Method, device and medium for evaluating uncertainty of flow field parameters in high-speed wind tunnel tests

Abstract

The present invention discloses a method, device and medium for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test, and relates to the field of wind tunnel tests for aerospace vehicles. The present invention, by virtue of the fact that a general aircraft aerodynamic database test requires a large number of wind blowing trips, ignores the influence of model condition replacement on the uncertainty of flow field control and measurement results, analyzes and classifies the test flow field conditions, and can conveniently obtain the statistical uncertainty of flow field parameters under the same test flow field conditions for dozens of times. The sample size is large, the credibility of the uncertainty results is high, and there is no need to increase the test content. The method is highly economical, the process is simple and easy, and it is easy to program and quickly implement, which can effectively improve the evaluation ability of the accuracy of aircraft aerodynamic characteristic data.

Description

Technical Field

[0001] The present invention relates to the field of wind tunnel tests for aerospace vehicles, and in particular to a method, a device and a medium for evaluating the uncertainty of flow field parameters in high-speed wind tunnel tests. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] With the development of aerospace technology, modern advanced aircraft have higher and higher requirements for flight performance, flight quality and other indicators. Aerodynamic characteristic data is an important input for aircraft aerodynamic shape design evaluation, flight mechanics characteristics, structural design, flight control and other disciplines, and is directly related to whether the design indicators can be successfully achieved. Therefore, obtaining high-precision aerodynamic characteristic data is crucial to improving the level of aircraft design. Wind tunnel testing is an important and reliable source of aerodynamic characteristic data. However, the structure of modern large-scale wind tunnel systems is very complex. Due to the constraints of the flow field noise and measurement control system of the wind tunnel test section, there is a large uncertainty in the test flow field and flow field parameters, which directly affects the accuracy of the aircraft's aerodynamic characteristics. Therefore, flow field uncertainty assessment must be carried out.

[0004] Traditional flow field uncertainty assessment methods are generally based on sensor calibration results, and the synthetic flow field uncertainty is obtained through the uncertainty propagation formula method. Ideally, this method can solve the uncertainty of accurate flow field parameters. However, unlike the sensor calibration environment, the actual working environment of high-speed wind tunnel tests is mixed with a series of dynamic, noise and other interference problems (prominent), and the comprehensive effect caused by control errors is not considered. The actual flow field uncertainty is often small and cannot reflect the real situation. Another common method is to obtain statistical results of flow field uncertainty through 7 or more repetitive test results, but the test cost is high, time-consuming, and the uncertainty will change to a certain extent when the number of samples increases. Although the magnitude will not change significantly, these deviations will also affect the evaluation of the final results when synthesizing the uncertainty of aerodynamic characteristics. To obtain more accurate results, there are only more repeated tests in the same state, which requires more costs.

[0005] Therefore, it is necessary to propose a low-cost and accurate method to obtain the uncertainty of the flow field in high-speed wind tunnel tests based on the existing test processes, methods and data, so as to evaluate the accuracy and reliability of the test results and meet the requirements of aircraft design. Summary of the invention

[0006] The purpose of the present invention is to provide a method, device and medium for evaluating the uncertainty of flow field parameters in high-speed wind tunnel tests, aiming at the uncertainty evaluation problem between the real flow field characteristic parameters and the nominal (expected) values ​​corresponding to the test conditions in the current high-speed (subsonic) wind tunnel, which is affected by the test section noise, sensor measurement noise, flow field control level, data processing method and other factors. On the basis of the wind tunnel test data of the existing aircraft aerodynamic database, the test states are summarized and grouped according to the flow field conditions, and the influence of the model conditions on the flow field uncertainty is ignored. The flow field characteristic parameters in the group are statistically analyzed in the aerodynamic linear change section to obtain the statistical results of the flow field parameter uncertainty under specific flow field conditions. The results can be directly used as the input conditions for the aerodynamic uncertainty analysis, or by analyzing the relationship between the flow field parameter uncertainty and the flow field conditions, a mathematical model for the flow field uncertainty to change with the flow field conditions is established, and the flow field uncertainty under all test conditions is predicted by the model, thereby solving the above-mentioned problem.

