Method for identifying turbulent and non-turbulent interfaces in hypersonic flow fields

Abstract

The present invention discloses a method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field, comprising the following steps: obtaining a velocity field in a hypersonic flow field; converting the velocity field into a vorticity field to obtain regional volume fractions corresponding to different vorticity ranges; analyzing a vorticity threshold platform region of a TNTI interface based on the regional volume fractions corresponding to the different vorticity ranges; selecting a target threshold in the vorticity threshold platform region, and dividing the vorticity field into a turbulent region and a non-turbulent region based on the target threshold; extracting a boundary line between the turbulent region and the non-turbulent region using a fuzzy clustering algorithm to obtain an isosurface corresponding to the target threshold; the present invention no longer artificially selects one or several thresholds for TNTI identification, but instead analyzes the vorticity threshold platform region, identifies all interfaces within the vorticity threshold range that meet the characteristics of the TNTI, and finally integrates them into a complete TNTI with thickness.

Description

Technical Field

[0001] The present invention relates to the field of aerospace technology, and more particularly to a method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field. Background Art

[0002] With the development of hypersonic vehicles, higher requirements are being placed on the aerodynamic thermal protection of the vehicle's exterior and the flow and combustion stability of its core engine. A common phenomenon is the transfer of matter and energy between turbulent and non-turbulent regions, which directly affects the overall safety, stability, and controllability of the vehicle. When studying turbulent and non-turbulent flow, the primary focus is on identifying the turbulent-non-turbulent interface (TNTI). This interface is actually a material layer with a very small thickness, which is divided into a viscous superlayer and an inviscid turbulent sublayer based on different properties. The two thin layers are on the Kolmogorov scale and Taylor scale, respectively. This makes it difficult to accurately extract such a small-scale TNTI in a complex flow field.

[0003] Currently, the commonly used methods for identifying TNTI based on thresholds of vorticity or pseudo-vorticity energy have two major drawbacks. First, the vorticity or pseudo-vorticity energy threshold corresponding to TNTI is relatively low. However, interference factors such as internal turbulent dissipation or shock waves can form localized small vorticity structures within turbulent regions or in non-turbulent areas. When extracting isosurfaces below a certain threshold, these small vorticity structures that are not TNTI can be mistakenly identified, resulting in certain errors. Especially when there is shock wave interference, the area near the shock wave and its interaction with the boundary layer is often accompanied by complex small vorticity regions. These small vorticity regions are not part of the energy and material transfer interface between the turbulent and non-turbulent regions that the study focuses on, and are regarded as error points. Direct application of the vorticity threshold method will identify these error points and affect the final quantitative analysis results; secondly, this method requires manual selection of a suitable threshold, and the common normalization method is relatively simple and is not applicable to hypersonic conditions, which ultimately causes more errors; moreover, the common vorticity threshold method mostly selects a certain threshold to identify TNTI, while the real TNTI is a material layer with thickness, not a simple surface. Simple visualization can present it in this way, but the physical laws presented by different layered regions within the TNTI are different during quantitative analysis.

[0004] Furthermore, the fuzzy clustering method can obtain clearer vortex structures and does not require manual threshold selection, thus reducing human selection errors. However, as the name suggests, the extracted interface is "fuzzy," making it difficult to determine the magnitude of vorticity or turbulent kinetic energy in the TNTI identified by this method, resulting in a lack of clear physical meaning.

[0005] Therefore, how to accurately and completely identify TNTI is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0006] In view of this, the present invention provides a method for identifying turbulent and non-turbulent interfaces in hypersonic flow fields. Instead of manually selecting one or several thresholds to identify TNTI, the method analyzes the vorticity threshold platform area and identifies all interfaces within the vorticity threshold range that meet the TNTI characteristics. Finally, the interfaces are integrated into a complete TNTI with thickness.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field comprises the following steps:

[0009] Obtain the velocity field under the hypersonic flow field.

[0010] The velocity field is converted into a vorticity field to obtain the regional volume fractions corresponding to different vorticity ranges.

[0011] According to the regional volume fractions corresponding to different vorticity ranges, the vorticity threshold platform area of the TNTI interface is analyzed.

[0012] A target threshold is selected in the vorticity threshold platform region, and the vorticity field is divided into a turbulent region and a non-turbulent region according to the target threshold.

