An analysis method and device for sonic boom test data

Abstract

An embodiment of the present invention discloses a method and device for analyzing sonic boom test data, relating to the technical field of wind tunnel sonic boom tests in the field of fluid mechanics, which can improve the accuracy and efficiency of the entire sonic boom test data analysis system, reduce the number of tests, and save test resources. The present invention includes: according to the data acquisition request sent by the client, reading the measurement data of the sonic boom test through a pressure measurement rail; analyzing and processing the initial data through a probability model stored in the server; solving the model parameters by using the maximum likelihood method; obtaining the model sonic boom signal to be measured through the parameter results, and finally returning the obtained results to the client. The present invention is applicable to equipment design involving wind tunnel sonic boom tests.

Description

Technical Field

[0001] The present invention relates to the technical field of wind tunnel sonic boom test in the field of body mechanics, and particularly to an analysis method and device for sonic boom test data. Background Art

[0002] With the development of aviation science and technology, supersonic civil aircraft have become one of the key directions for the development of civil aircraft. When an aircraft flies at supersonic speed, a sonic boom phenomenon will occur. A strong sonic boom will damage the ecological environment and affect people's normal life and work. Therefore, sonic boom and its suppression technology are the core key technologies that need to be broken through first in the development of a new generation of supersonic civil aircraft. Wind tunnel test is an important means to study sonic boom. The main purpose of a supersonic wind tunnel sonic boom test is to measure the spatial pressure distribution in the near field of an aircraft model. The sonic boom wind tunnel test will be interfered by various factors, which will affect the accuracy of the test to a certain extent and increase the technical difficulty of the test. It is difficult to measure the spatial pressure signal of a low sonic boom model using a wind tunnel test.

[0003] Internationally, the reference train method and spatial averaging technique are usually adopted to improve the measurement accuracy of wind tunnel sonic boom tests. The premise of the reference train method is to assume that the influence of the pressure measuring rail device on pressure is linear. The difference between the measurement train data when the model is fixed above the pressure measuring rail in the wind tunnel test and the reference train data measured after removing the model is considered as the spatial pressure signal generated by the model. However, in fact, the model will produce some coupling effects in the wind tunnel and the coupled part is difficult to quantify. All current spatial pressure measurement technologies cannot eliminate the measurement errors caused by the non-uniform disturbance of the wind tunnel flow field itself. The spatial averaging technique is an effective data correction method for the influence of this flow field non-uniformity. However, the curve obtained after correcting the data by the spatial averaging technique often still has local errors and is not smooth enough, resulting in difficulty in further improving the accuracy of the analysis results of sonic boom test data. Summary of the Invention

[0004] Embodiments of the present invention provide an analysis method and device for sonic boom test data, which can improve the accuracy of the entire sonic boom test data analysis system and can also reduce the number of tests, thereby saving test resources.

[0005] To achieve the above object, the embodiments of the present invention adopt the following technical solutions:

[0006] In a first aspect, the method provided by the embodiments of the present invention includes:

[0007] S1. The server receives a sonic boom test data request sent by the client;

[0008] S2. Read the sonic boom test data from the pressure measurement rail. The pressure measurement rail is installed in the wind tunnel and used to measure the sonic boom test data. The initially measured sonic boom test data is used as the initial data.

[0009] S3. Input the initial data into the probability model for processing. The probability model outputs model parameters.

[0010] S4. Use the model parameters output by the probability model to obtain the signal result of the sonic boom test model, and return the obtained signal result to the client.

[0011] In a second aspect, the device provided by an embodiment of the present invention includes:

[0012] A receiving module, configured to receive a sonic boom test data request sent by a client.

[0013] An acquisition module, configured to read the sonic boom test data from the pressure measurement rail. The pressure measurement rail is installed in the wind tunnel and used to measure the sonic boom test data. The initially measured sonic boom test data is used as the initial data.

[0014] A processing module, configured to input the initial data into the probability model for processing. The probability model outputs model parameters.

[0015] A result output module, configured to use the model parameters output by the probability model to obtain the signal result of the sonic boom test model, and return the obtained signal result to the client.

[0016] The analysis method and device for sonic boom test data provided by the embodiments of the present invention analyze the sonic boom test data based on a probability model. During use, each technician can use the information provided by the client, and the calculation entity runs on the server side. Among them, according to the data acquisition request sent by the client, the measurement data of the sonic boom test is read through the pressure measurement rail; the initial data is analyzed and processed by the probability model stored in the server; the maximum likelihood method is used to solve the model parameters; the model sonic boom signal to be measured is obtained through the parameter results, and the final result is returned to the client. The embodiments of the present invention improve the efficiency of the entire sonic boom test data analysis system and reduce the number of tests. Obvious improvements have been made both in terms of the accuracy of test data analysis and the utilization rate of test resources. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 Schematic diagram of the overall logic framework provided by the embodiments of the present invention;

[0019] Figure 2 Probability graph model used in the specific example provided by the embodiments of the present invention.