[0007] The technical solution of the present invention is as follows:

[0008] The uncertainty evaluation method of flow field parameters in high-speed wind tunnel tests includes:

[0009] Step S1: conducting a wind tunnel test to obtain aerodynamic characteristics wind tunnel test data;

[0010] Step S2: Screen, sort and group the wind tunnel test vehicle table in the obtained aerodynamic characteristics wind tunnel test data according to the test flow field conditions; the number of groups is G ;

[0011] Step S3: Plotting different trials M The lift curve under the number is calculated, and the linear interval of the angle of attack is determined according to the lift curve. The angle of attack interval is selected to generate the angle of attack sample points. α s ; M The number is expressed as the nominal Mach number;

[0012] Step S4: Set i= 1 ~G , the nominal flow field state corresponding to each group is M_i , P 0 _i , each group performs steps S5 to S10 respectively;

[0013] Step S5: i In the grouping, the number of each wind tunnel test vehicle is M~α Curve interpolation to angle of attack sample points α s On, get new M~α s curve; M~α The curve is expressed as a Mach number fluctuation curve;M~α s The curve represents the sample points of the Mach number fluctuation curve;

[0014] Step S6: i In the group, M~α s Same as the curve α s Sample point N times of car M Statistical analysis was performed to obtain M Sample RMS σM and angle of attack sample points α s The change relationship of σM ~ α s Change curve relationship;

[0015] Step S7: α s The sample points correspond to σM Take the average of the values ​​and get i Grouped M Sample Root Mean Square Error σM_i , that is M Uncertainty evaluation results of the number σM_i ;

[0016] Step S8: Using the method in steps S5 to S7, M The number is changed to the total starting pressure P 0, repeat steps S5 to S7 to obtain the total start-up pressure P Uncertainty evaluation results of 0 σP 0 _i ;

[0017] Step S9: Determine i Whether the number of wind tunnel test vehicles in the group is greater than the preset value; if so, directly proceed to step S10; if not, select the group evaluation result with the closest test conditions instead, or t Distribution principle, use T v=0.95 / 2 coefficient to correct uncertainty assessment results;

[0018] Step S10: Record the i The grouped nominal flow field parameters and uncertainty evaluation results can be used as input reference values ​​for the evaluation of the uncertainty results of aerodynamic tests in wind tunnel tests, and can be used by looking up tables;

[0019] Step S11: Based on the data recorded in step S10, the uncertainty of the corresponding flow field state is obtained.

[0020] Furthermore, in step S1, aerodynamic characteristics wind tunnel test data with a total number of wind tunnel test times exceeding 300 are obtained.

[0021] Furthermore, the step S2 comprises:

[0022] For the same test M Number and total start pressure P The wind tunnel test vehicles with 0 are grouped into one group. Let the total number of groups be G , the number of wind tunnel test vehicles included in each group is N If the number of wind tunnel test vehicles under the same flow field conditions is less than the preset value, the group is marked with P.

[0023] Furthermore, the step S3 comprises:

[0024] Step S31: Plotting different experiments M The lift curve under the number is C L ~α curve;

[0025] Step S32: Select in all trials M Count down, C L ~α The common angle of attack interval in which the curve can be linearly fitted, each interval Δ α Take angle of attack sample points α s .

[0026] Furthermore, the step S9 includes:

[0027] For the group marked as P in step S2, select the group evaluation result with the closest test conditions to replace it, or t Distribution principle, use T v=0.95 / 2 factor to correct the uncertainty assessment results.

[0028] Furthermore, the step S11 includes:

[0029] The uncertainty of the corresponding flow field state is obtained by table lookup method;

[0030] Or based on the data recorded in step S10, a mathematical model of flow field uncertainty is established, and then the mathematical model of flow field uncertainty is used to obtain the uncertainty of the corresponding flow field state.

[0031] Furthermore, the mathematical model of the flow field uncertainty is in the form of:

[0032]

[0033] .