[0013] A boundary line between the turbulent area and the non-turbulent area is extracted to obtain an isosurface corresponding to the target threshold.

[0014] Preferably, the step further includes: normalizing the data points in the vortex field.

[0015] Preferably, the normalization process comprises the following steps:

[0016]

[0017] Among them, ω * is the normalized vorticity, ω is the vorticity, v is the kinematic viscosity coefficient, δ is the local boundary layer thickness, u τ is the local friction velocity.

[0018] Preferably, the step of analyzing the vorticity threshold platform area of the TNTI interface includes:

[0019] Setting a vorticity threshold variable, and treating an area where the vorticity is greater than the threshold variable as a suspected turbulent area;

[0020] Based on the volume fractions of regions corresponding to different vorticity, a volume fraction curve of the suspected turbulent region under the change of vorticity threshold variable is generated;

[0021] A slope threshold is set, a portion of the volume fraction curve that is smaller than the slope threshold is extracted, and an interval formed by the end values of the vorticity threshold of the portion is used as the vorticity threshold platform area.

[0022] Preferably, the steps further include:

[0023] Multiple target threshold selections are performed in the vorticity threshold platform region, and multiple isosurfaces are correspondingly obtained as identification results of the turbulent and non-turbulent interface.

[0024] Preferably, a fuzzy clustering algorithm is used to extract the boundary line between the turbulent area and the non-turbulent area, and the steps include:

[0025] Initialization is performed based on the division of turbulent and non-turbulent areas;

[0026] Calculate the cluster center according to the membership matrix;

[0027] Based on the cluster center, calculate the distance between each data point and the cluster center and update the membership matrix;

[0028] Iterate until the objective function converges and output the final membership matrix and cluster centers.

[0029] Preferably, the objective function is:

[0030]

[0031] Among them, U is the membership matrix, V represents the cluster center, and u ij is the degree of membership, d ij is the distance from the data point to the cluster center, and m is the fuzzy index.

[0032] Preferably, a neural network algorithm is used to extract the boundary line between the turbulent area and the non-turbulent area.

[0033] A turbulent and non-turbulent interface identification system for a hypersonic flow field, comprising a data acquisition module, a preprocessing module, a data analysis module, and an interface generation module;

[0034] The data acquisition module is used to acquire the velocity field under the high sonic flow field;

[0035] The preprocessing module is used to convert the velocity field into a vortex field;

[0036] The data analysis module is used to analyze the volume fractions of different vorticity ranges in the vorticity field, and confirm the vorticity threshold platform area of the TNTI interface according to the regional volume fractions corresponding to the different vorticity ranges;

[0037] The interface generation module is used to select a target threshold in the vortex threshold platform area, and divide the vortex field into a turbulent area and a non-turbulent area according to the target threshold; and is used to extract the boundary line between the turbulent area and the non-turbulent area using a fuzzy clustering algorithm to obtain an isosurface corresponding to the target threshold.

[0038] Preferably, the preprocessing module is further used to perform normalization processing on the vorticity value in the vorticity field, and the normalization processing comprises the following steps:

[0039]

[0040] Among them, ω * is the normalized vorticity, ω is the vorticity, v is the kinematic viscosity coefficient, δ is the local boundary layer thickness, u τ is the local friction velocity.

[0041] Through the above technical solutions, it can be seen that compared with the prior art, the present invention discloses a method for identifying turbulent and non-turbulent interfaces in hypersonic flow fields. It no longer artificially selects one or several thresholds to identify TNTI, but analyzes the vortex threshold platform area, identifies all interfaces within the vortex threshold range that meet the characteristics of TNTI, and finally integrates them into a complete TNTI with thickness. Secondly, the present invention divides the original vortex field into two areas of "0" and "1" according to different thresholds in the vortex threshold platform area, extracts interfaces, and can obtain multiple isosurfaces with clear vortex values. The present invention uses local parameters for normalization processing in high-speed flow fields to avoid the influence of Reynolds number. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0043] Figure 1 A schematic diagram of a method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of the structure of a turbulent and non-turbulent interface identification system for a hypersonic flow field provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0046] Example 1

[0047] like Figure 1 The embodiment of the present invention discloses a method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field, comprising the following steps:

[0048] S1: Obtain the velocity field under hypersonic flow field.