[0020] Figure 3 Schematic diagram of reading the initial data of the sonic boom test in the specific example provided by the embodiments of the present invention;

[0021] Figure 4 Schematic diagram of the sonic boom test model signal result returned to the client in the specific example provided by the embodiments of the present invention;

[0022] Figures 5 - 6 Schematic diagram of the comparison between the model signal result and the result of the traditional method provided by the embodiments of the present invention.

[0023] Figure 7 Schematic diagram of the method flow provided by the embodiments of the present invention;

[0024] Figure 8 、 10 Schematic diagram of the device structure provided by the embodiments of the present invention;

[0025] Figure 9 Schematic diagram of the experimental environment provided by the embodiments of the present invention. Detailed implementation manners

[0026] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as limiting the present invention. Those skilled in the art of the present technology can understand that unless specifically stated, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The phrase "and / or" used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless defined as here.

[0027] An embodiment of the present invention provides an analysis method for sonic boom test data, as Figure 7 shown, including:

[0028] S1. The server receives a sonic boom test data request sent by the client.

[0029] S2. Read the sonic boom test data from the pressure measuring rail.

[0030] Wherein, the pressure measuring rail is installed in the wind tunnel and is used to measure the sonic boom test data, and the initially measured sonic boom test data is used as the initial data. In practical applications, some preprocessing programs can be executed on the collected initial data. The specific means for preprocessing the initial data can be the existing means according to the specific experimental environment, and these means are usually also well-known to technicians in the laboratory and engineers engaged in production work on the front line. Therefore, in this embodiment, no excessive limitation is imposed on how to perform the preprocessing.

[0031] In this embodiment, as Figure 9As shown, the pressure measuring rail is fixed on the side wall of the wind tunnel. The aircraft model is connected to the adapter support rod and the axial moving mechanism, and is fixed above the pressure measuring rail. Pressure measuring holes are distributed on the edge of the pressure measuring rail. The initial data includes: measurement data of the aircraft model at different axial positions. Among them, all test devices such as the aircraft model and the pressure measuring rail are in the flow field, and the pressure signal on the pressure measuring rail in the wind tunnel in this state is measured, and there is no need to measure the background signal (reference vehicle number) without placing the aircraft model in the test.

[0032] Specifically, the model is fixed above the pressure measuring rail, and the model and all test devices including the pressure measuring rail are kept in the flow field, and the pressure signal generated by the model in this state is measured through the pressure measuring holes distributed on the edge of the pressure measuring rail. In the process of reading the sonic boom test data from the pressure measuring rail, the aircraft model can be moved axially at fixed intervals, for example, the aircraft model is moved axially at fixed intervals for multiple times, and the test data of the aircraft model at different axial positions are measured. In the process of reading the sonic boom test data from the pressure measuring rail, the aircraft model can be moved axially at fixed intervals for multiple times, and the test data of the aircraft model at different axial positions are measured. The aircraft model is placed above the pressure measuring rail, and is moved axially at fixed intervals for multiple times, and multiple sets of test data of the model at different axial positions are measured.

[0033] S3. Input the initial data into a probability model for processing, and the probability model outputs model parameters.

[0034] S4. Using the model parameters output by the probability model, a signal result of the sonic boom test model is obtained, and the obtained signal result is returned to the client.

[0035] In this embodiment, by processing high-noise test data through a probability model, it is possible to effectively reduce errors such as human and technical errors, as well as errors in the subject data itself, and to largely eliminate various noise interferences that have a significant impact on the data results. For wind tunnel sonic boom tests where the real signal is weak and the noise interference is strong, it is proposed to use a probability model to analyze and model the wind tunnel test data to reduce the impact of noise and improve the analysis results. Through the analysis of the original data, it is found that the background environmental signal has a certain volatility, and using the reference train data as the background environmental signal will introduce new noise. The test data at different aircraft model positions have an overall environmental background signal offset change, which will have a certain impact on the results. For sonic boom test data that are interfered by multiple factors, the random interference factors are parameterized and the test data is probabilistically modeled.

