Solid rocket engine exhaust plume radiation calculation grid optimization method and device

Abstract

This invention relates to the field of infrared imaging technology, and particularly to a method and apparatus for optimizing the calculation grid of solid rocket motor exhaust radiation. The method includes: reading the number of grid points, grid surfaces, and variables from a grid file; the grid file is obtained by performing flow field calculations on the solid rocket motor exhaust using flow field calculation software, and the grid file contains internal and external flow field data; using the nozzle exit of the solid rocket motor as the interface between the internal and external flow fields, determining the axial and radial grid numbers in the external flow field; rearranging the grid points in the external flow field based on the axial and radial grid numbers to obtain a structured external flow field grid; and densifying the external flow field grid using a preset strategy to obtain an optimized target grid, whereby the grid densification level decreases layer by layer along the direction away from the engine nozzle. This invention can reasonably optimize the grid and improve the accuracy of infrared radiation calculation of the engine exhaust.

Description

Technical Field

[0001] This invention relates to the field of infrared imaging technology, and in particular to a method and apparatus for optimizing the computational grid of solid rocket motor exhaust radiation. Background Technology

[0002] The exhaust plume of a solid rocket motor is a multi-physics field that couples combustion, multiphase flow, convective heat transfer, and radiation. Identifying the type of exhaust plume is of great significance in the detection and identification of hypersonic targets.

[0003] In related technologies, plume type identification requires a mesh based on the plume flow field calculation. Computational Fluid Dynamics (CFD) methods are used in plume flow field calculations, while infrared radiation calculations are typically employed for plume type identification. Both of these calculations require the finite element method (FEM). Therefore, proper mesh generation for both plume and infrared radiation calculations is crucial. However, during plume flow field calculations, the flow field mesh near the solid rocket motor nozzle is locally refined. To ensure accuracy, both the internal and external flow fields of the engine are calculated using CFD, resulting in an unstructured mesh. In contrast, infrared radiation calculations typically require a structured mesh, and the number of mesh points in each dimension must be explicitly displayed; otherwise, the accuracy of the infrared radiation calculation will be affected.

[0004] Therefore, there is an urgent need for a method and apparatus for optimizing the computational grid of solid rocket motor exhaust radiation to solve the above problems. Summary of the Invention

[0005] This invention provides a method and apparatus for optimizing the calculation grid of solid rocket engine exhaust radiation, which can reasonably optimize the grid and improve the accuracy of infrared radiation calculation of engine exhaust.

[0006] In a first aspect, embodiments of the present invention provide a method for optimizing the computational grid for solid rocket motor exhaust radiation, including:

[0007] Read the number of grid points, grid surfaces, and variables from the grid file; the grid file is obtained by performing flow field calculations on the exhaust plume of a solid rocket motor using flow field calculation software, and the grid file contains internal flow field data and external flow field data.

[0008] Using the nozzle outlet of the solid rocket motor as the interface between the internal flow field and the external flow field, the axial grid number and radial grid number in the external flow field are determined.

[0009] The grid points in the external flow field are rearranged based on the axial grid number and the radial grid number to obtain a structured external flow field grid.

[0010] The external flow field mesh is densified using a preset strategy to obtain an optimized target mesh. The preset strategy is to gradually decrease the mesh densification level along the direction away from the engine nozzle.

[0011] Secondly, embodiments of the present invention also provide a grid optimization device for calculating the exhaust plume radiation of a solid rocket engine, comprising:

[0012] The reading module is used to read the number of grid points, the number of grid surfaces, and the number of variables in the grid file; the grid file is obtained by using flow field calculation software to perform flow field calculations on the exhaust plume of a solid rocket motor, and the grid file contains internal flow field data and external flow field data.

[0013] The determination module is used to determine the axial grid number and radial grid number in the outer flow field, taking the nozzle outlet of the solid rocket motor as the interface between the inner flow field and the outer flow field.

[0014] The structuring module is used to rearrange the grid points in the external flow field based on the axial grid number and the radial grid number to obtain a structured external flow field grid.

[0015] The encryption module is used to encrypt the external flow field grid using a preset strategy to obtain an optimized target grid. The preset strategy is to gradually decrease the grid encryption level along the direction away from the engine nozzle.

[0016] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.