[0049] S2: Convert the velocity field into the vorticity field to obtain the area volume fraction corresponding to different vorticity ranges;

[0050] S3: Analyze the vorticity threshold platform area of the TNTI interface according to the area volume fraction corresponding to different vorticity ranges;

[0051] S4: selecting a target threshold in the vorticity threshold platform area, and dividing the vorticity field into a turbulent area and a non-turbulent area according to the target threshold;

[0052] S5: Extract the boundary line between the turbulent area and the non-turbulent area, and obtain the isosurface corresponding to the target threshold.

[0053] In this embodiment, the present invention can extract a complete interface with thickness based on the vorticity field, that is, the vorticity threshold platform area, and by selecting the target threshold in the platform area for interface division, it can generate an isosurface with a clear vorticity level. By extracting all the vorticity in the complete TNTI interface, all the isosurfaces are obtained, and all the isosurfaces together constitute the final TNTI interface.

[0054] Regarding velocity field acquisition, in actual flight testing, various types of probes (such as pitot tubes and total pressure probes) can be installed to directly measure parameters such as static pressure, total pressure, and temperature around the aircraft, thereby calculating velocity field information. Ground-based radar or other remote sensing equipment can also be used to detect the flow field characteristics around the aircraft from a distance. Microsensor arrays can also be installed on the aircraft to directly measure the pressure distribution on the aircraft surface and infer the flow field velocity from the pressure distribution. Modern sensor technology allows operation under hypersonic conditions and can withstand extremely high temperatures.

[0055] Regarding the conversion of the velocity field to the vorticity field, vorticity is defined as the curl of the velocity field. In three-dimensional space, a data point in the velocity field is represented as u = (u, v, w), where u, v, and w represent the velocity components in the x, y, and z directions, respectively. The vorticity can be calculated using the following formula:

[0056]

[0057]

[0058] Among them, ω x 、ω y and ω z represent the vorticity components in the x, y and z directions respectively.

[0059] In order to further implement the above technical solution, the step also includes: normalizing the data points in the vortex field.

[0060] Further, the normalization process includes the following steps:

[0061]

[0062] Among them, ω * is the normalized vorticity, ω is the vorticity, v is the kinematic viscosity coefficient, δ is the local boundary layer thickness, u τ is the local friction velocity.

[0063] In this embodiment, the present invention uses the local boundary layer thickness and friction velocity to normalize the vortex field. Compared with the simple normalization method, it avoids the influence of changes in the Reynolds number and other factors in the hypersonic flow field.

[0064] In order to further implement the above technical solution, S3 specifically includes:

[0065] S31: Set the vorticity threshold variable and take the area where the vorticity is greater than the threshold variable as the suspected turbulent area;

[0066] S32: Based on the volume fractions of the regions corresponding to different vorticity, a volume fraction curve of the suspected turbulent region under the change of the vorticity threshold variable is generated;

[0067] S33: Setting a slope threshold, extracting a portion of the volume fraction curve that is less than the slope threshold, and using the interval formed by the end values of the vorticity threshold of this portion as a vorticity threshold platform area.

[0068] In this embodiment, due to the existence of TNTI, when the vorticity threshold variable changes from small to large or from large to small, there will be a slowly changing area in the volume fraction change curve of the suspected turbulent area. The present invention extracts the vorticity threshold corresponding to this slowly changing area as the vorticity range of the complete TNTI.

[0069] Regarding the calculation of volume fraction: the vorticity field is composed of the vorticity corresponding to multiple data points. By obtaining the number of data points that meet the threshold conditions and calculating the total proportion, the corresponding volume fraction can be obtained.

[0070] To further implement the above technical solution, S5 uses a fuzzy clustering algorithm to extract the boundary between the turbulent and non-turbulent areas. The steps include:

[0071] S51: Initialization is performed based on the division of turbulent areas and non-turbulent areas.

[0072] S52: Calculate the cluster center according to the membership matrix.

[0073] S53: Based on the cluster center, calculate the distance between each data point and the cluster center and update the membership matrix.

[0074] S54: Iterate until the objective function converges and output the final membership matrix and cluster centers.

