Optimization method of high-frequency dynamic temperature field on the wall of supersonic or hypersonic flow field

Abstract

The present application belongs to the field of flow field temperature testing technology and relates to a method for optimizing the high-frequency dynamic temperature field of the wall of a supersonic or hypersonic flow field, comprising: performing infrared temperature measurement in a supersonic or hypersonic flow field to obtain low-frequency temperature field data of the model wall and a compressed sensing orthogonal basis matrix; constructing a vector compressed sensing model and solving it to obtain a sparse matrix and an optimal observation matrix, and determining a sparse measurement point layout scheme of thermocouples with the optimal observation matrix; performing thermocouple temperature measurement based on the sparse measurement point layout scheme of the thermocouples to obtain high-frequency temperature field data of the model wall; reconstructing the vector compressed sensing model based on the high-frequency temperature field data of the model wall and solving it to obtain an optimal sparse matrix, and reconstructing the high-frequency dynamic temperature field data of the supersonic or hypersonic model wall. The present application can reconstruct the high-frequency dynamic temperature field of the supersonic or hypersonic model wall.

Description

Technical Field

[0001] The present application relates to the technical field of flow field temperature testing, and in particular to a method for optimizing the high-frequency dynamic temperature field of a supersonic or hypersonic flow field wall. Background Art

[0002] Measuring the model wall temperature distribution is one of the important technical means of supersonic / hypersonic flow field testing. The wall temperature distribution can be used to further obtain the distribution of wall heat flux, convective heat transfer coefficient, and Nusselt number. This can be used to evaluate the thermal protection performance and cooling efficiency of supersonic / hypersonic aircraft, and can also indirectly reflect complex flow mechanisms such as shock wave-boundary layer interference.

[0003] Compared with low-speed / subsonic flow fields, supersonic / hypersonic flow fields have the characteristics of high Mach number, high Reynolds number, high total temperature, and high total enthalpy. In addition, due to factors such as the compression heat of the shock wave upstream of the model and the viscous resistance in the boundary layer, the temperature field distribution in the near-wall area is more complex, which also puts higher requirements on the measurement of the flow field wall temperature.

[0004] Currently, two types of measurement methods are widely used in the field of supersonic / hypersonic flow field wall temperature measurement, namely: non-contact infrared temperature measurement technology and contact thermocouple temperature measurement technology.

[0005] Infrared temperature measurement technology can obtain wall temperature field distribution with high spatial resolution, but due to factors such as integration time and camera memory, its frame rate is generally limited to hundreds of frames per second (corresponding to an acquisition frequency of hundreds of Hz), which makes it difficult to meet the analysis needs of high-frequency dynamic temperature fields. High-frequency thermocouples can achieve higher-frequency temperature acquisition (up to approximately 50-100 kHz), but can only obtain local dynamic temperature data at discrete points. To improve their measurement spatial resolution, it is necessary to increase the number of measurement points on the model surface. However, installing more thermocouples will not only damage the integrity of the model surface, but also seriously change the flow structure such as shock waves in the flow field, and also result in higher testing costs. In addition, because the model length in supersonic / hypersonic flow fields is limited by the shock wave reflection diamond area, the layout space for thermocouples is also relatively limited.

[0006] For these reasons, there is currently a lack of methods for measuring the dynamic temperature field on the walls of supersonic / hypersonic flow models with high temporal and spatial resolution. With the increasing demands for the thermal protection performance of supersonic / hypersonic vehicles, the demand for testing the dynamic temperature field on the walls will inevitably increase. Therefore, it is necessary to develop a low-cost, highly reliable method for obtaining the dynamic temperature field on the walls with high temporal and spatial resolution. Summary of the Invention

[0007] Based on this, it is necessary to provide a high-frequency dynamic temperature field optimization method for the wall of supersonic or hypersonic flow field to address the above technical problems, which can reconstruct the high-frequency dynamic temperature field of the supersonic or hypersonic model wall.

[0008] The high-frequency dynamic temperature field optimization method for the wall of supersonic or hypersonic flow field includes:

[0009] In the supersonic or hypersonic flow field, infrared temperature measurement is performed to obtain the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix;

[0010] Based on the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix, a vector compressed sensing model is constructed and solved to obtain a sparse matrix and the optimal observation matrix. The sparse measurement point layout scheme of the thermocouple is determined with the optimal observation matrix.

