Aircraft aerodynamic simulation vortex characteristic analysis method, device, equipment and medium

Abstract

The present application discloses a method, apparatus, equipment and medium for analyzing vortex characteristics in aircraft aerodynamic simulation, and relates to the field of aircraft simulation technology, including: obtaining CFD simulation results of the flow around the target aircraft surface, wake or engine jet after three-dimensional CFD simulation to obtain flow field data; extracting vortex core lines in the flow field data of each time step based on Liutex vector field definition, and screening out target vortex core lines representing vortex structure from the vortex core lines; constructing a vortex core line similarity matrix based on the multi-dimensional attribute similarity of target vortex core lines in adjacent time steps, so as to detect aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results. The over-extraction problem caused by noise is solved by extracting vortex core lines through Liutex vector field, and the degree of coincidence between the real vortex axis is very high, so as to efficiently realize the accurate extraction and tracking of vortices in the analysis of vortex characteristics in aircraft aerodynamic simulation.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft simulation, and in particular to an aircraft aerodynamic simulation eddy flow characteristic analysis method, device, equipment and medium. Background Art

[0002] In the field of aircraft aerodynamic design, accurately analyzing the dynamic evolution of vortices in unsteady flow fields is crucial for optimizing aerodynamic performance, predicting instability risks, and reducing experimental costs. The spatiotemporal evolution of vortex structures, such as wing leading-edge vortices, wake vortices, and engine jet vortices, directly affects an aircraft's lift distribution, drag characteristics, and maneuverability. Traditional vortex analysis methods primarily rely on threshold segmentation techniques based on local velocity gradient tensors, extracting transient vortex structures using scalar field thresholds. However, existing techniques are complex and inefficient in describing complex vortex structures. For example, in the flow around a wing, the interaction between wingtip vortices and detached vortices is difficult to accurately distinguish using a single scalar threshold. Due to the strong unsteadiness of the flow field, vortex core lines undergo significant deformation due to advection, rotation, or topological changes (such as merging and splitting). Traditional matching algorithms based on spatial overlap or center of mass prediction are prone to tracking interruptions due to geometric deviations, making them inadequate for dynamic aircraft aerodynamic analysis.

[0003] With the expansion of the flight envelope and performance of new-generation aircraft, efficient and high-precision fluid simulation calculations are often required. High-precision fluid simulation generates massive amounts of data, and the speed at which simulation data is generated far exceeds the I / O speed of supercomputers, resulting in the inability to transmit and analyze simulation data in a timely manner. In-situ visualization is a key technology to address these issues. Because the data conversion process for in-situ visualization is completed at the computing node, it consumes a certain amount of computing resources, affecting computational efficiency. Traditional property consistency methods require the calculation of a large number of physical properties, resulting in a sharp increase in computing resource consumption. In addition, existing methods lack effective solutions to matching ambiguities in multi-vortex interaction scenarios (such as aerodynamic instability caused by vortex merging), limiting their application in in-situ visualization engineering practice. Moreover, aircraft design requires high-precision, real-time eddy flow analysis support, but existing methods struggle to strike a balance between efficiency and accuracy.

[0004] In summary, how to efficiently and accurately extract and track eddy currents during the aerodynamic simulation design of aircraft is a technical problem that needs to be solved in this field. Summary of the Invention

[0005] In view of this, the present invention aims to provide a method, apparatus, device, and medium for analyzing eddy flow characteristics in aircraft aerodynamic simulation, which can efficiently and accurately extract and track eddy flow during the aircraft aerodynamic simulation design process. The specific scheme is as follows:

[0006] In a first aspect, the present application discloses a method for analyzing vortex characteristics in aircraft aerodynamic simulation, comprising:

[0007] Obtain CFD simulation results of the flow around the target aircraft surface, wake, or engine jet after three-dimensional CFD simulation to obtain corresponding flow field data; wherein the flow field data is the velocity gradient matrix, vorticity vector, and spatial grid coordinates of the flow field grid points and the corresponding time steps;

[0008] Based on the Liutex vector field definition, vortex core lines are extracted from the flow field data at each time step, and target vortex core lines representing the vortex structure are screened out from the vortex core lines; wherein the vortex core lines include the vortex core lines of the wing leading edge vortex and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the magnitude of the Liutex vector field is defined as the local angular velocity;

[0009] A vortex core line similarity matrix is ​​constructed based on the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, so as to detect aircraft aerodynamic simulation vortex characteristic events and generate tracking results through the similarity matrix; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

[0010] Optionally, the extraction of vortex core lines in the flow field data at each time step based on the Liutex vector field definition includes:

[0011] Performing eigendecomposition on the velocity gradient matrix to obtain real eigenvalues ​​and imaginary parts of complex eigenvalues, and using the unit eigenvector of the real eigenvalues ​​as the local rotation direction of the Liutex vector field;

[0012] Determine the modulus of the Liutex vector field based on the imaginary part of the complex eigenvalue, the vorticity vector, and the local rotation direction to obtain the local angular velocity;

[0013] According to the cross product norm of the gradient of the local angular velocity of each flow field grid point and the vector of the local rotation direction, the flow field grid points whose cross product norm meets the first preset core point screening condition are screened as candidate vortex core points;

[0014] Based on the relative rotation strength of the candidate vortex core points, screening candidate vortex core points whose relative rotation strengths meet a second preset core point screening condition as target vortex core points;

[0015] The target vortex core point is used as a seed point, and integration is performed along the local rotation direction using the fourth-order Runge-Kutta method to generate the initial vortex core line of the wing leading edge vortex and the initial vortex core line of the trailing vortex respectively.

