A quantitative evaluation method and system for uncertainty measurement of multi-source aerodynamic data

Abstract

This invention provides a method for quantitatively evaluating the uncertainty of multi-source aerodynamic data, comprising: selecting benchmark data and determining the uncertainty of aerodynamic data for several sets of wind tunnel test data generated due to different wind tunnels or model scales; calculating benchmark data and determining the uncertainty of aerodynamic data for several sets of numerical simulation data generated due to different computational software or computational grids; fusing wind tunnel test data and numerical simulation data to determine the nominal value of multi-source aerodynamic data; and performing multi-source aerodynamic data uncertainty quantification to obtain the uncertainty quantification result of multi-source startup data. This method can be widely applied to the design of aerodynamic characteristic deviation zones for hypersonic vehicles. By combining flight environment profiles and comprehensively considering wind tunnel test data and numerical simulation data, it provides an aerodynamic database using a benchmark data correction + aerodynamic data uncertainty quantification approach.

Description

Technical Field

[0001] This invention relates to the field of aerodynamic design technology for hypersonic aircraft, and in particular to a quantitative evaluation method for uncertainty in multi-source aerodynamic data. Background Technology

[0002] The foundation and prerequisite for designing high-performance aircraft is the accurate prediction of their aerodynamic characteristics. Acquiring these characteristics typically relies on three methods: wind tunnel testing, numerical calculation, and flight testing. Wind tunnel testing offers a wealth of measurement methods but suffers from inconsistencies between the aircraft and the ground, such as model scaling and interference from tunnel walls and support structures. Numerical calculation is more economical and provides comprehensive flow information, but it lacks confidence assessment for complex flow models. Flight testing is the fundamental solution to the inconsistency between the aircraft and the ground, but due to the limited number of onboard measurement sensors, the amount of information obtained is far less than that from wind tunnel testing and numerical calculation. Therefore, the most economical and effective method is to leverage the advantages of each method, establishing a closed-loop aerodynamic research system that integrates them. This allows for mutual verification and promotion, achieving the goal of integrating aerodynamic performance prediction with aerodynamic problem research, effectively saving development costs and improving development efficiency. Summary of the Invention

[0003] The technical problem solved by this invention is: This invention provides a quantitative evaluation method for uncertainty of multi-source aerodynamic data, which comprehensively considers wind tunnel test and numerical simulation results, and corrects and quantifies the differences between air and ground, so as to reasonably and accurately give the deviation band of aerodynamic characteristics of the aircraft.

[0004] The technical solution adopted in this invention is: a quantitative evaluation method for uncertainty in multi-source aerodynamic data, comprising:

[0005] For several sets of wind tunnel test data generated due to different wind tunnels or different model scales, select the benchmark data and determine the uncertainty of the aerodynamic data;

[0006] For several sets of numerical simulation data generated due to different calculation software or different calculation grids, calculate the baseline data and determine the uncertainty of the aerodynamic data;

[0007] By fusing wind tunnel test data and numerical simulation data, the nominal values ​​of multi-source aerodynamic data are determined.

[0008] Uncertainty quantification of multi-source aerodynamic data is performed to obtain the uncertainty quantification results of multi-source start-up data.

[0009] Furthermore, for several sets of wind tunnel test data generated due to different wind tunnels or different model scales, the wind tunnel test data that is closest to the real flight environment is selected as the benchmark data by comparing the Reynolds numbers.

[0010] Furthermore, for several sets of wind tunnel test data generated due to different wind tunnels or different model scales, the determination of the uncertainty of the aerodynamic data includes: when the amount of wind tunnel test data is greater than a sets, the standard deviation of multiple sets of wind tunnel test data is used as the uncertainty of the aerodynamic data; when the amount of wind tunnel test data is less than or equal to a sets, the maximum residual method is used to calculate the uncertainty of multiple sets of wind tunnel test data; a is a set value.

[0011] Furthermore, for several sets of numerical simulation data generated due to different computing software or different computing grids, the average value of all numerical simulation data is used as the benchmark data.

[0012] Furthermore, for several sets of numerical simulation data generated due to different computational software or different computational grids, the determination of the uncertainty of the aerodynamic data includes:

[0013] When the numerical simulation data is greater than b sets, the standard deviation of the numerical simulation data is used as the uncertainty of the aerodynamic data; when the numerical simulation data is less than or equal to b sets, the maximum residual method is used to calculate the uncertainty, where b is a set value.