[0036] In this embodiment, before S3, the method further includes: establishing the probability model according to the sonic boom test data: Among them, B i,c is the cth sonic explosion test data of the ith pressure measuring hole, a i is the background signal of the ith pressure measuring hole, k tThe sonic boom signal is at a distance of t pressure measurement holes from the test model, b c is the overall environmental signal offset change of the c-th test. ε is the noise of other uncertain factors, following a normal distribution with a standard deviation of σ. By assuming ε as a random variable to explain all the uncertainties contained in the data, a i , k t , b c are all assumed to be parameters. The noise ε follows a normal distribution with a mean of 0 and a standard deviation of σ, and the probability graph model is as shown in Figure 2 .

[0037] The server inputs the initial data into the probability model for analysis and processing. The probability model is stored in the server for the processing and calculation of sonic boom data. The probability model can be obtained by analyzing the characteristics of the sonic boom test data accumulated in the database and establishing it. Specifically, it is necessary to perform probability modeling on the sonic boom wind tunnel measurement data through the analysis of the characteristics of the sonic boom test data.

[0038] In this embodiment, the process of inputting the initial data into the probability model for processing includes:

[0039] After inputting the initial data into the probability model, it is processed through the likelihood function to obtain the model parameters when the probability of the observed data is the largest. The observed data includes the sonic boom test data measured after the initial data. Among them, the likelihood function is:

[0040]

[0041] P is the probability of the data. Among them, using the model parameters calculated by the probability model in the server, the sonic boom test model signal result is obtained, and the obtained signal result is returned to the client. Specifically, the probability model uses the maximum likelihood method to solve for the parameters. By calculating the model parameters when the probability of the observed data is the largest, the observed data includes the sonic boom test data measured after the initial data. The result of the parameter k t is the sonic boom signal of the model that needs to be measured in the test. This parameter result is saved to the server and returned to the client.

[0042] Furthermore, in this embodiment, before S1, it also includes: The server receives the login information sent by the client. Extracts computing resources from the resource pool according to the login information and allocates them to the client. Specifically, as shown in Figure 1 , the client performs a user account login operation and sends the login information to the server. The server extracts computing resources from the resource pool according to the login information and allocates them to the account. Specifically, the required computing power can be estimated according to the size of the initial data volume, and the computing resources are extracted from the resource pool according to the estimation result and allocated to the client.

[0043] For example: After the client sends a data request to the server, the server reads multiple groups of initial data of the sonic boom test measured by the pressure measurement rail as Figure 3 shown. Further, the server processes and analyzes this set of data using the probability model in Equation 1, and uses the maximum likelihood method to solve the model parameters. The model likelihood function is as shown in Equation 2. The result of parameter k t is the sonic boom signal of the model to be measured in the test, as Figure 4 shown. Finally, this set of calculation results is saved to the server and returned to the client.

[0044] The main advantages of this embodiment are as follows: By using the probability model, the main noise sources in the sonic boom test data are decomposed and parameterized, and other unknown noises are simulated using the pure random variable ε of the normal distribution. The probability model is solved using the maximum likelihood method, which more reasonably explains the uncertainty of the test data, and smoother results are obtained in the comparison with the results of the traditional spatial averaging method. The traditional reference train number spatial averaging method uses the reference train number data as the background environment signal, but through the analysis of the original data, it is found that the background environment signal has certain fluctuations, and using the reference train number data as the background environment signal will introduce new noises. To reduce the noise sources, it is selected not to use the reference train number test data, and more accurate analysis results with smaller error fluctuations can be obtained with less test data, reducing the demand for test data and improving the test efficiency. The system can further improve the sonic boom data processing effect by adjusting and improving the probability model in the server and obtaining more test data.

[0045] In this embodiment, an analysis device for sonic boom test data is also provided. The device runs on the server, as Figure 8 shown, and the device includes:

[0046] A receiving module, configured to receive a sonic boom test data request sent by the client.

[0047] An acquisition module, configured to read sonic boom test data from the pressure measurement rail.

[0048] Wherein, the pressure measurement rail is installed in the wind tunnel and is used to measure sonic boom test data, and the initially measured sonic boom test data is used as the initial data. During the operation of the acquisition module, the aircraft model is moved axially at a fixed interval, and the test data of the aircraft model at different axial positions is measured.

[0049] A processing module, configured to input the initial data into the probability model for processing, and the probability model outputs model parameters.

[0050] A result output module, configured to obtain a signal result of the sonic boom test model by using the model parameters output by the probability model, and return the obtained signal result to the client.

[0051] Further, as Figure 10 shown, it further includes: a login module, configured to receive the login information sent by the client; a resource management module, configured to extract computing resources from a resource pool according to the login information and allocate them to the client.

