Aircraft pneumatic simulation eddy current characteristic analysis method, device, equipment and medium

Abstract

The invention discloses an aircraft pneumatic simulation vortex characteristic analysis method, device and equipment and a medium, and relates to the technical field of aircraft simulation, and the method comprises the steps: obtaining a CFD simulation result of aircraft surface streaming, wake flow or engine jet flow of a target aircraft after three-dimensional CFD simulation, so as to obtain flow field data; on the basis of Liutex vector field definition, vortex core lines in the flow field data of each time step are extracted, and target vortex core lines representing a vortex structure are screened out from the vortex core lines; and according to the multi-dimensional attribute similarity of the target vortex kernel lines of the adjacent time steps, constructing a vortex kernel line similarity matrix so as to detect an aircraft pneumatic simulation vortex characteristic event through the similarity matrix and generate a tracking result. The overlap ratio of the vortex kernel line extracted through the Liutex vector field and the real vortex axis is very high, the over-extraction problem caused by noise is solved, and accurate extraction and tracking of the vortex are efficiently achieved in aircraft pneumatic simulation vortex feature analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft simulation, and particularly to a method, device, equipment and medium for analyzing the vortex characteristics of aircraft aerodynamic simulation. Background Art

[0002] In the field of aircraft aerodynamic design, accurately analyzing the vortex dynamic evolution in the unsteady flow field is of great significance for optimizing aerodynamic performance, predicting instability risks and reducing experimental costs. The spatio-temporal evolution of vortex structures, such as leading-edge vortices of wings, wake vortices and engine jet vortices, directly affects the lift distribution, drag characteristics and control stability of aircraft. Traditional vortex analysis methods mainly rely on threshold segmentation techniques based on local velocity gradient tensors to extract instantaneous vortex structures through scalar field thresholds. However, the existing technologies have a more complex description and lower efficiency for complex vortex structures. For example, in the flow around a wing, the interaction effect between the wingtip vortex and the separation vortex is difficult to accurately distinguish through a single scalar threshold. Due to the strong unsteadiness of the flow field, the vortex core line undergoes significant deformation due to advection, rotation or topological changes (such as merging and splitting), and traditional matching algorithms based on spatial overlap or centroid prediction are prone to tracking interruption due to geometric deviations, unable to meet the requirements of aircraft dynamic aerodynamic analysis.

[0003] With the expansion of the flight envelope and flight performance of the new generation of aircraft, high-efficiency and high-precision fluid simulation calculations are usually required. High-precision fluid simulation generates a large amount of data, and the generation speed of simulation data has far exceeded the I / O speed of supercomputers, resulting in the inability to transmit and analyze simulation data in a timely manner. In-situ visualization is the key technology to solve these problems. Since the data conversion process of in-situ visualization is completed at the computing nodes, it will consume a certain amount of computing resources and affect the computing efficiency, while traditional attribute consistency methods need to calculate a large number of physical attributes, resulting in a sharp increase in computing resource consumption. In addition, the existing methods lack an effective solution to the matching ambiguity in multi-vortex interaction scenarios (such as aerodynamic instability caused by vortex merging), which limits their application in in-situ visualization engineering practice. Moreover, aircraft design requires high-precision and real-time vortex analysis support, but the existing methods are difficult to balance efficiency and precision.

[0004] In summary, how to efficiently achieve the accurate extraction and tracking of vortices in the process of aircraft aerodynamic simulation design is a technical problem to be solved in this field. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for analyzing the vortex characteristics of aircraft aerodynamic simulation, which can efficiently achieve the accurate extraction and tracking of vortices in the process of aircraft aerodynamic simulation design. The specific solutions are as follows: In the first aspect, the present application discloses a method for analyzing the vortex characteristics of aircraft aerodynamic simulation, including: Obtain the 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 are the flow field grid points and the velocity gradient matrix, vorticity vector and spatial grid coordinates at each corresponding time step. Extract the vortex core lines from the flow field data at each time step based on the definition of the Liutex vector field, and screen out the 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 vortices of the wing and the vortex core lines of the wake vortices, 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. Construct a vortex core line similarity matrix according to the multi-dimensional attribute similarities 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 a tracking result; wherein, the multi-dimensional attribute similarities include geometric similarity and physical feature similarity.

[0006] Optionally, the extracting the vortex core lines from the flow field data at each time step based on the definition of the Liutex vector field includes: Perform eigen-decomposition processing on the velocity gradient matrix to obtain real eigenvalues, the imaginary part of complex eigenvalues, and use the unit eigenvector of the real eigenvalues as the local rotation direction of the Liutex vector field. Determine the magnitude of the Liutex vector field 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 and the vector of the local rotation direction at each flow field grid point, screen out the flow field grid points whose cross product norm satisfies the first preset core point screening condition as candidate vortex core points. Based on the relative rotation intensity of the candidate vortex core points, screen out the candidate vortex core points whose relative rotation intensity satisfies the second preset core point screening condition as target vortex core points. Using the target vortex core points as seed points, integrate along the direction of the local rotation direction and adopt the fourth-order Runge-Kutta method to generate the initial vortex core lines of the leading-edge vortices of the wing and the initial vortex core lines of the wake vortices respectively.