[0016] Optionally, before constructing the vortex core line similarity matrix according to the multi-dimensional attribute similarities of the target vortex core lines at adjacent time steps, the method further includes:

[0017] Determining the spatial position distance and curve trend of the target vortex core line at adjacent time steps to obtain geometric similarity;

[0018] Determining the rotation intensity distance and the rotation direction distance of the target vortex core line at adjacent time steps to obtain the physical feature similarity;

[0019] The multi-dimensional attribute similarity of the target vortex core lines in adjacent time steps is obtained according to the geometric similarity and the physical feature similarity.

[0020] Optionally, detecting aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generating tracking results includes:

[0021] The quantitative correlation relationship of the target vortex core lines of the wing leading edge vortex and the trailing vortex in adjacent time steps is determined according to the matrix elements of the similarity matrix, so as to detect the aircraft aerodynamic simulation vortex characteristic events based on the quantitative correlation relationship and generate tracking results.

[0022] Optionally, the process of detecting aircraft aerodynamic simulation vortex characteristic events based on the quantitative correlation relationship further includes:

[0023] Determining whether a first event occurs across the span of the wing at a target vortex core line of the wing leading edge vortex in adjacent time steps based on the quantitative correlation relationship, wherein the type of the first event is a merging event type or a splitting event type;

[0024] Based on the quantitative correlation relationship, it is determined whether the target vortex core line of the wake vortex in adjacent time steps changes with the flight speed to cause a second event, and the type of the second event is a dissipation event type or a new event type.

[0025] Optionally, the characteristic vortex events of the aircraft aerodynamic simulation include characteristic events of the leading edge vortex of the wing and characteristic events of the wake vortex, wherein the characteristic event of the leading edge vortex of the wing includes an event of attenuation of the vortex core line intensity caused by a change in the angle of attack, and the characteristic event of the wake vortex includes an event of a sudden change in the helicity of the vortex core line caused by a change in the engine thrust.

[0026] In a second aspect, the present application discloses an aircraft aerodynamic simulation vortex characteristic analysis device, comprising:

[0027] A data acquisition module is used to obtain CFD simulation results of the flow around the target aircraft surface, wake flow, or engine jet flow after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the velocity gradient matrix, vorticity vector, and spatial grid coordinates of the flow field grid points and the corresponding time steps;

[0028] A vortex core line extraction module is used to extract vortex core lines from the flow field data at each time step based on the Liutex vector field definition, and to screen out target vortex core lines representing the vortex structure from the vortex core lines; wherein the vortex core lines include the vortex core lines of the leading edge vortex and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the magnitude of the Liutex vector field is defined as the local angular velocity;

[0029] The vortex analysis module is used to construct a vortex core line similarity matrix based on the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, so as to detect aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

[0030] In a third aspect, the present application discloses an electronic device, comprising:

[0031] Memory, used to store computer programs;

[0032] The processor is used to execute the computer program to implement the steps of the aforementioned aircraft aerodynamic simulation vortex characteristic analysis method.

[0033] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned aircraft aerodynamic simulation vortex characteristic analysis method are implemented.

[0034] It can be seen that the present application discloses a method for analyzing vortex characteristics of aircraft aerodynamic simulation, including: obtaining CFD simulation results of the flow around the aircraft surface, wake or engine jet of the target aircraft after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the velocity gradient matrix, vortex vector and spatial grid coordinates of the flow field grid points and the corresponding time steps; based on the Liutex vector field definition, vortex core lines in the flow field data of each time step are extracted, and target vortex core lines representing the vortex structure are screened out from the vortex core lines; wherein, the vortex core lines include the vortex core lines of the wing leading edge vortex and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the size of the Liutex vector field is defined as the local angular velocity; constructing a vortex core line similarity matrix according to the multi-dimensional attribute similarity of the target vortex core lines in adjacent time steps, so as to detect the aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity. Thus, by acquiring CFD data of the flow around the aircraft surface, wake, and engine jet, it is clear that the target vortex types are the wing leading edge vortex and the wake vortex, avoiding indiscriminate processing of the entire flow field. Furthermore, based on the Liutex vector field, the vortex core lines are extracted, retaining only the true vortex core points, reducing the simulation data processing range from the full flow field grid points to the local grid points. Moreover, the vortex core lines extracted by the Liutex vector field have a very high degree of coincidence with the true vortex axis, solving the problem of over-extraction caused by noise. The extraction process is highly automated and does not require manual parameter adjustment, making it suitable for cross-operating condition simulation. Furthermore, the precise initial vortex core line extraction reduces the probability of mismatching in the subsequent tracking stage, and finally, the accurate extraction and tracking of vortices are efficiently achieved in the vortex feature analysis of aircraft aerodynamic simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] 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.