[0017] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0018] This invention provides a method and apparatus for optimizing the grid for calculating the exhaust plume radiation of a solid rocket motor. The method is based on a flow field calculation grid file, which contains grids representing both the internal and external flow fields of the rocket motor. These grids are unstructured and cannot be directly applied to exhaust plume radiation calculations. Based on the grid characteristics of the exhaust plume flow field, this invention removes internal flow field data that is useless for radiation calculations, using the solid rocket motor nozzle exit as a boundary, retaining only the external flow field grid data. This improves computational efficiency while fully preserving the physical information of the multiphysics field within the flow field. Furthermore, by converting the external flow field grid data from an unstructured grid to a structured grid and densifying the grid near the engine nozzle, the accuracy of infrared radiation calculations can be further improved, providing technical support for engine exhaust plume identification. Therefore, this invention can reasonably optimize the grid and improve the accuracy of infrared radiation calculations for engine exhaust plumes. Attached Figure Description

[0019] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the structure of a solid rocket motor exhaust radiation computational grid optimization method provided in an embodiment of the present invention;

[0021] Figure 2 This is a partially enlarged view of the nozzle grid of a solid rocket motor provided in an embodiment of the present invention;

[0022] Figure 3 This is a vertical grid number calculation process provided by an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the flow field mesh after mesh optimization provided in an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of the process for encrypting an optimized mesh according to an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram of a mesh after the optimized mesh has been densified, according to an embodiment of the present invention;

[0026] Figure 7 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention;

[0027] Figure 8This is a structural diagram of a solid rocket engine exhaust radiation computational grid optimization device provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0029] In related technologies, to apply the mesh calculated from the rocket engine exhaust flow field to the radiation calculation of the exhaust plume, interpolation of the flow field mesh is typically used. This significantly increases the mesh size near the engine nozzle, thus affecting the accuracy of the infrared radiation calculation. Therefore, these technologies cannot meet the requirements for calculating the infrared radiation of engine exhaust plumes.

[0030] Please refer to Figure 1 This invention provides a method for optimizing the computational grid for solid rocket motor exhaust radiation, the method comprising:

[0031] Step 100: Read the number of grid points, grid surfaces, and variables in the grid file; the grid file is obtained by performing flow field calculations on the exhaust plume of a solid rocket motor using flow field calculation software, and the grid file contains internal flow field data and external flow field data;

[0032] Step 102: Using the nozzle outlet of the solid rocket motor as the interface between the inner flow field and the outer flow field, determine the axial grid number and radial grid number in the outer flow field;

[0033] Step 104: Rearrange the grid points in the external flow field based on the axial grid number and the radial grid number to obtain a structured external flow field grid;

[0034] Step 106: The external flow field mesh is densified using a preset strategy to obtain an optimized target mesh. The preset strategy is to gradually decrease the mesh densification level along the direction away from the engine nozzle.

[0035] In this step, the flow field calculation mesh file is used as a basis. However, this mesh file is unstructured and cannot be directly applied to the radiation calculation of the exhaust plume. Based on the mesh characteristics of the exhaust plume flow field, this invention removes internal flow field data that is useless for radiation calculation, using the nozzle exit of the solid rocket motor as the boundary, retaining only the mesh data of the external flow field. This improves computational efficiency while fully preserving the physical information of the multiphysics field within the flow field. Furthermore, by converting the external flow field mesh data from an unstructured mesh to a structured mesh and densifying the mesh near the engine nozzle, the accuracy of infrared radiation calculation can be further improved, providing technical support for engine exhaust plume identification. Therefore, this method can reasonably optimize the mesh and improve the accuracy of infrared radiation calculation of the engine exhaust plume.

[0036] The following description Figure 1 The execution method of each step is shown.

[0037] First, for step 100, the number of grid points, the number of grid faces, and the number of variables in the grid file are read. The specific implementation process is as follows:

[0038] Step A1: Read several rows of data in the grid file and determine the target row from the several rows of data; the target row includes a first key representing the number of grid points, a second key representing the number of grid faces, and at least one target separator; the first key includes a first character and a second character arranged in sequence, and the second key includes a third character and a fourth character arranged in sequence;

[0039] Step A2: Based on the position of the first keyword, the second keyword, and each target separator in the target row, determine the number of grid points and the number of grid faces;

[0040] Step A3: Determine the number of variables based on the position of the target row in the grid file.