[0007] 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, it further includes: Determine the spatial position distance and curve trend of the target vortex core lines at adjacent time steps to obtain geometric similarity. Determine the rotation intensity distance and rotation direction distance of the target vortex core lines at adjacent time steps to obtain physical feature similarity. Obtain the multi-dimensional attribute similarity of the target vortex core line at adjacent time steps based on the geometric similarity and the physical feature similarity.

[0008] Optionally, the detecting of the aircraft aerodynamic simulation vortex feature event and generating of the tracking result by using the similarity matrix includes: Judge the quantity correlation relationship of the target vortex core lines of the leading-edge vortex and the wake vortex at adjacent time steps according to the matrix elements of the similarity matrix, so as to detect the aircraft aerodynamic simulation vortex feature event based on the quantity correlation relationship and generate the tracking result.

[0009] Optionally, during the process of detecting the aircraft aerodynamic simulation vortex feature event based on the quantity correlation relationship, it further includes: Judge whether a first event of the target vortex core line of the leading-edge vortex at adjacent time steps crosses the wing span direction based on the quantity correlation relationship, and the type of the first event is a merging event type or a splitting event type; Judge whether a second event of the target vortex core line of the wake vortex at adjacent time steps occurs with the change of the flight speed based on the quantity correlation relationship, and the type of the second event is a dissipation event type or a newborn event type.

[0010] Optionally, the aircraft aerodynamic simulation vortex feature event includes a feature event of the leading-edge vortex and a feature event of the wake vortex. Among them, the feature event of the leading-edge vortex includes a vortex core line intensity attenuation event caused by the change of the angle of attack, and the feature event of the wake vortex includes a vortex core line helicity mutation event caused by the change of the engine thrust.

[0011] In a second aspect, the present application discloses an aircraft aerodynamic simulation vortex feature analysis device, including: A data acquisition module, configured to acquire the CFD simulation results of the flow around the aircraft surface, the wake flow or the engine jet flow after the three-dimensional CFD simulation of the target aircraft, so as to obtain the corresponding flow field data; wherein, the flow field data is the flow field grid points and the velocity gradient matrix, the vorticity vector and the spatial grid coordinates at each corresponding time step; A vortex core line extraction module, configured to extract the vortex core lines in the flow field data at each time step based on the definition of the Liutex vector field, and screen out the 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; An eddy current analysis module, configured to construct an eddy core line similarity matrix based on the multi-dimensional attribute similarity of the target eddy core line at adjacent time steps, so as to detect an aircraft aerodynamic simulation eddy current feature event through the similarity matrix and generate a tracking result; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

[0012] In a third aspect, the present application discloses an electronic device, including: A memory, configured to store a computer program; A processor, configured to execute the computer program to implement the steps of the aircraft aerodynamic simulation eddy current feature analysis method disclosed above.

[0013] In a fourth aspect, the present application discloses a computer-readable storage medium, configured to store a computer program; wherein, when the computer program is executed by a processor, the steps of the aircraft aerodynamic simulation eddy current feature analysis method disclosed above are implemented.

[0014] It can be seen that the present application discloses an aircraft aerodynamic simulation eddy current feature analysis method, including: obtaining CFD simulation results of the flow around the surface, wake flow or engine jet flow of a target aircraft after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the flow field grid points and the corresponding velocity gradient matrix, vorticity vector and spatial grid coordinates at each time step; extracting eddy core lines from the flow field data at each time step based on the definition of the Liutex vector field, and screening out target eddy core lines representing vortex structures from the eddy core lines; wherein, the eddy core lines include the eddy core lines of the leading-edge vortices of the wing and the eddy core lines of the wake vortices, 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; constructing an eddy core line similarity matrix based on the multi-dimensional attribute similarity of the target eddy core line at adjacent time steps, so as to detect an aircraft aerodynamic simulation eddy current feature event through the similarity matrix and generate a tracking result; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity. Thus, by obtaining the CFD data of the flow around the surface, wake flow, and engine jet flow of the aircraft, the types of target vortices are clarified as leading-edge vortices of the wing and wake vortices, avoiding the non-discriminatory processing of the entire flow field. Further, based on the Liutex vector field, eddy core lines are extracted, and only the real vortex core points are retained, reducing the simulation data processing range from the entire flow field grid points to local grid points. Moreover, the eddy core lines extracted by the Liutex vector field coincide very well with the real vortex axis, solving the problem of over-extraction caused by noise, and the extraction process has a high degree of automation, without manual parameter adjustment, and is applicable to cross-condition simulation. Further, the accurate extraction of the initial eddy core line reduces the probability of false matching in the subsequent tracking stage, and finally, the accurate extraction and tracking of eddy currents are efficiently realized in the aircraft aerodynamic simulation eddy current feature analysis. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained according to the provided accompanying drawings.

