An aircraft flow field reconstruction method and device based on snapshot weighting, equipment and storage medium

Abstract

The application discloses a snapshot weighting-based aircraft flow field reconstruction method and device, equipment and storage medium, and relates to the technical field of computers, and comprises the following steps: constructing a flow field snapshot matrix based on a target parameter affecting a target aircraft flow field and utilizing CFD software and spatial positions comprising preset flow field coordinate points to perform numerical simulation; constructing test observation data based on flow field data measured at each test observation point in a target test, determining target simulation position points satisfying a preset position condition by utilizing spatial positions and observation point positions corresponding to the test observation points, and constructing a projection snapshot by inversely interpolating distances from the target simulation position points to the observation point positions; and performing intrinsic orthogonal decomposition on the projection snapshot, determining a reconstructed flow field of the target aircraft based on obtained linear weighting vectors and the flow field snapshot matrix. The method avoids matrix ill-conditioning and regularization parameter selection difficulty in flow field reconstruction under multiple data sources, and improves the accuracy and coverage of flow field reconstruction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to an aircraft flow field reconstruction method and device based on snapshot weighting, equipment and storage medium. BACKGROUND

[0002] With the improvement of the complexity of the aircraft design and the fine requirement of the multidisciplinary comprehensive performance index, it is difficult to meet the modern design requirements only by relying on the integral aerodynamic force. The local details of the flow field information become indispensable key data. For example, the aircraft surface pressure distribution is directly related to the structure load spectrum, which is the basic input for strength checking and flutter analysis; the thermal environment parameters (temperature field, heat flux density) provide quantitative basis for thermal protection system design. The fine modeling and analysis of the flow field have become the key link of the multidisciplinary collaborative design of the aircraft.

[0003] Wind tunnel test and numerical simulation are the core technical means to obtain the flow field data of the aircraft, and each has its own distinct advantages and limitations. The advantage of wind tunnel test is that it can directly capture complex phenomena (such as vortex separation and shock formation) in real flow environment, but it is limited by experimental conditions, resulting in insufficient sensor arrangement density, and it is difficult to realize high-resolution measurement of full-field flow field parameters, especially in analyzing local flow details (such as pressure distribution and streamline trajectory). CFD (Computational Fluid Dynamics) technology can provide full-field flow field information (such as velocity field, pressure field and vortex structure) through numerical simulation, and can quickly evaluate different working conditions by adjusting parameters (such as attack angle and Mach number), significantly shorten the development cycle and reduce cost; however, its accuracy is highly dependent on the reasonableness of the physical model and the reliability of the numerical method. Due to the limitations of the understanding of complex flow phenomena such as turbulence, transition and real gas effects, the existing physical model still has an error that cannot be ignored. Since a single method has inherent limitations, data fusion technology has gradually become a key path to break through the technical bottleneck. Researchers are exploring the fusion of sparse measured data of wind tunnel test and full-field simulation results of CFD, in order to provide high-precision and rich flow field data for industrial applications.

[0004] Gappy-POD is a linear method capable of reconstructing flow fields in seconds. This method combines the dimensionality reduction capabilities of POD with the optimization capabilities of least squares to achieve efficient reconstruction of complete flow fields from sparse experimental data. It seeks a solution within the subspace spanned by POD modes that minimizes the difference from a few discrete reference points in the least-squares sense. While the Gappy-POD method has seen many successful applications in flow field reconstruction, it is prone to overfitting when the reference data is noisy (e.g., experimental measurement noise) or the number of sensors is too small. This means the reconstructed flow field perfectly matches the experimental data at the reference points, but spurious features (such as false eddies and shock wave jitter) appear throughout the field. To address this, regularization methods have been proposed, introducing regularization terms into the objective function to suppress excessive fluctuations in coefficients. However, currently, no regularization method is effective for all cases.

[0005] As can be seen from the above, how to avoid matrix ill-conditioning and difficulty in selecting regularization parameters in flow field reconstruction under multiple data sources, and improve the accuracy and coverage of flow field reconstruction, are urgent problems to be solved. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a method, apparatus, device, and storage medium for aircraft flow field reconstruction based on snapshot weighting, which can avoid matrix ill-conditioning and difficulties in selecting regularization parameters in flow field reconstruction under multiple data sources, and improve the accuracy and coverage of flow field reconstruction. The specific solution is as follows:

[0007] Firstly, this application provides a snapshot-weighted method for reconstructing the flow field of an aircraft, including:

[0008] The target parameters affecting the flow field of the target aircraft are determined. Based on the target parameters, numerical simulation is performed using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data. The flow field snapshot data is then used to construct a flow field snapshot matrix. The target parameters include incoming flow parameters and physical parameters.