[0041] In step A1, the first and second keywords are usually present in the header of the grid file. Therefore, the number of rows of data read is not less than the number of rows in the header. For example, the format of the header is shown in Table 1. This grid file was calculated by CFD++ flow field calculation software.

[0042] Table 1. Header file of the flow field calculated by CFD++ software

[0043]

[0044]

[0045] In Table 1, grid points are represented by Nodes, grid faces by Elements, and variables by Variables. Therefore, the first key is set to "N=", the second key is set to "E=", and the corresponding first character is "N", the second character is "=", the third character is "E", and the fourth character is "=". Read the Nline lines from the grid file; the number of Nline lines is greater than the number of lines in the file header. Typically, the file header is less than 60 lines, so Nline can be set to 100 lines. In the read Nline lines, search for the lines containing the keywords "N=" and "E=". N represents grid points (Nodes), and E represents grid faces (Elements). The target lines can then be represented by NEline. Furthermore, the target delimiter is a comma (",), and there can be multiple commas. However, not every target delimiter is valid; the valid delimiters need to be determined from among the multiple delimiters based on their positional relationship with the first and second keys. The number of grid points and grid faces is then determined based on the valid delimiters.

[0046] For step A2, based on the position of the first keyword, the second keyword, and each of the target delimiters in the target row, the number of grid points and the number of grid faces are determined, including:

[0047] The positions of each target separator are stored in the first array in ascending order;

[0048] Iterate through each position in the first array, and for each position reached, execute the following:

[0049] Determine if the position is greater than the position of the first character. If not, do not save the position and proceed to the next traversal. If so, determine the number represented by the string between the position and the position of the second character as the number of grid points and stop traversing.

[0050] Determine if the position is greater than the position of the third character. If not, do not save the position and proceed to the next traversal. If so, determine the number represented by the string between the position and the position of the fourth character as the number of grid faces, and stop traversing.

[0051] In this step, as shown in Table 1, for the string "N=121990,", the string between "=" and "," represents the number of grid points; for the string "E=121157,", the string between "=" and "," represents the number of grid faces. Therefore, to determine the number of grid points and grid faces, a valid target separator must be determined, as illustrated below:

[0052] 1) In the NEline, find the position information of the first keyword "N =" and the second keyword "E =", which can be represented as Index_N and Index_E. More specifically, Index_N and Index_E respectively represent the positions of the first character "N" and the third character "E", and Index_N + 1 and Index_E + 1 respectively represent the positions of the third character "=" and the fourth character "=".

[0053] 2) Determine the positions where the target delimiter "," appears. In the NEline row, the comma may appear multiple times. Store its position information in the first array Index_Dot in ascending order.

[0054] 3) Loop through and judge the elements Index_Dot(i) in the first array Index_Dot. When Index_Dot(i) < Index_N, do not save the value of Index_Dot(i). Until the first time Index_Dot(i) > Index_N appears, record the position information of the comma at this time as N_Index_Dot. The number represented by the string between Index_N + 1 and N_Index_Dot at this time is the number of grid points. Similarly, loop through the numbers in Index_Dot(i) until the first time Index_Dot(i) > Index_E appears, and record the position information represented by Index_Dot(i) at this time as E_Index_Dot. Then the number represented by the string between Index_E + 1 and E_Index_Dot is the number of grid surfaces.

[0055] For step A3, the number of variables is determined by the position where the NEline row is located. The calculation formula for the number of variables is Variables = NEline - 2.

[0056] Then, for step 102, use the nozzle exit of the solid rocket engine as the interface between the internal flow field and the external flow field, and determine the axial grid number and the radial grid number in the external flow field. The specific implementation process is as follows:

[0057] Record the axial coordinate value at the nozzle exit of the solid rocket engine as 0;

[0058] Read the axial coordinate value of each grid point and sequentially judge whether the axial coordinate value of each grid point is greater than 0; if not, do not save this grid point; if so, save all variables on this grid point and store this grid point in the first storage container until all grid points are read;

[0059] Set the radial direction grid point count storage container as the second storage container, and the initial value of the second storage container is 0;

[0060] Iterate through each grid point in the first storage container. For each grid point, determine whether its axial coordinate value is 0. If not, do not update the value of the second storage container. If yes, increment the value of the second storage container by 1, until all grid points in the first storage container have been traversed.