[0011] According to the sparse measurement point layout plan of thermocouples, thermocouple temperature measurement is carried out to obtain high-frequency temperature field data of the model wall;

[0012] According to the high-frequency temperature field data of the model wall, the vector compressed sensing model is reconstructed and solved to obtain the optimal sparse matrix, and the high-frequency dynamic temperature field data of the supersonic or hypersonic model wall is reconstructed.

[0013] In one embodiment, infrared temperature measurement is performed in a supersonic or hypersonic flow field to obtain low-frequency temperature field data of the model wall and a compressed sensing orthogonal basis matrix, including:

[0014] In the supersonic or hypersonic flow field, infrared temperature measurement is performed to obtain low-frequency temperature field data of the model wall;

[0015] According to the low-frequency temperature field data of the model wall, the intrinsic orthogonal decomposition is performed to obtain the intrinsic orthogonal decomposition spatial mode;

[0016] The intrinsic orthogonal decomposition spatial mode is used as the compressed sensing orthogonal basis matrix.

[0017] In one embodiment, a vector compressed sensing model is constructed based on the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix, and is solved to obtain a sparse matrix and an optimal measurement matrix. The sparse measurement point layout scheme of the thermocouple is determined based on the optimal measurement matrix, including:

[0018] Initialize the observation matrix and construct a vector compressed sensing model based on the observation matrix, the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix;

[0019] Compressed sensing model of vector is solved to obtain sparse matrix;

[0020] According to the sparse matrix, the reconstruction error is calculated to reconstruct the observation matrix;

[0021] When it is determined that the preset conditions are met, the measurement matrix corresponding to the minimum reconstruction error is output as the optimal measurement matrix;

[0022] According to the optimal observation matrix, the sparse measurement point layout scheme of thermocouples is determined.

[0023] In one embodiment, a measurement matrix is ​​initialized, and a vector compressed sensing model is constructed based on the measurement matrix, the model wall low-frequency temperature field data, and the compressed sensing orthogonal basis matrix, including:

[0024]

[0025] Where, is the low-dimensional measurement matrix, C is the observation matrix, is the high-dimensional matrix to be reconstructed, which is obtained from the low-frequency temperature field data of the model wall, Ψ is the compressed sensing orthogonal basis matrix, is a sparse matrix, p is the number of time sample points of dynamic measurement, and q is the q norm of the sparse matrix.

[0026] In one embodiment, calculating the reconstruction error includes:

[0027]

[0028] Where MSE is the reconstruction error and F is the Frobenius norm of the matrix.

[0029] In one embodiment, based on the high-frequency temperature field data of the model wall, a vector compressed sensing model is reconstructed and solved to obtain an optimal sparse matrix, and high-frequency dynamic temperature field data of the supersonic or hypersonic model wall is reconstructed, including:

[0030]

[0031] Where Y' is the high-frequency temperature field data of the model wall, X' is the high-frequency dynamic temperature field data, and S' is the optimal sparse matrix.

[0032] The above-mentioned high-frequency dynamic temperature field optimization method for the wall of a supersonic or hypersonic flow field overcomes the technical difficulty that the existing model wall temperature testing technology in supersonic / hypersonic flow fields is difficult to simultaneously achieve high-temporal and spatial resolution dynamic temperature field measurement. The low-frequency wall temperature field data obtained by infrared temperature measurement is combined with the high-frequency response thermocouple temperature test data of sparsely optimized points, and compressed sensing is used for data assimilation and reconstruction to obtain the high-temporal and spatial resolution model wall dynamic temperature field distribution under supersonic / hypersonic conditions. This method establishes a connection between infrared temperature measurement data and high-frequency response thermocouple data based on the compressed sensing method, and obtains the high-temporal and spatial resolution wall dynamic temperature field distribution under supersonic / hypersonic conditions through data assimilation and reconstruction. It can combine the advantages of high spatial resolution of infrared temperature measurement and high temporal resolution of high-frequency thermocouple temperature measurement, and can effectively reduce testing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 1 is a flow chart of a method for optimizing a high-frequency dynamic temperature field on a wall surface of a supersonic or hypersonic flow field in one embodiment;

[0034] Figure 2 Schematic diagram of the architecture of a method for optimizing the high-frequency dynamic temperature field on the wall of a supersonic or hypersonic flow field in one embodiment;

[0035] Figure 3 A side view of a random distribution of discrete thermocouple measurement points in one embodiment;

[0036] Figure 4 FIG. 1 is a top view of the random distribution of discrete thermocouple measurement points in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for explaining this application and are not intended to limit this application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this application without creative work are within the scope of protection of this application.

