Format dissipation adjustment method, device, equipment and medium based on scale filtering

Abstract

The present application discloses a format dissipation adjustment method, device, equipment and medium based on scale filtering, which relates to the field of fluid dynamics technology. The method comprises the following steps: performing feature analysis on a velocity field in flow field data, and projecting the feature field to a feature space to obtain feature variables of the velocity field; performing moving least square filtering on the feature variables; back-projecting the feature variables after the first filtering and the feature variables after the second filtering to a physical space to obtain the velocity field after the first filtering and the velocity field after the second filtering; calculating the square difference of the first high-frequency component and the square difference of the second high-frequency component; summing the square difference of the first high-frequency component and the square difference of the second high-frequency component to obtain the first summation result and the second summation result, and calculating the energy ratio; establishing a mapping relationship between the energy ratio and the dissipation adjustment factor, and calculating the dissipation adjustment factor; and using the dissipation adjustment factor to adaptively adjust the dissipation of the format, thereby solving the problem that the dissipation adjustment process depends on the specific format and the specific problem, and realizing adaptive adjustment of the format dissipation.

Description

Technical Field

[0001] The present invention relates to the field of fluid dynamics technology, and in particular to a format dissipation adjustment method, device, equipment and medium based on scale filtering. Background Art

[0002] At present, most dissipative control technologies adjust the dissipation of the format itself by adding a control factor, or combine two different formats through this factor to control the dissipation level of the hybrid format. For example, Deng and Jiang et al. used the BVD-AD dissipative control technology (Boundary Variation Diminishing-Adjustable Dissipation, based on unit boundary variation minimization-adjustable dissipation) in the finite volume framework. Through m+1 steps of BVD judgment and assignment operations, the candidate interface flux reconstructed by the nth-order polynomial and hyperbolic tangent function was obtained. From the above scheme, it can be seen that the adjustment parameters of the existing dissipative control technology are bound to the format, which is not universal, and different values ​​need to be assigned for different problems. It is dependent on the problem, and the size of the assignment often depends on experience. In addition, once a specific value is given, the dissipation level of the format is determined, and it cannot be adjusted adaptively according to the changes in the flow.

[0003] It can be seen from the above that how to solve the dissipative adjustment process depends on the specific format and the specific problem. Realizing adaptive adjustment of format dissipation based on scale filtering is a problem to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for adjusting format dissipation based on scale filtering, which can solve the problem that the dissipation adjustment process depends on specific formats and specific problems, and realize adaptive adjustment of format dissipation based on scale filtering. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a format dissipation adjustment method based on scale filtering, comprising:

[0006] Acquiring flow field data, performing feature analysis on a velocity field in the flow field data, and projecting the velocity field into a feature space to obtain a feature variable of the velocity field;

[0007] Performing a moving least squares filter on the characteristic variable of the velocity field to obtain a first filtered characteristic variable and a second filtered characteristic variable;

[0008] Back-projecting the first filtered characteristic variable and the second filtered characteristic variable into a physical space to obtain the first filtered velocity field and the second filtered velocity field at different scales;

[0009] Calculating a first high-frequency component square difference and a second high-frequency component square difference respectively using the velocity field, the first filtered velocity field and the second filtered velocity field;

[0010] Summing the square differences of the first high-frequency components and the square differences of the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculating an energy ratio based on the first summation result and the second summation result;

[0011] Establishing a mapping relationship between the energy ratio and the dissipation adjustment factor to calculate the dissipation adjustment factor;

[0012] The dissipation adjustment factor is used to adaptively adjust the dissipation of the format.

[0013] Optionally, the acquiring flow field data includes:

[0014] According to the compressible flow solver, flow field data is obtained by using a preset data acquisition method; the preset data acquisition method includes a direct numerical simulation method, a large eddy simulation method and a Reynolds average simulation method; the flow field data includes steady flow field data and non-flow field data.

[0015] Optionally, the performing feature analysis on the velocity field in the flow field data and projecting the velocity field into a feature space to obtain a feature variable of the velocity field includes:

[0016] Performing characteristic analysis on the velocity field in the flow field data to obtain a velocity field including a left characteristic matrix of velocity variables and a right characteristic matrix of velocity variables;

[0017] Calculating the product between the right characteristic matrix of the velocity variable and the local velocity field, and projecting it into the characteristic space to obtain the characteristic variable of the velocity field;

[0018] The formula for the feature analysis is:

[0019] ;

[0020] in, is the Jacobian matrix obtained from the local velocity field, is the left characteristic matrix of the velocity variable, is a diagonal matrix composed of the eigenvalues ​​of matrix A, S is the right eigenvalue matrix of the velocity variable, It is the inverse matrix of S;

[0021] The product is calculated as follows:

[0022] ;

[0023] in, is the velocity field, i=1,2,3, respectively represent the streamwise, normal and spanwise velocities, and n is the number of points on the template of the local velocity field to be filtered.

