Engine Multi-Field Coupling Simulation Method Based on Multi-Domain Radial Basis Function Mesh Deformation

Abstract

The present invention relates to an engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation, including: obtaining boundary nodes and volume nodes; randomly shuffling and grouping the boundary nodes to obtain several boundary node groups; constructing a parallel architecture and evenly distributing the boundary node groups and volume nodes to each process; each process calculates the wall distance from the volume nodes to all boundary node groups and classifies the volume nodes to obtain a classification result; each process calculates the radial basis interpolation function based on the boundary node groups; the radial basis interpolation functions of each boundary node group are broadcast to all processes; then, different grouped interpolation functions are used for interpolation calculation according to the classification results of the volume nodes to obtain an interpolation displacement result; displacement superposition is performed according to the interpolation displacement result, and the final grid deformation result is output. The present invention takes into account efficiency, accuracy and flexibility and can be applied to the deformation of complex large-scale grids.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid-structure interaction simulation, and particularly to a multi-field coupling simulation method for an engine based on multi-domain radial basis function grid deformation. Background Art

[0002] Currently, the technology of long-endurance air-breathing hypersonic vehicles is a key technology that major aerospace countries in the world give priority to developing. Its core is the scramjet engine technology. The scramjet engine involves strong multi-physical field coupling problems of heat, fluid, and solid at the same time, and faces severe thermal safety problems during long-endurance operation. The continuous action of extreme force / thermal loads (about 1000 s) and the vibration of the structure at high temperatures may cause fatigue, damage, and failure of the material, affecting the thermal matching and thermal sealing of the structure, and even damaging the structural integrity of the engine. The efficient and safe operation of the engine strongly depends on the reasonable design and effective matching of the flow, combustion, and structure. It is necessary to synchronously consider multi-physical field couplings such as heat transfer processes, structural deformations, and structural unsteady responses.

[0003] At present, multi-physical field coupling experiments are difficult, and numerical calculation is another practical research method, which strongly supports the design of scramjet engines. In the field of hypersonic fluid-structure thermal coupling research, a series of physical models for sub-disciplines such as aerodynamic force, aerodynamic heat, and nonlinear structures have been established, and various coupling strategies and coupling methods have been proposed. Grid deformation is a necessary link in fluid-structure thermal multi-field coupling and is an essential module for multi-physical field coupling simulation platforms. Currently, physical model methods represented by the spring method, partial differential equation methods represented by the elastic body method, and algebraic methods represented by radial basis function interpolation and inverse distance weighted interpolation methods have been proposed. These traditional methods still have problems of low efficiency and poor accuracy when applied to large-scale complex configurations. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a multi-field coupling simulation method for an engine based on multi-domain radial basis function grid deformation that is applicable to the internal and external flows of the engine and has high efficiency and high accuracy.

[0005] A multi-field coupling simulation method for an engine based on multi-domain radial basis function grid deformation, the method comprising:

[0006] Step 1, generating an engine flow field grid, obtaining boundary nodes and volume nodes; randomly shuffling and grouping the boundary nodes to obtain several boundary node groups of the same size;

[0007] Step 2, constructing a parallel architecture, the parallel architecture including two or more processes, and evenly distributing the boundary node groups and the volume nodes to each process;

[0008] Step 3: Each process calculates the wall distances from the volume nodes corresponding to it to all boundary node groups, classifies the volume nodes according to the wall distances, and obtains a classification result;

[0009] Step 4: Each process establishes an interpolation function based on the assigned boundary node group, and then solves it through a system of linear algebraic equations to obtain a radial basis function interpolation based on the boundary node group;

[0010] Step 5: The radial basis function interpolation of each boundary node group is broadcast to all processes; then different grouped interpolation functions are used for interpolation calculation according to the classification result of the volume nodes to obtain an interpolation displacement result; displacement superposition is performed according to the interpolation displacement result, and the final mesh deformation result is output.

