Method for evaluating uncertainty of wind tunnel balance calibration system based on weight loading

Abstract

The invention discloses a weight loading-based uncertainty evaluation method for a wind tunnel balance calibration system, relates to the technical field of aviation aerodynamic wind tunnel tests, and solves the problem that the uncertainty evaluation method for the wind tunnel balance calibration system in the prior art does not completely respond to the accurate test requirement of a wind tunnel model test. Firstly, balance calibration is completed through a calibration system, and a working formula matrix is obtained through least square solution based on an implicit formula; applying the same step load by adopting two modes of system loading and weight self-weight loading, and recording two groups of signal output sequences of the balance; then calculating two corresponding groups of calculation load sequences by using the working formula matrix and an eight-time iteration method; and finally, solving the difference between the two groups of calculation loads and taking an absolute value, and determining the maximum value of each component as the uncertainty of the loading load of the system. According to the method, system errors caused by an error theoretical analysis method are eliminated, and development of a wind tunnel balance calibration system and aerospace crafts is practically supported.

Description

Technical Field

[0001] This invention relates to the field of aerodynamic wind tunnel testing technology, specifically to a method for evaluating the uncertainty of a wind tunnel balance calibration system based on weight loading. Background Technology

[0002] Wind tunnel model testing is a crucial means of understanding aircraft performance and reducing development risks and costs during the aerospace vehicle development process. A wind tunnel balance is a high-precision measuring device that directly senses and measures aerodynamic forces and moments acting on the six degrees of freedom of the model. Before being used in wind tunnel testing, a wind tunnel balance must be calibrated using a calibration system to establish the relationship between the wind tunnel balance's measurement signals and the applied aerodynamic loads, i.e., to obtain the working formula matrix and other performance parameters. Therefore, the uncertainty of the wind tunnel balance calibration system directly determines the accuracy of the wind tunnel balance. Furthermore, GJB 2244A-2011, "Specification for Wind Tunnel Strain Balances," explicitly requires that "the measurement uncertainty of the calibration equipment should be 1 / 4 to 1 / 10 of the required measurement uncertainty of the balance being calibrated."

[0003] Currently, due to the serious shortcomings of single-component manual weight loading calibration systems in accurately simulating the multi-component load conditions of wind tunnel balances, six-component automatic loading calibration systems are now widely used. The six-component automatic loading calibration system utilizes multiple sets of weights or actuators plus force sensors as force sources, and simultaneously applies six-component loads through pulleys or variable-ratio levers. Data acquisition equipment collects the output signals, thereby establishing the working formula matrix of the wind tunnel balance and obtaining other performance parameters. The uncertainty of the six-component automatic loading calibration system is mainly determined by the uncertainty components of the calibration load and the data acquisition equipment. Currently, the uncertainty of the data acquisition equipment can be accurately obtained from the data provided by the calibration certificate; however, the uncertainty of the calibration load is mainly obtained through error theory analysis, lacking practical evaluation methods and failing to meet the needs of accurate testing in wind tunnel model experiments. Summary of the Invention

[0004] To address the problem that existing methods for evaluating the uncertainty of wind tunnel balance calibration systems do not fully meet the accuracy requirements of wind tunnel model tests, this invention provides a method for evaluating the uncertainty of wind tunnel balance calibration systems based on weight loading, comprising:

[0005] S1. The wind tunnel balance calibration system is used to calibrate a wind tunnel balance. The working formula matrix of the wind tunnel balance is obtained by solving the implicit formula using the least squares method. ;

[0006] S2. Apply a stepped load sequence to the wind tunnel balance at each loading point of the wind tunnel balance calibration system. And record the output sequence of each component signal of the wind tunnel balance. ;

[0007] S3. Applying the working formula matrix of the wind tunnel balance obtained in S1 The wind tunnel balance component signal output sequence obtained by S2 The calculated load sequence of each loading point of the wind tunnel balance calibration system was obtained by using an implicit formula iterative method of eight iterations. ;

[0008] S4. The loading head of the rotating wind tunnel balance calibration system is positioned so that the weight of the weights can be applied to each loading point, and the weights are applied to the stepped load sequence described in S2. A stepped load sequence with the same value was used to record the output signal sequence of each component of the wind tunnel balance. ;

[0009] S5. Applying the working formula matrix of the wind tunnel balance obtained in S1 The wind tunnel balance component signal output sequence obtained by S4 Using the implicit formula iterative method of eight iterations, the calculated load sequence of the step load sequence at each loading point under the self-weight of the weight is obtained. ;

[0010] S6. The computational load sequence obtained in S3 The computational load sequence obtained in S5 The differences are calculated, and the absolute values ​​of each value are taken. The maximum value of each component is then taken as the uncertainty of the calibration load applied to each loading point of the wind tunnel balance calibration system.