[0009] Experimental observation data is constructed based on the flow field data measured at each experimental observation point in the target experiment. The target simulation location point that meets the preset location conditions is determined by using the spatial location and the observation point location corresponding to the experimental observation point. Back-interpolation is performed based on the distance from the target simulation location point to the observation point location to construct a projection matrix. The projection snapshot is determined based on the flow field snapshot data and the projection matrix.

[0010] The projected snapshot is subjected to intrinsic orthogonal decomposition to obtain an orthogonal basis function space. The experimental observation data is projected onto the orthogonal basis function space to obtain a linear weighted vector. The reconstructed flow field of the target aircraft is determined based on the linear weighted vector and the flow field snapshot matrix.

[0011] Optionally, the step of determining the target parameters affecting the flow field of the target aircraft, and then performing numerical simulations based on the target parameters using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data, including:

[0012] Target parameters are constructed based on external flight condition parameters and internal physical parameters that affect the flow field of the target aircraft. The target parameters are then expanded using a preset expansion rule to obtain the expanded parameters.

[0013] The expanded parameters were sampled using the Latin hypercube sampling method to obtain simulated flow field samples;

[0014] Based on the simulated flow field samples and preset simulation conditions, numerical simulation is performed using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of simulated flow field data. The simulated flow field data is then determined as flow field snapshot data.

[0015] Optionally, constructing the flow field snapshot matrix using the flow field snapshot data includes:

[0016] Convert the flow field snapshot data into a flow field vector;

[0017] The flow field vectors are combined based on a preset dimension combination method to construct a flow field snapshot matrix.

[0018] Optionally, the step of constructing experimental observation data based on the flow field data measured at each experimental observation point in the target experiment, and determining the target simulation location point that meets the preset location conditions using the spatial location and the observation point location corresponding to the experimental observation point, includes:

[0019] The target test collects measured flow field data at each test observation point based on preset sensors set up at each test observation point, and uses the measured flow field data to construct test observation data; the target test includes flight tests corresponding to aircraft of the same type as the target aircraft;

[0020] In the preset flow field coordinates of the spatial location, the target simulation position point is determined based on the spatial dimension of the target aircraft flow field and the grid discretization form during data simulation, and the distance between the observation point corresponding to the test observation point satisfies the preset position conditions.

[0021] Optionally, determining the target simulation location point whose distance to the observation point corresponding to the experimental observation point satisfies the preset location condition, based on the spatial dimension of the target aircraft flow field and the grid discretization form during data simulation, within the preset flow field coordinates at the spatial location, includes:

[0022] If the spatial dimension of the flow field of the target aircraft is two-dimensional, then the first number of preset flow field coordinate points that are closest to the observation point corresponding to the test observation point are determined as the target simulation position points.

[0023] If the mesh discretization form of the target aircraft flow field is a triangular element, then the preset flow field coordinate points of the second target number that are closest to the observation point corresponding to the test observation point are determined as the target simulation position points;

[0024] If the grid discretization form of the target aircraft flow field is a quadrilateral cell, then the preset flow field coordinate points of the third target number that are closest to the observation point corresponding to the test observation point are determined as the target simulation position points.

[0025] Optionally, the step of performing back interpolation based on the distance from the target simulation location point to the observation point to construct a projection matrix, and determining the projection snapshot based on the flow field snapshot data and the projection matrix, includes:

[0026] Back-interpolation is performed based on the distance from the target simulation location point to the corresponding observation point to obtain interpolation coefficients. Based on each interpolation coefficient, a projection matrix is ​​constructed using the number of preset flow field coordinate points and the number of experimental observation points.

[0027] The flow field simulation projection vector is determined by multiplying the projection matrix with each of the flow field snapshot data, and the flow field simulation projection vectors are integrated to construct the projection snapshot.

[0028] Optionally, the step of performing intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space, projecting the experimental observation data onto the orthogonal basis function space to obtain a linear weighted vector, and determining the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix includes:

[0029] The projection snapshot is subjected to intrinsic orthogonal decomposition to obtain the orthogonal basis function space and eigenvector matrix, the mean vector of the projection snapshot and the coefficient vector of the orthogonal basis functions;

[0030] The expansion coefficients are determined based on the experimental observation data, the mean vector, and the orthogonal basis function space.

[0031] The linear weighting vector is determined using the expansion coefficient, the eigenvector matrix, and the meanization operator, and the reconstructed flow field of the target aircraft is determined based on the linear weighting vector and the flow field snapshot matrix.

[0032] Secondly, this application provides a snapshot-weighted aircraft flow field reconstruction device, comprising:

[0033] The numerical simulation module is used to determine the target parameters affecting the flow field of the target aircraft. Based on the target parameters, numerical simulation is performed using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data. The flow field snapshot data is then used to construct a flow field snapshot matrix. The target parameters include incoming flow parameters and physical parameters.

