Data optimization method, device, equipment and medium based on cubic spline interpolation

Abstract

The present application discloses a data optimization method, device, equipment and medium based on cubic spline interpolation, which relates to the field of data processing technology, including obtaining original pressure data of the surface of a wing model after a wind tunnel pressure test is completed, generating a pressure data matrix based on the original pressure data; calculating the pressure coefficient of each pressure measuring point in the pressure data matrix, obtaining the pressure coefficient matrix of all pressure measuring points, and setting a corresponding angle of attack sequence to be interpolated for each of the pressure measuring points; using the cubic spline interpolation method to interpolate the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding angle of attack sequence to be interpolated, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model; using the pressure coefficient interpolation results to optimize the wind tunnel pressure test data, which can avoid the Runge phenomenon caused by the Lagrange high-order interpolation polynomial, solve the data oscillation problem at the edge of the interpolation interval, improve the data rounding accuracy, and provide technical support for the optimization of the wind tunnel pressure test.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data optimization method, device, equipment and medium based on cubic spline interpolation. Background Art

[0002] In the process of aerospace vehicle development, wind tunnel tests provide important data support for aerodynamic performance research, layout optimization, and load design. However, due to the influence of wind loads, the model and support will produce elastic deformation, resulting in the inconsistency between the actual angle of attack of the model and the angle of attack of the wind tunnel mechanism. In order to facilitate the use of load data in the later stage, the structural load design unit often requires data standardization and provides result data with unified format and regular angle of attack, which requires the original data to be processed by angle rounding interpolation. The traditional angle rounding interpolation of wind tunnel test data adopts the Lagrangian interpolation method based on high-order polynomials. As a global interpolation method, when there are many original interpolation sample points, the higher the order of the interpolation polynomial function, the better it can simulate the real function curve, especially in the area with large curve inflection, the interpolation accuracy is higher than other general numerical methods. However, due to the unpredictability of the real polynomial function outside the interpolation interval, the high-order polynomial may cause the Runge phenomenon of violent oscillation at the edge of the interval, resulting in huge interpolation errors of the Lagrangian method at the edge angle of attack. The occurrence of Runge data oscillation is related to the characteristics of the interpolated function and the selection of interpolation nodes. For high-order polynomial interpolation with quasi-uniform nodes, as the order of the polynomial increases, the interpolation oscillation at the edge of the interval becomes more intense, and the interpolation error also increases significantly. In order to avoid Runge oscillation as much as possible, a series of solutions have been proposed in the field of numerical calculation, such as using Chebyshev nodes, increasing the number of nodes (especially near the end points of the interval), avoiding equidistant nodes, and limiting the order of the polynomial. However, for wind tunnel tests, the angle of attack nodes are determined in advance and cannot be adjusted at will. The method of limiting the order of the polynomial may reduce the risk of Runge oscillation, but it will also reduce the interpolation accuracy in the intermediate angle of attack area. Therefore, there is currently no better solution to the Runge oscillation problem in the Lagrange interpolation end point area in wind tunnel tests.

[0003] From the above, it can be seen that how to avoid the Runge phenomenon caused by Lagrange's high-order interpolation polynomials, solve the data oscillation problem at the edge of the interpolation interval, improve the accuracy of data rounding, and provide technical support for the optimization of wind tunnel pressure test are problems to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a data optimization method, device, equipment and medium based on cubic spline interpolation, which can avoid the Runge phenomenon caused by Lagrange high-order interpolation polynomials, solve the data oscillation problem at the edge of the interpolation interval, improve the data rounding accuracy, and provide technical support for the optimization of wind tunnel pressure test. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a data optimization method based on cubic spline interpolation, comprising:

[0006] Acquire original pressure data of the wing model surface after the wind tunnel pressure test is completed, and generate a pressure data matrix based on the original pressure data;

[0007] Calculating the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain a pressure coefficient matrix of all pressure measuring points, and setting a corresponding sequence of angles of attack to be interpolated for each pressure measuring point;

[0008] Using a cubic spline interpolation method, the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix is ​​interpolated onto the corresponding sequence of angles of attack to be interpolated, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model;

[0009] The pressure coefficient interpolation result is used to optimize the data of the wind tunnel pressure test.