[0061] The final value of the second storage container is determined as the radial grid number in the external flow field, and the quotient of the number of grid points in the first storage container and the radial grid number is determined as the axial grid number in the external flow field.

[0062] In this embodiment, such as Figure 2 The image shown is a magnified view of a solid rocket motor nozzle grid. It can be seen from the image that the grid includes both internal and external flow fields. Figure 2 In the corresponding mesh file, each mesh point stores a corresponding physical quantity, and the horizontal coordinate value (axial coordinate value) of the mesh point is one of these physical quantities. Therefore, the horizontal coordinate values ​​are denoted as X_store={X1,X1+Variables,X1+Variables2,X1+Variables3,...,X1+Variables(N-1)}, where N is the number of mesh points.

[0063] Since the flow field inside the engine nozzle is not considered as the region for calculating radiation, the engine nozzle exit is often set to X=0 when modeling the flow field mesh, such as... Figure 2 As shown. To select the external flow field of a solid rocket motor, using X≥0 as the criterion, the abscissa values ​​of N grid points are compared sequentially. If X≥0 is satisfied, all variables at that grid point are saved, and the number of all grid points with X≥0 is recorded as X_positive. Each grid point that meets the condition is stored in the first storage container, X_store. In this way, the grid points in the external flow field are obtained.

[0064] Then, the number of grid points in the vertical coordinate (radial direction) of the external flow field, i.e., the radial grid number, is calculated as follows:

[0065] 1) Set the storage container Y_num for the number of grid points in the radial direction (Y direction), which is the second storage container, and set its initial value to 0;

[0066] 2) Determine if the x-coordinate X_store(i) of the grid point in X_store is 0, and increment the loop by 1;

[0067] 3) If X_store(i) = 0, increment the value of the storage container Y_num by 1; otherwise, proceed to the next iteration of the loop.

[0068] 4) Check if the loop count i is less than the total number of grid points X_positive in X_store. If it is, proceed to the next loop; otherwise, exit the loop and output the result of Y_num to obtain the radial grid number. The calculation process is as follows: Figure 3 As shown.

[0069] Finally, the number of axial grids in the axial direction (i.e., the X direction) is X_num = X_positive / Y_num.

[0070] Next, regarding step 104, the grid points in the external flow field are rearranged based on the axial grid number and the radial grid number to obtain a structured external flow field grid, including:

[0071] For all grid points in the external flow field, the axial coordinate values ​​of each grid point are sorted in ascending order to obtain the axially optimized grid, denoted as GRID{i}, 1≤i≤Xnum, where Xnum is the number of axial grids in the external flow field;

[0072] For each axial coordinate point in the optimized axial mesh, all its corresponding radial coordinate values ​​are sorted in ascending order to obtain the optimized external flow field mesh, denoted as GRID{i,j}, 1≤j≤Ynum, where Ynum is the number of radial meshes in the external flow field.

[0073] The optimized flow field mesh diagram is shown below. Figure 4 As shown in the figure, the grid is a structured grid.

[0074] Finally, for step 106, the external flow field mesh is densified using a preset strategy to obtain an optimized target mesh. The preset strategy is to gradually decrease the mesh densification level along the direction away from the engine nozzle.

[0075] In this step, the apparent ray method can be used to refine the optimized mesh. Specifically, when calculating the external flow field of the exhaust plume using the apparent ray method, the mesh is often re-divided and recalculated. The physical quantity gradients near the engine nozzle change significantly, requiring appropriate mesh refinement. In the axial direction, the mesh is denser closer to the engine nozzle; in the radial direction, the mesh is denser closer to the axis.

[0076] In some implementations, the encryption process is as follows: Figure 5 As shown, the specific steps are as follows:

[0077] Step B1: Based on the overall size of the structured external flow field grid, it is re-divided to obtain the first number of grids in the axial direction and the second number of grids in the radial direction;

[0078] Step B2: Determine the first encryption coefficient and axial length in the axial direction, and the second encryption coefficient and radial length in the radial direction, respectively; the axial length is the axial projection length of the axial region of the structured grid under a preset viewing angle, and the preset viewing angle is the observation angle of the detector on the rocket engine;

[0079] Step B3: Determine the horizontal coordinates of each axial layer of the grid based on the first grid number, the first encryption coefficient, and the axial length; determine the vertical coordinates of each radial layer of the grid based on the second grid number, the second encryption coefficient, and the radial length, to obtain the optimized target grid.