[0034] The projection snapshot determination module is used to construct test observation data based on the flow field data measured at each test observation point in the target test, determine the target simulation position point that meets the preset position conditions using the spatial position and the observation point position corresponding to the test observation point, perform back interpolation based on the distance from the target simulation position point to the observation point position to construct a projection matrix, and determine the projection snapshot based on the flow field snapshot data and the projection matrix.

[0035] The reconstructed flow field determination module is used to perform intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space, project the experimental observation data onto the orthogonal basis function space to obtain a linear weighted vector, and determine the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix.

[0036] Thirdly, this application provides an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor is used to execute the computer program to implement the aforementioned snapshot-weighted aircraft flow field reconstruction method.

[0039] Fourthly, this application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned snapshot-weighted aircraft flow field reconstruction method.

[0040] This application identifies target parameters affecting the flow field of a target aircraft. Based on these target parameters, numerical simulations are performed using CFD software and the spatial positions of preset flow field coordinate points to obtain multiple sets of flow field snapshot data. A flow field snapshot matrix is ​​then constructed using these snapshot data. The target parameters include incoming flow parameters and physical parameters. Experimental observation data is constructed based on flow field data measured at various experimental observation points during the target experiment. Target simulation location points that meet preset location conditions are determined using the spatial positions and the corresponding observation point positions. Back-interpolation is performed based on the distance from the target simulation location point to the observation point to construct a projection matrix. Projected snapshots are determined based on the flow field snapshot data and the projection matrix. Intrinsic orthogonal decomposition is performed on the projected snapshots to obtain an orthogonal basis function space. The experimental observation data is projected onto the orthogonal basis function space to obtain a linear weighted vector. The reconstructed flow field of the target aircraft is determined based on the linear weighted vector and the flow field snapshot matrix.

[0041] As can be seen from the above, this application simulates the incoming flow parameters and physical parameters of the target aircraft. The simulated flow field snapshot matrix can obtain flow field information under all flight conditions and physical conditions of the target aircraft, ensuring the coverage of the flow field reconstruction. Based on the location of the experimental observation points, the target simulation location points are determined and linked with the flow field simulation. The distance is then back-interpolated to construct a projection matrix, so that the generated projection snapshot retains the flow field coverage of the simulation while incorporating the authenticity of the experimental observation data. After reducing the dimensionality of the flow field data through orthogonal decomposition, the experimental observation data is projected onto the orthogonal basis space to obtain a linear weighted vector. In this way, the reconstructed flow field of the target aircraft is determined based on the linear weighted vector and the flow field snapshot matrix. This combines the advantages of the global flow field of numerical simulation and experimental data, resulting in high-precision flow field data and improving the reliability of the flow field reconstruction. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0043] Figure 1 This is a flowchart of a snapshot-weighted aircraft flow field reconstruction method disclosed in this application;

[0044] Figure 2 A schematic diagram of a space shuttle surface grid provided in this application;

[0045] Figure 3This application provides a comparative schematic diagram of the heat flux distribution along the centerline of a space shuttle, wherein... Figure 3 Image (a) is a schematic diagram of the heat flow distribution along the centerline of the space shuttle according to this embodiment. Figure 3 (b) is a schematic diagram of the heat flux distribution along the centerline of the space shuttle using a Gappy POD method provided in this embodiment;

[0046] Figure 4 This is a schematic diagram of a snapshot-weighted aircraft flow field reconstruction device disclosed in this application;

[0047] Figure 5 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Currently, the Gappy-POD method combines the dimensionality reduction capability of POD with the optimization capability of least squares to achieve efficient reconstruction of the complete flow field from sparse experimental data. In implementation, a solution is sought in the subspace spanned by the POD modes that minimizes the difference from a few discrete reference points in the least squares sense. Although the Gappy-POD method has many successful applications in flow field reconstruction, it is prone to overfitting when the reference data is noisy or the number of sensors is too small. That is, the reconstructed flow field perfectly matches the experimental data at the reference points, but spurious features appear throughout the field. To address this, a regularization method has been proposed, introducing a regularization term into the objective function to suppress excessive fluctuations in coefficients. However, currently, no regularization method is effective for all cases. Therefore, this application provides a snapshot-weighted aircraft flow field reconstruction method. Based on the linear weighting vector and the flow field snapshot matrix, the reconstructed flow field of the target aircraft is determined. This method integrates the advantages of the global flow field from numerical simulation and experimental data, resulting in high-precision flow field data and improving the reliability of flow field reconstruction.