[0010] Optionally, the obtaining of original pressure data on the surface of the wing model after the wind tunnel pressure test is completed, and generating a pressure data matrix based on the original pressure data, includes:

[0011] After completing the pressure measurement test in the wind tunnel, the original pressure data of the wing model surface is obtained, and the original pressure data is sorted to generate a pressure data matrix; the first column in the pressure data matrix is ​​the model angle of attack, and the other columns except the first column are the pressure data of different pressure measurement points corresponding to the model angle of attack.

[0012] Optionally, calculating the pressure coefficient of each pressure measuring point in the pressure data matrix includes:

[0013] Reading the pressure data matrix, and calculating the pressure coefficient of each pressure measuring point in the pressure data matrix using a pressure coefficient calculation formula to obtain the pressure coefficient of each pressure measuring point;

[0014] The pressure coefficient calculation formula is:

[0015] ;

[0016] in, is the pressure coefficient, is the pressure at the ith pressure measuring point, is the static pressure of the wind tunnel flow, is the velocity pressure from the wind tunnel.

[0017] Optionally, the method of interpolating the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix onto the corresponding sequence of attack angles to be interpolated using a cubic spline interpolation method to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model includes:

[0018] The pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack is read in turn, and the pressure coefficient determinant of each pressure measuring point at different angles of attack is interpolated to the corresponding angle of attack sequence to be interpolated using the cubic spline interpolation method to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model.

[0019] Optionally, the pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack is sequentially read, and the pressure coefficient determinant of each pressure measuring point at different angles of attack is interpolated to a corresponding sequence of angles of attack to be interpolated using a cubic spline interpolation method to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model, including:

[0020] Reading a first pressure coefficient determinant of a first pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolating the first pressure coefficient determinant to a corresponding first sequence of angles of attack to be interpolated using a cubic spline interpolation method to obtain a first pressure coefficient interpolation result;

[0021] It is determined whether all the pressure measuring points have completed interpolation. If not, the pressure coefficient determinant reading process is repeated until all the pressure measuring points have completed interpolation to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model.

[0022] Optionally, the cubic spline interpolation method is in the form of:

[0023] ;

[0024] in, is the pressure coefficient obtained at the rounded angle of attack x, is the angle of attack in the test angle of attack sequence, For Adjacent angles of attack, is the interval between adjacent test angles of attack, and , is the original pressure coefficient value at the test angle of attack, is the second-order derivative at the test angle of attack, For Adjacent second-order derivatives.

[0025] In a second aspect, the present application discloses a data optimization device based on cubic spline interpolation, comprising:

[0026] A data acquisition module is used to acquire the original pressure data of the wing model surface after the wind tunnel pressure test is completed, and generate a pressure data matrix based on the original pressure data;

[0027] A pressure coefficient calculation module, used to calculate the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain a pressure coefficient matrix of all pressure measuring points, and to set a corresponding sequence of angles of attack to be interpolated for each pressure measuring point;

[0028] An interpolation module, used for interpolating the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding sequence of angles of attack to be interpolated using a cubic spline interpolation method, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model;

[0029] A data optimization module is used to optimize the data of the wind tunnel pressure test using the pressure coefficient interpolation result.

[0030] Optionally, the data acquisition module includes:

[0031] The data sorting and matrix generation module is used to obtain the original pressure data of the wing model surface after completing the pressure measurement test in the wind tunnel, and sort the original pressure data to generate a pressure data matrix; the first column in the pressure data matrix is ​​the model angle of attack, and the other columns except the first column are the pressure data of different pressure measurement points corresponding to the model angle of attack.

[0032] In a third aspect, the present application discloses an electronic device, comprising:

[0033] Memory, used to store computer programs;

[0034] A processor is used to execute the computer program to implement the aforementioned data optimization method based on cubic spline interpolation.

[0035] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned disclosed data optimization method based on cubic spline interpolation are implemented.

