryption method designed in step S3 is as follows:

[0018]

[0019] in, For encryption control factors; It is a relaxation factor;

[0020] 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.

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

[0022] S4.1. Update the value of each group point to obtain the updated value of each group point, expressed as:

[0023] ;

[0024] in, This refers to the updated point value for each group;

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

[0026]

[0027] 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;

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

[0029] .

[0030] Furthermore, the spatially uniform initial experimental scheme generated in step S5 is as follows:

[0031]

[0032] 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.

[0033] Furthermore, in step S6, a specified value is assigned 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:

[0034]

[0035]

[0036] 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 optimization design, For the first The dimension specifies a numerical value.

[0037] The beneficial effects of this invention are:

[0038] The present invention provides an edge-encryption experimental design method for multidisciplinary optimization design of aircraft, which can generate discontinuous data and design experimental schemes based on specified values. The present invention considers the strong nonlinear characteristics of aerodynamic data edges and encrypts the aerodynamic data edges while ensuring that the number of experimental design sample points remains unchanged, thereby effectively improving the accuracy of aircraft modeling and prediction data. Attached Figure Description

[0039] Figure 1 This is a flowchart of an edge encryption experimental design method for multidisciplinary optimization design of aircraft, as described in this invention.

[0040] Figure 2 This is a statistical chart showing the number of specified numerical data of different Ma dimensions in the experimental scheme designed using this invention;

[0041] Figure 3 This is a statistical chart showing the number of specified numerical data points of different AoA dimensions in the experimental scheme designed using this invention;

[0042] Figure 4 This is a statistical chart showing the number of specified numerical data of different δ dimensions in the experimental scheme designed using this invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0044] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0045] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 - Appendix Figure 4 Detailed explanation is as follows:

[0046] Example 1:

[0047] An edge-encrypted experimental design method for multidisciplinary optimization design of aircraft includes the following steps:

[0048] 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;

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

[0050] Furthermore, 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.

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

[0052] Furthermore, step S2 creates initial assignment groups for each dimension, and assigns the first... 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:

[0053]

[0054] Where i is any one of nv, where j is any one of nv. For the first The number of segments in a dimension.

[0055] S3. Design an edge encryption method;

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

[0057]

[0058] in, For encryption control factors; It is a relaxation factor;

[0059] 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.

[0060] 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;

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

[0062] S4.1. Update the value of each group point to obtain the updated value of each group point, expressed as:

[0063] ;

[0064] in, This refers to the updated point value for each group;

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

[0066]

[0067] 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;

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

[0069] .

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

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

[0072]

[0073] 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.

[0074] 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.

[0075] Furthermore, in step S6, a specified value is assigned 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:

[0076]

[0077]

[0078] 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 optimization design, For the first The dimension specifies a numerical value.

[0079] Taking the experimental design of the modeling data required for the full-state prediction of the aerodynamic characteristics of a certain aircraft as an example, the following implementation case is given to further illustrate the technical solution of the present invention. In this case, the required number of modeling data samples is 100, and each sample has 3 dimensions, namely Mach number Ma, angle of attack AoA, and rudder deflection angle δ. The specified values ​​for Ma are 0.3, 0.6, 0.8, 0.9, 1.2, and 1.5, a total of 6, requiring encryption on the left side; the specified values ​​for AoA are -4° to 16°, with an interval of 2°, a total of 11, requiring encryption on both the left and right sides; the specified values ​​for δ are -30° to 30°, with an interval of 10°, a total of 7, requiring encryption on the right side.

[0080] Initial assignment groups were created for each dimension, and the results are shown in Table 1:

[0081] Table 1

[0082]

[0083] By changing the group density, Ma, AoA, and δ represent left-end encryption, left-right-end encryption, and right-end encryption, respectively, with a relaxation factor of 0.5 for each, the results are shown in Table 2.

[0084] Table 2

[0085]

[0086] Update the assigned group, updating each point to... The assigned groups are shown in Table 3:

[0087] Table 3

[0088]

[0089] The initial experimental schemes generated based on the uniform design method are shown in Table 4:

[0090] Table 4

[0091]

[0092]

[0093] Each dimension of each sample in the initial experimental scheme was assigned a specified value according to the assigned value grouping, and the evaluation scheme for aerodynamic edge encryption generated by the method of the present invention is shown in Table 5:

[0094] Table 5

[0095]

[0096] Figures 2-4 The statistical data of different values ​​of Ma, AoA, and δ in the experimental scheme designed using the present invention are given respectively. It can be seen that the present invention can design experimental schemes according to specified values, and at the same time, it can consider the strong nonlinear characteristics of aerodynamic data edges and perform edge encryption on aerodynamic data experimental schemes.

[0097] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0098] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the 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 the number of values ​​to be generated for each dimension, wherein the sample is aircraft aerodynamic data; S2. For each dimension in the evaluation scheme to be generated obtained in step S1, create an initial assignment group; 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; S3. Design an edge encryption method; 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. 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 value 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, 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 refers to the updated point value for each group; 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: 。 4. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 3, 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.

5. The edge-encrypted experimental design method for multidisciplinary optimization design of aircraft according to claim 4, 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 being assigned a specified numerical value. For edge encryption experimental schemes used in multidisciplinary optimization design, For the first The dimension specifies a numerical value.

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