[0050] See Figure 1 As shown, this embodiment of the invention discloses a method for reconstructing the flow field of an aircraft based on snapshot weighting, including:

[0051] Step S11: Determine the target parameters that affect the flow field of the target aircraft. Based on the target parameters, numerical simulation is performed using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data. The flow field snapshot data is then used to construct a flow field snapshot matrix. The target parameters include incoming flow parameters and physical parameters.

[0052] In this embodiment, the target parameters that will change the flow field of the target spacecraft are first determined. Taking the space shuttle as an example, 11 physical parameters are considered to constitute the target parameters. Among them, the physical parameters of the target parameters include the chemical reaction rate, i.e. , , , , , , , The viscosity coefficient and specific heat capacity at isobaric pressure, multiplied by the target parameters by a uniform distribution. An uncertainty scaling factor is used to cover the actual fluctuation range of the target parameter. Then, the expanded parameter is sampled using the Latin hypercube sampling method to obtain multiple sets of simulated flow field samples. Based on the target parameter, numerical simulation of the space shuttle is performed using CFD software. Laminar flow simulation is used, the flux scheme is Steger-Warming (a flux splitting method for computational fluid dynamics), the limiter is Van Albada (a slope limiter for high-resolution finite volume methods), and the wall temperature is set to 1500K to make the simulation results more accurate and avoid unreasonable errors. Figure 2 This embodiment provides a schematic diagram of a space shuttle surface mesh, dividing the space shuttle surface into 53,655 spatial location points. After numerical simulation, multiple sets of simulated flow field data are obtained. Specifically, the process involves determining target parameters affecting the flow field of the target spacecraft, and performing numerical simulation based on these target parameters using CFD software and the spatial locations of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data. This includes: constructing target parameters based on external flight condition parameters and internal physical parameters affecting the flow field of the target spacecraft; expanding the target parameters using preset expansion rules to obtain expanded parameters; sampling the expanded parameters using the Latin hypercube sampling method to obtain simulated flow field samples; and performing numerical simulation based on the simulated flow field samples and preset simulation conditions using CFD software and the spatial locations of each preset flow field coordinate point to obtain multiple sets of simulated flow field data. These simulated flow field data are then identified as flow field snapshot data.

[0053] It is understandable that after obtaining multiple sets of flow field snapshot data, the flow field snapshot data is converted into vector form to obtain the flow field vector. ; where represents the first The snapshot data, the first of the flow field vectors element For the flow field in space The value at that location; The total number of preset flow field coordinate points in the spatial location is given, and then the flow field vectors are combined to construct a flow field snapshot matrix. , The total number of flow field snapshot data. Specifically, constructing a flow field snapshot matrix using the flow field snapshot data includes: converting the flow field snapshot data into flow field vectors; and combining the flow field vectors based on a preset dimension combination method to construct a flow field snapshot matrix.

[0054] Step S12: Construct test observation data based on the flow field data measured at each test observation point in the target test; determine the target simulation location point that meets the preset location conditions using the spatial location and the observation point location corresponding to the test observation point; perform back interpolation based on the distance from the target simulation location point to the observation point location to construct a projection matrix; and determine the projection snapshot based on the flow field snapshot data and the projection matrix.

[0055] In this embodiment, based on flight tests corresponding to aircraft of the same type as the target aircraft or other tests that can be used to calibrate the simulation data of the target aircraft, the measured flow field data at each test observation point is determined, thereby constructing test observation data. ; wherein, each element in the experimental observation data is A preset sensor in spatial position The flow field values ​​were measured at the locations; the experimental observation points were sparse locations where preset sensors were arranged. For each preset sensor's installation location... In the CFD simulation, a target simulation location point is found whose distance from the observation point corresponding to the experimental observation point meets the preset location conditions.

[0056] Specifically, the step of constructing experimental observation data based on flow field data measured at each experimental observation point in the target experiment, and determining the target simulation location point that meets the preset location conditions using the spatial location and the location of the observation point corresponding to the experimental observation point, includes: collecting measured flow field data at each experimental observation point based on preset sensors set at each experimental observation point in the target experiment, and constructing experimental observation data using the measured flow field data; the target experiment includes flight experiments corresponding to aircraft of the same type as the target aircraft; and determining the target simulation location point whose distance to the observation point corresponding to the experimental observation point meets the preset location conditions based on the spatial dimension of the flow field of the target aircraft and the grid discretization form during data simulation, within the preset flow field coordinate points of the spatial location.