[0014] Furthermore, the process of fusing wind tunnel test data and numerical simulation data to determine the nominal values ​​of multi-source aerodynamic data includes:

[0015] Determine the baseline data and select the wind tunnel test data that is closest to the flight environment as the initial nominal value;

[0016] Considering the differences between the sky and the ground, the initial nominal values ​​are corrected, and the corrected data is used as the nominal values ​​for the multi-source aerodynamic data.

[0017] Furthermore, the process of quantifying the uncertainty of multi-source aerodynamic data includes:

[0018] Limiting envelopes are applied to the uncertainty ranges of wind tunnel test data and numerical simulation data to obtain a data envelopment band. The data envelopment band is then compared with existing empirical values ​​of aerodynamic deviations. If the relative difference between the data envelopment band and the existing empirical values ​​of aerodynamic deviations is within 5%, the data envelopment band is taken as the quantitative result of the uncertainty of multi-source aerodynamic data.

[0019] A multi-source aerodynamic data uncertainty quantification system, comprising:

[0020] The first module involves selecting benchmark data and determining the uncertainty of aerodynamic data for several sets of wind tunnel test data generated due to different wind tunnels or different model scales.

[0021] The second module calculates the baseline data and determines the uncertainty of the aerodynamic data for several sets of numerical simulation data generated due to different calculation software or different calculation grids.

[0022] The third module integrates wind tunnel test data and numerical simulation data to determine the nominal values ​​of multi-source aerodynamic data.

[0023] The fourth module performs uncertainty quantification of multi-source aerodynamic data to obtain the uncertainty quantification results of multi-source startup data.

[0024] The beneficial effects of this invention compared to the prior art are:

[0025] This invention proposes a quantitative evaluation method for uncertainties in multi-source aerodynamic data, meeting the aerodynamic design requirements of hypersonic aircraft. This method is applicable to aerodynamic deviation zones where differences between air and ground are not significant, i.e., incoming flow Mach 0–7 and flight altitude 0–30 km. Compared to traditional design methods, it reduces local aerodynamic deviations by 50%, and its reliability has been verified by flight test data. This invention addresses multi-source aerodynamic data in the aircraft design process, comprehensively utilizing uncertainty data from numerical simulation and wind tunnel testing, combined with mathematical modeling and expert evaluation, to establish a quantitative model and standard for aerodynamic data uncertainty. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the method of the present invention.

[0027] Figure 2 This is a graph showing the uncertainty of wind tunnel test data.

[0028] Figure 3 This is a graph showing the uncertainty of numerical simulation data.

[0029] Figure 4 This is a graph showing the uncertainty of multi-source aerodynamic data. Detailed Implementation

[0030] The present invention will be described in conjunction with the accompanying drawings and embodiments.

[0031] A quantitative evaluation method for uncertainty in multi-source aerodynamic data includes the following steps:

[0032] 1) For multiple sets of wind tunnel test data generated due to different wind tunnels or different model scales, the wind tunnel test data that is closest to the real flight environment is selected as the benchmark data by comparing Reynolds numbers. Generally, this is wind tunnel test data of large-scale models (model scale value close to 1) or high enthalpy reproduction wind tunnel test data. When there are more than 7 sets of wind tunnel test data, the standard deviation of multiple sets of wind tunnel test data is used as the uncertainty of aerodynamic data. When the amount of data is less than or equal to 7 sets, the maximum residual method is used to calculate the uncertainty of multiple sets of wind tunnel test data.

[0033] 2) For multiple sets of numerical simulation data generated due to different calculation software or different calculation grids, a method similar to the uncertainty quantification of wind tunnel test data is adopted. First, the average value of all simulation data is used as the baseline data. When there are more than 7 sets of simulation data, the standard deviation of the simulation data is used as the uncertainty of the aerodynamic data. When the number of data sets is less than or equal to 7 sets, the maximum residual method is used to calculate the uncertainty. The calculation formula is the same as that for wind tunnel test data.

[0034] 3) Integrate wind tunnel test and numerical simulation aerodynamic data. First, determine the baseline data. Generally, select the wind tunnel test data that is closest to the flight environment as the initial nominal value. Then, consider the differences between the sky and the ground, such as strut interference and Reynolds number, and correct the initial nominal value. Use the corrected data as the nominal value of the multi-source aerodynamic data.

[0035] 4) Quantify the uncertainty of multi-source aerodynamic data: Based on the uncertainty of wind tunnel test data in step 1 and the uncertainty of numerical simulation data in step 4, limit the envelope range to obtain the data envelope band, and compare the data envelope band with the existing empirical values ​​of aerodynamic deviation. If the relative difference between the data envelope band and the existing empirical values ​​of aerodynamic deviation is within 5%, then the data envelope band is taken as the result of the quantity quantification of multi-source aerodynamic data.