[0024] Optionally, performing moving least square filtering on the characteristic variable of the velocity field to obtain the characteristic variable after the first filtering and the characteristic variable after the second filtering includes:

[0025] Using the first filtering calculation formula and the second filtering calculation formula, the characteristic variable of the velocity field is subjected to a first moving least squares filter and a second moving least squares filter to obtain the characteristic variable after the first filtering and the characteristic variable after the second filtering;

[0026] The first filtering calculation formula is:

[0027] ;

[0028] in, is the characteristic variable after the first filtering, MLS is the moving least squares filter, is the characteristic variable of the velocity field in different directions, is the filter width;

[0029] The second filtering calculation formula is:

[0030] ;

[0031] in, is the characteristic variable after the second filtering.

[0032] Optionally, the using the velocity field, the first filtered velocity field, and the second filtered velocity field to respectively calculate the first high-frequency component square difference and the second high-frequency component square difference comprises:

[0033] Calculating a first difference between the velocity field and the first filtered velocity field, and squaring the first difference to obtain a first high-frequency component square difference;

[0034] A second difference between the velocity field and the second filtered velocity field is calculated, and the second difference is squared to obtain a second high-frequency component square difference.

[0035] Optionally, summing the square differences of the first high-frequency components and the square differences of the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculating the energy ratio based on the first summation result and the second summation result includes:

[0036] summing the square differences of the first high-frequency components in different directions to obtain a first summation result, and summing the square differences of the second high-frequency components in different directions to obtain a second summation result;

[0037] The ratio between the first summation result and the second summation result is used as the energy ratio.

[0038] Optionally, the mapping relationship between the energy ratio and the dissipation adjustment factor is:

[0039] ;

[0040] in, is the dissipation adjustment factor at the i-th grid point and the n-th time step , for The multiplication factor of the change, a is the lower threshold of the energy ratio, b is the upper threshold of the energy ratio, and ER is the energy ratio.

[0041] In a second aspect, the present application discloses a format dissipation adjustment device based on scale filtering, comprising:

[0042] A projection module, used to obtain flow field data, perform feature analysis on a velocity field in the flow field data, and project the velocity field into a feature space to obtain a feature variable of the velocity field;

[0043] A filtering module, used for performing a moving least squares filter on the characteristic variable of the velocity field to obtain a first filtered characteristic variable and a second filtered characteristic variable;

[0044] A back-projection module, used for back-projecting the first filtered characteristic variable and the second filtered characteristic variable to a physical space to obtain the first filtered velocity field and the second filtered velocity field at different scales;

[0045] A square difference calculation module, used to calculate the square difference of the first high-frequency component and the square difference of the second high-frequency component respectively using the velocity field, the first filtered velocity field and the second filtered velocity field;

[0046] an energy ratio calculation module, configured to sum the square differences of the first high-frequency components and the square differences of the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculate the energy ratio based on the first summation result and the second summation result;

[0047] A mapping relationship establishing module, used to establish a mapping relationship between the energy ratio and the dissipation adjustment factor to calculate the dissipation adjustment factor;

[0048] The adjustment module is used to adaptively adjust the dissipation of the format using the dissipation adjustment factor.

[0049] In a third aspect, the present application discloses an electronic device, comprising:

[0050] Memory, used to store computer programs;

[0051] The processor is used to execute the computer program to implement the aforementioned format dissipation adjustment method based on scale filtering.

[0052] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned disclosed format dissipation adjustment method based on scale filtering are implemented.

[0053] It can be seen that the present application provides a format dissipation adjustment method based on scale filtering, including acquiring flow field data, performing feature analysis on the velocity field in the flow field data, and projecting the velocity field to the feature space to obtain the characteristic variables of the velocity field; performing moving least squares filtering on the characteristic variables of the velocity field to obtain the first filtered characteristic variables and the second filtered characteristic variables; back-projecting the first filtered characteristic variables and the second filtered characteristic variables to the physical space to obtain the first filtered velocity field and the second filtered velocity field at different scales; using the velocity field, the first filtered velocity field and the second filtered velocity field to respectively calculate the square difference of the first high-frequency component and the square difference of the second high-frequency component; summing the square difference of the first high-frequency component and the square difference of the second high-frequency component in different directions to obtain a first summation result and a second summation result, and calculating the energy ratio based on the first summation result and the second summation result; establishing a mapping relationship between the energy ratio and the dissipation adjustment factor to calculate the dissipation adjustment factor; and using the dissipation adjustment factor to adaptively adjust the dissipation of the format. The present application performs characteristic analysis on the velocity field in the flow field data, and projects the velocity field to the characteristic space for moving least squares filtering, and then projects the filtered characteristic variables back to the physical space through inverse characteristic projection, thereby obtaining the square difference of the high-frequency component of the velocity field. This process fully considers the high-frequency component, that is, the violent turbulent pulsation, that is, the characteristics of the small-scale flow structure, and performs dissipation adjustment based on this to make the method more physically meaningful. The energy ratio is calculated according to different square differences of high-frequency components, and a mapping relationship between the energy ratio and the dissipation adjustment factor is established to calculate the dissipation adjustment factor. The dissipation adjustment factor is used to realize adaptive adjustment of format dissipation based on scale filtering. The whole process does not depend on the specific format and is universal. The parameters used in the dissipation adjustment are adaptively determined according to the flow changes. While ensuring the calculation accuracy and stability, it does not depend on specific simulation problems and manual experience, and can be applied to high compressible turbulence simulation calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0055] Figure 1 A flow chart of a format dissipation adjustment method based on scale filtering disclosed in this application;