[0011] Furthermore, in S1, the implicit formula is expressed as:

[0012]

[0013] In the formula: n is the known number of wind tunnel balance components; The known calibration load value for the i-th component; It is the coefficient of the i-th component principal term to be determined; It is the known output increment value of the i-th component signal; These are the first-order disturbance correction coefficients for the i-th component caused by the other component loads to be determined; The known calibration load value for the j-th component that interferes with the i-th component; These are the second-order disturbance correction coefficients for the i-th component of each component load to be determined; It is the known calibration load value of the k-th component that interferes with the i-th component.

[0014] Furthermore, in S3 and S5, the implicit formula iteration method of eight times is as follows:

[0015] The formula for the first iteration is expressed as:

[0016]

[0017]

[0018]

[0019] The formula for the 2nd to 8th iterations is expressed as:

[0020]

[0021]

[0022] In the formula: n is the known number of wind tunnel balance components; It is the measured value of the i-th component load to be determined; The coefficient of the i-th component is known. It is the known output increment value of the i-th component signal; These are the known first-order disturbance correction coefficients for the i-th component caused by other component loads; It is the load value of the j-th component that interferes with the i-th component; These are the known second-order disturbance correction coefficients for each component load on the i-th component; It is the load value of the k-th component that interferes with the i-th component.

[0023] The beneficial effects of this invention are as follows: This invention provides an uncertainty evaluation method for a wind tunnel balance calibration system based on weight loading. It eliminates the systematic errors caused by error theory analysis methods, accurately quantifies the uncertainty of applying calibration loads at each loading point of the wind tunnel balance calibration system that conforms to actual working conditions, and effectively supports the development of wind tunnel balance calibration systems and aerospace vehicles. Attached Figure Description

[0024] Figure 1 This is a schematic diagram showing the location and direction of each loading point of the loading head. Detailed Implementation

[0025] The technical solution of the present invention will be further described below with reference to embodiments, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention. In the following embodiments, process equipment or devices not specifically specified are all conventional equipment or devices in the art. Unless specifically specified, the technical means used in the embodiments of the present invention are all conventional means well known to those skilled in the art.

[0026] Example 1, Reference Figure 1 This embodiment details a method for evaluating the uncertainty of a wind tunnel balance calibration system based on weight loading, comprising the following steps:

[0027] S1. The wind tunnel balance calibration system is used to calibrate a wind tunnel balance. The working formula matrix of the wind tunnel balance is obtained by solving the implicit formula using the least squares method. ;

[0028] The implicit formula is expressed as:

[0029]

[0030] In the formula: n is the known number of wind tunnel balance components; The known calibration load value for the i-th component; It is the coefficient of the i-th component principal term to be determined; It is the known output increment value of the i-th component signal; These are the first-order disturbance correction coefficients for the i-th component caused by the other component loads to be determined; The known calibration load value for the j-th component that interferes with the i-th component; These are the second-order disturbance correction coefficients for the i-th component of each component load to be determined; The known calibration load value for the k-th component that interferes with the i-th component;

[0031] Specifically, in this embodiment, the components of the calibrated wind tunnel balance are the normal force components. Pitch moment components Rolling torque component axial force component Lateral force components Yaw moment component The output increments of each component signal correspond to the output increments of the normal force component signal. Pitch torque component signal output increment Roll torque component signal output increment Axial force component signal output increment Lateral force component signal output increment Yaw torque component signal output increment The 144 component loads and corresponding signal output increments for calibrating the wind tunnel balance are shown in Table 1.

[0032] Table 1

[0033]

[0034]

[0035]

[0036] The working formula matrix of the wind tunnel balance is obtained by using implicit formulas and the least squares method. As shown in Table 2:

[0037] Table 2

[0038]

[0039] S2. Apply a stepped load sequence to the wind tunnel balance using the G1 loading point of the wind tunnel balance calibration system. And record the output sequence of each component signal of the wind tunnel balance. ;

[0040] For details, please refer to Figure 1 This figure shows the distribution of loading points G1-G17 on the loading head of the wind tunnel balance calibration system. With the origin of the loading head coordinate system as the reference, the orientation of the load application at each point in the X, Y, and Z axes is clearly defined. This is the key position and direction basis for accurately applying and calculating the load during the calibration uncertainty assessment of the system.

[0041] In this embodiment, a stepped load sequence is applied to the wind tunnel balance using the G1 loading point of the wind tunnel balance calibration system. As shown in Table 3:

[0042] Table 3

[0043] Record the output sequence of each component signal of the wind tunnel balance As shown in Table 4:

[0044] Table 4

[0045] S3. Applying the working formula matrix of the wind tunnel balance obtained in S1 The wind tunnel balance signal output sequences obtained from S2 The calculated load sequence of the step load sequence at the G1 loading point of the wind tunnel balance calibration system was obtained by using an implicit formula iterative method of eight iterations. ;

[0046] The implicit formula iterative method with eight iterations is as follows:

[0047] The formula for the first iteration is expressed as:

[0048]

[0049]

[0050]

[0051] The formula for the 2nd to 8th iterations is expressed as:

[0052]

[0053]