[0034] Furthermore, the mathematical model of the flow field uncertainty includes: a polynomial model, an exponential model, a logarithmic model, and a neural network model.

[0035] A high-speed wind tunnel test flow field parameter uncertainty assessment device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the high-speed wind tunnel test flow field parameter uncertainty assessment method as described above are implemented.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention, by taking advantage of the fact that the general aircraft aerodynamic database test requires more wind tunnel tests, ignores the influence of model condition changes on flow field control and measurement result uncertainty, analyzes and classifies the test flow field conditions, and can conveniently obtain the statistical uncertainty of flow field parameters under the same test flow field conditions for dozens of times. The sample size is large and the uncertainty results are highly credible. The method is based on the analysis of the wind tunnel test scheme and test data of the existing aircraft aerodynamic database. There is no need to increase the test content, it is highly economical, the process is simple and easy, and it is easy to program and implement quickly.

[0039] 2. The present invention can obtain the uncertainty of the flow field in a subsonic wind tunnel test during application, provide reliable input for evaluating the accuracy of the test results, and meet the requirements of aircraft design. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the uncertainty evaluation method for flow field parameters in high-speed wind tunnel tests;

[0041] Figure 2 This is an output example of the flow field parameter classification scheme and uncertainty result in the present invention. DETAILED DESCRIPTION

[0042] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0043] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0044] Embodiment 1

[0045] See also Figure 1 , the uncertainty evaluation method of flow field parameters in high-speed wind tunnel test includes the following steps:

[0046] Step S1: Conduct wind tunnel tests to obtain wind tunnel test data on aerodynamic characteristics; that is, conduct force measurement wind tunnel tests on the aircraft as planned to obtain wind tunnel test data on aerodynamic characteristics of a large number of vehicles. Generally, the total number of test vehicles should exceed 300. It should be noted that the wind tunnel test data on aerodynamic characteristics of the aircraft includes test data conducted continuously, intermittently, or at different times during the same period. The aircraft models may be the same, or different models with similar blockage degrees of the test sections may be selected, and there is no limitation on this.

[0047] Step S2: Screen, sort and group the wind tunnel test vehicle table in the obtained aerodynamic characteristics wind tunnel test data according to the test flow field conditions; the number of groups is G ;

[0048] Step S3: Plotting different trials M The lift curve under the number is calculated, and the linear interval of the angle of attack is determined according to the lift curve. The angle of attack interval is selected to generate the angle of attack sample points. α s ; M The number is expressed as the nominal Mach number;

[0049] Step S4: Set i= 1 ~G , the nominal flow field state corresponding to each group is M_i , P 0 _i , each group performs steps S5 to S10 respectively;

[0050] Step S5: i In the grouping, the number of each wind tunnel test vehicle is M~α Curve interpolation to angle of attack sample points α s On, get new M~α s curve; M~α The curve is expressed as a Mach number fluctuation curve; M~α s The curve represents the sample points of the Mach number fluctuation curve;

[0051] Step S6: i In the group, M~α s Same as the curve α s Sample point N times of car M Statistical analysis was performed to obtain M Sample RMS σM and angle of attack sample points α s The change relationship of σM ~ α s Change curve relationship;

[0052] Step S7: α s The sample points correspond to σM Take the average of the values ​​and get i Grouped M Sample Root Mean Square Error σM_i , that is M Uncertainty evaluation results of the number σM_i ;

[0053] Step S8: Using the method in steps S5 to S7, M The number is changed to the total starting pressure P 0, repeat steps S5 to S7 to obtain the total start-up pressure P Uncertainty evaluation results of 0 σP 0 _i That is, steps S5 to S7 are for M In this step, we change a parameter ( M The number is changed to the total starting pressure P 0), for the total driving pressure P 0Go again;

[0054] Step S9: Determine i Whether the number of wind tunnel test vehicles in the group is greater than the preset value; if so, directly proceed to step S10; if not, select the group evaluation result with the closest test conditions instead, or t Distribution principle, use Tv=0.95 / 2 coefficient to correct the uncertainty assessment results;

[0055] Step S10: Record the i The grouped nominal flow field parameters and uncertainty evaluation results can be used as input reference values ​​for the evaluation of the uncertainty results of aerodynamic tests in wind tunnel tests, and can be used by looking up tables;

[0056] Step S11: Based on the data recorded in step S10, the uncertainty of the corresponding flow field state is obtained.