[0038] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0039] In addition, the terms "first," "second," and so on, used in this application are for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "multiple groups" means at least two groups, such as two groups, three groups, and so on, unless otherwise specifically defined.

[0040] In this application, unless otherwise specified or limited, the terms "connect," "fix," etc. should be understood in a broad sense. For example, "fix" can mean a fixed connection, a detachable connection, or an integral connection; it can mean a mechanical connection, an electrical connection, a physical connection, or a wireless communication connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean internal communication between two elements or an interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0041] In addition, the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0042] This application provides a method for optimizing the high-frequency dynamic temperature field of the wall surface of a supersonic or hypersonic flow field, such as Figure 1 The flowchart shown, in one embodiment, includes:

[0043] Step 102 : In the supersonic or hypersonic flow field, infrared temperature measurement is performed to obtain low-frequency temperature field data of the model wall and a compressed sensing orthogonal basis matrix.

[0044] Specifically:

[0045] In the supersonic or hypersonic flow field, infrared temperature measurement is performed to obtain low-frequency temperature field data of the model wall;

[0046] According to the low-frequency temperature field data of the model wall, the Proper Orthogonal Decomposition (POD) is carried out to obtain the Proper Orthogonal Decomposition spatial mode;

[0047] The intrinsic orthogonal decomposition spatial mode is used as the compressed sensing orthogonal basis matrix.

[0048] In this step, infrared temperature measurement is performed to obtain low-frequency temperature field data on the model wall. The following steps are performed: The flat plate to be measured is fixed to the support structure of the hypersonic wind tunnel base, maintaining the plate horizontally. The plate surface is coated with a specific non-metallic material with a certain emissivity (such as Bakelite). The leading edge of the plate is formed into a wedge shape to eliminate the bow-shaped shock wave upstream of the plate under hypersonic conditions and weaken it into an oblique shock wave. Dynamic temperature field measurements of the flat plate surface are performed using an infrared temperature camera at a fixed inflow Mach number and different inflow total temperatures. The video capture frame rate is set to 100 fps, and the temperature distribution is recorded for a period of time after the wind tunnel is started and the flow conditions stabilize.

[0049] It should be noted that how to perform infrared temperature measurement to obtain low-frequency temperature field data of the model wall belongs to the existing technology and will not be described in detail here.

[0050] Step 104 : construct a vector compressed sensing model based on the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix, solve it, obtain a sparse matrix, and obtain an optimal measurement matrix. The sparse measurement point layout plan of the thermocouple is determined based on the optimal measurement matrix.

[0051] Specifically:

[0052] Initialize the observation matrix and construct a vector compressed sensing model based on the observation matrix, the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix;

[0053] Compressed sensing model of vector is solved to obtain sparse matrix;

[0054] According to the sparse matrix, the reconstruction error is calculated to reconstruct the observation matrix;

[0055] When it is determined that the preset conditions are met, the measurement matrix corresponding to the minimum reconstruction error is output as the optimal measurement matrix;

[0056] According to the optimal observation matrix, the sparse measurement point layout scheme of thermocouples is determined.