[0057] It is understood that, for each experimental observation location, i.e., the installation location of each of the preset sensors, a preset number of flow field coordinate points closest to the experimental observation point are found in the spatial location of the simulated flow field as target simulation location points. Specifically, determining the target simulation location points whose distance to the observation point corresponding to the experimental observation point satisfies preset location conditions among the preset flow field coordinate points in the spatial location, based on the spatial dimension of the target aircraft flow field and the grid discretization form during data simulation, includes: if the spatial dimension of the target aircraft flow field is two-dimensional, then the first target number of preset flow field coordinate points closest to the observation point corresponding to the experimental observation point are determined as target simulation location points; if the grid discretization form of the target aircraft flow field is triangular elements, then the second target number of preset flow field coordinate points closest to the observation point corresponding to the experimental observation point are determined as target simulation location points; if the grid discretization form of the target aircraft flow field is quadrilateral elements, then the third target number of preset flow field coordinate points closest to the observation point corresponding to the experimental observation point are determined as target simulation location points.

[0058] In this embodiment, after obtaining the target simulation location point, the back interpolation coefficients are determined based on the distance from the target simulation location point to the corresponding observation point. The closer the distance, the larger the back interpolation coefficients. A projection matrix is ​​then constructed based on these back interpolation coefficients. The projection matrix is... ,in, The number of the experimental observation points; To determine the number of preset flow field coordinate points, the flow field simulation projection vector is determined based on the projection matrix and the flow field vector, using the following formula:

[0059] ;

[0060] in, The flow field vector is the vector form corresponding to the flow field snapshot data. The projection matrix; The sequence number of the flow field snapshot data. ; This represents the total number of flow field snapshot data, i.e., the number of samples in the simulated flow field. The simulated flow field projection vectors are integrated to construct the projection snapshots.

[0061] Specifically, the step of performing back interpolation based on the distance from the target simulation location point to the observation point to construct a projection matrix, and determining the projection snapshot based on the flow field snapshot data and the projection matrix, includes: performing back interpolation based on the distance from the target simulation location point to the corresponding observation point to obtain interpolation coefficients; constructing a projection matrix based on each interpolation coefficient using the number of preset flow field coordinate points and the number of experimental observation points; determining the flow field simulation projection vector based on the product of the projection matrix and each flow field snapshot data; and integrating the flow field simulation projection vector to construct the projection snapshot.

[0062] Step S13: Perform intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space, project the experimental observation data onto the orthogonal basis function space to obtain a linear weighted vector, and determine the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix.

[0063] In this embodiment, after obtaining the projection snapshot, an intrinsic orthogonal decomposition is performed on the projection snapshot, and the corresponding formula is as follows:

[0064] ;

[0065] in, For the projection snapshot; The mean vector of the projected snapshot; It is an orthogonal basis function space; is the coefficient vector of the orthogonal basis functions; s is the number of eigenvectors that are truncated; The number of simulated projection vectors for each flow field in the projected snapshot; The number of experimental observation points is given. After obtaining the orthogonal basis function space, the truncated eigenvectors are determined based on the orthogonal basis function space, and the corresponding formula is as follows:

[0066] ;

[0067] in, Let be the orthogonal basis function space; For the projection snapshot; The truncated feature vector matrix is ​​a matrix composed of the core features of the target that best represent the experimental observation point data, based on the matrix corresponding to the projection snapshot.

[0068] It is understandable that by projecting the observation data of a certain experiment onto the orthogonal basis function space, the expansion coefficients can be obtained, and the corresponding formulas are as follows:

[0069] ;

[0070] in, This is the transpose of the orthogonal basis function space; The experimental observation data; Let be the mean vector of the projected snapshot. Then, a linear weighted vector is determined based on the expansion coefficients, with the corresponding formula as follows:

[0071] ;

[0072] in, For the mean-averaging operator, ; This refers to the total number of the flow field snapshot data, i.e., the number of samples in the simulated flow field; For transpose; The truncated eigenvector matrix; Here is the coefficient vector of the orthogonal basis functions. After obtaining the linear weighted vector, it is applied to the flow field snapshot matrix to obtain the reconstructed flow field of the target aircraft, as shown in the following formula:

[0073] ;

[0074] in, This is the flow field snapshot matrix; Let be the linear weighted vector.

[0075] Specifically, the step of performing intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space, projecting the experimental observation data onto the orthogonal basis function space to obtain a linear weighted vector, and determining the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix includes: performing intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space and an eigenvector matrix, a mean vector of the projected snapshot, and coefficient vectors of the orthogonal basis functions; determining expansion coefficients based on the experimental observation data, the mean vector, and the orthogonal basis function space; determining a linear weighted vector using the expansion coefficients, the eigenvector matrix, and a meanization operator; and determining the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix.