[0057] In this embodiment, specifically, step S2 includes:

[0058] For the same test M Number and total start pressure P The wind tunnel test vehicles with 0 are grouped into one group. Let the total number of groups be G , the number of wind tunnel test vehicles included in each group is N , if the number of wind tunnel test vehicles under the same flow field conditions is less than the preset value, then the group is marked with P; it should be noted that the preset value can be any smaller sample number, such as 5, 9, etc.; in this embodiment, the preset value is 7;

[0059] It should be noted that, in order to prevent excessive grouping, the minimum distinguishing unit of the nominal value can be set (such as Δ M <0.02,Δ P 0<10KPa).

[0060] In this embodiment, specifically, step S3 includes:

[0061] Step S31: Plotting different experiments M The lift curve under the number is C L ~α curve;

[0062] Step S32: Select in all trials M Count down, C L ~α The common angle of attack interval in which the curve can be linearly fitted, each interval Δ α Take angle of attack sample points α s .

[0063] In this embodiment, specifically, step S9 includes:

[0064] For the group marked as P in step S2, the uncertainty result obtained is less reliable due to the small number of samples; therefore, the evaluation result of the group with the closest test conditions can be selected instead, or according to tDistribution principle, use T v=0.95 / 2 factor to correct the uncertainty assessment results.

[0065] In this embodiment, specifically, step S11 includes:

[0066] The uncertainty of the corresponding flow field state is obtained by the table lookup method; that is, in similar tests of other aircraft in the future, if the same test wind tunnel is used, the acquisition and control system is collected, and the blockage degree of the test model is not much different, under the same flow field conditions ( M , P 0) The flow field uncertainty results obtained by the above method can be directly used as the input of aerodynamic uncertainty; the test content in the database can also be added to the same grouping above to increase the number of samples and obtain a more reliable flow field uncertainty output;

[0067] Or, based on the data recorded in step S10, a mathematical model of flow field uncertainty is established, and then the mathematical model of flow field uncertainty is used to obtain the uncertainty of the corresponding flow field state; that is, for the conditions described in step S2, if enough grouping results have been obtained and the range is wide enough (covering the flow field states required by most experiments), M , P 0 range), then the obtained data variable M , P 0 and the dependent variable σM , σP 0, and establish a mathematical model applicable to flow field uncertainty. The mathematical model of flow field uncertainty is as follows:

[0068]

[0069]

[0070] In this embodiment, it should also be noted that the mathematical model of the flow field uncertainty generally includes: a polynomial model, an exponential model, a logarithmic model, a neural network model, etc.

[0071] This embodiment also proposes a high-speed wind tunnel test flow field parameter uncertainty assessment device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the high-speed wind tunnel test flow field parameter uncertainty assessment method as described above when executing the computer program; preferably, the computer program can be executed on a terminal device, such as a personal computer.

[0072] This embodiment also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the high-speed wind tunnel test flow field parameter uncertainty assessment method as described above are implemented.

[0073] However, the apparatus of the present invention is not limited thereto, and in the present invention, the readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device.

[0074] The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more conductors, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0075] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0076] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0077] Embodiment 2

[0078] Embodiment 2 is a specific application of the uncertainty evaluation method for flow field parameters in high-speed wind tunnel tests proposed in Embodiment 1.

[0079] The test data of this embodiment comes from a subsonic force measurement wind tunnel test of a conventional aircraft. The purpose of the test is to build a flight aerodynamic database for the aircraft. M The range of the number is 0.4~0.92. In order to evaluate the uncertainty of the test results of the aerodynamic database, the uncertainty of the test flow field parameters needs to be obtained as input.

