Method and device for analyzing uncertainty of aerodynamic data of waverider aircraft

Abstract

The invention provides a wave-rider aircraft aerodynamic data uncertainty analysis method and device, and the method comprises the steps: obtaining the numerical calculation aerodynamic data of a wave-rider aircraft; factors influencing the uncertainty of the numerical calculation pneumatic data are determined; carrying out uncertainty analysis on the basis of pneumatic data, calculating model uncertainty by adopting a range method, designing an orthogonal test according to factors, calculating numerical value and input parameter uncertainty, and carrying out significance analysis on factor influence and interaction according to the orthogonal test; and calculating the total uncertainty according to the model uncertainty, the numerical value and the input parameter uncertainty, and verifying the reliability of the total uncertainty by adopting new sample data. According to the method, the uncertainty of the pneumatic data is comprehensively and effectively obtained, meanwhile, the influence factors of the uncertainty of the numerical calculation pneumatic data and the significance of the interaction are visually obtained, and support is provided for the reliability of the numerical calculation pneumatic data.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method and apparatus for analyzing the uncertainty of aerodynamic data of a waverider aircraft. Background Technology

[0002] The aerodynamics of an aircraft is influenced by a combination of factors, including altitude, Mach number, angular velocity, angle of attack, sideslip angle, aerodynamic rudder, and control jet, and is highly nonlinear.

[0003] In related technologies, the fitting ability of traditional aerodynamic models using nonlinear polynomial models is limited. They require simplification of complex physical phenomena and have certain requirements for mesh accuracy and computational resources. Numerical calculations suffer from various error factors, including physical model errors, discretization errors of control equations and boundary conditions, stage errors caused by limitations in computer data storage word length, and convergence errors caused by judging convergence in iterative calculations. Furthermore, the Navier-Stokes equations solved by numerical simulations are strongly nonlinear equations, making it impossible to analyze the interaction laws between influencing factors. Therefore, the aerodynamic models of aircraft are inaccurate, and the aerodynamic data of aircraft obtained through numerical calculations contain certain errors.

[0004] Numerical calculation of aerodynamic data uncertainty is the foundation for conducting aerodynamic layout research, control system design and evaluation, flight trajectory design and virtual flight tests. It is of great significance for improving aircraft performance and flight safety. The uncertainty of aerodynamic data is an important basis for evaluating the reliability and usability of the aerodynamic model.

[0005] Based on the above analysis of the development status of this technology field, existing technologies lack a solution that simultaneously considers the uncertainty of input parameters, the uncertainty of the calculation model, the uncertainty of numerical values, as well as the significance analysis of the influence and interaction of factors. Summary of the Invention

[0006] The purpose of this invention is to provide a method and apparatus for analyzing the uncertainty of aerodynamic data of waverider aircraft, aiming to solve the above-mentioned problems in the prior art.

[0007] According to a first aspect of the present invention, a method for analyzing aerodynamic data uncertainty of a waverider aircraft is provided, comprising: Acquire numerical aerodynamic data for waverider aircraft; Identify the factors that affect the uncertainty of aerodynamic data in numerical calculations; Uncertainty analysis was performed based on aerodynamic data. The range method was used to calculate the model uncertainty. Orthogonal experiments were designed based on the factors to calculate the numerical and input parameter uncertainties. The significance of the influence and interaction of the factors was analyzed based on the orthogonal experiments. The total uncertainty is calculated based on the model uncertainty and the uncertainties of the numerical and input parameters, and the reliability of the total uncertainty is verified using new sample data.

[0008] According to a second aspect of the present invention, an apparatus for analyzing aerodynamic data uncertainty of a waverider aircraft is provided, comprising: The initial calculation module is used to acquire aerodynamic data for numerical calculation of waverider aircraft; The factor determination module is used to determine the factors that affect the uncertainty of aerodynamic data in numerical calculations. The uncertainty analysis module is used to perform uncertainty analysis based on aerodynamic data. It uses the range method to calculate model uncertainty, designs orthogonal experiments based on factors, calculates numerical and input parameter uncertainties, and performs significance analysis of factor influence and interaction based on orthogonal experiments. The validation module is used to calculate the total uncertainty based on the model uncertainty and the uncertainties of the numerical and input parameters, and to verify the reliability of the total uncertainty using new sample data.