[0052] This embodiment relates to the technical field of wind tunnel sonic boom tests, and can improve the efficiency and accuracy of a sonic boom test data analysis system. Using the information provided by the client, the main body of the method flow runs on the server side. The main methods include: reading the measurement data of the sonic boom test according to the data acquisition request sent by the client; analyzing and processing the initial data through a probability model stored in the server; solving the model parameters by using the maximum likelihood method; obtaining the model sonic boom signal to be measured through the parameter results, and finally returning the obtained result to the client. By using the sonic boom test data processing method of the present invention, the accuracy of the sonic boom test data result and the efficiency of the sonic boom test can be improved, test resources can be saved, and the test cost can be reduced.

[0053] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, it is described relatively simply, and for the relevant parts, reference can be made to the partial description of the method embodiment. As described above, only the specific implementation manners of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for analyzing sonic boom test data, characterized in that Including: S1. The server receives a sonic boom test data request sent by the client. S2. Read the sonic boom test data. Among them, the pressure measurement rail is installed in the wind tunnel and used to measure the sonic boom test data. The initially measured sonic boom test data is used as the initial data. S3. Input the initial data into the probability model for processing, and the probability model outputs model parameters. S4. Use the model parameters output by the probability model to obtain the signal result of the sonic boom test model, and return the obtained signal result to the client. During the process of reading the sonic boom test data from the pressure measurement rail, move the aircraft model axially at a fixed interval, and measure the test data of the aircraft model at different axial positions. Before S3, it further includes: establishing the probability model according to the sonic boom test data: where B i,c is the sonic boom test data of the c-th sonic boom test for the i-th pressure measurement hole, a i is the background signal of the i-th pressure measurement hole, k t is the sonic boom signal of the pressure measurement hole t away from the test model, b c is the overall environmental signal offset change of the c-th test, and ε is the noise of other uncertain factors, following a normal distribution with a standard deviation of σ.

2. The method according to claim 1, characterized in that, The pressure measurement rail is fixed on the side wall of the wind tunnel. The aircraft model is connected to the adapter strut and the axial movement mechanism and is fixed above the pressure measurement rail. Pressure measurement holes are distributed on the edge of the pressure measurement rail. The initial data includes: the measurement data of the aircraft model at different axial positions.

3. The method according to claim 1, characterized in that The inputting the initial data into the probability model for processing includes: After inputting the initial data into the probability model, perform maximum likelihood processing through the likelihood function to obtain the model parameters when the probability of the observed data is the largest. The observed data includes the sonic boom test data measured after the initial data. Among them, the likelihood function is: P is the probability of the data.

4. The method according to claim 1, characterized in that, Before S1, it also includes: The server receives the login information sent by the client. Extract computing resources from the resource pool according to the login information and allocate them to the client.

5. An analysis device for sonic boom test data, characterized in that, The device runs on the server. The device includes: A receiving module, used to receive the sonic boom test data request sent by the client. An acquisition module, used to read the sonic boom test data from the pressure measurement rail. Among them, the pressure measurement rail is installed in the wind tunnel and used to measure the sonic boom test data. The initially measured sonic boom test data is used as the initial data. A processing module, used to input the initial data into the probability model for processing, and the probability model outputs model parameters. A result output module, used to use the model parameters output by the probability model to obtain the signal result of the sonic boom test model, and return the obtained signal result to the client. During the process of reading the sonic boom test data from the pressure measurement rail, move the aircraft model axially at a fixed interval, and measure the test data of the aircraft model at different axial positions. Before S3, it further includes: establishing the probability model according to the sonic boom test data: where B i,c is the sonic boom test data of the i-th pressure measuring hole for the c-th time, a i is the background signal of the i-th pressure measuring hole, k t is the sonic boom signal of the pressure measuring hole t away from the test model, b c is the overall environmental signal offset change of the c-th test, and ε is the noise of other uncertain factors, following a normal distribution with a standard deviation of σ.

6. The device according to claim 5, wherein, The pressure measurement rail is fixed on the side wall of the wind tunnel. The aircraft model is connected to the adapter strut and the axial movement mechanism and is fixed above the pressure measurement rail. Pressure measurement holes are distributed on the edge of the pressure measurement rail. The initial data includes: the measurement data of the aircraft model at different axial positions.

7. The device according to claim 6, characterized in that, During the operation of the acquisition module, move the aircraft model axially at a fixed interval, and measure the test data of the aircraft model at different axial positions.

8. The device according to claim 5, characterized in that, It also includes: A login module, used to receive the login information sent by the client. A resource management module, used to extract computing resources from the resource pool according to the login information and allocate them to the client.

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