[0016] Figure 1 Flowchart of a method for analyzing the vortex characteristics of aircraft aerodynamic simulation disclosed in this application; Figure 2 Flowchart of a specific method for analyzing the vortex characteristics of aircraft aerodynamic simulation disclosed in this application; Figure 3 Original state diagram of a three-dimensional isentropic vortex data flow field t = 45 disclosed in this application; Figure 4 Spatial curve of an isentropic vortex flow field t = 45 satisfying the C-1 criterion disclosed in this application; Figure 5 Spatial curve of an isentropic vortex flow field t = 45 satisfying the C-1 and C-2 criteria disclosed in this application; Figure 6 Vortex core line extracted from an isentropic vortex flow field t = 45 according to C-1, C-2, and C-3 disclosed in this application; Figures 7(a) to 7(c) are vortex core lines of a three-dimensional semi-cylindrical turbulent flow field from t = 65 to t = 67 time steps disclosed in this application. Among them, Figure 7(a) represents the vortex core line of the three-dimensional semi-cylindrical turbulent flow field at the t = 65 time step, Figure 7(b) represents the vortex core line of the three-dimensional semi-cylindrical turbulent flow field at the t = 66 time step, and Figure 7(c) represents the vortex core line of the three-dimensional semi-cylindrical turbulent flow field at the t = 67 time step; Figure 8 Similarity matrix of the vortex core lines of a three-dimensional semi-cylindrical turbulent flow field at t = 65 and t = 66 disclosed in this application; Figure 9 Flowchart of a method for tracking the vortex characteristics of aircraft aerodynamic simulation disclosed in this application; Figure 10 Directed acyclic graph of the tracking results of the vortex core lines of a three-dimensional semi-cylindrical turbulent flow field at t = 65 and t = 66 disclosed in this application; Figure 11 Structural schematic diagram of a device for analyzing the vortex characteristics of aircraft aerodynamic simulation disclosed in this application; Figure 12 Structural diagram of an electronic device disclosed in this application. Detailed implementation manners

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] In the field of aircraft aerodynamic design, accurately analyzing the vortex dynamic evolution in the unsteady flow field is of great significance for optimizing aerodynamic performance, predicting instability risks, and reducing experimental costs. The spatio-temporal evolution of vortex structures, such as leading-edge vortices of wings, wake vortices, and engine jet vortices, directly affects the lift distribution, drag characteristics, and handling stability of aircraft. Traditional vortex analysis methods mainly rely on threshold segmentation techniques based on local velocity gradient tensors to extract instantaneous vortex structures through scalar field thresholds. However, the existing technologies have a more complex description and lower efficiency for complex vortex structures. For example, in the flow around a wing, the interaction effect between the tip vortex and the separation vortex is difficult to accurately distinguish through a single scalar threshold. Due to the strong unsteadiness of the flow field, the vortex core line undergoes significant deformation due to advection, rotation, or topological changes (such as merging and splitting). Traditional matching algorithms based on spatial overlap or centroid prediction are prone to tracking interruptions due to geometric deviations and cannot meet the requirements of aircraft dynamic aerodynamic analysis.

[0019] In aircraft aerodynamic simulation, the scale of flow field data is huge (such as tens of millions of grid points). Traditional property consistency methods need to calculate a large number of physical properties, resulting in a sharp increase in computational resource consumption. In addition, the existing methods lack effective solutions to the matching ambiguity in multi-vortex interaction scenarios (such as aerodynamic instability caused by vortex merging), which limits their application in engineering practice. Moreover, aircraft design requires high-precision and real-time vortex analysis support, but the existing methods are difficult to balance efficiency and accuracy.

[0020] Therefore, the present invention provides a vortex feature analysis solution for aircraft aerodynamic simulation, which can efficiently achieve the accurate extraction and tracking of vortices during the aircraft aerodynamic simulation design process.

[0021] Refer to Figure 1 As shown, the embodiments of the present invention disclose a method for analyzing vortex features in aircraft aerodynamic simulation, including: Step S11: Obtain the CFD simulation results of the flow around the aircraft surface, wake flow, or engine jet flow of the target aircraft after three-dimensional CFD simulation to obtain corresponding flow field data; wherein, the flow field data is the flow field grid points and the corresponding velocity gradient matrix, vorticity vector, and spatial grid coordinates at each time step.

[0022] In this embodiment, through aircraft wind tunnel experiments or CFD (Computational Fluid Dynamics) simulation software, flow field data is output with a time step of Δt = 0.001 s, and the flow field data of the target aircraft in the aerodynamic simulation scenario is collected in chronological order. Among them, the flow field data specifically includes velocity field data (three-dimensional velocity components ), vorticity vector ( ), and spatial grid coordinates (spatial discrete point coordinates ). The data format is HDF5 (Hierarchical Data Format 5), and the grid resolution is 1 mm. Gradient operations are performed on the velocity field data at each time step to obtain a velocity gradient matrix.

[0023] Step S12: Extract vortex core lines from the flow field data at each time step based on the definition of the Liutex vector field, and screen out target vortex core lines representing vortex structures from the vortex core lines; among them, the vortex core lines include the vortex core lines of the leading-edge vortices of the wing and the vortex core lines of the wake vortices. 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.