[0076] Furthermore, to verify the accuracy of the flow field reconstruction proposed in this scheme, Figure 3 This embodiment provides a comparative schematic diagram of the heat flux distribution along the centerline of a space shuttle, wherein... Figure 3 Image (a) is a schematic diagram of the heat flow distribution along the centerline of the space shuttle according to this embodiment. Figure 3Figure (b) is a schematic diagram of the heat flow distribution along the space shuttle centerline using the Gappy POD method provided in this embodiment. The horizontal axis X represents the spatial coordinates of the space shuttle centerline, i.e., the linear distance from the nose to the tail of the space shuttle. L represents the total length of the space shuttle centerline, and X / L represents the ratio of the spatial coordinates of the space shuttle centerline to the total length, resulting in a dimensionless position. This is to eliminate differences between spacecraft of different sizes and make the coordinates more universal. For example, X / L=0 corresponds to the top of the nose, X / L=1 corresponds to the end of the tail, and X / L=0.5 corresponds to the midpoint of the centerline. The vertical axis represents the heat flow density at the corresponding position along the space shuttle centerline, i.e., the heat passing through a unit area per unit time. A larger value indicates more intense heating at that position. It can be seen that the flow field reconstruction results for spacecraft including the space shuttle are very consistent with CFD simulations, and avoid the oscillations in the reconstructed heat flow field that the Gappy-POD method fails to eliminate.

[0077] As can be seen from the above, this application simulates the incoming flow parameters and physical parameters of the target aircraft. The simulated flow field snapshot matrix can obtain flow field information under all flight conditions and physical conditions of the target aircraft, ensuring the coverage of the flow field reconstruction. Based on the location of the experimental observation points, the target simulation location points are determined and linked with the flow field simulation. The distance is then back-interpolated to construct a projection matrix, so that the generated projection snapshot retains the flow field coverage of the simulation while incorporating the authenticity of the experimental observation data. After reducing the dimensionality of the flow field data through orthogonal decomposition, the experimental observation data is projected onto the orthogonal basis space to obtain a linear weighted vector. In this way, the reconstructed flow field of the target aircraft is determined based on the linear weighted vector and the flow field snapshot matrix. This combines the advantages of the global flow field of numerical simulation and experimental data, resulting in high-precision flow field data and improving the reliability of the flow field reconstruction.

[0078] Accordingly, see Figure 4 As shown, this application also provides a snapshot-weighted aircraft flow field reconstruction device, comprising:

[0079] The numerical simulation module 11 is used to determine the target parameters affecting the flow field of the target aircraft, and to perform numerical simulation based on the target parameters using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data, and to construct a flow field snapshot matrix using the flow field snapshot data; the target parameters include incoming flow parameters and physical parameters;

[0080] The projection snapshot determination module 12 is used to construct test observation data based on the flow field data measured at each test observation point in the target test, determine the target simulation position point that meets the preset position conditions using the spatial position and the observation point position corresponding to the test observation point, perform back interpolation based on the distance from the target simulation position point to the observation point position to construct a projection matrix, and determine the projection snapshot based on the flow field snapshot data and the projection matrix.

[0081] The reconstructed flow field determination module 13 is used to perform intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space, project the experimental observation data onto the orthogonal basis function space to obtain a linear weighted vector, and determine the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix.

[0082] In some specific embodiments, the numerical simulation module 11 may specifically include:

[0083] The parameter expansion unit is used to construct target parameters based on external flight condition parameters and internal physical parameters that affect the flow field of the target aircraft, and to expand the target parameters using preset expansion rules to obtain the expanded parameters.

[0084] The parameter sampling unit is used to sample the expanded parameters using the Latin hypercube sampling method to obtain simulated flow field samples.

[0085] The numerical simulation unit is used to perform numerical simulation based on the simulated flow field sample and preset simulation conditions, using CFD software and the spatial position of each preset flow field coordinate point, so as to obtain multiple sets of simulated flow field data, and to determine the simulated flow field data as flow field snapshot data.

[0086] In some specific embodiments, the numerical simulation module 11 may specifically include:

[0087] A snapshot data conversion unit is used to convert the flow field snapshot data into a flow field vector;

[0088] The flow field vector combination unit is used to combine the flow field vectors based on a preset dimension combination method to construct a flow field snapshot matrix.

[0089] In some specific embodiments, the projection snapshot determination module 12 may specifically include:

[0090] The observation data construction unit is used to collect measured flow field data at each of the test observation points based on preset sensors set at each test observation point in the target test, and to construct test observation data using the measured flow field data; the target test includes flight tests corresponding to aircraft of the same type as the target aircraft;

[0091] The target location point determination unit is used to determine, based on the spatial dimension of the target aircraft flow field and the grid discretization form during data simulation, the target simulation location point whose distance from the observation point corresponding to the test observation point satisfies the preset location conditions.

[0092] In some specific embodiments, the projection snapshot determination module 12 may specifically include:

[0093] The first position point determination unit is used to determine the first number of preset flow field coordinate points that are closest to the observation point corresponding to the test observation point as the target simulation position point in the preset flow field coordinate points of the spatial position if the spatial dimension of the target aircraft flow field is two-dimensional.