[0056] Figure 2 A specific flow chart of format dissipation regulation disclosed in this application;

[0057] Figure 3 (1) A WENO7Z double Mach density contour map disclosed in this application;

[0058] Figure 3 (2) A partial enlarged view of the double Mach density contour of WENO7Z disclosed in this application;

[0059] Figure 4 (1) An Improved WENO7Z double Mach density contour map disclosed in this application;

[0060] Figure 4 (2) A partial enlarged view of the double Mach density contour of the Improved WENO7Z disclosed in this application;

[0061] Figure 5 A density contour map of a WENO7Z two-dimensional Riemann problem disclosed in this application;

[0062] Figure 6 An Improved WENO7Z two-dimensional Riemann problem density contour map disclosed in this application;

[0063] Figure 7 A vortex structure diagram obtained by the Q criterion under a WENO7Z coarse grid disclosed in this application;

[0064] Figure 8 This is a vortex structure diagram obtained by the Q criterion under an Improved WENO7Z coarse grid disclosed in this application;

[0065] Fig. 9 A graph of the evolution of turbulent kinetic energy over time disclosed in the present application;

[0066] Fig.10 This is a schematic diagram of the structure of a format dissipation adjustment device based on scale filtering disclosed in this application;

[0067] Fig.11 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] At present, most dissipative control technologies adjust the dissipation of the format itself by adding a control factor, or combine two different formats through this factor to control the dissipation level of the hybrid format. For example, Deng and Jiang et al.'s BVD-AD dissipative control technology in a finite volume framework is based on unit boundary variation minimization-adjustable dissipation). Through m+1 steps of BVD judgment and assignment operations, the candidate interface flux reconstructed by n-order polynomials and hyperbolic tangent functions is obtained. From the above scheme, it can be seen that the adjustment parameters of the existing dissipative control technology are bound to the format, which is not universal, and different values ​​need to be assigned for different problems. It is dependent on the problem, and the size of the assignment often depends on experience. In addition, once a specific value is given, the dissipation level of the format is determined, and it cannot be adjusted adaptively according to the change of the flow. As can be seen from the above, how to solve the dissipative adjustment process depends on the specific format and the specific problem. Realizing adaptive adjustment of format dissipation based on scale filtering is a problem to be solved in this field.

[0070] For example, most of the popular dissipative control techniques now are to adjust the dissipation of the format itself by adding a control factor, or to combine two different formats through this factor to control the dissipation level of the hybrid format. The dissipative control technology in the finite volume framework is to obtain the candidate interface flux reconstructed by the n-order polynomial and hyperbolic tangent function through m+1 steps of BVD judgment and assignment operations. The m-th step mainly obtains the n-order polynomial interpolation reconstruction value and introduces the dissipative control factor to adjust the dissipation. The process is as follows:

[0071] ;

[0072] in, is the dissipation control factor, which ranges from 0.5 to 1.0. When , the format is an n+2 order non-dissipative central format; when When the format is a high-order upwind format, by selecting different The dissipation of the format can be controlled, and Usually specified according to the specific simulation problem. is the interpolated reconstruction value at the left unit interface obtained by the n-th order polynomial:

[0073] ;

[0074] in, is the average value of the flow in the jth unit, Similarly;

[0075] Earlier dissipative control methods, such as Roe (Riemann-Oreskovich-Eager, finite volume method-finite element method-Euler format), split the flow into a central part and a dissipative part from the beginning, and control the dissipation level of the format by the Jacobian matrix obtained by the Roe average. Taking one-dimensional flow as an example, the dissipative control process is as follows:

[0076] ;

[0077] in, is the Jacobian matrix obtained by Roe averaging, which is in the following form:

[0078] ;

[0079] in, and is the velocity and total enthalpy obtained by Roe averaging, which is in the following form:

[0080] ;

[0081] The values ​​with subscript L are the values ​​of the left grid points of the interface being sought, and the values ​​with subscript R are the values ​​of the right grid points of the interface being sought;

[0082] When the flow is smooth, When the value of is close to 0, the format is a central format with low dissipation. When the flow contains discontinuities or shock waves, According to the change of local flow field, the dissipation level of the format is not zero, so as to adjust the dissipation level of the format;

[0083] In summary, the control factors of existing dissipative control technologies are either specified according to specific simulation problems, are highly empirical, and cannot be adjusted adaptively with flow conditions. Once given, the dissipation level of the format is determined; or the dissipation adjustment is bound to the specific format and is difficult to generalize and apply to other formats.

[0084] See also Figure 1 As shown, the embodiment of the present invention discloses a format dissipation adjustment method based on scale filtering, which may specifically include:

[0085] Step S11: acquiring flow field data, performing feature analysis on the velocity field in the flow field data, and projecting the velocity field into a feature space to obtain feature variables of the velocity field.