[0024] In this embodiment, the velocity gradient matrix is subjected to eigen - decomposition processing to obtain real eigenvalues, the imaginary parts of complex eigenvalues, and the unit eigen - vectors of the real eigenvalues are used as the local rotation directions of the Liutex vector field; based on the imaginary parts of the complex eigenvalues, the vorticity vector, and the local rotation directions, the magnitudes of the Liutex vector field are determined to obtain the local angular velocity; according to the cross - product norms of the gradients of the local angular velocities of each flow - field grid point and the vectors of the local rotation directions, the flow - field grid points that satisfy the first preset core - point screening condition for the cross - product norms are selected as candidate vortex core points; based on the relative rotation intensities of the candidate vortex core points, the candidate vortex core points that satisfy the second preset core - point screening condition for the relative rotation intensities are selected as target vortex core points; with the target vortex core points as seed points, integration is performed along the direction of the local rotation direction using the fourth - order Runge - Kutta method to generate the initial vortex core lines of the leading - edge vortices of the wing and the initial vortex core lines of the wake vortices respectively. It can be understood that for the vortices in the engine jet, it further includes extracting the vortex core lines in the interaction region between the jet and the free stream, and the interaction region is defined by the ratio of the jet exit velocity to the free stream velocity. The Liutex vortex core lines of the flow - field vortex characteristics in the flow field formed after the aerodynamic simulation of the above - mentioned aircraft are extracted, where the vortex core line is a topological structure describing 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 mathematical definition of the Liutex vector field is as follows: ; where, denotes the Liutex vector, a vector used to characterize the vortex characteristics at a certain point in the flow field, comprehensively reflecting the intensity and direction information of the vortex, denotes the Liutex scalar, representing the local rotation intensity, used to quantify the intensity characteristics of the vortex, denotes the vorticity vector, is the real eigenvalue of the velocity gradient matrix is the unit eigen - vector of, 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 .

[0025] Based on the above - mentioned Liutex vector - field definition, the velocity gradient matrix is subjected to eigen - decomposition processing to obtain real eigenvalues and the imaginary parts of complex eigenvalues, and the unit eigen - vectors of the real eigenvalues are used as the local rotation directions of the Liutex vector field to calculate the local angular velocities, that is, the Liutex scalars, of each flow - field grid point . Calculate the Liutex scalar The cross - product norm of the gradient and the local rotation axis vector, and filter the flow field grid points that satisfy the cross - product norm as candidate vortex core points, where ε is a preset extremely small positive number used to determine whether a grid point in the flow field is on the vortex core line; Therefore, the first preset core point screening condition is the screening condition that the deviation degree between the change direction of the rotation intensity of the flow field grid point and the rotation axis direction is less than ε, that is, the C - 1 criterion. As Figure 2 shown, take the data after aircraft aerodynamic simulation as the data set, and based on this data set, identify and extract the vortex core line. First, calculate the cross - product norm of the gradient of the Liutex scalar R of each flow field grid point and the local rotation axis vector, and then determine whether the calculated cross - product norm satisfies the C - 1 criterion. As Figure 3 is the original flow field morphology of a three - dimensional isentropic vortex.

[0026] Furthermore, for the candidate vortex core points that currently satisfy the C - 1 criterion, the change direction of the Liutex scalar is consistent with the local rotation axis direction, but it cannot distinguish 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 flow region, and the of these regions may also be less than ε, but they are not vortex core points. Therefore, for the candidate vortex core points in the flow field regions of the leading - edge vortex and wake vortex of the wing, determine whether the candidate vortex core points satisfy the second preset core point screening condition. Specifically, calculate the relative rotation intensity of the candidate vortex core point, and preset a target intensity threshold . If the relative rotation intensity of the current candidate vortex core point, this condition ensures that the identified points not only satisfy the C - 1 criterion but also have sufficient rotation intensity (satisfy the C - 2 criterion), thus distinguishing the vortex region and the non - vortex region, then 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, different problems can be adapted to ensure accurate identification of vortex core points in various situations.

[0027] As Figure 2 shown, the formula for calculating the relative rotation intensity of the candidate vortex core points that satisfy the C - 1 criterion is as follows: ; Specifically, taking the target vortex core point as the seed point, integrating along the direction of the local rotation direction using the fourth-order Runge-Kutta method to respectively generate the initial vortex core line of the leading-edge vortex of the wing and the initial vortex core line of the wake vortex. It can be understood that in the three-dimensional flow field obtained by CFD simulation, taking the target vortex core point as the seed point, starting from each seed point, integrating along the direction of the Liutex vector using the fourth-order Runge-Kutta method to generate the initial vortex core line.

[0028] The specific process of integrating using the fourth-order Runge-Kutta method is as follows: Select a sufficiently small integration step size , usually smaller than the minimum value of the flow field grid interval, to ensure the accuracy of the integration. Starting from the spatial position of each seed point at the time step, integrate along the direction of the Liutex vector R to update the spatial position of each target vortex core point at the time step. The position update formula is as follows: ; where is the Liutex unit vector at the point , , , , represent intermediate calculation quantities, which are incremental terms obtained through operations at different positions and are used to gradually calculate the weighted value of the position update. represents the integration step size, which represents a small distance increment and is used to control the step size of the position update. The termination conditions for the integration include that the magnitude of the Liutex vector is zero, reaching the boundary of the computational domain, the integration step size is less than the minimum step size, reaching the maximum number of integration steps or length, and the convergence of the vortex core line. Figure 4 is the three-dimensional flow field space curve generated by the point integration that satisfies the C-1 criterion. The points screened by the C-1 criterion and the C-2 criterion can obtain the flow field space curve as shown in Figure 5 after integration processing.