[0011] The above engine multi-field coupling simulation method based on multi-domain radial basis function mesh deformation generates an engine flow field mesh to obtain boundary nodes and volume nodes; randomly shuffles and groups the boundary nodes to obtain several boundary node groups of the same size; constructs a parallel architecture, which includes more than two processes, and evenly distributes the boundary node groups and volume nodes to each process; each process calculates the wall distances from the volume nodes corresponding to it to all boundary node groups, classifies the volume nodes according to the wall distances, and obtains a classification result; each process establishes an RBF interpolation function based on the assigned boundary node group, and then solves it through a system of linear algebraic equations to obtain a radial basis function interpolation based on the boundary node group; the radial basis function interpolation of each boundary node group is broadcast to all processes; then different grouped interpolation functions are used for interpolation calculation according to the classification result of the volume nodes to obtain an interpolation displacement result; displacement superposition is performed according to the interpolation displacement result, and the final mesh deformation result is output.

[0012] In the present invention, the boundary nodes are randomly shuffled and grouped, and then a parallel strategy is used for calculation, which significantly improves the construction and solution efficiency of the system of linear equations. The volume nodes are classified according to the wall distances from the volume nodes to the boundary node groups, and different grouped interpolation functions are used for interpolation calculation for different types of volume nodes. The differential processing method can more accurately simulate the deformation of nodes at different positions, ensure the smooth propagation of the displacement field from the boundary to the space, improve the accuracy of the mesh deformation calculation, and make the simulation results more in line with the actual situation of engine multi-field coupling. In addition, for the method provided by the present invention, the grouping process and the calculation of the wall distances do not depend on the specific deformation, so they can be completed in the pre-processing, taking into account efficiency, accuracy and flexibility, and can be applied to the deformation of complex large-scale meshes. Description of the Drawings

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0014] Figure 1 It is a schematic flow diagram of the multi-field coupling simulation of an engine based on multi-domain radial basis function grid deformation in an embodiment;

[0015] Figure 2 It is a schematic framework diagram of parallel architecture calculation in an embodiment;

[0016] Figure 3 It is a schematic diagram of boundary node grouping and volume node classification in an embodiment, where Figure 3 (a) is a schematic diagram of all boundary nodes, Figure 3 (b) is a schematic diagram of the boundary nodes of boundary node group 1, Figure 3 (c) is a schematic diagram of the boundary nodes of boundary node group 2, Figure 3 (d) is a schematic diagram of the boundary nodes of boundary node group 3; Figure 3 (e) is a schematic diagram of volume node classification;

[0017] Figure 4 It is a schematic diagram of the grid deformation result of a 2D NACA0012 airfoil in an embodiment, where Figure 4 (a) is a schematic diagram of the calculation result of the traditional greedy algorithm, Figure 4 (b) is a schematic diagram of the calculation result of the multi-scale method, Figure 4 (c) is a schematic diagram of the calculation result obtained when the present invention uses 4 groups, Figure 4 (d) is a schematic diagram of the calculation result obtained when the present invention uses 8 groups;

[0018] Figure 5 It is a schematic diagram of the grid deformation result of a large-scale configuration aircraft with complex internal and external flow integration in an embodiment, where Figure 5 (a) is a schematic diagram of the grid of the flow direction cross-section of the large-scale aircraft before deformation; Figure 5 (b) is a schematic diagram of the grid on the surface of the large-scale aircraft before deformation; Figure 5 (c) is a schematic diagram of the grid of the flow direction cross-section of the large-scale aircraft after deformation and its distortion distribution; Figure 5 (d) is a schematic diagram of the grid of the spanwise cross-section of the large-scale aircraft after deformation and its distortion distribution; Figure 5 (e) is a schematic diagram of the curve of the average distortion of the grid and the boundary changing with the number of groups; Figure 5(f) is a schematic diagram of the curve of computing time varying with the number of groups.

[0019] The realization of the object, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] It can be understood that the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0022] The following will describe the embodiments of the present invention in detail with reference to the accompanying drawings in the embodiments of the present invention.

[0023] Embodiment 1

[0024] In this embodiment, for the grid deformation of large-scale fluid-structure-thermal coupling problems, especially the fluid-structure-thermal coupling calculation and evaluation of scramjet engines, an engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation is proposed based on the idea of domain division. By randomly shuffling and grouping the boundary nodes and then adopting a parallel strategy for calculation, the construction and solution efficiency of the linear equations are significantly improved. The volume nodes are classified according to the wall distance from the volume nodes to the boundary node groups, and different grouping interpolation functions are used for interpolation calculation for different types of volume nodes. The differential processing method can more accurately simulate the deformation of nodes at different positions and ensure the smooth propagation of the displacement field from the boundary to the space, improving the accuracy of the grid deformation calculation and making the simulation results more in line with the actual situation of the engine multi-field coupling. In addition, for the method provided by the present invention, the grouping process and the wall distance calculation do not depend on the specific deformation, so they can be completed in the pre-processing, taking into account efficiency, accuracy and flexibility, and can be applied to the deformation of complex large-scale grids.