[0094] The second position point determination unit is used to determine the preset flow field coordinate points of the second target number that are closest to the observation point corresponding to the test observation point as the target simulation position points if the grid discretization form of the target aircraft flow field is a triangular element.

[0095] The third position point determination unit is used to determine the preset flow field coordinate points of the third target number that are closest to the observation point corresponding to the test observation point as the target simulation position points if the grid discretization form of the target aircraft flow field is a quadrilateral element.

[0096] In some specific embodiments, the projection snapshot determination module 12 may specifically include:

[0097] The inverse interpolation unit is used to perform inverse interpolation based on the distance from the target simulation location point to the corresponding observation point location to obtain interpolation coefficients, and to construct a projection matrix based on each interpolation coefficient using the number of preset flow field coordinate points and the number of experimental observation points;

[0098] The projection vector integration unit is used to determine the flow field simulation projection vector based on the product of the projection matrix and each of the flow field snapshot data, and to integrate the flow field simulation projection vectors to construct the projection snapshot.

[0099] In some specific embodiments, the reconstructed flow field determination module 13 may specifically include:

[0100] An orthogonal decomposition unit is used to perform intrinsic orthogonal decomposition on the projected snapshot to obtain the orthogonal basis function space and eigenvector matrix, the mean vector of the projected snapshot, and the coefficient vector of the orthogonal basis functions;

[0101] The expansion coefficient determination unit is used to determine the expansion coefficients based on the experimental observation data, the mean vector, and the orthogonal basis function space.

[0102] The reconstructed flow field determination unit is used to determine a linear weighted vector using the expansion coefficient, the eigenvector matrix, and the meanization operator, and to determine the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix.

[0103] Furthermore, embodiments of this application also disclose an electronic device, Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. Specifically, the electronic device 20 may 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 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the snapshot-weighted aircraft flow field reconstruction method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0104] 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 can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0105] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0106] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the snapshot-weighted aircraft flow field reconstruction method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0107] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned snapshot-weighted aircraft flow field reconstruction method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0108] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0109] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software 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 beyond the scope of this application.

[0110] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0111] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0112] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for reconstructing the flow field of an aircraft based on snapshot weighting, characterized in that, include: Determine the target parameters that affect the flow field of the target aircraft, and perform numerical simulation based on the target parameters and using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data, and construct a flow field snapshot matrix using the flow field snapshot data; The target parameters include incoming flow parameters and physical parameters; Experimental observation data is constructed based on the flow field data measured at each experimental observation point in the target experiment. The target simulation location point that meets the preset location conditions is determined by using the spatial location and the observation point location corresponding to the experimental observation point. Back-interpolation is performed based on the distance from the target simulation location point to the observation point location to construct a projection matrix. The projection snapshot is determined based on the flow field snapshot data and the projection matrix. The projected snapshot is subjected to intrinsic orthogonal decomposition to obtain an orthogonal basis function space. The experimental observation data is projected onto the orthogonal basis function space to obtain a linear weighted vector. The reconstructed flow field of the target aircraft is determined based on the linear weighted vector and the flow field snapshot matrix. The step of constructing experimental observation data based on flow field data measured at each experimental observation point in the target experiment, and determining the target simulation location point that meets the preset location conditions using the spatial location and the location of the observation point corresponding to the experimental observation point, includes: collecting measured flow field data at each experimental observation point based on preset sensors set at each experimental observation point in the target experiment, and constructing experimental observation data using the measured flow field data; the target experiment includes flight experiments corresponding to aircraft of the same type as the target aircraft; and determining the target simulation location point whose distance to the observation point corresponding to the experimental observation point meets the preset location conditions based on the spatial dimension of the flow field of the target aircraft and the grid discretization form during data simulation, within the preset flow field coordinate points of the spatial location. The step of determining the target simulation location point whose distance to the observation point corresponding to the test observation point satisfies the preset location conditions among the preset flow field coordinate points in the spatial location, based on the spatial dimension of the target aircraft flow field and the grid discretization form during data simulation, includes: among the preset flow field coordinate points in the spatial location, if the spatial dimension of the target aircraft flow field is two-dimensional, then the first number of preset flow field coordinate points closest to the observation point corresponding to the test observation point are determined as the target simulation location point; if the grid discretization form of the target aircraft flow field is triangular elements, then the second number of preset flow field coordinate points closest to the observation point corresponding to the test observation point are determined as the target simulation location point; if the grid discretization form of the target aircraft flow field is quadrilateral elements, then the third number of preset flow field coordinate points closest to the observation point corresponding to the test observation point are determined as the target simulation location point.