[0086] In this embodiment, the flow field data is obtained according to the compressible flow solver and a preset data acquisition method; the preset data acquisition method includes a direct numerical simulation method, a large eddy simulation method, and a Reynolds average simulation method; the flow field data includes steady flow field data and non-flow field data;

[0087] Then, characteristic analysis is performed on the velocity field in the flow field data to obtain a velocity field including a left characteristic matrix of velocity variables and a right characteristic matrix of velocity variables;

[0088] Calculating the product between the right characteristic matrix of the velocity variable and the local velocity field, and projecting it into the characteristic space to obtain the characteristic variable of the velocity field;

[0089] The formula for the feature analysis is:

[0090] ;

[0091] in, is the Jacobian matrix obtained from the local velocity field, is the left characteristic matrix of the velocity variable, is a diagonal matrix composed of the eigenvalues ​​of matrix A, S is the right eigenvalue matrix of the velocity variable, It is the inverse matrix of S;

[0092] The product is calculated as follows:

[0093] ;

[0094] in, is the velocity field, i=1,2,3, respectively represent the streamwise, normal and spanwise velocities, and n is the number of points on the template of the local velocity field to be filtered.

[0095] Specifically, firstly, steady flow field data / unsteady flow field data are obtained according to the compressible flow solver, which can be obtained by direct numerical simulation, large eddy simulation, Reynolds average simulation and other methods. According to characteristic analysis, a velocity field including a left characteristic matrix of velocity variables and a right characteristic matrix of velocity variables is obtained. The right characteristic matrix is ​​multiplied by the local velocity field to be filtered, and it is projected into the characteristic space to obtain the characteristic variables of the velocity field.

[0096] Step S12: performing moving least square filtering on the characteristic variables of the velocity field to obtain characteristic variables after first filtering and characteristic variables after second filtering.

[0097] In this embodiment, the first filtering calculation formula and the second filtering calculation formula are used to perform the first moving least squares filtering and the second moving least squares filtering on the characteristic variables of the velocity field to obtain the characteristic variables after the first filtering and the characteristic variables after the second filtering;

[0098] The first filtering calculation formula is:

[0099] ;

[0100] in, is the characteristic variable after the first filtering, MLS is the moving least squares filter, is the characteristic variable of the velocity field in different directions, is the filter width;

[0101] The second filtering calculation formula is:

[0102] ;

[0103] in, is the characteristic variable after the second filtering.

[0104] MLS (Moving Least Squares) is a moving least squares filter, which uses the velocity field Take it as an example, and its MLS filtering is as follows:

[0105] ;

[0106] Among them, b is the basis function, which generally has linear basis and quadratic basis. Here, quadratic basis is selected, and its specific form in two-dimensional and three-dimensional cases is:

[0107] ;

[0108] Among them, a is the optimal coefficient, which is derived from the error function, P is the basis function matrix, and W is the weight matrix. The specific form is as follows:

[0109] ;

[0110] ;

[0111] is a weight function. The closer the position is to the filter point, the greater the corresponding weight is, and vice versa. Its specific form is as follows:

[0112] ;

[0113] in, represents the filter width, When different values ​​are taken, different filtered characteristic variable fields can be obtained. and .

[0114] Step S13: back-projecting the first filtered characteristic variables and the second filtered characteristic variables into a physical space to obtain the first filtered velocity field and the second filtered velocity field at different scales.

[0115] In this embodiment, the filtered characteristic variables are multiplied by the left characteristic matrix and then back-projected into the physical space to obtain the first filtered velocity field and the second filtered velocity field at different scales:

[0116] .

[0117] Step S14: Calculate the first high-frequency component square difference and the second high-frequency component square difference respectively using the velocity field, the first filtered velocity field and the second filtered velocity field.

[0118] In this embodiment, a first difference between the velocity field and the first filtered velocity field is calculated, and the first difference is squared to obtain a first high-frequency component square difference, and a second difference between the velocity field and the second filtered velocity field is calculated, and the second difference is squared to obtain a second high-frequency component square difference.

[0119] Specifically, the velocity field that takes density changes into account before filtering is used Subtract the first filtered velocity field that takes density changes into account And the velocity field after the second filter And square it to get the square difference of the first high-frequency component of the velocity field and the square difference of the second high frequency component :

[0120] ;

[0121] .

[0122] Step S15: summing the square differences of the first high-frequency components and the square differences of the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculating an energy ratio based on the first summation result and the second summation result.

[0123] In this embodiment, the square differences of the first high-frequency components in different directions are summed to obtain a first summation result, and the square differences of the second high-frequency components in different directions are summed to obtain a second summation result, and the ratio between the first summation result and the second summation result is used as the energy ratio.

[0124] Specifically, the square difference of high-frequency components in different directions is Sum to obtain the sum of the square differences of the high-frequency components and use the first filter width to get the first summation result The second summation result obtained by comparing the second filter width , get the energy ratio ER:

[0125] ;

[0126] ;

[0127] ;

[0128] in, and The n in the formula represents the filtering order, that is, n=1 represents the first filtering, that is, When n=2, it means the second filtering, that is, Filtering.

[0129] Step S16: establishing a mapping relationship between the energy ratio and the dissipation adjustment factor to calculate the dissipation adjustment factor.