[0080] In some implementations, step B3 is specifically implemented as follows:

[0081] In the axial direction, the x-coordinate of the first layer of mesh is calculated according to the first formula, which is expressed as follows:

[0082]

[0083] The x-coordinate of the position of the i-th grid (excluding the first grid layer) is calculated using the second formula, which is expressed as follows:

[0084] L(i)=L(i-1)+L0×RatioL i-1

[0085] In the formula, L0 is the abscissa of the first layer of grid, L(i) is the abscissa of the i-th layer of grid, L is the axial length, NPL is the number of the first grid, RatioL is the first encryption coefficient, and 1≤i≤NPL-1;

[0086] In the radial direction, the second grid number is numbered from bottom to top. Selecting the grid in the region Y>=0, the first layer of grids in that region is numbered NPW2=INT(NPW / 2). The ordinate of the first layer of grids is calculated using the third formula, which is expressed as:

[0087]

[0088] The ordinate of the j-th grid layer (excluding the first layer) is calculated using the fourth formula, which is expressed as follows:

[0089] R(j)=R(j-1)+R0×RatioR j-1 , where 1≤j≤NPW2;

[0090] In the region where Y <= 0, the symmetric calculation method of the fifth formula is applied. The expression of the fifth formula is:

[0091] R(j) = R(NPW-j+1); where NPW2 < j ≤ NPW;

[0092] In the formula, R0 is the ordinate of the first layer of grid; R(j) is the ordinate of the j-th layer of grid; R is the radial length; NPW is the second grid number; RatioR is the second encryption coefficient; INT() is the rounding symbol;

[0093] Based on the calculation results of the first, second, third, fourth, and fifth formulas, the optimized target mesh is obtained.

[0094] The encrypted grid diagram is as follows Figure 6 As shown in the figure, after densification, the mesh becomes denser closer to the engine nozzle in the axial direction, and denser closer to the axis in the radial direction. This improves the accuracy of radiation calculations without significantly increasing the computational load, resulting in high computational efficiency.

[0095] like Figure 7 , Figure 8 As shown, this embodiment of the invention provides a grid optimization device for calculating exhaust plume radiation from a solid rocket engine. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 7 The diagram shown is a hardware architecture diagram of an electronic device for optimizing the computational grid of exhaust plume radiation from a solid rocket engine, as provided in an embodiment of the present invention. (Except for...) Figure 7 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 8 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0096] This embodiment provides a grid optimization device for calculating exhaust plume radiation from a solid rocket engine, comprising:

[0097] The reading module 800 is used to read the number of grid points, the number of grid surfaces, and the number of variables in the grid file; the grid file is obtained by using flow field calculation software to perform flow field calculations on the exhaust flame of a solid rocket motor, and the grid file contains internal flow field data and external flow field data.

[0098] The determination module 802 is used to determine the axial grid number and radial grid number in the outer flow field, taking the nozzle outlet of the solid rocket motor as the interface between the inner flow field and the outer flow field.

[0099] The structuring module 804 is used to rearrange the grid points in the external flow field based on the axial grid number and the radial grid number to obtain a structured external flow field grid.

[0100] The encryption module 806 is used to encrypt the external flow field grid using a preset strategy to obtain an optimized target grid. The preset strategy is that the grid encryption level decreases layer by layer along the direction away from the engine nozzle.

[0101] In some implementations, when reading the number of grid points, grid faces, and variables in the grid file, the reading module 800 performs the following operations:

[0102] Read several lines of data from the grid file and determine a target line from the several lines of data; the target line includes a first key representing the number of grid points, a second key representing the number of grid faces, and at least one target separator; the first key includes a first character and a second character arranged in sequence, and the second key includes a third character and a fourth character arranged in sequence;

[0103] Based on the position of the first keyword, the second keyword, and each target separator in the target row, determine the number of grid points and the number of grid faces;

[0104] The number of variables is determined based on the position of the target row in the grid file.