[0080] In view of the above test purpose and flow field uncertainty assessment requirements, the specific operation process of this embodiment includes the following steps:

[0081] Step S1: Carry out the force measurement wind tunnel test of the aircraft as planned, and obtain a large number of wind tunnel test data of aerodynamic characteristics. Generally, the total number of test runs should exceed 300. For example, the main test contents carried out in this example include basic longitudinal aerodynamic force test, supercharged variable Reynolds number wind tunnel test, deflection efficiency test of elevator, aileron, rudder, horizontal tail and other control surfaces, and the total number of test runs is 479.

[0082] Step S2: For the obtained wind tunnel test vehicle table, filter, sort and group according to the test flow field conditions, and for the same test nominal Mach number (hereinafter referred to as M Number) and total starting pressure (hereinafter referred to as P 0) are grouped together. To prevent excessive grouping, the minimum distinguishing unit of the nominal value can be set (such as Δ M <0.02,Δ P 0<10KPa); Assume the total number of groups obtained G , the number of cars contained in each group is N If the number of test vehicles under the same flow field conditions is less than 7, then the group is marked with P. M , P The 0 parameter principle can be divided into G = 19 groups, each with 2 to 57 cars. For details, refer to Figure 2 .

[0083] Step S3: Plotting different trials M The lift curve under the number is C L ~ α curve, select in all trials M The common angle of attack range in which the curve can be linearly fitted under the following conditions, within which every interval Δ α Take angle of attack sample points α s. As in this example, M Count C L The linear range of the curve is inconsistent. Select a common linear range. α Within plus or minus 2 degrees, take the interval Δ α =0.5°, then the angle of attack sample point data α s =-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2°.

[0084] Step S4: Set i =1~ G , the nominal flow field state corresponding to each group is M_i , P 0_ i , each group performs steps S5 to S10 respectively;

[0085] Step S5: i In each test group, M ~ α Curve interpolation to angle of attack sample points α s On, get new M ~ α s curve.

[0086] Step S6: i In the group, M ~ α Same as the curve α s point N The second car M Statistical analysis was performed to obtain M Sample RMS σ M ~ α s Change curve relationship.

[0087] Step S7: α s The σ corresponding to the sample point M Take the average of the values ​​and get i Grouped M The root mean square error of the sample is σ M (Results of the uncertainty assessment).

[0088] Step S8: Using a similar method as above, M Total pressure P 0, repeat steps S5 to S7 to obtain the total pressure P Uncertainty evaluation result σ P 0.

[0089] Step S9: For the group marked P in step S2, since the number of samples is small, the uncertainty result obtained is less reliable. The group evaluation result closest to the nominal flow field condition of the test can be selected instead. t Distribution principle, use T v=0.95 / 2 coefficient to correct the uncertainty assessment result. In this example, in group 14, M =0.86, P The result of 0=185KPa can be obtained from Group 13. M =0.85 instead.

[0090] Step S10: Record the i The grouped nominal flow field parameters and uncertainty evaluation results can be used as input reference values ​​for the evaluation of the uncertainty results of the wind tunnel test aerodynamic test, and can be used by looking up the table. M , P 0. N , Mark , σ M , σ P 0 (see Figure 2 It should be noted that the data in the dark area in the figure indicates that the data is replaced by data in other rows).

[0091] Step S11: For the conditions described in step S2, if the grouping results are sufficient and the range is wide enough (covering the flow field states required for most experiments), M , P 0 range), then the obtained data variable M , P 0 and the dependent variable σ M , σ P 0, and establish a mathematical model applicable to flow field uncertainty.