2. The aircraft flow field reconstruction method based on snapshot weighting according to claim 1, characterized in that, The process involves determining the target parameters that influence the flow field of the target aircraft, and then performing numerical simulations based on these parameters using CFD software and the spatial positions of various preset flow field coordinate points to obtain multiple sets of flow field snapshot data, including: Target parameters are constructed based on external flight condition parameters and internal physical parameters that affect the flow field of the target aircraft. The target parameters are then expanded using a preset expansion rule to obtain the expanded parameters. The expanded parameters were sampled using the Latin hypercube sampling method to obtain simulated flow field samples; Based on the simulated flow field samples and preset simulation conditions, numerical simulation is performed using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of simulated flow field data. The simulated flow field data is then determined as flow field snapshot data.

3. The aircraft flow field reconstruction method based on snapshot weighting according to claim 1, characterized in that, The step of constructing a flow field snapshot matrix using the flow field snapshot data includes: Convert the flow field snapshot data into a flow field vector; The flow field vectors are combined based on a preset dimension combination method to construct a flow field snapshot matrix.

4. The aircraft flow field reconstruction method based on snapshot weighting according to any one of claims 1 to 3, characterized in that, The process of performing back interpolation based on the distance from the target simulation location point to the observation point location to construct a projection matrix, and determining the projection snapshot based on the flow field snapshot data and the projection matrix, includes: Back-interpolation is performed based on the distance from the target simulation location point to the corresponding observation point to obtain interpolation coefficients. Based on each interpolation coefficient, a projection matrix is ​​constructed using the number of preset flow field coordinate points and the number of experimental observation points. The flow field simulation projection vector is determined by multiplying the projection matrix with each of the flow field snapshot data, and the flow field simulation projection vectors are integrated to construct the projection snapshot.

5. The aircraft flow field reconstruction method based on snapshot weighting according to claim 4, characterized in that, The process of performing intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space, projecting the experimental observation data onto the orthogonal basis function space to obtain a linear weighted vector, and determining the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix includes: The projection snapshot is subjected to intrinsic orthogonal decomposition to obtain the orthogonal basis function space and eigenvector matrix, the mean vector of the projection snapshot and the coefficient vector of the orthogonal basis functions; The expansion coefficients are determined based on the experimental observation data, the mean vector, and the orthogonal basis function space. The linear weighting vector is determined using the expansion coefficient, the eigenvector matrix, and the meanization operator, and the reconstructed flow field of the target aircraft is determined based on the linear weighting vector and the flow field snapshot matrix.

6. A snapshot-weighted aircraft flow field reconstruction device, characterized in that, include: The numerical simulation module is used to determine the target parameters that affect the flow field of the target aircraft. Based on the target parameters, numerical simulation is performed using CFD software and the spatial positions of each preset flow field coordinate point to obtain multiple sets of flow field snapshot data. The flow field snapshot data is then used to construct a flow field snapshot matrix. The target parameters include incoming flow parameters and physical parameters; The projection snapshot determination module is used to construct test observation data based on the flow field data measured at each test observation point in the target test, determine the target simulation position point that meets the preset position conditions using the spatial position and the observation point position corresponding to the test observation point, perform back interpolation based on the distance from the target simulation position point to the observation point position to construct a projection matrix, and determine the projection snapshot based on the flow field snapshot data and the projection matrix. The reconstructed flow field determination module is used to perform intrinsic orthogonal decomposition on the projected snapshot to obtain an orthogonal basis function space, project the experimental observation data onto the orthogonal basis function space to obtain a linear weighted vector, and determine the reconstructed flow field of the target aircraft based on the linear weighted vector and the flow field snapshot matrix; Specifically, the projection snapshot determination module is used to collect measured flow field data at each test observation point based on preset sensors set at each test observation point in the target test, and to construct test observation data using the measured flow field data; the target test includes flight tests corresponding to aircraft of the same type as the target aircraft; in the preset flow field coordinate points in the spatial location, the target simulation position point whose observation point position distance satisfies the preset position condition is determined based on the spatial dimension of the target aircraft flow field and the grid discretization form during data simulation; Specifically, the projection snapshot determination module is used to determine the target simulation location points as follows: if the spatial dimension of the target aircraft flow field is two-dimensional, the first number of preset flow field coordinate points closest to the observation point corresponding to the test observation point are determined as target simulation location points; if the mesh discretization form of the target aircraft flow field is triangular elements, the second number of preset flow field coordinate points closest to the observation point corresponding to the test observation point are determined as target simulation location points; if the mesh discretization form of the target aircraft flow field is quadrilateral elements, the third number of preset flow field coordinate points closest to the observation point corresponding to the test observation point are determined as target simulation location points.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the snapshot-weighted aircraft flow field reconstruction method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the snapshot-weighted aircraft flow field reconstruction method as described in any one of claims 1 to 5.

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