[0029] In this embodiment, the first spatial distance between the initial vortex core lines in each vortex core line group of the leading-edge wing vortex and the second spatial distance between the initial vortex core lines in each vortex core line group of the wake vortex are determined respectively; wherein, the vortex core line group is constructed based on any two initial vortex core lines; according to each of the first spatial distances and each of 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 screened respectively, 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 values of the local angular velocities of the integral starting points of the initial vortex core lines in the first vortex core line group and the second vortex core line group, the initial vortex core lines with spatial gradient values meeting the preset spatial gradient threshold are screened to obtain the target vortex core line of the leading-edge wing vortex and the target vortex core line of the wake vortex respectively. It can be understood that the obtained initial vortex core lines are screened. In an ideal situation, 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 spurious core lines. The existence of spurious core lines will interfere with the accurate identification and analysis of the vortex structure. To ensure that the generated core lines can truly reflect the physical characteristics of the vortices in the flow field, the C-3 criterion is used to identify and delete these spurious core lines to ensure the uniqueness of the core lines, where the C-3 criterion is to judge whether the relevant information of the initial vortex core line meets the spatial distance threshold condition and the spatial gradient threshold condition, and finally the target vortex core lines that meet the C-1, C-2, and C-3 criteria are obtained as described in Figure 6 to describe the target vortex core lines of the vortex characteristics in the isentropic vortex flow field. Specifically, for the leading-edge wing vortex, calculate the minimum spatial distance dis between any two initial vortex core lines in each vortex core line group of the leading-edge wing vortex. When there are two curves, curve A and curve B, which are represented by the point sets and respectively. For each point on curve A, calculate its distance from all points on curve B. The distance between the point pair ( , ) can be calculated by the Euclidean distance formula: ; where represents the distance between two points, , , are the three-dimensional spatial coordinates of the point , , , are the three-dimensional spatial coordinates of the point .

[0030] For each point on curve A, find the point on curve B with the distance The nearest point , and record this minimum distance. This process can be expressed as: ; where is the minimum distance from point to curve B. To obtain the average distance between two curves, the average of the minimum distances of all point pairs can be calculated to obtain the first spatial distance: ; where N is the number of points on curve A.

[0031] Based on the first spatial distance, determine whether 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, satisfying the first spatial threshold condition, then it is considered that these two initial vortex core lines belong to the same group to obtain the target vortex core line group, and the target vortex core line groups are merged to obtain the first vortex core line group.

[0032] In the first vortex core line group, select the vortex core line generated by the point with the minimum Liutex gradient at the integral starting point Li as the most accurate core line as a representative, and delete the remaining core lines. After successfully identifying and calculating the target vortex core line of the leading-edge vortex of the wing, use the target vortex core line as the skeleton to track the vortex characteristics. The final vortex core line will be expressed as: ; where is the time step, is the number of the vortex in a single time step, represents the vortex core line with the time step number , represents that there are a total of or features in the flow field at the current time step, that is, or vortex core lines are extracted, represents the total number of data points on the vortex core line numbered in the flow field at the current time step, represents the spatial position at point , represents the magnitude of the Liutex vector of the vortex core line numbered at point , represents the Liutex vector of the vortex core line numbered at point . For ease of description, the vortex core line is simplified as: ; Indicates the point on the vortex core line numbered in the previous time step , Indicates the vortex core line numbered in the previous time step Indicates the point on the vortex core line numbered in the subsequent time step , Indicates the vortex core line numbered in the subsequent time step

[0033] The process of screening the target vortex core line for the wake vortex is the same as that for the leading-edge wing vortex described above, and will not be elaborated here.

[0034] Step S13: Construct a vortex core line similarity matrix based on the multi-dimensional attribute similarity of the target vortex core lines in adjacent time steps, so as to detect the aerodynamic simulation vortex flow characteristic events of the aircraft through the similarity matrix and generate a tracking result; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

[0035] In this embodiment, before constructing the vortex core line similarity matrix based on the multi-dimensional attribute similarity of the target vortex core lines in adjacent time steps, it further includes: determining the spatial position distance and curve trend of the target vortex core lines in adjacent time steps to obtain geometric similarity; determining the rotation intensity distance and rotation direction distance of the target vortex core lines in adjacent time steps to obtain physical feature similarity; obtaining the multi-dimensional attribute similarity of the target vortex core lines in adjacent time steps according to the geometric similarity and the physical feature similarity. It can be understood that the Liutex vortex core line definition contains 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 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 the Euclidean distance, and the trend similarity between curves can be represented by the covariance. The geometric similarity can be expressed as: ; wherein, and are two target vortex core lines, is the geometric shape similarity. DTSM is a distribution curve similarity measurement method, which is used to calculate the geometric similarity between target vortex core lines in this embodiment.

[0036] ; ; ; wherein, Represents geometric similarity, Represents a curve and The covariance between, Represents a curve The standard deviation of, Represents a curve The standard deviation of, Represents a curve and The Euclidean distance between, Represents the spatial distance between curves, Represents a curve The th component of, Represents the quantity curve The th component of, while Represents the global correlation between curves. Is a penalty factor used to assign weights to the global correlation and spatial distance. Calculate the similarity between two curves based on these weighted similarity algorithms. The higher the value, the greater the weight of the global correlation. By default, set = 1.

[0037] Furthermore, calculate the physical feature similarity between vortices in the flow field, that is, the physical property similarity between the target vortex core lines. Use the following algorithm to calculate the similarity between the physical features of the vortices contained in the Liutex vortex core line, such as the rotation direction Liutex vector And the magnitude of the rotation intensity Liutex vector .