[0036] Figure 1 This is a flow chart of a method for analyzing vortex characteristics in aircraft aerodynamic simulation disclosed in this application;

[0037] Figure 2 This is a flow chart of a specific aircraft aerodynamic simulation vortex characteristic analysis method disclosed in this application;

[0038] Figure 3 This is a three-dimensional isentropic vortex data flow field t=45 original state diagram disclosed in this application;

[0039] Figure 4 This is a spatial curve of an isentropic eddy flow field t=45 disclosed in this application that meets the C-1 criterion;

[0040] Figure 5 This is a spatial curve of an isentropic eddy flow field t=45 disclosed in this application that meets the C-1 and C-2 criteria;

[0041] Figure 6 It is an isentropic eddy flow field disclosed in this application, t=45, and the vortex core line extracted according to C-1, C-2, and C-3;

[0042] Figures 7(a) to 7(c) are vortex core lines of a three-dimensional semi-cylindrical turbulent flow field disclosed in this application from time step t=65 to time step t=67, wherein Figure 7(a) represents the vortex core line of the three-dimensional semi-cylindrical turbulent flow field at time step t=65, Figure 7(b) represents the vortex core line of the three-dimensional semi-cylindrical turbulent flow field at time step t=66, and Figure 7(c) represents the vortex core line of the three-dimensional semi-cylindrical turbulent flow field at time step t=67;

[0043] Figure 8 The similarity matrix of the vortex core lines of t=65 and t=66 in a three-dimensional semi-cylindrical turbulent flow field disclosed in this application;

[0044] Figure 9 This is a flow chart of a method for tracking vortex characteristics in aircraft aerodynamic simulation disclosed in this application;

[0045] Figure 10 A directed acyclic graph of the vortex core line tracking results of a three-dimensional semi-cylindrical turbulent flow field at t=65 and t=66 disclosed in this application;

[0046] Figure 11 This is a schematic structural diagram of an aircraft aerodynamic simulation vortex characteristic analysis device disclosed in this application;

[0047] Figure 12 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described 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.

[0049] In the field of aircraft aerodynamic design, accurately analyzing the dynamic evolution of vortices in unsteady flow fields is crucial for optimizing aerodynamic performance, predicting instability risks, and reducing experimental costs. The spatiotemporal evolution of vortex structures, such as wing leading-edge vortices, wake vortices, and engine jet vortices, directly affects an aircraft's lift distribution, drag characteristics, and maneuverability. Traditional vortex analysis methods primarily rely on threshold segmentation techniques based on local velocity gradient tensors, extracting transient vortex structures using scalar field thresholds. However, existing techniques are complex and inefficient in describing complex vortex structures. For example, in the flow around a wing, the interaction between wingtip vortices and detached vortices is difficult to accurately distinguish using a single scalar threshold. Due to the strong unsteadiness of the flow field, vortex core lines undergo significant deformation due to advection, rotation, or topological changes (such as merging and splitting). Traditional matching algorithms based on spatial overlap or center of mass prediction are prone to tracking interruptions due to geometric deviations, making them inadequate for dynamic aircraft aerodynamic analysis.

[0050] In aircraft aerodynamic simulations, flow field data is massive (e.g., tens of millions of grid points). Traditional attribute consistency methods require the calculation of numerous physical properties, resulting in a significant increase in computing resources. Furthermore, existing methods lack effective solutions for matching ambiguities in multi-vortex interaction scenarios (such as aerodynamic instability caused by vortex mergers), limiting their practical application in engineering. Furthermore, aircraft design requires high-precision, real-time eddy flow analysis, but existing methods struggle to balance efficiency and accuracy.

[0051] To this end, the present invention provides an aircraft aerodynamic simulation vortex feature analysis solution, which can efficiently realize the accurate extraction and tracking of vortices during the aircraft aerodynamic simulation design process.

[0052] Reference Figure 1 As shown, the embodiment of the present invention discloses a method for analyzing vortex characteristics of aircraft aerodynamic simulation, including:

[0053] Step S11: Obtain CFD simulation results of the flow around the target aircraft surface, wake or engine jet after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the velocity gradient matrix, vortex vector and spatial grid coordinates of the flow field grid points and the corresponding time steps.

[0054] In this embodiment, the flow field data of the target aircraft in the aerodynamic simulation scenario is outputted by an aircraft wind tunnel experiment or CFD (Computational Fluid Dynamics) simulation software with a time step of Δt = 0.001 seconds, and the flow field data of the target aircraft in the aerodynamic simulation scenario is collected in chronological order, wherein the flow field data specifically includes velocity field data (three-dimensional velocity components ), vorticity vector ( ), spatial grid coordinates (spatial discrete point coordinates The data format is HDF5 (Hierarchical Data Format 5), and the grid resolution is 1 mm. A gradient operation is performed on the velocity field data at each time step to obtain the velocity gradient matrix.

[0055] Step S12: based on the Liutex vector field definition, vortex core lines are extracted from the flow field data of each time step, and target vortex core lines representing the vortex structure are screened out from the vortex core lines; wherein, the vortex core lines include the vortex core lines of the leading edge vortex of the wing and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the magnitude of the Liutex vector field is defined as the local angular velocity.

[0056] In this embodiment, the velocity gradient matrix is ​​subjected to eigendecomposition processing to obtain real eigenvalues ​​and imaginary parts of complex eigenvalues, and the unit eigenvector of the real eigenvalues ​​is used as the local rotation direction of the Liutex vector field; the modulus of the Liutex vector field is determined based on the imaginary part of the complex eigenvalues, the vorticity vector, and the local rotation direction to obtain the local angular velocity; according to the cross product norm of the gradient of the local angular velocity of each flow field grid point and the vector of the local rotation direction, the flow field grid points whose cross product norm meets the first preset core point screening condition are screened as candidate vortex core points; based on the relative rotation strength of the candidate vortex core points, the candidate vortex core points whose relative rotation strength meets the second preset core point screening condition are screened as target vortex core points; with the target vortex core point as the seed point, integration is performed along the direction of the local rotation direction using the fourth-order Runge-Kutta method to generate the initial vortex core line of the leading edge vortex and the initial vortex core line of the wake vortex, respectively. It can be understood that for the vortex in the engine jet, it further includes the extraction of the vortex core line of the interaction area between the jet and the free stream, and the interaction area is defined by the ratio of the jet outlet velocity to the free stream velocity. The Liutex vortex core line of the flow field vortex characteristics in the flow field formed after the above-mentioned aircraft aerodynamic simulation is extracted, wherein the vortex core line is a topological structure that describes the vortex characteristics in the flow field. The Liutex vortex core line is uniquely generated by the Liutex vector field. The direction of the Liutex vector is defined as the local rotation axis, and the magnitude of the Liutex vector is defined as the local angular velocity, that is, the Liutex vector field is mathematically defined as follows:

[0057] ;

[0058] in, Represents Liutex vector, a vector used to characterize the vortex characteristics of a certain point in the flow field, which comprehensively reflects the intensity and direction information of the vortex. represents the Liutex scalar, which represents the local rotation intensity and is used to quantify the intensity characteristics of the vortex. represents the vorticity vector, is the real eigenvalue of the velocity gradient matrix The unit eigenvector of , which is used to characterize the direction of the local rotation axis of the vortex, is the imaginary part of the complex eigenvalue of the velocity gradient matrix, and the real part is .

[0059] Based on the above definition of Liutex vector field, the velocity gradient matrix is ​​decomposed to obtain the real eigenvalue and the imaginary part of the complex eigenvalue, and the unit eigenvector of the real eigenvalue is used as the local rotation direction of the Liutex vector field to calculate the local angular velocity of each flow field grid point, that is, the Liutex scalar Calculate the Liutex scalar for each flow field grid point The cross product norm of the gradient and the local rotation axis vector is selected to meet the cross product norm The flow field grid points are selected as candidate vortex core points, where ε is a preset minimum positive number used to determine whether a grid point in the flow field is located on the vortex core line; therefore, the first preset core point screening condition is that the deviation between the direction of the rotation intensity change of the selected flow field grid point and the direction of the rotation axis is less than ε, which is the C-1 criterion. Figure 2 As shown in the figure, the data after the aircraft aerodynamic simulation is used as a data set, and the vortex core line is identified and extracted based on the data set. First, the cross product norm of the gradient of the Liutex scalar R and the local rotation axis scalar of each flow field grid point is calculated, and then it is determined whether the calculated cross product norm meets the C-1 criterion. Figure 3 It is the original flow field form of three-dimensional isentropic vortex.

[0060] Furthermore, the candidate vortex core point that currently meets the C-1 criterion, Liutex scalar The direction of change is consistent with the direction of the local rotation axis, but it cannot distinguish between the vortex region and the non-vortex region. In the fluid, in addition to the vortex region, there are other regions, such as the laminar region. It may also be smaller than ε, but they are not vortex core points. Therefore, for the candidate vortex core points in the flow field area of ​​the wing leading edge vortex and the wake vortex, it is judged whether the candidate vortex core points meet the second preset core point screening condition. Specifically, the relative rotation intensity of the candidate vortex core points is calculated. , and pre-set a target intensity threshold , if the relative rotation strength of the current candidate vortex core point This condition ensures that the identified point not only meets the C-1 criterion, but also has sufficient rotation intensity (meeting the C-2 criterion), thereby distinguishing the vortex area from the non-vortex area. The current candidate vortex core point is the target vortex core point. It should be noted that different fluid dynamics problems may have different vortex characteristics and intensities. By adjusting the intensity threshold in the C-2 criterion , which can be adapted to different problems and ensure that the vortex core point can be accurately identified in various situations.

[0061] like Figure 2 As shown, the relative rotation strength of the candidate vortex core point that meets the C-1 criterion The calculation formula is as follows:

[0062] ;

[0063] Specifically, the target vortex core point is used as the seed point, and the fourth-order Runge-Kutta method is used to integrate along the direction of the local rotation direction to generate the initial vortex core line of the wing leading edge vortex and the initial vortex core line of the wake vortex, respectively. It can be understood that in the three-dimensional flow field obtained by CFD simulation, the target vortex core point is used as the seed point, starting from each seed point, the fourth-order Runge-Kutta method is used to integrate along the direction of the Liutex vector to generate the initial vortex core line.

[0064] The specific process of using the fourth-order Runge-Kutta method for integration is as follows: choose a sufficiently small integration step size , which is usually smaller than the minimum value of the flow field grid interval to ensure the accuracy of the integration. Spatial position under time step Initially, integrate along the direction of Liutex vector R and update the core points of each target vortex at Spatial position under time step , the position update formula is as follows:

[0065] ;

[0066] in, It's on point Liutex unit vector at , 、 、 、 Represents the intermediate calculation amount, which is the incremental item obtained by calculating different positions, and is used to gradually calculate the weighted value of the position update. The integration step size is a small distance increment used to control the position update step size. The termination conditions for the integration include the Liutex vector modulus reaching zero, reaching the computational domain boundary, the integration step size being less than the minimum step size, reaching the maximum number of integration steps or length, and convergence of the vortex core line. Figure 4 It is a three-dimensional flow field space curve generated by the point integral that meets the C-1 criterion. After the points selected by the C-1 criterion and the C-2 criterion are integrated, the following can be obtained: Figure 5 The flow field space curve is shown.