[0105] In some implementations, when the reading module 800 determines the number of grid points and the number of grid faces based on the position of the first keyword, the second keyword, and each of the target delimiters in the target line, it performs the following operations:

[0106] The positions of each target separator are stored in the first array in ascending order;

[0107] Iterate through each position in the first array, and for each position reached, execute the following:

[0108] Determine if the position is greater than the position of the first character. If not, do not save the position and proceed to the next traversal. If so, determine the number represented by the string between the position and the position of the second character as the number of grid points and stop traversing.

[0109] Determine if the position is greater than the position of the third character. If not, do not save the position and proceed to the next traversal. If so, determine the number represented by the string between the position and the position of the fourth character as the number of grid faces, and stop traversing.

[0110] In some embodiments, when determining the axial and radial grid numbers in the outer flow field, using the nozzle exit of the solid rocket motor as the interface between the inner and outer flow fields, the determining module 802 performs the following operations:

[0111] The axial coordinate value at the nozzle exit of the solid rocket engine is recorded as 0;

[0112] Read the axial coordinate value of each grid point and determine whether the axial coordinate value of each grid point is greater than 0. If not, do not save the grid point. If yes, save all variables on the grid point and store the grid point in the first storage container until all grid points have been read.

[0113] Set the radial direction grid point count storage container as the second storage container, and the initial value of the second storage container is 0;

[0114] Iterate through each grid point in the first storage container. For each grid point, determine whether its axial coordinate value is 0. If not, do not update the value of the second storage container. If yes, increment the value of the second storage container by 1, until all grid points in the first storage container have been traversed.

[0115] The final value of the second storage container is determined as the radial grid number in the external flow field, and the quotient of the number of grid points in the first storage container and the radial grid number is determined as the axial grid number in the external flow field.

[0116] In some implementations, when the structuring module 804 rearranges the grid points in the external flow field based on the axial grid number and the radial grid number to obtain a structured external flow field grid, it performs the following operations:

[0117] For all grid points in the external flow field, the axial coordinate values ​​of each grid point are sorted in ascending order to obtain the axially optimized grid, denoted as GRID{i}, 1≤i≤Xnum, where Xnum is the number of axial grids in the external flow field;

[0118] For each axial coordinate point in the optimized axial mesh, all its corresponding radial coordinate values ​​are sorted in ascending order to obtain the optimized external flow field mesh, denoted as GRID{i,j}, 1≤j≤Ynum, where Ynum is the number of radial meshes in the external flow field.

[0119] In some implementations, when the encryption module 806 encrypts the external flow field mesh using a preset strategy to obtain an optimized target mesh, it performs the following operations:

[0120] Based on the overall size of the structured external flow field grid, it is re-divided to obtain the first number of grids in the axial direction and the second number of grids in the radial direction;

[0121] The first encryption coefficient and axial length in the axial direction, and the second encryption coefficient and radial length in the radial direction are determined respectively; the axial length is the axial projection length of the axial region of the structured grid under a preset viewing angle, and the preset viewing angle is the observation angle of the detector on the rocket engine;

[0122] The horizontal coordinates of each axial layer of the grid are determined based on the first grid number, the first encryption coefficient, and the axial length, and the vertical coordinates of each radial layer of the grid are determined based on the second grid number, the second encryption coefficient, and the radial length, to obtain the optimized target grid.

[0123] In some implementations, when the encryption module 806 determines the abscissa of each axial layer of the grid based on the first grid number, the first encryption coefficient, and the axial length, and determines the ordinate of each radial layer of the grid based on the second grid number, the second encryption coefficient, and the radial length, to obtain the optimized target grid, it performs the following operations:

[0124] In the axial direction, the x-coordinate of the first layer of mesh is calculated according to the first formula, which is expressed as follows:

[0125]

[0126] The x-coordinate of the position of the i-th grid (excluding the first grid layer) is calculated using the second formula, which is expressed as follows:

[0127] L(i)=L(i-1)+L0×RatioL i-1

[0128] In the formula, L0 is the abscissa of the first layer of grid, L(i) is the abscissa of the i-th layer of grid, L is the axial length, NPL is the number of the first grid, RatioL is the first encryption coefficient, and 1≤i≤NPL-1;