[0038] ; ; ; Among them, Represents the physical feature similarity between curves. Is a scale parameter used to adjust the influence of the distance metric. It usually represents the standard deviation of the data, reflecting the degree of dispersion of the data, and Respectively represent the distance metrics of the vectors and their magnitudes on the curve, N is the number of points on the curve, Is The rotation intensity value at the point, µ is the average value of the rotation intensities of all points. The two curves are respectively and , and each curve is composed of a series of points, and each point is respectively composed of a scalar and a vector Composed. Its scalar The similarity metric between them is obtained through the Euclidean distance: ; The vector on curve is represented as , and curve is composed of vectors . For each vector on curve and each vector on curve , calculate their cosine similarity: ; where, represents the component of the vector at the point on curve . 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.

[0039] ; In this embodiment, by integrating the geometric similarity and the physical feature similarity, the final multi-dimensional attribute similarity S between the target vortex core lines is obtained, and the formula is as follows: ; where, and are attribute weights, and different weights are assigned according to the influence degree of the attributes on the vortex characteristics of the flow field. By default in this embodiment, is 0.2, is 0.8, and different flow fields are slightly adjusted according to the actual situation.

[0040] In this embodiment, according to the calculated multi-dimensional attribute similarity, a similarity matrix of n×m 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 element is a decimal between [0, 1]. If the similarity between two vortex core lines is greater than the minimum similarity threshold, it is considered that the vortex core lines are similar, that is, the represented vortex characteristics are similar. As shown in the schematic diagrams of the target vortex core lines in the three-dimensional semi-cylindrical turbulent flow field at adjacent time steps in FIGS. 7(a) to 7(c), and calculating according to the vortex characteristics in FIGS. 7(a) and 7(b) will obtain a vortex characteristic similarity matrix as shown in Figure 8 . As shown in Figure 9As shown, a corresponding similarity matrix is constructed through the multi-dimensional attribute similarity of the above-mentioned target vortex core line. Based on the quantitative correlation relationship between the matrix elements in the similarity matrix and the vortex core line, the similar vortex features are determined, and the final feature events are determined according to the pre-set correspondence between the quantity and the event.

[0041] In this embodiment, the quantitative correlation relationship of the target vortex core lines of the leading-edge vortex and the wake vortex of the wing at adjacent time steps is judged according to the matrix elements of the similarity matrix, so as to detect the aerodynamic simulation vortex feature events of the aircraft and generate a tracking result based on the quantitative correlation relationship. Wherein, in the process of detecting the aerodynamic simulation vortex feature events of the aircraft based on the quantitative correlation relationship, it further includes: judging whether a first event of the target vortex core line of the leading-edge vortex of the wing at adjacent time steps crosses the wing span direction based on the quantitative correlation relationship, and the type of the first event is a merging event type or a splitting event type; judging whether a second event occurs to the target vortex core line of the wake vortex of the wing at adjacent time steps with the change of the flight speed based on the quantitative correlation relationship, and the type of the second event is a dissipation event type or a newborn event type. The aerodynamic simulation vortex feature events of the aircraft include the feature events of the leading-edge vortex of the wing and the feature events of the wake vortex of the wing. Among them, the feature events of the leading-edge vortex of the wing include the event of the attenuation of the vortex core line intensity caused by the change of the angle of attack, and the feature events of the wake vortex of the wing include the event of the sudden change of the helicity of the vortex core line caused by the change of the engine thrust. It can be understood that according to the definition of the feature events, there are a total of 5 types of events: continuation, splitting, merging, generation, and dissipation. The obtained similarity matrix is traversed, and the events that occur are judged according to the feature correspondence relationship. When the feature quantity correspondence relationship is: 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, a directed acyclic graph of the feature tracking result is generated according to the detected feature correlation relationship and the feature events. Figure 8 Traversing the shown vortex feature similarity matrix can obtain Figure 10 The shown three-dimensional cylindrical turbulent flow vortex feature tracking result and the directed acyclic graph.

[0042] It can be seen that the present application discloses a method for analyzing the vortex characteristics of aircraft aerodynamic simulation, including: obtaining the CFD simulation results of the flow around the aircraft surface, wake flow or engine jet flow after the three-dimensional CFD simulation of the target aircraft to obtain the corresponding flow field data; wherein, the flow field data is the flow field grid points and the velocity gradient matrix, vorticity vector and spatial grid coordinates at each corresponding time step; extracting the vortex core lines in the flow field data at each time step based on the definition of the Liutex vector field, and screening out the 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 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; constructing a vortex core line similarity matrix according to the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps to detect the aircraft aerodynamic simulation vortex characteristic events through the similarity matrix and generate a tracking result; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity. Thus, by obtaining the CFD data of the flow around the aircraft surface, wake flow and engine jet flow, the types of target vortices are clarified as the leading-edge vortex of the wing and the wake vortex, avoiding the non-discriminatory processing of the entire flow field. Further, the vortex core lines are extracted based on the Liutex vector field, and only the real vortex core points are retained, reducing the simulation data processing range from the entire flow field grid points to local grid points. Moreover, the coincidence degree between the vortex core lines extracted by the Liutex vector field and the real vortex axis is very high, solving the problem of over-extraction caused by noise, and the extraction process has a high degree of automation, without manual parameter adjustment, applicable to cross-condition simulation. Further, the accurate extraction of the initial vortex core lines reduces the probability of false matching in the subsequent tracking stage, and finally efficiently realizes the accurate extraction and tracking of vortices in the analysis of aircraft aerodynamic simulation vortex characteristics.