[0067] In this embodiment, the first spatial distance between the initial vortex core lines in each vortex core line group of the wing leading edge vortex and the second spatial distance between the initial vortex core lines in each vortex core line group of the trailing vortex are determined respectively; wherein, the vortex core line group is constructed based on two random initial vortex core lines; according to each of the first spatial distances and the second spatial distances, the target vortex core line groups that meet the first spatial distance threshold condition and the second spatial distance threshold condition are respectively screened, and the target vortex core line groups are merged to obtain the first vortex core line group and the second vortex core line group respectively; according to the spatial gradient value of the local angular velocity of the integral starting point of each initial vortex core line in the first vortex core line group and the second vortex core line group, the initial vortex core lines whose spatial gradient value meets the preset spatial gradient threshold are screened to obtain the target vortex core line of the wing leading edge vortex and the target vortex core line of the trailing vortex respectively. It is understandable that the obtained initial vortex core lines are screened. Ideally, the vortex core line should be unique, but due to numerical discretization, multiple core lines may be generated at close positions. These additional core lines are called false core lines. The existence of false core lines will interfere with the accurate identification and analysis of vortex structures. In order to ensure that the generated core lines can truly reflect the physical characteristics of the vortex in the flow field, the C-3 criterion is used to identify and delete these false core lines to ensure the uniqueness of the core lines. The C-3 criterion is to determine whether the relevant information of the initial vortex core line meets the spatial distance threshold condition and the spatial gradient threshold condition. Finally, the C-1, C-2, and C-3 criteria are met. Figure 6 The target vortex core line that describes the vortex characteristics in the isentropic vortex flow field is shown. Specifically, for the wing leading edge vortex, the minimum spatial distance dis between each pair of initial vortex core lines in each vortex core line group of the wing leading edge vortex is calculated. When two curves, curve A and curve B, are respectively formed by the point set and For every point on curve A , calculate its intersection with all points on curve B The distance between the points ( , ) can be calculated using the Euclidean distance formula:

[0068] ;

[0069] in, represents the distance between two points, , , Yes The three-dimensional space coordinates of , , Yes The three-dimensional space coordinates of .

[0070] For every point on curve A , find the distance on curve B The nearest point , and record the minimum distance. This process can be expressed as:

[0071] ;

[0072] in, Yes The minimum distance to curve B. To get the average distance between the two curves, the minimum distances of all pairs of points can be averaged to get the first spatial distance:

[0073] ;

[0074] Where N is the number of points on curve A.

[0075] Based on the first spatial distance, it is judged whether the two initial vortex core lines belong to the same group. If the first spatial distance dis between the two initial vortex core lines is less than the characteristic parameter σ, that is, dis<σ, and the first spatial threshold condition is met, then the two initial vortex core lines are considered to belong to the same group to obtain the target vortex core line group, and each target vortex core line group is merged and processed to obtain the first vortex core line group.

[0076] In the first vortex core line group, the vortex core line generated by the minimum Liutex gradient point at the integration starting point is selected as the most accurate core line as the representative, and the remaining core lines are deleted. After successfully identifying and calculating the target vortex core line of the wing leading edge vortex, the target vortex core line is used as the skeleton to track the vortex characteristics. The final vortex core line is expressed as:

[0077] ;

[0078] in, is the time step, is the number of vortices in a single time step, express The time step number is The vortex core line, Indicates that there are a total of or Features, that is, extracted or vortex core line, Indicates that the number in the current time step flow field is The total number of data points on the vortex core line, Indicates a point The spatial position of Indicates the number The vortex core line is The modulus of the Liutex vector at the point, Indicates the number The vortex core line is Liutex vector at point . For ease of description, the vortex core line is simplified as:

[0079] ;

[0080] Indicates that the number in the previous time step is Points on the vortex core line , Indicates that the number in the previous time step is vortex core line. Indicates that the number of the subsequent time step is Points on the vortex core line , Indicates that the number of the subsequent time step is vortex core line.

[0081] The target vortex core line screening process for the wake vortex is the same as the target vortex core line screening process for the wing leading edge vortex mentioned above, and will not be described in detail.

[0082] Step S13: constructing a vortex core line similarity matrix based on the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, so as to detect aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

[0083] In this embodiment, before constructing the vortex core line similarity matrix based on the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, it also includes: determining the spatial position distance and curve trend of the target vortex core lines at adjacent time steps to obtain geometric similarity; determining the rotation intensity distance and rotation direction distance of the target vortex core lines at adjacent time steps to obtain physical feature similarity; obtaining the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps based on the geometric similarity and the physical feature similarity. It can be understood that the Liutex vortex core line definition includes both the geometric information and physical information of the vortex. By calculating the geometric similarity and physical similarity of the vortex core lines, the similarity between the two vortex core lines can be obtained, providing data support for vortex core line matching. The geometric information of the curve includes its spatial position distance and curve trend. The spatial distance of the curve can be represented by Euclidean distance, and the covariance represents the trend similarity between curves. Geometric similarity can be expressed as:

[0084] ;

[0085] in, and are two target vortex core lines, DTSM is a distribution curve similarity measurement method, which is used to calculate the geometric similarity between target vortex core lines in this embodiment.

[0086] ;

[0087] ;

[0088] ;

[0089] in, represents geometric similarity, Representation curve and The covariance between Representation curve The standard deviation of Representation curve The standard deviation of Representation curve and The Euclidean distance between represents the spatial distance between curves, Representation curve No. A quantity, Representation curve No. A quantity, and Represents the global correlation between curves. is a penalty factor that assigns weights to global correlation and spatial distance. Based on these weighted similarity algorithms, the similarity between two curves is calculated. The higher the value, the more weight is given to global relevance. By default, the setting =1.

[0090] Furthermore, the similarity of physical characteristics between vortices in the flow field, that is, the similarity of physical properties between target vortex core lines, is calculated. The following algorithm is used to calculate the similarity between the physical characteristics of the flow field vortices contained in the Liutex vortex core line, such as the rotation direction Liutex vector and the modulus of the rotation intensity Liutex vector .