[0025] As Figure 1 and Figure 2 shown, the engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation provided by this embodiment includes the following steps:

[0026] Step 1: Generate the engine flow field grid and obtain the boundary nodes and volume nodes. Randomly shuffle and group the boundary nodes to obtain several boundary node groups of the same size.

[0027] Step 2: Construct a parallel architecture. The parallel architecture includes more than two processes, and evenly distribute the boundary node groups and volume nodes to each process.

[0028] Step 3: Each process calculates the wall distance from the volume nodes to all boundary node groups, and classify the volume nodes according to the wall distance to obtain the classification result.

[0029] Step 4: Each process establishes an interpolation function based on the assigned boundary node groups, and then solves it through a system of linear algebraic equations to obtain the radial basis interpolation function based on the boundary node groups.

[0030] Step 5: Broadcast the radial basis interpolation functions of each boundary node group to all processes; then perform interpolation calculations using different grouped interpolation functions according to the classification results of the volume nodes to obtain the interpolation displacement results; perform displacement superposition according to the interpolation displacement results and output the final grid deformation result.

[0031] In the specific implementation process of Step 1, first generate the engine flow field grid, obtain the coordinates and boundary displacements of the boundary nodes, and obtain the coordinates of the volume nodes. At the same time, perform initialization settings, and preset the number of boundary node groups , the number of parallel cores and the support radius . Then randomly shuffle and group the boundary nodes to obtain several boundary node groups of the same size.

[0032] More specifically, calculate and output the coordinates and boundary displacements of the boundary nodes through CSD, or directly give the boundary displacements manually; CFD outputs the coordinates of all flow field grid nodes, which cover the volume nodes; the grid deformation program reads the coordinates of the boundary nodes, the coordinates of the volume nodes and the boundary displacements.

[0033] Set the number of boundary node groups , the number of parallel cores and the support radius . Since the number of nodes in each boundary node group is the same, is also set to an integer multiple of the number of parallel cores, so that the computational workload of each process is roughly the same, thus achieving parallel load balancing. The nodes of each boundary node group can better represent the boundary shape, so the setting of the support radius is the same as the traditional greedy method.

[0034] It should be noted that the mesh deformation method based on RBF operates on point clouds. Therefore, only the coordinates of all nodes and the displacements of boundary node positions are required, and mesh topology information is not needed. The RBF-based mesh deformation method usually adopts the compactly supported Wendland’s C 2 function as the radial basis function, and its specific form is:

[0035] (1)

[0036] In the formula, ; represents the position vector at any position in space, represents the position vector of the i th RBF support point, R represents the support radius.

[0037] Randomly shuffle and group the boundary nodes to obtain several boundary node groups of the same size. Among them, the Knuth-Durstenfeld random shuffle algorithm is used to randomly shuffle the boundary nodes and then group them. The specific steps are as follows:

[0038] Step 101, establish an index array arr of size , where is the total number of boundary nodes; the numbers 1, 2,..., , are stored in the index array arr respectively; set ;

[0039] Step 102, generate a random integer from 1 to ;

[0040] Step 103, obtain the value of arr[r], and swap the value of arr[r] with the value of the index array arr[n + 1];

[0041] Step 104, n = n - 1;

[0042] Step 105, repeat Step 102, Step 103, and Step 104 until n = 1, completing the random shuffling of the boundary nodes;

[0043] Step 106, based on the index array arr, group the boundary nodes into boundary node groups of the same size.

[0044] By randomly shuffling the boundary node indices and storing them in the index array arr. Then, according to the index array arr, group the boundary nodes into copies. As Figure 3 shown in (a), assuming that all boundary nodes are 657, the boundary nodes are randomly grouped by random shuffling to obtain boundary node groups as shown in Figure 3 (b) to Figure 3 (d), and each boundary node group is assigned 219 boundary nodes. Figure 3 (e) is a schematic diagram of volume node classification. In this embodiment, the boundary nodes are randomly grouped by random shuffling, which can ensure that the node density after grouping is similar to the original nodes, and each group can represent the original boundary shape to a certain extent. In this way, the number of nodes is reduced and the node interval is increased, which is beneficial to alleviating the ill-conditioning of the system of linear algebraic equations.