[0036] It can be seen that the present application provides a data optimization method based on cubic spline interpolation, including obtaining the original pressure data of the surface of the wing model after the wind tunnel pressure test is completed, and generating a pressure data matrix based on the original pressure data; calculating the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain the pressure coefficient matrix of all pressure measuring points, and setting a corresponding angle of attack sequence to be interpolated for each pressure measuring point; using the cubic spline interpolation method to interpolate the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding angle of attack sequence to be interpolated, so as to obtain the pressure coefficient interpolation result of all pressure measuring points of the wing model; and using the pressure coefficient interpolation result to perform data optimization on the wind tunnel pressure test. The present application obtains the original pressure data of the wing model surface after the wind tunnel pressure test is completed, generates a pressure data matrix, and then calculates the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain the pressure coefficient matrix, and sets a corresponding angle of attack sequence to be interpolated for each of the pressure measuring points, and uses the cubic spline interpolation method to interpolate the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding angle of attack sequence to be interpolated to obtain the pressure coefficient interpolation result. The cubic spline interpolation method divides the entire angle of attack interpolation interval into multiple small intervals, and uses a low-order polynomial to interpolate in each small interval. It can avoid the Runge phenomenon caused by Lagrange's high-order interpolation polynomial and solve the data oscillation problem at the edge of the interpolation interval. The smaller segmented interval ensures the interpolation accuracy of the low-order interpolation polynomial, so that the entire angle of attack interval has a higher interpolation fidelity and improves the data rounding accuracy. The pressure coefficient interpolation result is used to optimize the wind tunnel pressure test data, providing technical support for the optimization of the wind tunnel pressure test. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0038] Figure 1 A flow chart of a data optimization method based on cubic spline interpolation disclosed in this application;

[0039] Figure 2 A specific flow chart of data optimization based on cubic spline interpolation disclosed in this application;

[0040] Figure 3 A comparison diagram of the interpolation results of the pressure coefficient of the pressure measuring point disclosed in the present application compared with the traditional Lagrange interpolation method;

[0041] Figure 4This is a schematic diagram of the structure of a data optimization device based on cubic spline interpolation disclosed in this application;

[0042] Figure 5 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In the process of aerospace vehicle development, wind tunnel tests provide important data support for aerodynamic performance research, layout optimization, and load design. However, due to the influence of wind loads, the model and support will produce elastic deformation, resulting in the inconsistency between the actual angle of attack of the model and the angle of attack of the wind tunnel mechanism. In order to facilitate the use of load data in the later stage, the structural load design unit often requires data standardization and provides result data with unified format and regular angle of attack, which requires the original data to be processed by angle rounding interpolation. The traditional angle rounding interpolation of wind tunnel test data adopts the Lagrangian interpolation method based on high-order polynomials. As a global interpolation method, when there are many original interpolation sample points, the higher the order of the interpolation polynomial function, the better it can simulate the real function curve, especially in the area with large curve inflection, the interpolation accuracy is higher than other general numerical methods. However, due to the unpredictability of the real polynomial function outside the interpolation interval, the high-order polynomial may cause the Runge phenomenon of violent oscillation at the edge of the interval, resulting in huge interpolation errors of the Lagrangian method at the edge angle of attack. The occurrence of Runge data oscillation is related to the characteristics of the interpolated function and the selection of interpolation nodes. For high-order polynomial interpolation with quasi-uniform nodes, as the order of the polynomial increases, the interpolation oscillation at the edge of the interval becomes more intense, and the interpolation error also increases significantly. In order to avoid Runge oscillation as much as possible, a series of solutions have been proposed in the field of numerical calculation, such as using Chebyshev nodes, increasing the number of nodes (especially near the end points of the interval), avoiding equidistant nodes, and limiting the order of the polynomial. However, for wind tunnel tests, the angle of attack nodes are determined in advance and cannot be adjusted at will. The method of limiting the order of the polynomial may reduce the risk of Runge oscillation, but it will also reduce the interpolation accuracy in the intermediate angle of attack area. Therefore, there is currently no better solution to solve the Runge oscillation problem in the Lagrangian interpolation endpoint area in wind tunnel tests. As can be seen from the above, how to avoid the Runge phenomenon caused by Lagrangian high-order interpolation polynomials, solve the data oscillation problem at the edge of the interpolation interval, improve the accuracy of data rounding, and provide technical support for the optimization of wind tunnel pressure test are problems to be solved in this field.