[0091] ;

[0092] ;

[0093] ;

[0094] in, Indicates the similarity of physical characteristics between curves. Is a scale parameter used to adjust the influence of distance metric. It usually represents the standard deviation of the data, reflecting the degree of dispersion of the data. and Represent the distance metric of the vector and its modulus on the curve, N is the number of points on the curve, yes The rotation intensity value at the point, μ is the average value of the rotation intensity of all points. The two curves are and , each curve consists of a series of points, each of which is represented by a scalar and vector Its scalar The similarity measure between them is obtained by Euclidean distance:

[0095] ;

[0096] curve The up vector is represented as ,curve By vector Composition. For curves Every vector on and curves Every vector on , calculate the cosine similarity between them:

[0097] ;

[0098] in, Indicates that the curve is at point Vector , calculate the vector distance between each pair of nearest neighbor points, and then take the average of these distances as the distance between the two curves.

[0099] ;

[0100] In this embodiment, the geometric similarity and physical feature similarity are combined to obtain the final multi-dimensional attribute similarity S between the target vortex core lines. The formula is as follows:

[0101] ;

[0102] in, and is the attribute weight, which is assigned different weights according to the degree of influence of the attribute on the vortex characteristics of the flow field. In this embodiment, by default, is 0.2, It is 0.8, and different flow fields can be slightly adjusted according to actual conditions.

[0103] In this embodiment, based on the calculated multi-dimensional attribute similarity, an n×m similarity matrix of vortex core lines is constructed. n represents the number of vortex core lines at time t, m represents the number of vortex core lines at time t+1, the row number of the constructed similarity matrix represents the number of the vortex core line at time t, and the column number of the matrix represents the number of the vortex core line at time t+1. The matrix elements are decimals between [0,1]. If the similarity of two vortex core lines is greater than the minimum similarity threshold, it is considered that the vortex core lines are similar, that is, the vortex characteristics they represent are similar. As shown in Figures 7 (a) to 7 (c), the target vortex core lines of the three-dimensional semi-cylindrical turbulent flow field at adjacent time steps are schematic diagrams, and the calculation based on the vortex characteristics in Figures 7 (a) and 7 (b) will be as follows Figure 8 The eddy feature similarity matrix is ​​shown in . Figure 9 As shown, the corresponding similarity matrix is ​​constructed through the multi-dimensional attribute similarity of the target vortex core line mentioned above, and the vortex characteristics similar to it are determined through the quantitative correlation relationship between the matrix elements in the similarity matrix and the vortex core line, and the final characteristic event is determined according to the predetermined quantity and event correspondence.

[0104] In this embodiment, the quantitative correlation between the target vortex core lines of the wing leading edge vortex and the wake vortex at adjacent time steps is determined based on the matrix elements of the similarity matrix, so as to detect characteristic vortex events in aircraft aerodynamic simulation based on the quantitative correlation and generate tracking results. The process of detecting characteristic vortex events in aircraft aerodynamic simulation based on the quantitative correlation further includes: determining, based on the quantitative correlation, whether the target vortex core line of the wing leading edge vortex at adjacent time steps undergoes a first event across the wing span, wherein the first event type is a merging event type or a splitting event type; and determining, based on the quantitative correlation, whether the target vortex core line of the wake vortex at adjacent time steps undergoes a second event with changes in flight speed, wherein the second event type is a dissipating event type or a new generation event type. Aircraft aerodynamic simulation vortex characteristic events include characteristic events of the wing leading edge vortex and characteristic events of the wake vortex. The characteristic events of the wing leading edge vortex include vortex core line intensity attenuation events caused by changes in angle of attack, and the characteristic events of the wake vortex include vortex core line helicity mutation events caused by changes in engine thrust. It can be understood that according to the definition of feature events, there are a total of 5 types of events: continuation, splitting, merging, generation, and dissipation. The similarity matrix obtained by traversing is used to determine the event that occurred based on the feature correspondence. When the feature quantity corresponds to: 1" to 1: continuation event; 1" to many: splitting event; many to 1: merging event; 0" to 1: generation event; 1" to 0: dissipation event. Finally, based on the detected feature association relationship and feature events, a directed acyclic graph of feature tracking results is generated. Figure 8 Traversing the eddy feature similarity matrix shown in the figure can obtain Figure 10 The three-dimensional cylindrical spoiler vortex feature tracking results and directed acyclic graph are shown.

[0105] It can be seen that the present application discloses a method for analyzing vortex characteristics of aircraft aerodynamic simulation, including: obtaining CFD simulation results of the flow around the aircraft surface, wake or engine jet of the target aircraft after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the velocity gradient matrix, vortex vector and spatial grid coordinates of the flow field grid points and the corresponding time steps; based on the Liutex vector field definition, vortex core lines in the flow field data of each time step are extracted, and target vortex core lines representing the vortex structure are screened out from the vortex core lines; wherein, the vortex core lines include the vortex core lines of the wing leading edge vortex and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the size of the Liutex vector field is defined as the local angular velocity; constructing a vortex core line similarity matrix according to the multi-dimensional attribute similarity of the target vortex core lines in adjacent time steps, so as to detect the aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity. Thus, by acquiring CFD data of the flow around the aircraft surface, wake, and engine jet, it is clear that the target vortex types are the wing leading edge vortex and the wake vortex, avoiding indiscriminate processing of the entire flow field. Furthermore, based on the Liutex vector field, the vortex core lines are extracted, retaining only the true vortex core points, reducing the simulation data processing range from the full flow field grid points to the local grid points. Moreover, the vortex core lines extracted by the Liutex vector field have a very high degree of coincidence with the true vortex axis, solving the problem of over-extraction caused by noise. The extraction process is highly automated and does not require manual parameter adjustment, making it suitable for cross-operating condition simulation. Furthermore, the precise initial vortex core line extraction reduces the probability of mismatching in the subsequent tracking stage, and finally, the accurate extraction and tracking of vortices are efficiently achieved in the vortex feature analysis of aircraft aerodynamic simulation.