[0009] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the waverider aerodynamic data uncertainty analysis method provided in the first aspect of the present disclosure.

[0010] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which an information transmission implementation program is stored, which, when executed by a processor, implements the steps of the waverider aerodynamic data uncertainty analysis method provided in the first aspect of the present disclosure.

[0011] The technical solution provided by the embodiments of the present invention has the following beneficial effects: by analyzing the main sources of uncertainty in numerical aerodynamic data, a comprehensive analysis scheme is determined, including the analysis of model uncertainty and numerical and input parameter uncertainty. The essential differences between the calculation model and the real physical system are measured respectively, as well as the range of solution variation caused by uncertainty. While comprehensively and effectively obtaining the uncertainty of aerodynamic data, the significance of the influencing factors and interactions of the uncertainty of numerical aerodynamic data is intuitively obtained, thus providing support for the reliability of numerical aerodynamic data.

[0012] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the aerodynamic data uncertainty analysis method for waverider aircraft according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the relative importance of significant influencing factors in embodiments of the present invention; Figure 3 This is a schematic diagram of the verification points and uncertainty range of an embodiment of the present invention; Figure 4 This is a schematic diagram of the aerodynamic data uncertainty analysis device for waverider aircraft according to an embodiment of the present invention; Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0016] Method Implementation Examples According to embodiments of the present invention, a method for analyzing the uncertainty of aerodynamic data of a waverider aircraft is provided. Figure 1 This is a flowchart of the aerodynamic data uncertainty analysis method for waverider aircraft according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method for analyzing aerodynamic data uncertainty of a waverider aircraft according to an embodiment of the present invention specifically includes: In step S110, the numerical calculation aerodynamic data of the waverider aircraft are obtained, specifically including: A mesh is drawn based on the shape of the waverider aircraft. The aerodynamic coefficients and aerodynamic moment coefficients of the waverider aircraft are obtained through numerical calculation. The axial force coefficient and normal force coefficient are selected from the aerodynamic coefficients, and the pitching moment coefficient is selected from the aerodynamic moment coefficients. Axial force coefficient, normal force coefficient, and pitching moment coefficient are used as aerodynamic data.

[0017] Choosing other types of data is also within the scope of protection of this invention.

[0018] In step S120, the factors affecting the uncertainty of the numerical calculation aerodynamic data are determined, specifically including: The calculation of the shape, state, and numerical calculation method of the waverider aircraft was analyzed to identify the factors affecting the uncertainty of the numerical calculation aerodynamic data. The identified factors are grid density, turbulence model, boundary conditions, and flight altitude.

[0019] In step S130, uncertainty analysis is performed based on aerodynamic data. The range method is used to calculate the model uncertainty. Orthogonal experiments are designed according to the factors, and the numerical and input parameter uncertainties are calculated. The significance analysis of the influence and interaction of factors is performed based on the orthogonal experiments, specifically including: In this embodiment of the invention, the uncertainty analysis method is determined by analyzing the sources of uncertainty in numerically calculated aerodynamic data. There are three main sources of uncertainty in numerically calculated aerodynamic data: input parameter uncertainty, calculation model uncertainty, and numerical uncertainty.

[0020] Model uncertainty refers to the uncertainty caused by the assumptions and simplifications made during model construction, which is determined by the architecture of the physical model. The physical model parameters are the parameters contained in the model architecture. In order to quantify model uncertainty, the model parameters must be fixed, and only one set of model parameters can be selected to quantify model uncertainty.