[0057] More specifically:

[0058] Initialize the observation matrix and build the initial model based on the observation matrix, the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix:

[0059] Y=CX=CΨS

[0060] Where, is a low-dimensional measurement matrix, As the measurement matrix (a Gaussian matrix can be selected as the measurement matrix, and each element thereof satisfies the Gaussian probability density distribution with a mean of 0), the high-dimensional matrix x can be projected into the low-dimensional space to obtain the measurement matrix S. is a high-dimensional matrix to be reconstructed, which is obtained from the low-frequency temperature field data of the model wall at different times with a certain frame rate. is the compressed sensing orthogonal basis matrix, is a sparse matrix, p is the number of time sample points of dynamic measurement (number of transient snapshots);

[0061] Since the vector equation has an underdetermined characteristic, in order to solve it, a minimum mixing l for solving the sparse matrix S is defined. 1,q Norm problem:

[0062]

[0063] Where n is the number of rows of the sparse matrix S, i is the row index of the sparse matrix S, and j is the column index of the sparse matrix S;

[0064] The compressed sensing optimization problem is transformed to obtain a vector compressed sensing model:

[0065]

[0066] Where q is the q norm of the sparse matrix;

[0067] The vector compressed sensing model is solved using convex optimization algorithm, greedy algorithm, particle swarm optimization algorithm or Bayesian algorithm to obtain a sparse matrix;

[0068] According to the sparse matrix, the reconstruction error is calculated to evaluate the accuracy of reconstructing the high spatial resolution wall temperature field from sparse measurement points at different times:

[0069]

[0070] Where MSE is the reconstruction error, and F is the Frobenius norm of the matrix;

[0071] When it is determined that the preset conditions are met, the measurement matrix corresponding to the minimum reconstruction error is output as the optimal measurement matrix C;

[0072] According to the optimal measurement matrix C, the non-zero coordinates of each column vector of the optimal measurement matrix are used as the optimal sparse measurement points, so as to determine the sparse measurement point layout scheme of the thermocouples to meet the measurement accuracy requirements of a fixed number of different moments.

[0073] In this step, the initialized observation matrix can be constructed by a Gaussian matrix, a Bernoulli matrix, etc., and each element thereof satisfies a Gaussian probability density distribution with a mean of 0.

[0074] The reconstruction error can evaluate the accuracy of reconstructing the high spatial resolution wall temperature field from sparse measurement points at different times. With the goal of minimizing the reconstruction error, the optimal observation matrix can be obtained.

[0075] Step 106 : Perform thermocouple temperature measurement according to the sparse measurement point layout scheme of the thermocouples to obtain high-frequency temperature field data of the model wall.

[0076] In this step, if Figure 3 and Figure 4 As shown (J represents an oblique shock wave, R represents a coaxial thermocouple, B represents a flat plate, arrows represent the incoming flow direction, circles represent the measurement point locations, and the dashed line represents the boundary between the plate head and the main test section), based on the sparse thermocouple measurement point layout, multiple sets of miniature high-frequency coaxial thermocouples are threaded onto the flat plate model surface, with their leads extending through the wind tunnel floor for thermocouple temperature measurement. Under identical incoming flow conditions (total incoming flow temperature, Mach number, etc.), high-frequency temperature field data are acquired at discrete points on the model wall using the thermocouples and arranged into a test matrix Y'. The specific method for performing thermocouple temperature measurement and obtaining high-frequency temperature field data on the model wall based on this layout is known in the art and will not be elaborated here.

[0077] Step 108 : reconstruct a vector compressed sensing model based on the high-frequency temperature field data of the model wall, solve it, obtain the optimal sparse matrix, and reconstruct the high-frequency dynamic temperature field data of the supersonic or hypersonic model wall.

[0078] Specifically:

[0079]

[0080] Where Y' is the high-frequency temperature field data of the model wall, X' is the high-frequency dynamic temperature field data, and S' is the optimal sparse matrix.

[0081] In this step, the optimal sparse matrix S' is obtained through the convex optimization algorithm according to the sparse test data Y' at different times, and then the high-frequency dynamic temperature field data X' is reconstructed, thereby realizing the data assimilation and reconstruction of the high-frequency dynamic temperature field.

[0082] The above-mentioned high-frequency dynamic temperature field optimization method for the wall of a supersonic or hypersonic flow field overcomes the technical difficulty that the existing model wall temperature testing technology in supersonic / hypersonic flow fields is difficult to simultaneously achieve high-temporal and spatial resolution dynamic temperature field measurement. The low-frequency wall temperature field data obtained by infrared temperature measurement is combined with the high-frequency response thermocouple temperature test data of sparsely optimized points, and compressed sensing is used for data assimilation and reconstruction to obtain the high-temporal and spatial resolution model wall dynamic temperature field distribution under supersonic / hypersonic conditions. This method establishes a connection between infrared temperature measurement data and high-frequency response thermocouple data based on the compressed sensing method, and obtains the high-temporal and spatial resolution wall dynamic temperature field distribution under supersonic / hypersonic conditions through data assimilation and reconstruction. It can combine the advantages of high spatial resolution of infrared temperature measurement and high temporal resolution of high-frequency thermocouple temperature measurement, and can effectively reduce testing costs.