[0106] Reference Figure 11 As shown, the present invention also discloses an aircraft aerodynamic simulation vortex characteristic analysis device, comprising:

[0107] The data acquisition module 11 is used to obtain CFD simulation results of the flow around the target aircraft surface, wake flow, or engine jet flow after the three-dimensional CFD simulation to obtain corresponding flow field data; wherein the flow field data is the velocity gradient matrix, vorticity vector, and spatial grid coordinates of the flow field grid points and the corresponding time steps;

[0108] a vortex core line extraction module 12 for extracting vortex core lines from the flow field data at each time step based on the Liutex vector field definition, and screening target vortex core lines representing the vortex structure from the vortex core lines; wherein the vortex core lines include the vortex core lines of the leading edge vortex and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the magnitude of the Liutex vector field is defined as the local angular velocity;

[0109] The vortex analysis module 13 is used to construct a vortex core line similarity matrix based on the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, so as to detect the aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

[0110] It can be seen that the present application discloses obtaining CFD simulation results of the flow around the surface of the target aircraft, the wake or the engine jet after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the velocity gradient matrix, vortex vector and spatial grid coordinates of the flow field grid points and the corresponding time steps; based on the Liutex vector field definition, the vortex core lines in the flow field data of each time step are extracted, and the target vortex core lines representing the vortex structure are screened out from the vortex core lines; wherein, the vortex core lines include the vortex core lines of the leading edge vortex of the wing and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the size of the Liutex vector field is defined as the local angular velocity; a vortex core line similarity matrix is ​​constructed according to the multi-dimensional attribute similarity of the target vortex core lines of adjacent time steps, so as to detect the aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity. Thus, by acquiring CFD data of the flow around the aircraft surface, wake, and engine jet, it is clear that the target vortex types are the wing leading edge vortex and the wake vortex, avoiding indiscriminate processing of the entire flow field. Furthermore, based on the Liutex vector field, the vortex core lines are extracted, retaining only the true vortex core points, reducing the simulation data processing range from the full flow field grid points to the local grid points. Moreover, the vortex core lines extracted by the Liutex vector field have a very high degree of coincidence with the true vortex axis, solving the problem of over-extraction caused by noise. The extraction process is highly automated and does not require manual parameter adjustment, making it suitable for cross-operating condition simulation. Furthermore, the precise initial vortex core line extraction reduces the probability of mismatching in the subsequent tracking stage, and finally, the accurate extraction and tracking of vortices are efficiently achieved in the vortex feature analysis of aircraft aerodynamic simulation.

[0111] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 12 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram should not be considered as any limitation to the scope of application of the present application.

[0112] Figure 12This is a schematic diagram of the structure of an electronic device 20 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 of the aircraft aerodynamic simulation vortex characteristic analysis method disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0113] In this embodiment, the power supply 23 is used to provide operating 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. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this 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. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0114] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0115] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0116] The operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, enabling the processor 21 to calculate and process the massive amount of data 223 in the memory 22. The operating system 221 can be Windows Server, NetWare, Unix, Linux, etc. In addition to including computer programs capable of performing the aircraft aerodynamic simulation vortex characteristic analysis method performed by the electronic device 20 as disclosed in any of the aforementioned embodiments, the computer programs 222 can further include computer programs capable of performing other specific tasks. Data 223 can include data received by the electronic device from external devices as well as data collected by its own input / output interface 25.

[0117] Furthermore, this application discloses a computer-readable storage medium for storing a computer program. When executed by a processor, the computer program implements the aforementioned method for analyzing vortex flow characteristics in aircraft aerodynamic simulation. The specific steps of this method can be found in the corresponding contents disclosed in the aforementioned embodiments and will not be further described here.

[0118] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0119] Professionals may further appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory RAM (Random Access Memory), memory, read-only memory ROM (Read Only Memory), electrically programmable EPROM (Electrically Programmable Read Only Memory), electrically erasable programmable EEPROM (Electric Erasable Programmable Read Only Memory), registers, hard disk, removable disk, CD-ROM (Compact Disc-Read Only Memory), or any other form of storage medium known in the technical field.

[0120] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0121] The above is a detailed introduction to the solution provided by the present invention. Specific examples are used herein 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 ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for analyzing vortex characteristics in aircraft aerodynamic simulation, characterized in that: include: Obtain CFD simulation results of the flow around the target aircraft surface, wake, or engine jet after three-dimensional CFD simulation to obtain corresponding flow field data; wherein the flow field data is the velocity gradient matrix, vorticity vector, and spatial grid coordinates of the flow field grid points and the corresponding time steps; Based on the Liutex vector field definition, vortex core lines are extracted from the flow field data at each time step, and target vortex core lines representing the vortex structure are screened out from the vortex core lines; wherein the vortex core lines include the vortex core lines of the wing leading edge vortex and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the magnitude of the Liutex vector field is defined as the local angular velocity; A vortex core line similarity matrix is ​​constructed based on the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, so as to detect aircraft aerodynamic simulation vortex characteristic events and generate tracking results through the similarity matrix; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