[0021] Numerical uncertainty mainly includes spatial discretization error, temporal discretization error, and iteration error. Estimating spatial discretization error typically requires designing multiple meshes, consuming significant computer resources for analysis.

[0022] Different models may exist to describe the same physical phenomenon. For example, changes in different turbulence models can have a significant impact on the calculation results, such as the setting of flight altitude and boundary conditions.

[0023] (1) Model uncertainty Quantification was achieved by comparing computational and experimental data, using data analysis methods from process statistical control. , , ;in, These are the mean and squared difference of the systematic bias; Therefore, the forms of Formula 1 and Formula 2 are derived. Obtain aerodynamic data from numerical simulation experiments of current waverider aircraft, and calculate the standard deviation using Formula 1. The model uncertainty is calculated using the range method based on Formula 2. : Formula 1; Formula 2; in, This represents the calculated aerodynamic data. This represents the aerodynamic data from the simulation experiment. The correction coefficients depend on the number of data points. This represents the coverage factor, and different confidence levels correspond to different values ​​of the coverage factor. When coverage factor When the coverage factor is set to 1, the confidence level of the uncertainty is 68%; when it is set to 2, the confidence level is 95%; and when it is set to 3, the confidence level is 99.73%. By selecting different coverage factors, uncertainties at different confidence levels can be obtained.

[0024] Preferably, if multiple experimental and numerical calculation data exist, Formula 1 can be used. , This represents the maximum difference within a group. .

[0025] The uncertainty of aerodynamic data for waverider aircraft generally does not consider the influence of random parameters. In second-order Monte Carlo simulations, the inner iterations disappear, and only the outer cognitive uncertainty parameters are simulated. By employing orthogonal experimental design from mathematical statistics, the number of samples to be simulated can be greatly reduced, and the interaction of uncertainties from different sources is considered, resulting in more reliable uncertainty results.

[0026] (2) Numerical and input parameter uncertainty The corresponding horizontal influence values ​​were set for grid density, turbulence model, boundary conditions and flight altitude. The axial force coefficient, normal force coefficient and pitching moment coefficient corresponding to the preset group of samples were obtained by numerical calculation and used as orthogonal verification data. In this embodiment of the invention, the grid density is influenced by 3 horizontal factors, the turbulence model by 2 horizontal factors, the boundary conditions by 3 horizontal factors, the flight altitude by 3 horizontal factors, and the horizontal influence values ​​are the specific parameter settings under the horizontal number. A total of 36 sets of combined samples are used to perform numerical calculations to obtain the axial force coefficient, normal force coefficient, and pitching moment coefficient. The current 36 sets of samples may include the aerodynamic data from the initial solution; The current orthogonal array is used to calculate numerical and input parameter uncertainties. The total number of trials is n, and the experimental results are assumed to be... The total variance of the experiment is shown below:

[0027] The calculation results were processed using standard statistical methods to obtain the numerical and input parameter uncertainties. Formula 3 was then used to calculate the numerical and input parameter uncertainties. : Formula 3; in, Indicates coverage factor. This indicates the number of samples in the preset group. This represents the average of all experimental results in the orthogonal validation data. This represents the corresponding value of the orthogonal validation data.

[0028] When coverage factor When the value is 1, the confidence level of the uncertainty is 68%; when the value is 2, the confidence level of the uncertainty is 95%; and when the value is 3, the confidence level of the uncertainty is 99.73%. In this embodiment of the invention, =3.