[0130] In this embodiment, the mapping relationship between the energy ratio and the dissipation adjustment factor is:

[0131] ;

[0132] in, is the dissipation adjustment factor at the i-th grid point and the n-th time step , for The multiple factor of the change, a is the lower limit threshold of the energy ratio, b is the upper limit threshold of the energy ratio, and ER is the energy ratio. By selecting appropriate a and b, information that fully reflects the magnitude of flow field disturbance, the magnitude of pulsation intensity, or the degree of flow analysis can be obtained. To ensure The upper and lower limits of the ER threshold are selected as .

[0133] Step S17: Adaptively adjust the dissipation of the format using the dissipation adjustment factor.

[0134] In this embodiment, according to the dissipation adjustment factor , adjust the dissipation of the specific format, take the WENO7Z format as an example, adjust its dissipation. It should be noted that the dissipation adjustment factor Different formats can be embedded in different forms to adaptively adjust their dissipation:

[0135] ;

[0136] in, , , are the weight, optimal weight, and smoothing factor corresponding to the kth sub-template in the WENO7Z format, is a global smoothing factor of WENO7Z.

[0137] The specific process of format dissipation adjustment based on scale filtering proposed in this application is as follows: Figure 2 As shown, first, flow field data is obtained according to the compressible flow solver; then, the velocity field in the flow field data is subjected to feature analysis, and the velocity field is projected into the feature space to obtain the characteristic variables of the velocity field; then, the characteristic variables of the velocity field are subjected to two moving least squares filtering of different widths to obtain the characteristic variables after the first filtering and the characteristic variables after the second filtering; then, the characteristic variables after the first filtering and the characteristic variables after the second filtering are back-projected into the physical space to obtain the velocity fields after the first filtering and the velocity fields after the second filtering at different scales; then, the square difference of the first high-frequency component and the square difference of the second high-frequency component are calculated respectively using the velocity field, the velocity field after the first filtering and the velocity field after the second filtering; then, the square difference of the first high-frequency component and the square difference of the second high-frequency component in different directions are summed to obtain the first summation result and the second summation result, and the energy ratio is calculated based on the first summation result and the second summation result; then, a mapping relationship between the energy ratio and the dissipation adjustment factor is established to calculate the dissipation adjustment factor; finally, the dissipation of the format is adaptively adjusted using the dissipation adjustment factor.

[0138] Taking the double Mach reflection problem of flow Mach number Ma=10, the two-dimensional Riemann problem and the instantaneous flow field structure and statistical results of subsonic uniform isotropic turbulence in different formats as examples, the double Mach density contours of WENO7Z are as follows: Figure 3 (1) shows that the local enlarged image of the double Mach density contour of WENO7Z is shown in Figure 3 (2) shows that the double Mach density contours of Improved WENO7Z are as follows: Figure 4 (1) shows a partial enlarged view of the double Mach density contour of Improved WENO7Z. Figure 4 As shown in (2), under fine grid conditions, for the double Mach reflection problem, the Improved WENO7Z with the dissipation adjustment factor is more refined than WENO7Z in describing the flow field and can display more flow details. The density contours of the 2D Riemann problem of WENO7Z are shown in Figure 5 As shown, the density contours of the Improved WENO7Z two-dimensional Riemann problem are as follows Figure 6 As shown in Figure 2, for the two-dimensional Riemann problem, under fine grid conditions, the Improved WENO7Z can significantly improve the resolution of the convection field and display more flow details. The vortex structure obtained by the Q criterion under the coarse grid of WENO7Z is shown in Figure 2. Figure 7 As shown, Q = 3, streamwise velocity coloring, the vortex structure obtained by the Q criterion under the ImprovedWENO7Z coarse grid is as follows Figure 8As shown in the figure, the instantaneous vortex structure diagram of the flow field is obtained by using the Q criterion for the flow field results obtained by the two formats. Under the coarser grid, it can be easily seen that the flow field resolution of WENO7Z is low and can only capture a small number of medium-scale structures. In contrast, the flow field resolution of Improved WENO7Z, which is the format using the scale-filter-based nonlinear format dissipative adjustment technology of the present invention, is greatly improved, and can capture flow field structures of multiple scales. The evolution of turbulent kinetic energy over time is shown in Fig. 9 As shown in the figure, the flow field results are statistically averaged and quantitative analysis is given in two formats. It can be clearly seen that Improved WENO7Z is closer to the reference value of direct numerical simulation than WENO7Z.

[0139] Compared with the existing dissipative regulation technology, the present application performs moving least squares filtering on the velocity field feature projection, and then transforms it into physical space through inverse feature projection to obtain the high-frequency component of the velocity field. This process fully considers the high-frequency component, namely the violent turbulent pulsation, that is, the characteristics of the small-scale flow structure. Dissipative regulation is performed based on this, making the method more physically meaningful. Most importantly, the entire technical implementation process does not depend on a specific format and is universal. The parameters used in the dissipative regulation are adaptively determined according to the flow changes and are independent of specific simulation problems and manual experience.

[0140] In addition, the nonlinear format dissipation regulation technology based on scale filtering in the present application may not use characteristic analysis, or filter other variables (such as momentum, pressure and temperature, etc.), or adopt other low-pass filtering methods (such as Gaussian filtering and Chebyshev filtering).