[0129] In the radial direction, the second grid number is numbered from bottom to top. Selecting the grid in the region Y>=0, the first layer of grids in that region is numbered NPW2=INT(NPW / 2). The ordinate of the first layer of grids is calculated using the third formula, which is expressed as:

[0130]

[0131] The ordinate of the j-th grid layer (excluding the first layer) is calculated using the fourth formula, which is expressed as follows:

[0132] R(j)=R(j-1)+R0×RatioR j-1 , where 1≤j≤NPW2;

[0133] In the region where Y <= 0, the symmetric calculation method of the fifth formula is applied. The expression of the fifth formula is:

[0134] R(j) = R(NPW-j+1); where NPW2 < j ≤ NPW;

[0135] In the formula, R0 is the ordinate of the first layer of grid; R(j) is the ordinate of the j-th layer of grid; R is the radial length; NPW is the second grid number; RatioR is the second encryption coefficient; INT() is the rounding symbol;

[0136] Based on the calculation results of the first, second, third, fourth, and fifth formulas, the optimized target mesh is obtained.

[0137] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a computational grid optimization device for solid rocket motor exhaust radiation. In other embodiments of the present invention, a computational grid optimization device for solid rocket motor exhaust radiation may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0138] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0139] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a solid rocket engine exhaust radiation computational grid optimization method according to any embodiment of this invention.

[0140] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a solid rocket motor exhaust radiation computational grid optimization method according to any embodiment of this invention.

[0141] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0142] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0143] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0144] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0145] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0146] 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.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the computational grid of exhaust plume radiation from a solid rocket motor, characterized in that, include: Read the number of grid points, grid faces, and variables from the grid file; The mesh file is obtained by performing flow field calculations on the exhaust plume of a solid rocket motor using flow field calculation software. The mesh file contains internal flow field data and external flow field data. Using the nozzle outlet of the solid rocket motor as the interface between the internal flow field and the external flow field, the axial grid number and radial grid number in the external flow field are determined. The grid points in the external flow field are rearranged based on the axial grid number and the radial grid number to obtain a structured external flow field grid. The external flow field mesh is densified using a preset strategy to obtain an optimized target mesh. The preset strategy is to gradually reduce the mesh density layer by layer along the direction away from the engine nozzle.

2. The method according to claim 1, characterized in that, The reading of the number of grid points, grid faces, and variables in the grid file includes: Read several lines of data from the grid file and determine a target line from the several lines of data; the target line includes a first key representing the number of grid points, a second key representing the number of grid faces, and at least one target separator; the first key includes a first character and a second character arranged in sequence, and the second key includes a third character and a fourth character arranged in sequence; Based on the position of the first keyword, the second keyword, and each target separator in the target row, determine the number of grid points and the number of grid faces; The number of variables is determined based on the position of the target row in the grid file.

3. The method according to claim 2, characterized in that, The step of determining the number of grid points and the number of grid faces based on the positions of the first keyword, the second keyword, and each of the target delimiters in the target row includes: The positions of each target separator are stored in the first array in ascending order; Iterate through each position in the first array, and for each position reached, execute the following: Determine if the position is greater than the position of the first character. If not, do not save the position and proceed to the next traversal. If so, determine the number represented by the string between the position and the position of the second character as the number of grid points and stop traversing. Determine if the position is greater than the position of the third character. If not, do not save the position and proceed to the next traversal. If so, determine the number represented by the string between the position and the position of the fourth character as the number of grid faces, and stop traversing.

4. The method according to claim 1, characterized in that, The step of using the nozzle exit of the solid rocket motor as the interface between the internal and external flow fields, and determining the axial and radial grid numbers in the external flow field, includes: The axial coordinate value at the nozzle exit of the solid rocket engine is recorded as 0; Read the axial coordinate value of each grid point and determine whether the axial coordinate value of each grid point is greater than 0. If not, do not save the grid point. If yes, save all variables on the grid point and store the grid point in the first storage container until all grid points have been read. Set the radial direction grid point count storage container as the second storage container, and the initial value of the second storage container is 0; Iterate through each grid point in the first storage container. For each grid point, determine whether its axial coordinate value is 0. If not, do not update the value of the second storage container. If yes, increment the value of the second storage container by 1, until all grid points in the first storage container have been traversed. The final value of the second storage container is determined as the radial grid number in the external flow field, and the quotient of the number of grid points in the first storage container and the radial grid number is determined as the axial grid number in the external flow field.