[0043] Referring to Figure 11 as shown, the present invention also correspondingly discloses an apparatus for analyzing the vortex characteristics of aircraft aerodynamic simulation, including: A data acquisition module 11, configured to obtain the CFD simulation results of the flow around the aircraft surface, wake flow or engine jet flow after the three-dimensional CFD simulation of the target aircraft to obtain the corresponding flow field data; wherein, the flow field data is the flow field grid points and the velocity gradient matrix, vorticity vector and spatial grid coordinates at each corresponding time step; A vortex core line extraction module 12, configured to extract the vortex core lines in the flow field data at each time step based on the definition of the Liutex vector field, and screen out the 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 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; An eddy current analysis module 13 is configured to construct an eddy core line similarity matrix based on the multi-dimensional attribute similarity of the target eddy core line at adjacent time steps, so as to detect an aircraft aerodynamic simulation eddy current feature event through the similarity matrix and generate a tracking result; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

[0044] It can be seen that the present application discloses obtaining CFD simulation results of the flow around the surface of an aircraft, wake flow, or engine jet flow after three-dimensional CFD simulation of a target aircraft to obtain corresponding flow field data; wherein, the flow field data is the velocity gradient matrix, vorticity vector, and spatial grid coordinates at each time step corresponding to the flow field grid points; extracting eddy core lines from the flow field data at each time step based on the definition of the Liutex vector field, and screening out target eddy core lines representing vortex structures from the eddy core lines; wherein, the eddy core lines include the eddy core lines of the leading-edge vortices of the wing and the eddy core lines of the wake vortices, 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; constructing an eddy core line similarity matrix based on the multi-dimensional attribute similarity of the target eddy core line at adjacent time steps, so as to detect an aircraft aerodynamic simulation eddy current feature event through the similarity matrix and generate a tracking result; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity. Thus, by obtaining the CFD data of the flow around the surface of the aircraft, wake flow, and engine jet flow, the types of target vortices are clarified as leading-edge vortices of the wing and wake vortices, avoiding non-discriminatory processing of the entire flow field. Further, eddy core lines are extracted based on the Liutex vector field, and only the real vortex core points are retained, reducing the scope of simulation data processing from the entire flow field grid points to local grid points. Moreover, the coincidence degree between the eddy core lines extracted by the Liutex vector field and the real vortex axis is very high, solving the problem of over-extraction caused by noise, and the extraction process has a high degree of automation, without the need for manual parameter adjustment, and is applicable to cross-condition simulations. Further, accurate extraction of the initial eddy core lines reduces the probability of false matching in the subsequent tracking stage, and finally, accurate extraction and tracking of eddy currents are efficiently realized in the analysis of aircraft aerodynamic simulation eddy current features.

[0045] Further, the embodiment of the present application also discloses an electronic device Figure 12 It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment, and the content in the figure should not be regarded as any limitation on the scope of use of the present application.

[0046] Figure 12Schematic diagram of the structure of an electronic device 20 provided by 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. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the aircraft aerodynamic simulation vortex feature analysis method disclosed in any of the foregoing embodiments. Additionally, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0047] 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 external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of the present application, and no specific limitation is imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application needs, and no specific limitation is made here.

[0048] 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 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0049] In addition, as a carrier for resource storage, the memory 22 may be a read-only memory, a random access memory, a disk, or an optical disc, etc. The resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.

[0050] Among them, the operating system 221 is used to manage and control each hardware device and computer program 222 on the electronic device 20, so as to implement the operation and processing of the processor 21 on the massive data 223 in the memory 22. It can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of implementing the aircraft aerodynamic simulation vortex feature analysis method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks. The data 223 may include not only the data transmitted by external devices received by the electronic device, but also the data collected by its own input / output interface 25, etc.

[0051] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the aircraft aerodynamic simulation vortex feature analysis method disclosed above is implemented. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.

[0052] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts between the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description of the method part for the relevant parts.

[0053] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application. The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable EPROM (EEPROM), registers, hard disks, removable disks, CD-ROM (Compact Disc-Read Only Memory), or any other form of storage medium known in the technical field.