[0092] The mathematical model is in the form of:

[0093]

[0094]

[0095] As in this example, a simple M and P The uncertainty of flow field parameters is fitted by the quadratic polynomial product form of 0, and the mathematical model is:

[0096]

[0097] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

[0098] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A method for evaluating the uncertainty of flow field parameters in high-speed wind tunnel tests, characterized in that: include: Step S1: conducting a wind tunnel test to obtain aerodynamic characteristics wind tunnel test data; Step S2: Screen, sort and group the wind tunnel test vehicle table in the obtained aerodynamic characteristics wind tunnel test data according to the test flow field conditions; the number of groups is G ; Step S3: Plotting different trials M The lift curve under the number is calculated, and the linear interval of the angle of attack is determined according to the lift curve. The angle of attack interval is selected to generate the angle of attack sample points. α s ; M The number is expressed as the nominal Mach number; Step S4: Set i= 1 ~G , the nominal flow field state corresponding to each group is M_i , P 0 _i , each group performs steps S5 to S10 respectively; Step S5: i In the grouping, the number of each wind tunnel test vehicle is M~α Curve interpolation to angle of attack sample points α s On, get new M~α s curve; M~α The curve is expressed as a Mach number fluctuation curve; M~α s The curve represents the sample points of the Mach number fluctuation curve; Step S6: i In the group, M~α s Same as the curve α s Sample point N times of car M Statistical analysis was performed to obtain M Sample RMS σM and angle of attack sample points α s The change relationship of σM~α s Change curve relationship; Step S7: α s The sample points correspond to σM Take the average of the values ​​and get i Grouped M Sample Root Mean Square Error σM_ i , that is M Uncertainty evaluation results of the number σM_i ; Step S8: Using the method in steps S5 to S7, M The number is changed to the total starting pressure P 0, repeat steps S5 to S7 to obtain the total start-up pressure P Uncertainty evaluation results of 0 σP 0 _i ; Step S9: Determine i Whether the number of wind tunnel test vehicles in the group is greater than a preset value; if so, directly proceed to step S10; If not, select the group evaluation result with the closest test conditions instead, or t Distribution principle, use T v=0.95 / 2 coefficient to correct uncertainty assessment results; Step S10: Record the i The grouped nominal flow field parameters and uncertainty evaluation results can be used as input reference values ​​for the evaluation of the uncertainty results of aerodynamic tests in wind tunnel tests, and can be used by looking up tables; Step S11: Based on the data recorded in step S10, the uncertainty of the corresponding flow field state is obtained.

2. The method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test according to claim 1 is characterized in that: In the step S1, aerodynamic characteristics wind tunnel test data with a total number of wind tunnel test times exceeding 300 are obtained.

3. The method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test according to claim 2 is characterized in that: The step S2 comprises: For the same test M Number and total start pressure P The wind tunnel test vehicles with 0 are grouped into one group. Let the total number of groups be G , the number of wind tunnel test vehicles included in each group is N If the number of wind tunnel test vehicles under the same flow field conditions is less than the preset value, the group is marked with P.

4. The method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test according to claim 3 is characterized in that: The step S3 comprises: Step S31: Plotting different experiments M The lift curve under the number is C L ~α curve; Step S32: Select in all trials M Count down, C L ~α The common angle of attack interval in which the curve can be linearly fitted, each interval Δ α Take angle of attack sample points α s .

5. The method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test according to claim 4 is characterized in that: The step S9 comprises: For the group marked as P in step S2, select the group evaluation result with the closest test conditions to replace it, or t Distribution principle, use T v=0.95 / 2 factor to correct the uncertainty assessment results.

6. The method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test according to claim 1 is characterized in that: The step S11 comprises: The uncertainty of the corresponding flow field state is obtained by table lookup method; Or based on the data recorded in step S10, a mathematical model of flow field uncertainty is established, and then the mathematical model of flow field uncertainty is used to obtain the uncertainty of the corresponding flow field state.

7. The method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test according to claim 6 is characterized in that: The mathematical model of the flow field uncertainty is in the form of: 。 8. The method for evaluating the uncertainty of flow field parameters in a high-speed wind tunnel test according to claim 7 is characterized in that: The mathematical model of flow field uncertainty includes: a polynomial model, an exponential model, a logarithmic model, and a neural network model.

9. High-speed wind tunnel test flow field parameter uncertainty assessment device, including: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for uncertainty assessment of flow field parameters in a high-speed wind tunnel test as described in any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating uncertainty of flow field parameters in a high-speed wind tunnel test are implemented.

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