[0029] (3) Significance analysis Orthogonal arrays can also be used to study each factor of uncertainty and whether the pairwise interactions are significant; for example, orthogonal arrays can be used to study the interactions between three factors A, B, and C and AB, BC, and AC, with the 7th column not arranged as a random error column. Construct an orthogonal array for significance analysis; Place factors and second-order interaction factors at the top of the orthogonal array. Place grid density, turbulence model, grid density × turbulence model, boundary condition, grid density × boundary condition, turbulence model × boundary condition, height, grid density × height, turbulence model × height, and boundary condition × height in columns 1-10 of the orthogonal array, respectively. Set a random error column e in the last column of the orthogonal array, and set it to 3 levels. The number of levels for each factor is fixed, let's assume... As factors The level number, the first One factor (i.e.) (Column) i Levels (i.e.) i If the number of occurrences of line () is s, then ; No. The sum of squared deviations of the factors is: ,by Indicator Factors of i Formula 4 can be derived from the sum of the results of the horizontal tests; Using Formula 4 to represent the first Sum of squared deviations of the column : Formula 4; in, Indicates the first The number of levels for each factor indicates the number of factors influencing that level. This indicates the number of samples in the preset group. Indicator Factors of The sum of experimental results under the horizontal influence value, This represents the sum of the test results; Once the orthogonal array is completed, the sum of squared deviations for each column can be calculated. .

[0030] Compare the sum of squared deviations in each column. With random error column If it exists Then the corresponding sum of squared deviations in that column will be included in the sum of squared errors, which means all of these will be included. Incorporating the sum of squared errors Among them, it became ; In the embodiments of the present invention That is, the effects of factors or interactions in columns 4, 5, 8, 9, and 10 are not significant, and the sum of squared errors is: ; Calculate the hypothesis test statistic using Formula 5. : Formula 5; in, Indicates the first Subtract 1 from the number of levels in the column. This represents the number of levels in the random error column minus 1; the number of levels minus 1 represents the degrees of freedom. This represents the sum of squares of all deviations that satisfy the conditions, combined with the sum of squares of the errors. Indicates distribution, Describing the degrees of freedom as The F-distribution; because ,therefore ; Compare the hypothesis test statistic with the critical value in the distribution table. contrast, The distribution is given by looking up the table. The value was set to 0.01, and the significance analysis results were obtained.

[0031] The comparison shows that the effects of grid density, turbulence model, interaction between grid density and turbulence model, interaction between turbulence model and boundary conditions, and altitude on the uncertainty of the axial force coefficient of the flight state are significant. Among these, the influence of turbulence model is obvious, while the coupling effect of altitude is very small and can be ignored.

[0032] In step S140, the total uncertainty is calculated based on the model uncertainty and the uncertainties of the numerical and input parameters, and the reliability of the total uncertainty is verified using new sample data. Specifically, this includes: Using orthogonal experimental design in the measurement of input parameter uncertainty can ensure that various factors are fully combined, and the simulation results can be directly processed to obtain the total input parameter uncertainty. The interaction law between various factors has been implicitly considered.

[0033] Formula 6 is used to combine the model uncertainty and the numerical and input parameter uncertainties to obtain the total uncertainty. : Formula 6; in, Indicates the model uncertainty. This indicates the uncertainty of the numerical values ​​and input parameters.

[0034] Within a given measurement error range, new sample data is acquired. The significance of the influencing factors and interactions of aerodynamic data uncertainty is calculated based on numerical values. In this embodiment, 10 sets of numerical values ​​and input parameter combinations with grid density level 3, boundary condition level 2, and height level 2 are selected for calculation to obtain aerodynamic data. All 10 sets of numerically calculated aerodynamic data fell within the uncertainty range, and the confidence level of the uncertainty of this invention was 99.73%, verifying the reliability of the numerical calculation uncertainty of this invention.

[0035] The above technical solutions of the embodiments of the present invention will be illustrated with reference to the following accompanying drawings.

[0036] Figure 2 This is a schematic diagram illustrating the relative importance of significant influencing factors in embodiments of the present invention, such as... Figure 2 As shown, the results of the significant factor F value of the uncertainty of the CA axial force coefficient, CN normal force coefficient, and Cmz pitching moment coefficient are presented. Figure 3 This is a schematic diagram of the verification points and uncertainty range of an embodiment of the present invention, as shown below. Figure 3 As shown, the corresponding verifications all fall within the corresponding range.