[0054] In the formula: n is the known number of wind tunnel balance components; It is the measured value of the i-th component load to be determined; The coefficient of the i-th component is known. It is the known output increment value of the i-th component signal; These are the known first-order disturbance correction coefficients for the i-th component caused by other component loads; It is the load value of the j-th component that interferes with the i-th component; These are the known second-order disturbance correction coefficients for each component load on the i-th component; It is the load value of the k-th component that interferes with the i-th component;

[0055] Specifically, in this embodiment, the calculated load sequence of the G1 loading point stepped load sequence of the wind tunnel balance calibration system is obtained. As shown in Table 5:

[0056] Table 5

[0057] S4. The loading head of the rotating wind tunnel balance calibration system is brought into position such that the weight of the weights can be used to load the G1 loading point, and the same stepped load sequence as S2 is applied using the weights. Record the output sequence of each component signal of the wind tunnel balance. ;

[0058] Specifically, in this embodiment, the output sequence of each component signal of the wind tunnel balance is as follows: As shown in Table 6:

[0059] Table 6

[0060] S5. Applying the working formula matrix of the wind tunnel balance obtained in S1 The wind tunnel balance signal output sequences obtained from S4 The calculated load sequence of the step load sequence at loading point G1 was obtained by using an implicit formula iterative method of eight iterations. ;

[0061] Specifically, in this embodiment, the calculated load sequence is obtained by calculating the stepped load sequence of the weight self-weight loading point G1. As shown in Table 7:

[0062] Table 7

[0063] S6. To The obtained computational load sequence and The obtained computational load sequence The corresponding differences are calculated, and the absolute values ​​of each value are taken. The maximum value of each component is taken as the uncertainty of the calibration load applied to the G1 loading point of the wind tunnel balance calibration system.

[0064] Specifically, in this embodiment, for The obtained computational load sequence and The obtained computational load sequence The corresponding differences are shown in Table 8:

[0065] Table 8

[0066] The uncertainty of applying the calibration load at the G1 loading point of the wind tunnel balance calibration system is:

[0067] Although the invention has been described with reference to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative and not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A method for evaluating the uncertainty of a wind tunnel balance calibration system based on weight loading, characterized in that, include: S1. The wind tunnel balance calibration system is used to calibrate a wind tunnel balance. The working formula matrix of the wind tunnel balance is obtained by solving the implicit formula using the least squares method. ; S2. Apply a stepped load sequence to the wind tunnel balance at each loading point of the wind tunnel balance calibration system. And record the output sequence of each component signal of the wind tunnel balance. ; S3. Applying the working formula matrix of the wind tunnel balance obtained in S1 The wind tunnel balance component signal output sequence obtained by S2 The calculated load sequence of each loading point of the wind tunnel balance calibration system was obtained by using an implicit formula iterative method of eight iterations. ; S4. The loading head of the rotating wind tunnel balance calibration system is positioned so that the weight of the weights can be applied to each loading point, and the weights are applied to the stepped load sequence described in S2. A stepped load sequence with the same value was used to record the output signal sequence of each component of the wind tunnel balance. ; S5. Applying the working formula matrix of the wind tunnel balance obtained in S1 The wind tunnel balance component signal output sequence obtained by S4 Using the implicit formula iterative method of eight iterations, the calculated load sequence of the step load sequence at each loading point under the self-weight of the weight is obtained. ; S6. The computational load sequence obtained in S3 The computational load sequence obtained in S5 The differences are calculated, and the absolute values ​​of each value are taken. The maximum value of each component is then taken as the uncertainty of the calibration load applied to each loading point of the wind tunnel balance calibration system.

2. The uncertainty evaluation method for a wind tunnel balance calibration system based on weight loading according to claim 1, characterized in that, In S1, the implicit formula is expressed as: ； In the formula: n is the known number of wind tunnel balance components; The known calibration load value for the i-th component; It is the coefficient of the i-th component principal term to be determined; It is the known output increment value of the i-th component signal; These are the first-order disturbance correction coefficients for the i-th component caused by the other component loads to be determined; The known calibration load value for the j-th component that interferes with the i-th component; These are the second-order disturbance correction coefficients for the i-th component of each component load to be determined; It is the known calibration load value of the k-th component that interferes with the i-th component.

3. The uncertainty evaluation method for a wind tunnel balance calibration system based on weight loading according to claim 1, characterized in that, In S3 and S5, the implicit formula iterative method of eight iterations is as follows: The formula for the first iteration is expressed as: ； ； ； The formula for the 2nd to 8th iterations is expressed as: ； ； In the formula: n is the known number of wind tunnel balance components; It is the measured value of the i-th component load to be determined; The coefficient of the i-th component is known. It is the known output increment value of the i-th component signal; These are the known first-order disturbance correction coefficients for the i-th component caused by other component loads; It is the load value of the j-th component that interferes with the i-th component; These are the known second-order disturbance correction coefficients for each component load on the i-th component; It is the load value of the k-th component that interferes with the i-th component.

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