[0045] In the specific implementation process of step 2, a parallel architecture is constructed by using a hybrid Message Passing Interface (MPI) and OpenMP technology, and then the grouped boundary node groups are evenly distributed to each process; at the same time, the volume nodes to be interpolated are evenly distributed to each process according to the node index. In this step, when constructing the parallel architecture, the number of processes can be equal to or less than the number of boundary node groups , when the number of processes is equal to the number of boundary node groups , each process is assigned a boundary node group; when the number of processes is less than the number of boundary node groups , the number of boundary node groups assigned to the processes may be different. Preferably, the number of boundary node groups can be set to an integer multiple of the number of processes for easy equal distribution, so as to ensure parallel load balancing.

[0046] It can be understood that a parallel architecture generally includes child processes and a main process. In the processes of this embodiment, both the main process and the child processes participate in the calculation.

[0047] In the specific implementation process of step 3, when each process performs calculations, first, a K-dimensional tree (K-D tree) structure is established K-D trees. Then, the nearest neighbor search algorithm is used to calculate the wall distance from the volume nodes to all boundary node groups, and the minimum wall distance of each volume node is obtained.

[0048] According to the minimum wall distance, the volume nodes are divided into near-wall volume nodes, intermediate volume nodes, and far-wall volume nodes. The classification rules are as follows:

[0049] (2)

[0050] In the formula, indicates that the volume node belongs to the near-wall region; Indicates that the volume node belongs to the intermediate transition region; Indicates that the volume node belongs to the region far from the wall; Indicates the minimum distance between the i-th volume point and the wall; Indicates an adjustable characteristic length parameter; Indicates another adjustable coefficient that affects the number of intermediate nodes, and its value range is from 0 to 1.

[0051] It can be understood that the purpose of classification is to adopt different interpolation methods according to different node types in the subsequent interpolation stage. More support points can be adopted for the near-wall nodes, and fewer support points can be adopted for the nodes far from the wall to ensure the accuracy of grid deformation.

[0052] In the specific implementation process of step 4, each process establishes an RBF interpolation function based on the allocated boundary node group, constructs a system of linear algebraic equations on each group of nodes, solves the system of linear algebraic equations, and obtains the radial basis interpolation function based on each grouping.

[0053] Among them, the expression of the RBF interpolation function is:

[0054] (3)

[0055] In the formula, represents the position vector at any position in space, represents the position vector of the i-th RBF support point; represents the displacement field vector at the position; represents the weight coefficient vector of the i-th RBF support point; represents the number of boundary nodes contained in this boundary node group; represents the support radius.

[0056] In formula (3), the left end of the approximate equal sign is the exact displacement field, and the right end is the constructed radial basis interpolation function. In order to obtain the unknown coefficient of the interpolation function and make the interpolation function exactly equal to the known displacement at the boundary node, a system of linear algebraic equations is constructed, and the expression is:

[0057] (4)

[0058] In the formula, represents the influence of the support point on the support point , where represents the position vector of the support point , Denote the position vector of the support point ; Denote the support point corresponding to the unknown coefficient of the th dimension; is the th dimensional value of the displacement field on the support point .

[0059] It should be noted that, in this embodiment, it is assumed that the displacement field is a 3D vector field, the matrix size is , and the value range of the support point is . Then, for the three-dimensional space, the constructed system of linear algebraic equations is expressed as follows:

[0060] (5)

[0061] Then, based on formula (5), solve the unknown coefficient of the interpolation function to obtain the radial basis interpolation function based on the boundary node group, and then obtain the displacement field vector. It should be noted that the system of linear algebraic equations is solved by using the LDL T Cholesky method of the open-source Eigen library.

[0062] In the specific implementation process of step 5, different grouped interpolation functions are used for interpolation calculation according to the classification results of the volume nodes. For the volume node , there are the following situations:

[0063] (1) If the minimum wall distance , it means that the node is not affected, the interpolation displacement is 0, and no additional calculation is required.