[0045] See also Figure 1 As shown, the embodiment of the present invention discloses a data optimization method based on cubic spline interpolation, which may specifically include:

[0046] Step S11: acquiring original pressure data of the wing model surface after the wind tunnel pressure test is completed, and generating a pressure data matrix based on the original pressure data.

[0047] In this embodiment, after completing the pressure measurement test in the wind tunnel, the original pressure data of the wing model surface is obtained, and the original pressure data is sorted to generate a pressure data matrix; the first column in the pressure data matrix is ​​the model angle of attack, and the other columns except the first column are the pressure data of different pressure measurement points corresponding to the model angle of attack.

[0048] Step S12: Calculate the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain a pressure coefficient matrix of all pressure measuring points, and set a corresponding angle of attack sequence to be interpolated for each pressure measuring point.

[0049] In this embodiment, the pressure data matrix is ​​read, and the pressure coefficient calculation formula is used to calculate the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain the pressure coefficient of each pressure measuring point;

[0050] The pressure coefficient calculation formula is:

[0051] ;

[0052] in, is the pressure coefficient, is the pressure at the ith pressure measuring point, is the static pressure of the wind tunnel flow, It is the wind tunnel flow velocity pressure; wind tunnel flow velocity pressure, wind tunnel flow static pressure and wind tunnel flow velocity pressure are obtained by pressure sensors arranged in the tunnel during wind tunnel pressure measurement test.

[0053] Then, a pressure coefficient matrix is ​​generated based on the pressure coefficients of all pressure measuring points, and a corresponding sequence of angles of attack to be interpolated is set for each of the pressure measuring points. For example, the pressure coefficient matrix data of the first pressure measuring point at different angles of attack are read, and the sequence of angles of attack to be interpolated is set to -8°, -6°-4°, -2°, 0°, 2°, 4°, 6°, 8°, 10°, 12°, 14°, 16°. It is worth noting that the angle of attack to be interpolated should be an integer and should not exceed the angle of attack range of the pressure measuring point to avoid data extrapolation.

[0054] Step S13: using the cubic spline interpolation method, interpolate the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding sequence of angles of attack to be interpolated, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model.

[0055] In this embodiment, the pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack is read in turn, and the pressure coefficient determinant of each pressure measuring point at different angles of attack is interpolated to the corresponding sequence of angles of attack to be interpolated using the cubic spline interpolation method to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model.

[0056] Specifically, read the first pressure coefficient determinant of the first pressure measuring point in the pressure coefficient matrix at different angles of attack, and use the cubic spline interpolation method to interpolate the first pressure coefficient determinant to the corresponding first angle of attack sequence to be interpolated to obtain the first pressure coefficient interpolation result; determine whether all pressure measuring points have completed interpolation, if not all differences are completed, repeat the process of reading the pressure coefficient determinant until all pressure measuring points have completed differences, so as to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model. That is to say, after reading the first pressure coefficient determinant of the first pressure measuring point at different angles of attack, perform cubic spline interpolation to obtain the first pressure coefficient interpolation result, and then determine whether all test points have completed interpolation, if not all, repeat the above steps until all test points have completed interpolation, that is, complete the interpolation of the rounded pressure coefficients of all pressure measuring points on the model surface, thereby obtaining the pressure coefficient interpolation results of the pressure measuring points on the entire surface of the model.

[0057] The definition and solution method of the cubic spline interpolation method proposed in this application are as follows:

[0058] The interpolation function is defined by a cubic polynomial between every two adjacent pressure coefficient data points. The coefficients of these interpolation function polynomials are determined by minimizing the square error criterion of the curve. The constructed polynomial requires that not only the function value is equal at the data point, but also the first-order and second-order derivatives are equal, thus ensuring the smoothness of the curve. The equation system for solving the spline interpolation polynomial is an underdetermined equation system, and the boundary conditions of the endpoints need to be added to meet the positive solution requirements. The polynomial form constructed by the spline interpolation method is:

[0059] ;

[0060] in, is the pressure coefficient obtained at the rounded angle of attack x, is the angle of attack in the test angle of attack sequence, For Adjacent angles of attack, is the interval between adjacent test angles of attack, and , is the original pressure coefficient value at the test angle of attack, is the second-order derivative at the test angle of attack, For Adjacent second-order derivatives.