5. The method according to claim 1, characterized in that, The rearrangement of grid points in the external flow field based on the axial grid number and the radial grid number to obtain a structured external flow field grid includes: For all grid points in the external flow field, the axial coordinate values ​​of each grid point are sorted in ascending order to obtain the axially optimized grid, denoted as GRID{i}, 1≤i≤Xnum, where Xnum is the number of axial grids in the external flow field; For each axial coordinate point in the optimized axial mesh, all its corresponding radial coordinate values ​​are sorted in ascending order to obtain the optimized external flow field mesh, denoted as GRID{i,j}, 1≤j≤Ynum, where Ynum is the number of radial meshes in the external flow field.

6. The method according to claim 1, characterized in that, The step of refining the external flow field mesh using a preset strategy to obtain an optimized target mesh includes: Based on the overall size of the structured external flow field grid, it is re-divided to obtain the first number of grids in the axial direction and the second number of grids in the radial direction; The first encryption coefficient and axial length in the axial direction, and the second encryption coefficient and radial length in the radial direction are determined respectively; the axial length is the axial projection length of the axial region of the structured grid under a preset viewing angle, and the preset viewing angle is the observation angle of the detector on the rocket engine; The horizontal coordinates of each axial layer of the grid are determined based on the first grid number, the first encryption coefficient, and the axial length, and the vertical coordinates of each radial layer of the grid are determined based on the second grid number, the second encryption coefficient, and the radial length, to obtain the optimized target grid.

7. The method according to claim 6, characterized in that, The optimized target mesh is obtained by determining the abscissa of each axial layer of mesh based on the first mesh number, the first refinement coefficient, and the axial length, and determining the ordinate of each radial layer of mesh based on the second mesh number, the second refinement coefficient, and the radial length, respectively. In the axial direction, the x-coordinate of the first layer of mesh is calculated according to the first formula, which is expressed as follows: The x-coordinate of the position of the i-th grid (excluding the first grid layer) is calculated using the second formula, which is expressed as follows: L(i)=L(i-1)+L0×RatioL i-1 In the formula, L0 is the abscissa of the first layer of grid, L(i) is the abscissa of the i-th layer of grid, L is the axial length, NPL is the number of the first grid, RatioL is the first encryption coefficient, and 1≤i≤NPL-1; In the radial direction, the second grid number is numbered from bottom to top. Selecting the grid in the region Y>=0, the first layer of grids in that region is numbered NPW2=INT(NPW / 2). The ordinate of the first layer of grids is calculated using the third formula, which is expressed as: The ordinate of the j-th grid layer (excluding the first layer) is calculated using the fourth formula, which is expressed as follows: R(j)=R(j-1)+R0×RatioR j-1 , where 1≤j≤NPW2; In the region where Y <= 0, the symmetric calculation method of the fifth formula is applied. The expression of the fifth formula is: R(j) = R(NPW-j+1); where NPW2 < j ≤ NPW; In the formula, R0 is the ordinate of the first layer of grid; R(j) is the ordinate of the j-th layer of grid; R is the radial length; NPW is the second grid number; RatioR is the second encryption coefficient; INT() is the rounding symbol; Based on the calculation results of the first, second, third, fourth, and fifth formulas, the optimized target mesh is obtained.

8. A grid optimization device for calculating exhaust plume radiation from a solid rocket motor, characterized in that, include: The read module is used to read the number of grid points, grid faces, and variables in the grid file; The mesh file is obtained by performing flow field calculations on the exhaust plume of a solid rocket motor using flow field calculation software. The mesh file contains internal flow field data and external flow field data. The determination module is used to determine the axial grid number and radial grid number in the outer flow field, taking the nozzle outlet of the solid rocket motor as the interface between the inner flow field and the outer flow field. The structuring module is used to rearrange the grid points in the external flow field based on the axial grid number and the radial grid number to obtain a structured external flow field grid. The encryption module is used to encrypt the external flow field grid using a preset strategy to obtain an optimized target grid. The preset strategy is that the grid encryption level decreases layer by layer along the direction away from the engine nozzle.

9. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of any one of claims 1-7.

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