[0141] In this embodiment, flow field data is acquired, feature analysis is performed on the velocity field in the flow field data, and the velocity field is projected into a feature space to obtain feature variables of the velocity field; moving least square filtering is performed on the feature variables of the velocity field to obtain first filtered feature variables and second filtered feature variables; the first filtered feature variables and the second filtered feature variables are back-projected into a physical space to obtain first filtered velocity fields and second filtered velocity fields at different scales; the first high-frequency component square difference and the second high-frequency component square difference are calculated using the velocity field, the first filtered velocity field and the second filtered velocity field respectively; the first high-frequency component square difference and the second high-frequency component square difference in different directions are summed to obtain a first summation result and a second summation result, and an energy ratio is calculated based on the first summation result and the second summation result; a mapping relationship between the energy ratio and a dissipation adjustment factor is established to calculate the dissipation adjustment factor; and the dissipation of the format is adaptively adjusted using the dissipation adjustment factor. The present application performs characteristic analysis on the velocity field in the flow field data, and projects the velocity field to the characteristic space for moving least squares filtering, and then projects the filtered characteristic variables back to the physical space through inverse characteristic projection, thereby obtaining the square difference of the high-frequency component of the velocity field. This process fully considers the high-frequency component, that is, the violent turbulent pulsation, that is, the characteristics of the small-scale flow structure, and performs dissipation adjustment based on this to make the method more physically meaningful. The energy ratio is calculated according to different square differences of high-frequency components, and a mapping relationship between the energy ratio and the dissipation adjustment factor is established to calculate the dissipation adjustment factor. The dissipation adjustment factor is used to realize adaptive adjustment of format dissipation based on scale filtering. The whole process does not depend on the specific format and is universal. The parameters used in the dissipation adjustment are adaptively determined according to the flow changes. While ensuring the calculation accuracy and stability, it does not depend on specific simulation problems and manual experience, and can be applied to high compressible turbulence simulation calculations.

[0142] See also Fig.10 As shown, the embodiment of the present invention discloses a format dissipation adjustment device based on scale filtering, which may specifically include:

[0143] The projection module 11 is used to obtain flow field data, perform feature analysis on the velocity field in the flow field data, and project the velocity field into a feature space to obtain a feature variable of the velocity field;

[0144] A filtering module 12, configured to perform a moving least squares filter on the characteristic variable of the velocity field to obtain a first filtered characteristic variable and a second filtered characteristic variable;

[0145] A back-projection module 13, configured to back-project the first filtered characteristic variable and the second filtered characteristic variable to a physical space to obtain a first filtered velocity field and a second filtered velocity field at different scales;

[0146] A square difference calculation module 14, used to calculate a first high-frequency component square difference and a second high-frequency component square difference respectively using the velocity field, the first filtered velocity field and the second filtered velocity field;

[0147] An energy ratio calculation module 15, configured to sum the square differences of the first high-frequency components and the square differences of the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculate an energy ratio based on the first summation result and the second summation result;

[0148] A mapping relationship establishing module 16, used to establish a mapping relationship between the energy ratio and the dissipation adjustment factor to calculate the dissipation adjustment factor;

[0149] The adjustment module 17 is used to adaptively adjust the dissipation of the format using the dissipation adjustment factor.

[0150] In this embodiment, flow field data is acquired, feature analysis is performed on the velocity field in the flow field data, and the velocity field is projected into a feature space to obtain feature variables of the velocity field; moving least square filtering is performed on the feature variables of the velocity field to obtain first filtered feature variables and second filtered feature variables; the first filtered feature variables and the second filtered feature variables are back-projected into a physical space to obtain first filtered velocity fields and second filtered velocity fields at different scales; the first high-frequency component square difference and the second high-frequency component square difference are calculated using the velocity field, the first filtered velocity field and the second filtered velocity field respectively; the first high-frequency component square difference and the second high-frequency component square difference in different directions are summed to obtain a first summation result and a second summation result, and an energy ratio is calculated based on the first summation result and the second summation result; a mapping relationship between the energy ratio and a dissipation adjustment factor is established to calculate the dissipation adjustment factor; and the dissipation of the format is adaptively adjusted using the dissipation adjustment factor. The present application performs characteristic analysis on the velocity field in the flow field data, and projects the velocity field to the characteristic space for moving least squares filtering, and then projects the filtered characteristic variables back to the physical space through inverse characteristic projection, thereby obtaining the square difference of the high-frequency component of the velocity field. This process fully considers the high-frequency component, that is, the violent turbulent pulsation, that is, the characteristics of the small-scale flow structure, and performs dissipation adjustment based on this to make the method more physically meaningful. The energy ratio is calculated according to different square differences of high-frequency components, and a mapping relationship between the energy ratio and the dissipation adjustment factor is established to calculate the dissipation adjustment factor. The dissipation adjustment factor is used to realize adaptive adjustment of format dissipation based on scale filtering. The whole process does not depend on the specific format and is universal. The parameters used in the dissipation adjustment are adaptively determined according to the flow changes. While ensuring the calculation accuracy and stability, it does not depend on specific simulation problems and manual experience, and can be applied to high compressible turbulence simulation calculations.