[0061] Solution The relationship between the values ​​is:

[0062] ;

[0063] ;

[0064] ;

[0065] in, is the third-order difference quotient. The system of equations for solving the M relation is an underdetermined system of equations, and the boundary conditions of the endpoints are required to meet the requirements of positive solution. Common boundary conditions include natural boundary (the second-order derivative of the two endpoints is zero), clamped boundary (the first-order derivative of the two endpoints is given) and periodic boundary (the function value, first-order and second-order derivatives of the two endpoints are equal). The system of equations for the three boundary conditions are as follows:

[0066] Natural Boundaries:

[0067] ;

[0068] Clamping Boundary:

[0069] ;

[0070] Periodic Boundary:

[0071] .

[0072] Step S14: Optimizing the data of the wind tunnel pressure test using the pressure coefficient interpolation result.

[0073] The specific process of implementing data optimization based on cubic spline interpolation in this application is as follows Figure 2 As shown, the steps are as follows:

[0074] Step 1: Complete the pressure test in the wind tunnel to obtain the original pressure data of the model surface, and organize the original pressure data into a pressure data matrix; the first column of the matrix is ​​the model angle of attack, and the second to last columns are the pressure data at the corresponding angle of attack from the first pressure measuring point to the last pressure measuring point on the model surface;

[0075] Step 2: Read the pressure data matrix, and calculate the pressure coefficient of each pressure measuring point in the pressure data matrix using the pressure coefficient calculation formula to obtain the pressure coefficient matrix of all pressure measuring points;

[0076] Step 3: reading the first pressure coefficient determinant of the first pressure measuring point in the pressure coefficient matrix at different angles of attack, and setting a corresponding first angle of attack sequence to be interpolated for the first pressure measuring point;

[0077] Step 4: interpolate the first pressure coefficient determinant onto the first angle of attack sequence to be interpolated using the cubic spline interpolation method to obtain the first pressure coefficient interpolation result of the rounded angle of attack at the first pressure measuring point;

[0078] Step 5: Determine whether all the pressure measuring points have completed the interpolation. If not, repeat steps 3-4 to obtain the determinant of the pressure coefficient of the next pressure measuring point at different angles of attack, until the rounded pressure coefficient interpolation of all pressure measuring points on the model surface is completed, and the pressure coefficient interpolation results of the pressure measuring points on the entire surface of the model are obtained. Finally, the pressure coefficient interpolation results are used to optimize the data of the wind tunnel pressure test.

[0079] The interpolation results of the pressure coefficients at the pressure measuring points compared with the technical solution of the present application and the traditional Lagrange interpolation method are shown in Figure 2. Figure 3 As shown, the cubic spline interpolation method proposed in the present application is to divide the entire angle of attack interpolation interval into multiple small intervals, and use a low-order polynomial for interpolation in each small interval, thereby avoiding the Runge phenomenon caused by the traditional use of Lagrange high-order interpolation polynomials, and better solve the data oscillation problem at the edge of the angle of attack interpolation interval. In addition, the smaller segmented interval ensures the interpolation accuracy of the low-order interpolation polynomial, so that the method has a higher interpolation fidelity in the entire angle of attack range, can greatly improve the rounding accuracy, and provides important technical support for the refined pressure interpolation of wind tunnel tests.