[0036] For uncertain quantification results that are much smaller than empirical values ​​(uncertain quantification results deviating from empirical values ​​by more than 25%), further analysis is required.

[0037] First, determine whether the number of sample points in the wind tunnel test and numerical simulation data source is too small. If the number of any group of samples in the wind tunnel test or numerical simulation data source is less than 2, it indicates that there is a defect in the diversity of sample points. In this case, empirical values ​​should be used as the result of uncertainty quantification.

[0038] If both wind tunnel tests and numerical simulations have two or more data points, it is necessary to further determine whether the numerical simulations are based on the same grid or the wind tunnel test data are based on the same test model. If either of these conditions is met, it indicates that there is a defect in the diversity of data points and the uncertainty quantification assessment results have a large deviation. In this case, empirical values ​​should be used as the uncertainty quantification results. Otherwise, the uncertainty quantification assessment results of multi-source aerodynamic data can be accepted.

[0039] A multi-source aerodynamic data uncertainty quantification system, comprising:

[0040] The first module involves selecting benchmark data and determining the uncertainty of aerodynamic data for several sets of wind tunnel test data generated due to different wind tunnels or different model scales.

[0041] The second module calculates the baseline data and determines the uncertainty of the aerodynamic data for several sets of numerical simulation data generated due to different calculation software or different calculation grids.

[0042] The third module integrates wind tunnel test data and numerical simulation data to determine the nominal values ​​of multi-source aerodynamic data.

[0043] The fourth module performs uncertainty quantification of multi-source aerodynamic data to obtain the uncertainty quantification results of multi-source startup data.

[0044] Example:

[0045] like Figure 1 As shown, this invention provides a method for quantitative evaluation of uncertainty in multi-source aerodynamic data, comprising the following steps:

[0046] 1) such as Figure 2 As shown, for multiple sets of wind tunnel test data generated due to different wind tunnels or different model scales, the wind tunnel test data that is closest to the actual flight environment is selected as the benchmark data by comparing Reynolds numbers. This is generally large-scale model wind tunnel test data or high-enthalpy reproducible wind tunnel test data. The standard deviation of multiple sets of wind tunnel test data is used as one of the uncertainties of the aerodynamic data. For repeatable data that is limited (less than 7 times), the maximum residual method is used to calculate the uncertainty.

[0047] Taking the axial force coefficient as an example, the formula for calculating the repeatability uncertainty is as follows:

[0048] Standard deviation method:

[0049]

[0050] Among them, C X,U For the axial force uncertainty, C X,i C is the axial force coefficient for repeatable blowing. X,AVE is the average value of various repetitive blowing data; n is the number of repetitions.

[0051] Maximum residual method:

[0052]

[0053] Among them, C X,U For the axial force uncertainty, C X,i C is the repeatability axial force coefficient. X,AVE The average value of various repetitive data; coefficient K' n The reciprocal is shown in the attached table below.

[0054] n 2 3 4 5 6 7 8 <![CDATA[1K' n ]]> 1.77 1.02 0.83 0.74 0.68 0.64 0.61

[0055] For other aerodynamic coefficients, the normal force coefficient C Y,ULateral force coefficient C Z,U Rolling moment coefficient C MX,U Yaw moment coefficient C MY,U Pitch moment coefficient C MZ,U Uncertainty measurement quantification method and C X,U Consistent.

[0056] 2) such as Figure 3 As shown, for multiple sets of numerical simulation data generated due to different calculation software or different calculation grids, a method similar to that used for wind tunnel test data uncertainty quantification is adopted. First, the average value of all simulation data is used as the baseline data, and the standard deviation of the simulation data is used as the uncertainty of the aerodynamic data. For repeatable data that is limited (less than 7 times), the maximum residual method is used to calculate the uncertainty, and the calculation formula is the same as that for wind tunnel test data.

[0057] 3) For the fusion of wind tunnel test and numerical simulation aerodynamic data, the reference data is first determined. Generally, the wind tunnel test data that is closest to the flight environment is selected as the initial nominal value. Then, considering the differences between the sky and the ground, such as strut interference and Reynolds number, the initial nominal value is corrected and the corrected data is used as the nominal value of the multi-source aerodynamic data.

[0058] 4) such as Figure 4 As shown, for the uncertainty measurement of multi-source aerodynamic data, considering the uncertainty measurement results of wind tunnel test data in step 1 and numerical simulation data in step 2, the envelope range is given a limit envelope, and compared with the aerodynamic deviation of previous models to correct the uncertainty measurement value; for states with significant differences, separate analysis is required, and multiple factors are combined to determine the uncertainty measurement value.