2. The method for analyzing eddy flow characteristics of an aircraft aerodynamic simulation according to claim 1, characterized in that: The extraction of vortex core lines in the flow field data of each time step based on the Liutex vector field definition includes: Performing eigendecomposition on the velocity gradient matrix to obtain real eigenvalues ​​and imaginary parts of complex eigenvalues, and using the unit eigenvector of the real eigenvalues ​​as the local rotation direction of the Liutex vector field; Determine the modulus of the Liutex vector field based on the imaginary part of the complex eigenvalue, the vorticity vector, and the local rotation direction to obtain the local angular velocity; According to the cross product norm of the gradient of the local angular velocity of each flow field grid point and the vector of the local rotation direction, the flow field grid points whose cross product norm meets the first preset core point screening condition are screened as candidate vortex core points; Based on the relative rotation strength of the candidate vortex core points, screening candidate vortex core points whose relative rotation strengths meet a second preset core point screening condition as target vortex core points; The target vortex core point is used as a seed point, and integration is performed along the local rotation direction using the fourth-order Runge-Kutta method to generate the initial vortex core line of the wing leading edge vortex and the initial vortex core line of the trailing vortex respectively.

3. The method for analyzing eddy current characteristics of aircraft aerodynamic simulation according to claim 2, characterized in that: The step of selecting a target vortex core line representing a vortex structure from the vortex core line comprises: Determining respectively a first spatial distance between initial vortex core lines in each vortex core line group of the wing leading edge vortex and a second spatial distance between initial vortex core lines in each vortex core line group of the trailing vortex; wherein the vortex core line group is constructed based on two random initial vortex core lines; Filter target vortex core line groups that meet the first spatial distance threshold condition and the second spatial distance threshold condition according to each of the first spatial distances and each of the second spatial distances, and merge the target vortex core line groups to obtain a first vortex core line group and a second vortex core line group, respectively; According to the spatial gradient value of the local angular velocity of the integral starting point of each initial vortex core line in the first vortex core line group and the second vortex core line group, the initial vortex core lines whose spatial gradient values ​​meet the preset spatial gradient threshold are screened to obtain the target vortex core line of the wing leading edge vortex and the target vortex core line of the wake vortex, respectively.

4. The method for analyzing eddy flow characteristics of an aircraft aerodynamic simulation according to claim 1, wherein: Before constructing the vortex core line similarity matrix according to the multi-dimensional attribute similarities of the target vortex core lines at adjacent time steps, the method further includes: Determining the spatial position distance and curve trend of the target vortex core line at adjacent time steps to obtain geometric similarity; Determining the rotation intensity distance and the rotation direction distance of the target vortex core line at adjacent time steps to obtain the physical feature similarity; The multi-dimensional attribute similarity of the target vortex core lines in adjacent time steps is obtained according to the geometric similarity and the physical feature similarity.

5. The method for analyzing eddy flow characteristics of aircraft aerodynamic simulation according to claim 1, characterized in that: The detecting of aircraft aerodynamic simulation vortex characteristic events by using the similarity matrix and generating tracking results includes: The quantitative correlation relationship of the target vortex core lines of the wing leading edge vortex and the trailing vortex in adjacent time steps is determined according to the matrix elements of the similarity matrix, so as to detect the aircraft aerodynamic simulation vortex characteristic events based on the quantitative correlation relationship and generate tracking results.

6. The method for analyzing eddy flow characteristics of aircraft aerodynamic simulation according to claim 5, characterized in that: The process of detecting aircraft aerodynamic simulation vortex characteristic events based on the quantitative correlation relationship further includes: Determining whether a first event occurs across the span of the wing at a target vortex core line of the wing leading edge vortex in adjacent time steps based on the quantitative correlation relationship, wherein the type of the first event is a merging event type or a splitting event type; Based on the quantitative correlation relationship, it is determined whether the target vortex core line of the wake vortex in adjacent time steps changes with the flight speed to cause a second event, and the type of the second event is a dissipation event type or a new event type.

7. The method for analyzing vortex characteristics of aircraft aerodynamic simulation according to any one of claims 1 to 6, characterized in that: The characteristic vortex events of aircraft aerodynamic simulation include characteristic events of wing leading edge vortex and characteristic events of wake vortex, wherein the characteristic event of the wing leading edge vortex includes the vortex core line intensity attenuation event caused by the change of attack angle, and the characteristic event of the wake vortex includes the vortex core line helicity mutation event caused by the change of engine thrust.

8. An aircraft aerodynamic simulation vortex characteristic analysis device, characterized in that: include: A data acquisition module is used to obtain CFD simulation results of the flow around the target aircraft surface, wake flow, or engine jet flow after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the velocity gradient matrix, vorticity vector, and spatial grid coordinates of the flow field grid points and the corresponding time steps; A vortex core line extraction module is used to extract vortex core lines from the flow field data at each time step based on the Liutex vector field definition, and to screen out target vortex core lines representing the vortex structure from the vortex core lines; wherein the vortex core lines include the vortex core lines of the leading edge vortex and the vortex core lines of the wake vortex, the direction of the Liutex vector field is defined as the local rotation direction, and the magnitude of the Liutex vector field is defined as the local angular velocity; The vortex analysis module is used to construct a vortex core line similarity matrix based on the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, so as to detect aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the aircraft aerodynamic simulation vortex characteristic analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store computer programs; wherein, when the computer program is executed by a processor, the steps of the aircraft aerodynamic simulation vortex characteristic analysis method according to any one of claims 1 to 7 are implemented.

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