[0080] In this embodiment, the original pressure data of the wing model surface after the wind tunnel pressure test is completed is obtained, and a pressure data matrix is ​​generated based on the original pressure data; the pressure coefficient of each pressure measuring point in the pressure data matrix is ​​calculated to obtain the pressure coefficient matrix of all pressure measuring points, and a corresponding angle of attack sequence to be interpolated is set for each pressure measuring point; the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix is ​​interpolated to the corresponding angle of attack sequence to be interpolated using the cubic spline interpolation method to obtain the pressure coefficient interpolation result of all pressure measuring points of the wing model; and the wind tunnel pressure test data is optimized using the pressure coefficient interpolation result. The present application obtains the original pressure data of the wing model surface after the wind tunnel pressure test is completed, generates a pressure data matrix, and then calculates the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain the pressure coefficient matrix, and sets a corresponding angle of attack sequence to be interpolated for each of the pressure measuring points, and uses the cubic spline interpolation method to interpolate the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding angle of attack sequence to be interpolated to obtain the pressure coefficient interpolation result. The cubic spline interpolation method divides the entire angle of attack interpolation interval into multiple small intervals, and uses a low-order polynomial to interpolate in each small interval. It can avoid the Runge phenomenon caused by Lagrange's high-order interpolation polynomial and solve the data oscillation problem at the edge of the interpolation interval. The smaller segmented interval ensures the interpolation accuracy of the low-order interpolation polynomial, so that the entire angle of attack interval has a higher interpolation fidelity and improves the data rounding accuracy. The pressure coefficient interpolation result is used to optimize the wind tunnel pressure test data, providing technical support for the optimization of the wind tunnel pressure test.

[0081] See also Figure 4 As shown, the embodiment of the present invention discloses a data optimization device based on cubic spline interpolation, which may specifically include:

[0082] A data acquisition module 11 is used to acquire the original pressure data of the wing model surface after the wind tunnel pressure test is completed, and generate a pressure data matrix based on the original pressure data;

[0083] A pressure coefficient calculation module 12 is used to calculate the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain a pressure coefficient matrix of all pressure measuring points, and to set a corresponding sequence of angles of attack to be interpolated for each pressure measuring point;

[0084] An interpolation module 13 is used to interpolate the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding sequence of attack angles to be interpolated using a cubic spline interpolation method, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model;

[0085] The data optimization module 14 is used to optimize the data of the wind tunnel pressure test using the pressure coefficient interpolation result.

[0086] In this embodiment, the original pressure data of the wing model surface after the wind tunnel pressure test is completed is obtained, and a pressure data matrix is ​​generated based on the original pressure data; the pressure coefficient of each pressure measuring point in the pressure data matrix is ​​calculated to obtain the pressure coefficient matrix of all pressure measuring points, and a corresponding angle of attack sequence to be interpolated is set for each pressure measuring point; the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix is ​​interpolated to the corresponding angle of attack sequence to be interpolated using the cubic spline interpolation method to obtain the pressure coefficient interpolation result of all pressure measuring points of the wing model; and the wind tunnel pressure test data is optimized using the pressure coefficient interpolation result. The present application obtains the original pressure data of the wing model surface after the wind tunnel pressure test is completed, generates a pressure data matrix, and then calculates the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain the pressure coefficient matrix, and sets a corresponding angle of attack sequence to be interpolated for each of the pressure measuring points, and uses the cubic spline interpolation method to interpolate the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding angle of attack sequence to be interpolated to obtain the pressure coefficient interpolation result. The cubic spline interpolation method divides the entire angle of attack interpolation interval into multiple small intervals, and uses a low-order polynomial to interpolate in each small interval. It can avoid the Runge phenomenon caused by Lagrange's high-order interpolation polynomial and solve the data oscillation problem at the edge of the interpolation interval. The smaller segmented interval ensures the interpolation accuracy of the low-order interpolation polynomial, so that the entire angle of attack interval has a higher interpolation fidelity and improves the data rounding accuracy. The pressure coefficient interpolation result is used to optimize the wind tunnel pressure test data, providing technical support for the optimization of the wind tunnel pressure test.

[0087] In some specific embodiments, the data acquisition module 11 may specifically include:

[0088] The data sorting and matrix generation module is used to obtain the original pressure data of the wing model surface after completing the pressure measurement test in the wind tunnel, and sort the original pressure data to generate a pressure data matrix; the first column in the pressure data matrix is ​​the model angle of attack, and the other columns except the first column are the pressure data of different pressure measurement points corresponding to the model angle of attack.