[0083] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0084] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for optimizing the high-frequency dynamic temperature field of a supersonic or hypersonic flow field wall, characterized in that: include: In the supersonic or hypersonic flow field, infrared temperature measurement is performed to obtain low-frequency temperature field data of the model wall, perform intrinsic orthogonal mode decomposition, and obtain the compressed sensing orthogonal basis matrix; Based on the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix, a vector compressed sensing model is constructed and solved to obtain a sparse matrix and the optimal observation matrix. The sparse measurement point layout scheme of the thermocouple is determined with the optimal observation matrix. According to the sparse measurement point layout plan of thermocouples, thermocouple temperature measurement is carried out to obtain high-frequency temperature field data of the model wall; According to the high-frequency temperature field data of the model wall, a vector compressed sensing model is reconstructed and solved to obtain an optimal sparse matrix, and the high-frequency dynamic temperature field data of the supersonic or hypersonic model wall is reconstructed; according to the high-frequency temperature field data of the model wall, a vector compressed sensing model is reconstructed and solved to obtain an optimal sparse matrix, and the high-frequency dynamic temperature field data of the supersonic or hypersonic model wall is reconstructed, including: Where, is the high-frequency temperature field data of the model wall, is the high-frequency dynamic temperature field data, is the optimal sparse matrix, is the observation matrix, is the compressed sensing orthogonal basis matrix.

2. The method for optimizing the high-frequency dynamic temperature field of the wall surface of a supersonic or hypersonic flow field according to claim 1, characterized in that: In the supersonic or hypersonic flow field, infrared temperature measurement is performed to obtain the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix, including: In the supersonic or hypersonic flow field, infrared temperature measurement is performed to obtain low-frequency temperature field data of the model wall; According to the low-frequency temperature field data of the model wall, the intrinsic orthogonal decomposition is performed to obtain the intrinsic orthogonal decomposition spatial mode; The intrinsic orthogonal decomposition spatial mode is used as the compressed sensing orthogonal basis matrix.

3. The method for optimizing the high-frequency dynamic temperature field of the wall surface of a supersonic or hypersonic flow field according to claim 2, characterized in that: Based on the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix, a vector compressed sensing model is constructed and solved to obtain a sparse matrix and the optimal observation matrix. The sparse measurement point layout scheme of the thermocouple is determined with the optimal observation matrix, including: Initialize the observation matrix and construct a vector compressed sensing model based on the observation matrix, the low-frequency temperature field data of the model wall and the compressed sensing orthogonal basis matrix; Compressed sensing model of vector is solved to obtain sparse matrix; According to the sparse matrix, the reconstruction error is calculated to reconstruct the observation matrix; When it is determined that the preset conditions are met, the measurement matrix corresponding to the minimum reconstruction error is output as the optimal measurement matrix; According to the optimal observation matrix, the sparse measurement point layout scheme of thermocouples is determined.

4. The method for optimizing the high-frequency dynamic temperature field of the wall surface of a supersonic or hypersonic flow field according to claim 3, characterized in that: Initialize the observation matrix and build a vector compressed sensing model based on the observation matrix, the model wall low-frequency temperature field data and the compressed sensing orthogonal basis matrix, including: Where, is a low-dimensional measurement matrix, is the observation matrix, is the high-dimensional matrix to be reconstructed, obtained from the low-frequency temperature field data of the model wall. is the compressed sensing orthogonal basis matrix, is a sparse matrix, is the number of time sample points for dynamic measurement, is the q-norm of the sparse matrix.

5. The method for optimizing the high-frequency dynamic temperature field of the wall surface of a supersonic or hypersonic flow field according to claim 4, characterized in that: Calculate the reconstruction error, including: Where, is the reconstruction error, is the Frobenius norm of the matrix.

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