[0059] The parts of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A method for multi-source aerodynamic data uncertainty quantification evaluation, characterized in that, The application relates to a method for quantifying uncertainty of multi-source aerodynamic data. The method comprises the following steps: For several groups of wind tunnel test data generated due to different wind tunnels or different model scaling ratios, reference data are selected and uncertainty of aerodynamic data is determined; For several groups of numerical simulation data generated due to different calculation software or different calculation grids, reference data are calculated and uncertainty of aerodynamic data is determined; Wind tunnel test data and numerical simulation data are fused to determine nominal values of multi-source aerodynamic data; Uncertainty quantification of multi-source aerodynamic data is performed to obtain uncertainty quantification results of multi-source aerodynamic data; The step of performing uncertainty quantification of multi-source aerodynamic data comprises the following steps:

2. The method of claim 1, wherein, An envelope of uncertainty of wind tunnel test data and uncertainty results of numerical simulation data is obtained to obtain a data envelope band, and the data envelope band is compared with existing aerodynamic deviation empirical values; if a relative difference between the data envelope band and the existing aerodynamic deviation empirical values is within 5%, the data envelope band is taken as the uncertainty quantification results of multi-source aerodynamic data.

3. The method of claim 2, wherein, For several groups of wind tunnel test data generated due to different wind tunnels or different model scaling ratios, wind tunnel test data closest to a real flight environment are selected as reference data by comparing Reynolds numbers.

4. The method of claim 1, wherein, For several groups of wind tunnel test data generated due to different wind tunnels or different model scaling ratios, the step of determining uncertainty of aerodynamic data comprises the following steps: when the quantity of wind tunnel test data is greater than a group, standard deviations of the multiple groups of wind tunnel test data are taken as the uncertainty of aerodynamic data; when the quantity of wind tunnel test data is less than or equal to a group, a maximum residual method is adopted to calculate the uncertainty of the multiple groups of wind tunnel test data; a is a set value.

5. The method of claim 4, wherein, For several groups of numerical simulation data generated due to different calculation software or different calculation grids, average values of all the numerical simulation data are taken as reference data. For several groups of numerical simulation data generated due to different calculation software or different calculation grids, the step of determining uncertainty of aerodynamic data comprises the following steps:

6. The method of claim 1, wherein, When the quantity of numerical simulation data is greater than b groups, standard deviations of the numerical simulation data are taken as the uncertainty of aerodynamic data; when the quantity of numerical simulation data is less than or equal to b groups, a maximum residual method is adopted to calculate the uncertainty; b is a set value. The step of fusing wind tunnel test data and numerical simulation data to determine nominal values of multi-source aerodynamic data comprises the following steps: Reference data are determined, and wind tunnel test data closest to a flight environment are selected as initial nominal values; 7. The method of claim 6, wherein, The initial nominal values are corrected by considering differences between the sky and the earth, and the corrected data are taken as the nominal values of multi-source aerodynamic data. The step of performing uncertainty quantification of multi-source aerodynamic data further comprises the following steps:

8. The method of claim 7, wherein, If the uncertainty quantification results deviate from empirical values by more than 25%, the quantity of wind tunnel test and numerical simulation data sources is judged; if the quantity of any one of the wind tunnel test or numerical simulation data sources is less than 2, it is indicated that sample point diversity has defects, and the empirical values are taken as the uncertainty quantification results. The step of performing uncertainty quantification of multi-source aerodynamic data further comprises the following steps: For the uncertainty quantification result deviates from the empirical value more than 25%, if the sample points of wind tunnel test and numerical simulation data source are all greater than or equal to 2, further judge whether the numerical simulation is based on the same set of grid or the wind tunnel test data is based on the same test model: if any of the above conditions is met, it indicates that the sample point diversity is defective, and the uncertainty quantification evaluation result deviates greatly, at this time, the empirical value is taken as the uncertainty quantification result; otherwise, the multi-source aerodynamic data uncertainty quantification evaluation result is adopted.

9. A multi-source aerodynamic data uncertainty quantification system, comprising: It comprises: A first module, for several groups of wind tunnel test data produced by different wind tunnels or different model scaling, selects the reference data and determines the uncertainty of aerodynamic data; A second module, for several groups of numerical simulation data produced by different calculation software or different calculation grids, calculates the reference data and determines the uncertainty of aerodynamic data; A third module, fuses the wind tunnel test data and the numerical simulation data to determine the nominal value of the multi-source aerodynamic data; A fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The fourth module, performs multi-source aerodynamic data uncertainty quantification to obtain the multi-source aerodynamic data uncertainty quantification result; The

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