[0151] In some specific embodiments, the projection module 11 may specifically include:

[0152] The data acquisition module is used to acquire flow field data according to a compressible flow solver and a preset data acquisition method; the preset data acquisition method includes a direct numerical simulation method, a large eddy simulation method, and a Reynolds average simulation method; the flow field data includes steady flow field data and non-flow field data.

[0153] In some specific embodiments, the projection module 11 may specifically include:

[0154] A feature analysis module, used for performing feature analysis on the velocity field in the flow field data to obtain a velocity field including a left feature matrix of velocity variables and a right feature matrix of velocity variables;

[0155] A product projection module, used to calculate the product between the right characteristic matrix of the velocity variable and the local velocity field, and project it into the characteristic space to obtain the characteristic variable of the velocity field;

[0156] The formula for the feature analysis is:

[0157] ;

[0158] in, is the Jacobian matrix obtained from the local velocity field, is the left characteristic matrix of the velocity variable, is a diagonal matrix composed of the eigenvalues ​​of matrix A, S is the right eigenvalue matrix of the velocity variable, It is the inverse matrix of S;

[0159] The product is calculated as follows:

[0160] ;

[0161] in, is the velocity field, i=1,2,3, respectively represent the streamwise, normal and spanwise velocities, and n is the number of points on the template of the local velocity field to be filtered.

[0162] In some specific embodiments, the filtering module 12 may specifically include:

[0163] A moving least squares filtering module is used to perform a first moving least squares filter and a second moving least squares filter on the characteristic variable of the velocity field by using a first filtering calculation formula and a second filtering calculation formula to obtain a characteristic variable after the first filtering and a characteristic variable after the second filtering;

[0164] The first filtering calculation formula is:

[0165] ;

[0166] in, is the characteristic variable after the first filtering, MLS is the moving least squares filter, is the characteristic variable of the velocity field in different directions, is the filter width;

[0167] The second filtering calculation formula is:

[0168] ;

[0169] in, is the characteristic variable after the second filtering.

[0170] In some specific embodiments, the square difference calculation module 14 may specifically include:

[0171] a first high-frequency component square difference calculation module, used for calculating a first difference between the velocity field and the first filtered velocity field, and squaring the first difference to obtain a first high-frequency component square difference;

[0172] The second high-frequency component square difference calculation module is used to calculate the second difference between the velocity field and the second filtered velocity field, and square the second difference to obtain the second high-frequency component square difference.

[0173] In some specific embodiments, the energy ratio calculation module 15 may specifically include:

[0174] A square difference summing module, configured to sum the square differences of the first high-frequency components in different directions to obtain a first summing result, and to sum the square differences of the second high-frequency components in different directions to obtain a second summing result;

[0175] A ratio calculation module is used to use the ratio between the first summation result and the second summation result as the energy ratio.

[0176] In some specific embodiments, the mapping relationship between the energy ratio and the dissipation adjustment factor is:

[0177] ;

[0178] in, is the dissipation adjustment factor at the i-th grid point and the n-th time step , for The multiplication factor of the change, a is the lower threshold of the energy ratio, b is the upper threshold of the energy ratio, and ER is the energy ratio.

[0179] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the format dissipation adjustment method based on scale filtering performed by the electronic device disclosed in any of the aforementioned embodiments.

[0180] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0181] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0182] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, so as to realize the operation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the format dissipation adjustment method based on scale filtering and executed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can also further include a computer program that can be used to complete other specific tasks. In addition to including data transmitted from an external device received by the format dissipation adjustment device based on scale filtering, the data 223 can also include data collected by its own input and output interface 25, etc.

[0183] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0184] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the format dissipation adjustment method based on scale filtering disclosed in any of the aforementioned embodiments are implemented.

[0185] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0186] The above is a detailed introduction to the format dissipation adjustment method, device, equipment and storage medium based on scale filtering provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A format dissipation adjustment method based on scale filtering, characterized in that: include: Acquiring flow field data, performing feature analysis on a velocity field in the flow field data, and projecting the velocity field into a feature space to obtain a feature variable of the velocity field; Performing a moving least squares filter on the characteristic variable of the velocity field to obtain a first filtered characteristic variable and a second filtered characteristic variable; Back-projecting the first filtered characteristic variable and the second filtered characteristic variable into a physical space to obtain the first filtered velocity field and the second filtered velocity field at different scales; Calculating a first high-frequency component square difference and a second high-frequency component square difference respectively using the velocity field, the first filtered velocity field and the second filtered velocity field; Summing the square differences of the first high-frequency components and the square differences of the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculating an energy ratio based on the first summation result and the second summation result; Establishing a mapping relationship between the energy ratio and the dissipation adjustment factor to calculate the dissipation adjustment factor; Adaptively adjusting the dissipation of the format using the dissipation adjustment factor; The characteristic analysis of the velocity field in the flow field data and projecting the velocity field to the characteristic space to obtain the characteristic variables of the velocity field includes: characteristic analysis of the velocity field in the flow field data to obtain a velocity field including a left characteristic matrix of velocity variables and a right characteristic matrix of velocity variables; calculating the product between the right characteristic matrix of velocity variables and the local velocity field, and projecting the product to the characteristic space to obtain the characteristic variables of the velocity field; The formula for the feature analysis is: ； in, is the Jacobian matrix obtained from the local velocity field, is the left characteristic matrix of the velocity variable, is a diagonal matrix composed of the eigenvalues ​​of matrix A, S is the right eigenvalue matrix of the velocity variable, It is the inverse matrix of S; The product is calculated as follows: ； in, is the velocity field, i=1,2,3, respectively represent the streamwise, normal and spanwise velocities, and n is the number of points on the template of the local velocity field to be filtered.