[0037] In summary, to address the existing problems, this invention presents a method for analyzing the uncertainty of aerodynamic data for waverider aircraft. By analyzing the main sources of uncertainty in numerically calculated aerodynamic data, a comprehensive analysis scheme is determined, including the analysis of model uncertainty and the uncertainty of numerical and input parameters. This method measures the essential differences between the computational model and the real physical system, as well as the range of solution variation caused by uncertainty. While comprehensively and effectively obtaining the uncertainty of aerodynamic data, it also intuitively reveals the significance of the influencing factors and interactions of the uncertainty in numerically calculated aerodynamic data, thus providing support for the reliability of numerically calculated aerodynamic data.

[0038] Device Examples According to an embodiment of the present invention, an apparatus for analyzing aerodynamic data uncertainty of a waverider aircraft is provided. Figure 4 This is a schematic diagram of the aerodynamic data uncertainty analysis device for waverider aircraft according to an embodiment of the present invention, as shown below. Figure 4 As shown, the aerodynamic data uncertainty analysis device for waverider aircraft according to an embodiment of the present invention specifically includes: The initial calculation module 40 is used to acquire the aerodynamic data for numerical calculation of the waverider vehicle, specifically for: A mesh is drawn based on the shape of the waverider aircraft. The aerodynamic coefficients and aerodynamic moment coefficients of the waverider aircraft are obtained through numerical calculation. The axial force coefficient and normal force coefficient are selected from the aerodynamic coefficients, and the pitching moment coefficient is selected from the aerodynamic moment coefficients. Axial force coefficient, normal force coefficient, and pitching moment coefficient are used as aerodynamic data.

[0039] Factor determination module 42 is used to determine the factors affecting the uncertainty of aerodynamic data in numerical calculations, specifically for: The determining factors are grid density, turbulence model, boundary conditions, and flight altitude.

[0040] Uncertainty analysis module 44 is used for uncertainty analysis based on aerodynamic data. It calculates model uncertainty using the range method, designs orthogonal experiments based on factors, calculates numerical and input parameter uncertainties, and performs significance analysis of factor influences and interactions based on the orthogonal experiments. Specifically, it is used for: Obtain aerodynamic data from numerical simulation experiments of current waverider aircraft, and calculate the standard deviation using Formula 1. The model uncertainty is calculated using the range method based on Formula 2. : Formula 1; Formula 2; in, This represents the calculated aerodynamic data. This represents the aerodynamic data from the simulation experiment. Indicates the correction factor. This represents the coverage factor, and different confidence levels correspond to different values ​​of the coverage factor.

[0041] The corresponding horizontal influence values ​​were set for grid density, turbulence model, boundary conditions and flight altitude. The axial force coefficient, normal force coefficient and pitching moment coefficient corresponding to the preset group of samples were obtained by numerical calculation and used as orthogonal verification data. Calculate the numerical and input parameter uncertainties using Formula 3. : Formula 3; in, Indicates coverage factor. This indicates the number of samples in the preset group. This represents the average of all experimental results in the orthogonal validation data. This represents the corresponding value of the orthogonal validation data.

[0042] Construct an orthogonal array for significance analysis; Place the factors and second-order interaction factors at the top of the orthogonal array, and set a random error column in the last column of the orthogonal array; Using Formula 4 to represent the first Sum of squared deviations of the column : Formula 4; in, Indicates the first The number of levels of the factors, This indicates the number of samples in the preset group. Indicator Factors of The sum of experimental results under the horizontal influence value, This represents the sum of the test results; If it exists Then, the corresponding sum of squared deviations in this column is included in the sum of squared errors, and the test hypothesis statistic is calculated using Formula 5. : Formula 5; in, Indicates the first Subtract 1 from the number of levels in the column. This indicates that the number of levels in the random error column is reduced by 1. This represents the sum of squares of all deviations that satisfy the conditions, combined with the sum of squares of the errors. Indicates distribution, Describing the degrees of freedom as The F-distribution; The significance analysis results are obtained by comparing the hypothesis test statistic with the critical value in the distribution table.