[0075] Furthermore, the objective function is:

[0076]

[0077] Among them, U is the membership matrix, V represents the cluster center, d ij is the distance from the data point to the cluster center, m is the fuzzy index; n is the total number of data points contained in the data set, k is the total number of cluster centers, i represents the i-th data point, and j represents the j-th cluster center. u ij Indicates the membership of the i-th data point to the j-th cluster center.

[0078] In this embodiment, after obtaining the complete TNTI threshold range, the present invention needs to separately identify each threshold within that range. Prior to identification, certain processing is required: the original vorticity field is divided into arrays of "0" and "1" labels based on the threshold. Vorticity fields greater than the threshold are classified as "0," and those less than or equal to the threshold are classified as "1." This array is then input into the fuzzy clustering recognition component to identify all faces within the threshold range. The core of this component remains the fuzzy clustering algorithm, which initializes the cluster center and membership matrix to determine the cluster to which each data point belongs. The distance from the data point to the cluster center is then calculated to analyze the minimum value of the objective function under the constraints. Finally, convergence is determined to produce the clustering result. Compared to conventional fuzzy clustering, the input of the present invention is no longer a velocity field, but an array of "0" and "1" with clear physical meaning. The resulting interface at a given vorticity threshold is no longer ambiguous. Repeating this clustering process yields TNTI at different thresholds until all faces within the threshold range in the first component are identified. Combining these results yields a complete TNTI result.

[0079] In another embodiment, a neural network algorithm can be used to extract the boundary between the turbulent area and the non-turbulent area. The specific steps include:

[0080] Step 0: Build a 3-layer neural network with an input layer, a hidden layer, and an output layer.

[0081] Step 1: The number of nodes in the input layer is determined by the number of classes in the data, which is 2. Similarly, the number of nodes in the output layer is determined by the number of classes we have, which is also 2. (Since we only have 2 classes, we could actually use just one output node to predict 0 or 1, but having 2 makes it easier to scale the network to more classes).

[0082] Step 2: You can choose the dimension of the hidden layer. The more nodes you place in the hidden layer, the more complex the function that can be accommodated.

[0083] Step 3: Choose an activation function for the hidden layer. An activation function transforms a layer's input into its output. Non-linear activation functions allow us to fit non-linear assumptions. Common choices for activation functions are tanh, sigmoid function, or ReLUs.

[0084] Step 4: The activation function of the output layer is softmax, which is a way to convert raw scores into probabilities. You can think of softmax as its generalization to multiple classes.

[0085] Step 5: Train the model by learning the fitting parameters and minimizing the error of the training data in an iterative process.

[0086] Step 6: Predict the data based on the trained model, divide the original data points into two parts, and obtain the interface between the two parts.

[0087] Step 7: Use this model to analyze the “0” and “1” label arrays at different thresholds, obtain the interface, and integrate it into a complete TNTI.

[0088] Example 2

[0089] like Figure 2 Based on the same inventive concept, an embodiment of the present invention discloses a system for identifying turbulent and non-turbulent interfaces in a hypersonic flow field, comprising a data acquisition module, a preprocessing module, a data analysis module, and an interface generation module;

[0090] The data acquisition module is used to obtain the velocity field under the high sonic flow field;

[0091] The preprocessing module is used to convert the velocity field into the vorticity field;

[0092] The data analysis module is used to analyze the volume fractions of different vorticity ranges in the vorticity field and confirm the vorticity threshold platform area of the TNTI interface based on the regional volume fractions corresponding to different vorticity ranges;

[0093] The interface generation module is used to select the target threshold in the vorticity threshold platform area and divide the vorticity field into turbulent area and non-turbulent area according to the target threshold; it is used to extract the boundary line between turbulent area and non-turbulent area using fuzzy clustering algorithm to obtain the isosurface corresponding to the target threshold.

[0094] In order to further implement the above technical solution, the preprocessing module is also used to normalize the vorticity value in the vorticity field. The normalization process includes the following steps:

[0095]

[0096] Among them, ω * is the normalized vorticity, ω is the vorticity, v is the kinematic viscosity coefficient, δ is the local boundary layer thickness, u τ is the local friction velocity.