2. The format dissipation adjustment method based on scale filtering according to claim 1 is characterized in that: The obtaining of flow field data comprises: According to the compressible flow solver, flow field data is obtained by using a preset data acquisition method; the preset data acquisition method includes a direct numerical simulation method, a large eddy simulation method and a Reynolds average simulation method; the flow field data includes steady flow field data and non-flow field data.

3. The format dissipation adjustment method based on scale filtering according to claim 1 is characterized in that: The performing moving least square filtering on the characteristic variable of the velocity field to obtain the characteristic variable after the first filtering and the characteristic variable after the second filtering includes: Using the first filtering calculation formula and the second filtering calculation formula, the characteristic variable of the velocity field is subjected to a first moving least squares filter and a second moving least squares filter to obtain the characteristic variable after the first filtering and the characteristic variable after the second filtering; The first filtering calculation formula is: ； in, is the characteristic variable after the first filtering, MLS is the moving least squares filter, is the characteristic variable of the velocity field in different directions, is the filter width; The second filtering calculation formula is: ； in, is the characteristic variable after the second filtering.

4. The format dissipation adjustment method based on scale filtering according to claim 1 is characterized in that: The method of calculating the first high-frequency component square difference and the second high-frequency component square difference respectively by using the velocity field, the first filtered velocity field and the second filtered velocity field comprises: Calculating a first difference between the velocity field and the first filtered velocity field, and squaring the first difference to obtain a first high-frequency component square difference; A second difference between the velocity field and the second filtered velocity field is calculated, and the second difference is squared to obtain a second high-frequency component square difference.

5. The format dissipation adjustment method based on scale filtering according to claim 1 is characterized in that: The step of summing the square differences of the first high-frequency components and the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculating the energy ratio based on the first summation result and the second summation result includes: summing the square differences of the first high-frequency components in different directions to obtain a first summation result, and summing the square differences of the second high-frequency components in different directions to obtain a second summation result; The ratio between the first summation result and the second summation result is used as the energy ratio.

6. The format dissipation adjustment method based on scale filtering according to any one of claims 1 to 5, characterized in that: The mapping relationship between energy ratio and dissipation adjustment factor is: ； in, is the dissipation adjustment factor at the i-th grid point and the n-th time step , for The multiplication factor of the change, a is the lower threshold of the energy ratio, b is the upper threshold of the energy ratio, and ER is the energy ratio.

7. A format dissipation adjustment device based on scale filtering, characterized in that: include: A projection module, used to obtain flow field data, perform feature analysis on a velocity field in the flow field data, and project the velocity field into a feature space to obtain a feature variable of the velocity field; A filtering module, used for performing a moving least squares filter on the characteristic variable of the velocity field to obtain a first filtered characteristic variable and a second filtered characteristic variable; A back-projection module, used for back-projecting the first filtered characteristic variable and the second filtered characteristic variable to a physical space to obtain the first filtered velocity field and the second filtered velocity field at different scales; A square difference calculation module, used to calculate the square difference of the first high-frequency component and the square difference of the second high-frequency component respectively using the velocity field, the first filtered velocity field and the second filtered velocity field; an energy ratio calculation module, configured to sum the square differences of the first high-frequency components and the second high-frequency components in different directions to obtain a first summation result and a second summation result, and calculate the energy ratio based on the first summation result and the second summation result; A mapping relationship establishing module, used to establish a mapping relationship between the energy ratio and the dissipation adjustment factor to calculate the dissipation adjustment factor; An adjustment module, used for adaptively adjusting the dissipation of the format using the dissipation adjustment factor; The characteristic analysis of the velocity field in the flow field data and projecting the velocity field to the characteristic space to obtain the characteristic variables of the velocity field includes: characteristic analysis of the velocity field in the flow field data to obtain a velocity field including a left characteristic matrix of velocity variables and a right characteristic matrix of velocity variables; calculating the product between the right characteristic matrix of velocity variables and the local velocity field, and projecting the product to the characteristic space to obtain the characteristic variables of the velocity field; The formula for the feature analysis is: ； in, is the Jacobian matrix obtained from the local velocity field, is the left characteristic matrix of the velocity variable, is a diagonal matrix composed of the eigenvalues ​​of matrix A, S is the right eigenvalue matrix of the velocity variable, It is the inverse matrix of S; The product is calculated as follows: ； in, is the velocity field, i=1,2,3, respectively represent the streamwise, normal and spanwise velocities, and n is the number of points on the template of the local velocity field to be filtered.

8. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the format dissipation adjustment method based on scale filtering as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the format dissipation adjustment method based on scale filtering as described in any one of claims 1 to 6 is implemented.

Images