[0043] The verification module 46 is used to calculate the total uncertainty based on the model uncertainty and the uncertainties of the numerical and input parameters, and to verify the reliability of the total uncertainty using new sample data. Specifically, it is used for: Formula 6 is used to combine the model uncertainty and the numerical and input parameter uncertainties to obtain the total uncertainty. : Formula 6; in, Indicates the model uncertainty. This indicates the uncertainty of the numerical values ​​and input parameters.

[0044] In summary, to address the existing problems, this invention provides an aerodynamic data uncertainty analysis device for waverider aircraft. By analyzing the main sources of uncertainty in numerically calculated aerodynamic data, a comprehensive analysis scheme is determined, including the analysis of model uncertainty and the uncertainty of numerical and input parameters. This assesses the essential differences between the computational model and the real physical system, as well as the range of solution variation caused by uncertainty. While comprehensively and effectively obtaining the aerodynamic data uncertainty, it also intuitively reveals the significance of the influencing factors and interactions of the uncertainty in numerically calculated aerodynamic data, thus providing support for the reliability of numerically calculated aerodynamic data.

[0045] Electronic device examples Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 500 may include at least one processor 510 and a memory 520. The processor 510 can execute instructions stored in the memory 520. The processor 510 is communicatively connected to the memory 520 via a data bus. In addition to the memory 520, the processor 510 can also be communicatively connected to an input device 530, an output device 540, and a communication device 550 via the data bus.

[0046] Processor 510 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0047] The memory 520 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0048] In this embodiment of the present disclosure, the memory 520 stores executable instructions, and the processor 510 can read the executable instructions from the memory 520 and execute the instructions to implement all or part of the steps of the waverider aerodynamic data uncertainty analysis method in any of the exemplary embodiments described above.

[0049] Computer-readable storage medium embodiments In addition to the methods and apparatus described above, exemplary embodiments of this disclosure may also be a computer program product or a computer-readable storage medium storing the computer program product, the computer product including computer program instructions that can be executed by a processor to implement all or part of the steps described in any of the waverider aerodynamic data uncertainty analysis methods in the exemplary embodiments described above.

[0050] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. Programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages, and scripting languages ​​(e.g., Python). The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0051] Computer-readable storage media may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media include: static random access memory (SRAM) having one or more electrically connected wires; electrically erasable programmable read-only memory (EEPROM); erasable programmable read-only memory (EPROM); programmable read-only memory (PROM); read-only memory (ROM); magnetic storage; flash memory; magnetic disk or optical disk; or any suitable combination thereof.

[0052] Finally, it should be noted that: the contents not described in detail in this specification are common knowledge to those skilled in the art; the above embodiments are only used to illustrate the technical solutions of this invention, and not to limit it; although this invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this invention.

Claims

1. A method for analyzing the uncertainty of aerodynamic data of a waverider aircraft, characterized in that, include: Acquire numerical aerodynamic data for waverider aircraft; Identify the factors that affect the uncertainty of aerodynamic data in numerical calculations; Uncertainty analysis was performed based on the aerodynamic data, and the model uncertainty was calculated using the range method. Orthogonal experiments were designed based on the factors to calculate the numerical and input parameter uncertainties. The significance of the influence and interaction of the factors was analyzed based on the orthogonal experiments. The total uncertainty is calculated based on the model uncertainty and the numerical and input parameter uncertainties, and the reliability of the total uncertainty is verified using new sample data.

2. The method according to claim 1, characterized in that, The acquisition of numerical calculation aerodynamic data for waverider aircraft specifically includes: A mesh is drawn based on the shape of the waverider aircraft. The aerodynamic coefficients and aerodynamic moment coefficients of the waverider aircraft are obtained by numerical calculation. The axial force coefficient and normal force coefficient are selected from the aerodynamic coefficients, and the pitching moment coefficient is selected from the aerodynamic moment coefficients. The axial force coefficient, the normal force coefficient, and the pitching moment coefficient are used as aerodynamic data.