[0089] In some specific embodiments, the pressure coefficient calculation module 12 may specifically include:

[0090] A pressure coefficient calculation module is used to read the pressure data matrix and calculate the pressure coefficient of each pressure measuring point in the pressure data matrix using a pressure coefficient calculation formula to obtain the pressure coefficient of each pressure measuring point;

[0091] The pressure coefficient calculation formula is:

[0092] ;

[0093] in, is the pressure coefficient, is the pressure at the ith pressure measuring point, is the static pressure of the wind tunnel flow, is the velocity pressure from the wind tunnel.

[0094] In some specific embodiments, the interpolation module 13 may specifically include:

[0095] The pressure coefficient interpolation result determination module is used to sequentially read the pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack, and use the cubic spline interpolation method to interpolate the pressure coefficient determinant of each pressure measuring point at different angles of attack to the corresponding sequence of angles of attack to be interpolated, so as to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model.

[0096] In some specific embodiments, the interpolation module 13 may specifically include:

[0097] a first pressure coefficient interpolation result determination module, configured to read the first pressure coefficient determinant of the first pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolate the first pressure coefficient determinant to the corresponding first sequence of angles of attack to be interpolated using a cubic spline interpolation method, so as to obtain a first pressure coefficient interpolation result;

[0098] The judgment module is used to judge whether all the pressure measuring points have completed the interpolation. If not, the pressure coefficient determinant reading process is repeated until all the pressure measuring points have completed the interpolation to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model.

[0099] In some specific embodiments, the cubic spline interpolation method is in the form of:

[0100] ;

[0101] in, is the pressure coefficient obtained at the rounded angle of attack x, is the angle of attack in the test angle of attack sequence, For Adjacent angles of attack, is the interval between adjacent test angles of attack, and , is the original pressure coefficient value at the test angle of attack, is the second-order derivative at the test angle of attack, For Adjacent second-order derivatives.

[0102] Figure 5A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the data optimization method based on cubic spline interpolation performed by the electronic device disclosed in any of the aforementioned embodiments.

[0103] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0104] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0105] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20 to realize the operation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the data optimization method based on cubic spline interpolation performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to including data transmitted from an external device received by the data optimization device based on cubic spline interpolation, the data 223 can also include data collected by its own input and output interface 25, etc.

[0106] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0107] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the data optimization method based on cubic spline interpolation disclosed in any of the aforementioned embodiments are implemented.

[0108] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used 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 presence of other identical elements in the process, method, article or device including the elements.

[0109] The above is a detailed introduction to a data optimization method, device, equipment and storage medium based on cubic spline interpolation provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A data optimization method based on cubic spline interpolation, characterized in that: include: Acquire original pressure data of the wing model surface after the wind tunnel pressure test is completed, and generate a pressure data matrix based on the original pressure data; Calculating the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain a pressure coefficient matrix of all pressure measuring points, and setting a corresponding sequence of angles of attack to be interpolated for each pressure measuring point; Using a cubic spline interpolation method, the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix is ​​interpolated onto the corresponding sequence of angles of attack to be interpolated, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model; Using the pressure coefficient interpolation result to optimize the data of the wind tunnel pressure test; Wherein, the method of interpolating the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding sequence of angles of attack to be interpolated to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model by using the cubic spline interpolation method comprises: sequentially reading the pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolating the pressure coefficient determinant of each pressure measuring point at different angles of attack to the corresponding sequence of angles of attack to be interpolated by using the cubic spline interpolation method to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model; The method sequentially reads the pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolates the pressure coefficient determinant of each pressure measuring point at different angles of attack to a corresponding sequence of angles of attack to be interpolated using a cubic spline interpolation method, so as to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model, including: reading the first pressure coefficient determinant of the first pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolating the first pressure coefficient determinant to a corresponding sequence of first angles of attack to be interpolated using a cubic spline interpolation method, so as to obtain the first pressure coefficient interpolation result; and judging whether all the pressure measuring points have completed the interpolation, and if not, repeating the process of reading the pressure coefficient determinant until all the pressure measuring points have completed the interpolation, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model.