[0097] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0098] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field, characterized in that: The following steps are involved: Obtain the velocity field under the hypersonic flow field; Regarding the acquisition of the velocity field, various types of probes are installed to directly measure the parameters around the aircraft and calculate the velocity field information; or remote sensing equipment is used to detect the flow field characteristics around the aircraft to obtain velocity field information; or micro sensor arrays are installed on the aircraft to measure the pressure distribution on the aircraft surface and infer the flow field velocity from the pressure distribution; Converting the velocity field into a vorticity field and normalizing the data points in the vorticity field to obtain the area volume fractions corresponding to different vorticity ranges; The normalization process includes: Among them, ω * is the normalized vorticity, ω is the vorticity, v is the kinematic viscosity coefficient, δ is the local boundary layer thickness, and uτ is the local friction velocity; Analyze the vorticity threshold platform area of the TNTI interface based on the regional volume fraction corresponding to different vorticity ranges. The specific steps include: Setting a vorticity threshold variable, and treating an area where the vorticity is greater than the threshold variable as a suspected turbulent area; Based on the volume fractions of regions corresponding to different vorticity, a volume fraction curve of the suspected turbulent region under the change of vorticity threshold variable is generated; Setting a slope threshold, extracting a portion of the volume fraction curve that is smaller than the slope threshold, and using the interval formed by the end values of the vorticity threshold of the portion as the vorticity threshold platform area; Selecting a target threshold in the vorticity threshold platform region, and dividing the vorticity field into a turbulent region and a non-turbulent region according to the target threshold; A boundary line between the turbulent area and the non-turbulent area is extracted to obtain an isosurface corresponding to the target threshold.

2. The method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field according to claim 1, wherein: The steps also include: Multiple target threshold selections are performed in the vorticity threshold platform region, and multiple isosurfaces are correspondingly obtained as identification results of the turbulent and non-turbulent interface.

3. The method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field according to claim 1, wherein: The fuzzy clustering algorithm is used to extract the boundary line between the turbulent area and the non-turbulent area, and the steps include: Initialization is performed based on the division of turbulent and non-turbulent areas; Calculate the cluster center according to the membership matrix; Based on the cluster center, calculate the distance between each data point and the cluster center and update the membership matrix; Iterate until the objective function converges and output the final membership matrix and cluster centers.

4. The method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field according to claim 3, wherein: The objective function is: Among them, U is the membership matrix, V represents the cluster center, and u ij is the degree of membership, d ij is the distance from the data point to the cluster center, and m is the fuzzy index.

5. The method for identifying turbulent and non-turbulent interfaces in a hypersonic flow field according to claim 1, characterized in that: A neural network algorithm is used to extract the boundary line between the turbulent area and the non-turbulent area.

6. A system for identifying turbulent and non-turbulent interfaces in a hypersonic flow field, characterized in that: It includes data acquisition module, pre-processing module, data analysis module and interface generation module; The data acquisition module is used to acquire the velocity field under the high-speed sound flow field. Regarding the acquisition of the velocity field, various types of probes are installed to directly measure the parameters around the aircraft to calculate the velocity field information; or remote sensing equipment is used to detect the flow field characteristics around the aircraft to obtain the velocity field information; or a micro sensor array is installed on the aircraft to measure the pressure distribution on the aircraft surface and infer the flow field velocity based on the pressure distribution. The pre-processing module is used to convert the velocity field into a vortex field, which includes normalizing the data points in the vortex field: Among them, ω * is the normalized vorticity, ω is the vorticity, ν is the kinematic viscosity coefficient, δ is the local boundary layer thickness, u τ is the local friction velocity; The data analysis module is used to analyze the volume fractions of different vorticity ranges in the vorticity field; Used to set a vorticity threshold variable, and regard the area with vorticity greater than the threshold variable as a suspected turbulent area; based on the volume fraction of the area corresponding to different vorticities, generate a volume fraction curve of the suspected turbulent area under the change of the vorticity threshold variable; set a slope threshold, extract the part of the volume fraction curve that is less than the slope threshold, and regard the interval formed by the end value of the vorticity threshold of this part as the vorticity threshold platform area; The interface generation module is used to select a target threshold in the vortex threshold platform area, and divide the vortex field into a turbulent area and a non-turbulent area according to the target threshold; and is used to extract the boundary line between the turbulent area and the non-turbulent area using a fuzzy clustering algorithm to obtain an isosurface corresponding to the target threshold.

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