3. The method according to claim 1, characterized in that, The factors that affect the uncertainty of numerical aerodynamic data include grid density, turbulence model, boundary conditions, and flight altitude.

4. The method according to claim 1, characterized in that, The calculation of model uncertainty using the range method specifically includes: Obtain aerodynamic data from numerical simulation experiments of current waverider aircraft, and calculate the standard deviation using Formula 1. The uncertainty of the model is calculated using the range method according to Formula 2. : Official 1; Official 2; in, This represents the calculated aerodynamic data. This represents the aerodynamic data from the simulation experiment. Indicates the correction factor. This represents the coverage factor, and different confidence levels correspond to different values ​​of the coverage factor.

5. The method according to claim 1, characterized in that, The design of orthogonal experiments based on the aforementioned factors, and the calculation of numerical and input parameter uncertainties, specifically include: The corresponding horizontal influence values ​​were set for grid density, turbulence model, boundary conditions and flight altitude. The axial force coefficient, normal force coefficient and pitching moment coefficient corresponding to the preset group of samples were obtained by numerical calculation and used as orthogonal verification data. Calculate the numerical and input parameter uncertainties using Formula 3. : Official 3; in, Indicates coverage factor. This indicates the number of samples in the preset group. This represents the average of all experimental results in the orthogonal validation data. This represents the corresponding value of the orthogonal validation data.

6. The method according to claim 1, characterized in that, The significance analysis of factor influences and interactions based on the orthogonal experiment specifically includes: Construct an orthogonal array for significance analysis; Place the factors and second-order interaction factors at the top of the orthogonal array, and set a random error column in the last column of the orthogonal array; Using Formula 4 to represent the first Sum of squared deviations of the column : Official 4; in, Indicates the first The number of levels of the factors, This indicates the number of samples in the preset group. Indicator Factors of The sum of experimental results under the horizontal influence value, This represents the sum of the test results; If it exists , If the column represents a random error, then the sum of squared deviations corresponding to that column is included in the sum of squared errors, and the test hypothesis statistic is calculated using Formula 5. : Official 5; in, Indicates the first Subtract 1 from the number of levels in the column. This indicates that the number of levels in the random error column is reduced by 1. This represents the sum of squares of all deviations that satisfy the conditions, combined with the sum of squares of the errors. Indicates distribution, Describing the degrees of freedom as The F-distribution; The significance analysis results are obtained by comparing the test hypothesis statistic with the critical value in the distribution table.

7. The method according to claim 1, characterized in that, The calculation of the total uncertainty based on the model uncertainty and the numerical and input parameter uncertainties specifically includes: Using Formula 6, the model uncertainty and the numerical and input parameter uncertainties are combined to obtain the total uncertainty. : Official 6; in, Indicates the model uncertainty. This indicates the uncertainty of the numerical values ​​and input parameters.

8. A device for analyzing aerodynamic data uncertainty of a waverider aircraft, characterized in that, include: The initial calculation module is used to acquire aerodynamic data for numerical calculation of waverider aircraft; The factor determination module is used to determine the factors that affect the uncertainty of aerodynamic data in numerical calculations. The uncertainty analysis module is used to perform uncertainty analysis based on the aerodynamic data, calculate the model uncertainty using the range method, design orthogonal experiments based on the factors, calculate the numerical and input parameter uncertainties, and perform significance analysis of the influence and interaction of factors based on the orthogonal experiments. The verification module is used to calculate the total uncertainty based on the model uncertainty and the uncertainty of the numerical values ​​and input parameters, and to verify the reliability of the total uncertainty using new sample data.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for analyzing the aerodynamic data uncertainty of a waverider aircraft as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an implementation program for information transmission, which, when executed by a processor, implements the steps of the method for analyzing the aerodynamic data uncertainty of a waverider aircraft as described in any one of claims 1 to 7.

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