2. The data optimization method based on cubic spline interpolation according to claim 1, characterized in that: The method of obtaining the original pressure data of the wing model surface after the wind tunnel pressure test is completed, and generating a pressure data matrix based on the original pressure data, comprises: After completing the pressure measurement test in the wind tunnel, the original pressure data of the wing model surface is obtained, and the original pressure data is sorted to generate a pressure data matrix; the first column in the pressure data matrix is ​​the model angle of attack, and the other columns except the first column are the pressure data of different pressure measurement points corresponding to the model angle of attack.

3. The data optimization method based on cubic spline interpolation according to claim 1, characterized in that: The calculating the pressure coefficient of each pressure measuring point in the pressure data matrix comprises: Reading the pressure data matrix, and calculating the pressure coefficient of each pressure measuring point in the pressure data matrix using a pressure coefficient calculation formula to obtain the pressure coefficient of each pressure measuring point; The pressure coefficient calculation formula is: ； in, is the pressure coefficient, is the pressure at the ith pressure measuring point, is the static pressure of the wind tunnel flow, is the velocity pressure from the wind tunnel.

4. The data optimization method based on cubic spline interpolation according to any one of claims 1 to 3, characterized in that: The cubic spline interpolation method is in the form of: ； in, is the pressure coefficient obtained at the rounded angle of attack x, is the angle of attack in the test angle of attack sequence, For Adjacent angles of attack, is the interval between adjacent test angles of attack, and , is the original pressure coefficient value at the test angle of attack, is the second-order derivative at the test angle of attack, For Adjacent second-order derivatives.

5. A data optimization device based on cubic spline interpolation, characterized in that: include: A data acquisition module is used to acquire the original pressure data of the wing model surface after the wind tunnel pressure test is completed, and generate a pressure data matrix based on the original pressure data; A pressure coefficient calculation module, used to calculate the pressure coefficient of each pressure measuring point in the pressure data matrix to obtain a pressure coefficient matrix of all pressure measuring points, and to set a corresponding sequence of angles of attack to be interpolated for each pressure measuring point; An interpolation module, used for interpolating the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding sequence of angles of attack to be interpolated using a cubic spline interpolation method, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model; A data optimization module, used for optimizing the data of the wind tunnel pressure test by using the pressure coefficient interpolation result; Wherein, the method of interpolating the pressure coefficient determinant of the pressure measuring point in the pressure coefficient matrix to the corresponding sequence of angles of attack to be interpolated to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model by using the cubic spline interpolation method comprises: sequentially reading the pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolating the pressure coefficient determinant of each pressure measuring point at different angles of attack to the corresponding sequence of angles of attack to be interpolated by using the cubic spline interpolation method to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model; The method sequentially reads the pressure coefficient determinant of each pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolates the pressure coefficient determinant of each pressure measuring point at different angles of attack to a corresponding sequence of angles of attack to be interpolated using a cubic spline interpolation method, so as to obtain the pressure coefficient interpolation results of all pressure measuring points of the wing model, including: reading the first pressure coefficient determinant of the first pressure measuring point in the pressure coefficient matrix at different angles of attack, and interpolating the first pressure coefficient determinant to a corresponding sequence of first angles of attack to be interpolated using a cubic spline interpolation method, so as to obtain the first pressure coefficient interpolation result; and judging whether all the pressure measuring points have completed the interpolation, and if not, repeating the process of reading the pressure coefficient determinant until all the pressure measuring points have completed the interpolation, so as to obtain the pressure coefficient interpolation results of all the pressure measuring points of the wing model.

6. The data optimization device based on cubic spline interpolation according to claim 5, characterized in that: The data acquisition module comprises: The data sorting and matrix generation module is used to obtain the original pressure data of the wing model surface after completing the pressure measurement test in the wind tunnel, and sort the original pressure data to generate a pressure data matrix; the first column in the pressure data matrix is ​​the model angle of attack, and the other columns except the first column are the pressure data of different pressure measurement points corresponding to the model angle of attack.

7. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the data optimization method based on cubic spline interpolation as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the data optimization method based on cubic spline interpolation as described in any one of claims 1 to 4 is implemented.

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