[0054] Finally, it should also be noted that in this document, relational terms such as "first" and "second" are only used 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 term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitations, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0055] The solutions provided by the present invention have been introduced in detail above. Specific examples have been used herein to illustrate the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for analyzing the vortex characteristics of an aircraft's aerodynamic simulation, characterized in that, Including: Obtaining the 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 are the flow field grid points and the velocity gradient matrix, vorticity vector and spatial grid coordinates at corresponding time steps. Extracting vortex core lines from the flow field data at each time step based on the definition of the Liutex vector field, and screening out target vortex core lines representing vortex structures from the vortex core lines; wherein, the vortex core lines include the vortex core lines of the leading-edge vortices of the wing and the vortex core lines of the wake vortices, 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. Constructing a vortex core line similarity matrix according to the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, so as to detect aircraft aerodynamic simulation vortex feature events through the similarity matrix and generate tracking results; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

2. The method for analyzing the vortex characteristics of the aerodynamic simulation of an aircraft according to claim 1, wherein The extracting vortex core lines from the flow field data at each time step based on the definition of the Liutex vector field includes: Performing eigen-decomposition processing on the velocity gradient matrix to obtain real eigenvalues, imaginary parts of complex eigenvalues, and taking the unit eigenvector of the real eigenvalues as the local rotation direction of the Liutex vector field. Determining the magnitude 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 and the vector of the local rotation direction at each flow field grid point, screening out the flow field grid points whose cross product norm satisfies the first preset core point screening condition as candidate vortex core points. Based on the relative rotation intensity of the candidate vortex core points, screening out the candidate vortex core points whose relative rotation intensity satisfies the second preset core point screening condition as target vortex core points. Taking the target vortex core points as seed points, integrating along the direction of the local rotation direction using the fourth-order Runge-Kutta method to respectively generate the initial vortex core lines of the leading-edge vortices of the wing and the initial vortex core lines of the wake vortices.

3. The method for analyzing the vortex characteristics of the aerodynamic simulation of the aircraft according to claim 2, wherein The screening out target vortex core lines representing vortex structures from the vortex core lines includes: Respectively determining the first spatial distance between the initial vortex core lines in each vortex core line group of the leading-edge vortices of the wing and the second spatial distance between the initial vortex core lines in each vortex core line group of the wake vortices; wherein, the vortex core line group is constructed based on any two initial vortex core lines randomly. Respectively screening out the target vortex core line groups that satisfy the first spatial distance threshold condition and the second spatial distance threshold condition according to the first spatial distances and the second spatial distances, and performing merging processing on the target vortex core line groups to respectively obtain the first vortex core line group and the second vortex core line group. According to the spatial gradient values of the local angular velocities at the integral starting points of the initial vortex core lines in the first vortex core line group and the second vortex core line group, screening out the initial vortex core lines whose spatial gradient values satisfy the preset spatial gradient threshold to respectively obtain the target vortex core lines of the leading-edge vortices of the wing and the target vortex core lines of the wake vortices.

4. The method for analyzing the vortex characteristics of the aerodynamic simulation of an aircraft according to claim 1, wherein 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, the following steps are further included: Determine the spatial position distance and curve trend of the target vortex core lines at adjacent time steps to obtain the geometric similarity; Determine the rotation intensity distance and rotation direction distance of the target vortex core lines at adjacent time steps to obtain the physical feature similarity; Obtain 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.

5. The method for analyzing the vortex characteristics of the aerodynamic simulation of an aircraft according to claim 1, wherein The detection of the aircraft aerodynamic simulation vortex flow feature events and the generation of tracking results through the similarity matrix include: Judge the quantity correlation relationship of the target vortex core lines of the leading-edge vortices and wake vortices under adjacent time steps according to the matrix elements of the similarity matrix, and detect the aircraft aerodynamic simulation vortex flow feature events and generate tracking results based on the quantity correlation relationship.

6. The method for analyzing the vortex characteristics of the aerodynamic simulation of an aircraft according to claim 5, wherein During the process of detecting the aircraft aerodynamic simulation vortex flow feature events based on the quantity correlation relationship, the following steps are further included: Judge whether a first event of the target vortex core line of the leading-edge vortex under adjacent time steps occurs across the wing span based on the quantity correlation relationship, and the type of the first event is a merging event type or a splitting event type; Judge whether a second event of the target vortex core line of the wake vortex under adjacent time steps occurs with the change of the flight speed based on the quantity correlation relationship, and the type of the second event is a dissipation event type or a newborn event type.

7. The method for analyzing the vortex characteristics of the aerodynamic simulation of an aircraft according to any one of claims 1 to 6, characterized in that The aircraft aerodynamic simulation vortex flow feature events include the feature events of the leading-edge vortices and the feature events of the wake vortices. Among them, the feature events of the leading-edge vortices include the vortex core line intensity attenuation events caused by the change of the angle of attack, and the feature events of the wake vortices include the vortex core line helicity mutation events caused by the change of the engine thrust.

8. An aerodynamic simulation vortex feature analysis device for an aircraft, characterized in that It includes: A data acquisition module, configured to acquire the CFD simulation results of the flow around the aircraft surface, wake flow or engine jet flow 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, vorticity vector and spatial grid coordinates corresponding to each time step of the flow field grid points; A vortex core line extraction module, configured to extract the vortex core lines in the flow field data of each time step based on the definition of the Liutex vector field, and screen out the 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 vortices and the vortex core lines of the wake vortices, 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 flow analysis module, configured to construct a vortex core line similarity matrix according to the multi-dimensional attribute similarity of the target vortex core lines at adjacent time steps, and detect the aircraft aerodynamic simulation vortex flow feature events and generate tracking results through the similarity matrix; wherein, the multi-dimensional attribute similarity includes geometric similarity and physical feature similarity.

9. An electronic device, characterized in that, It includes: A memory, configured to store a computer program; A processor, configured to execute the computer program to implement the steps of the aircraft aerodynamic simulation vortex flow feature analysis method according to any one of claims 1 to 7.

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

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