[0064] (2) If , then randomly select one of the radial basis interpolation functions of all boundary node groups for interpolation calculation to obtain the interpolation displacement result of the far-wall volume node. Preferably, the first interpolation function is selected here; this is because the displacement field rapidly decays as it propagates from the boundary to the space, and only using 1 grouped node has sufficient accuracy.

[0065] (3) If , select the boundary node groups with the closest wall distance from the boundary node groups, and use the weighted average of the interpolation results of the boundary node groups to obtain the interpolation displacement result of the near-wall volume node. This is because by selecting the actual number of support points used can be determined, and the form of calculation based on the weight coefficient ensures that the closer the group, the greater its influence. Among them, the expression of the weight coefficient is:

[0066] (6)

[0067] In the formula, represents the distance between the current volume node and the th boundary node group; represents the distance between the current volume node and the th boundary node group.

[0068] (4) If , use trigonometric functions to perform weighted calculation on the calculation results of the interpolation displacement results of the near-wall volume nodes and the far-wall volume nodes to obtain the interpolation displacement result of the intermediate nodes. It can be understood that the intermediate nodes are used to smooth two different regions, and using trigonometric functions can well achieve the smooth transition of the displacement field.

[0069] The calculation expression for weighting using trigonometric functions is:

[0070] (7)

[0071] In the formula, represents the interpolation displacement result of the intermediate nodes; represents the interpolation displacement result of the near-wall volume nodes; represents the interpolation displacement result of the far-wall volume nodes; , represent the weight coefficients; represents the minimum distance between the th volume point and the wall; represents the adjustable characteristic length parameter.

[0072] (5) If the minimum wall distance , it means that the node is on the surface. Find the boundary node group containing this node to calculate the interpolation displacement result. It can be understood that selecting the boundary node group containing this node for calculation can ensure that the volume node moves to the correct position and ensure the accurate boundary shape.

[0073] Finally, according to the interpolation displacement results interpolated by each process, superimpose them on the initial position vectors of the corresponding volume nodes respectively, and then obtain the position vectors of all volume nodes after deformation. Then, the main process outputs the final mesh deformation result based on these deformed position vectors.

[0074] In one embodiment, as Figure 4 shown, a schematic diagram of the mesh deformation result of the 2D NACA0012 airfoil is provided to verify the effectiveness of the method proposed by the present invention.

[0075] The wing is horizontal before deformation. The wing mesh contains 657 boundary nodes, 110,300 volume nodes, and 110,345 quadrilateral meshes. After rotating the wing surface counterclockwise by 45° around the leading edge, the mesh is deformed. The mesh orthogonality quality defined in the Fluent software q is used as a measure to characterize the mesh quality. represents the change in mesh orthogonality relative to the initial value. Figure 4 (a) shows the calculation results using the traditional greedy algorithm. In the figure, represents the number of boundary nodes used by the greedy algorithm; Figure 4 (b) shows the calculation results of the multi-scale method. In the figure, represents the number of basic points used by the multi-scale method; Figure 4 (c) and Figure 4 (d) show the calculation results of the method of the present invention. In the figure, represents the number of groups. It can be seen that the method of the present invention has the same deformation accuracy as the traditional method.

[0076] To quantitatively compare the calculation efficiency of different methods, Table 1 gives the CPU time and total calculation time of different methods in stages such as grouping, wall distance calculation, linear algebraic equation system construction, linear algebraic equation system solution, and interpolation calculation. Among them, the greedy algorithm uses 132 nodes as the reduced data set, the multi-scale method also uses 132 nodes as the basic point set, and the method proposed in the present invention uses 4 groups and performs serial and parallel calculations respectively. It can be seen that the method provided by the present invention has the highest efficiency even when implemented serially, and the efficiency is further improved after adopting parallel technology.

[0077] Table 1 Comparison of the efficiency of 2D NACA0012 airfoil mesh deformation methods

[0078]

[0079] In one embodiment, as Figure 5 shown, a schematic diagram of the mesh deformation result of a large-scale aircraft with complex internal and external flow integration is provided to verify the accuracy and efficiency of the method of the present invention on a large-scale configuration.

[0080] The large-scale aircraft mesh uses a hybrid hexahedron and tetrahedron mesh, including 338,000 boundary nodes, 8.647 million volume nodes, and 16.052 million elements. The mesh skewness defined in Fluent is used as a measure of the quality of the unstructured hybrid mesh. The aircraft is initially in a horizontal attitude and then rotates clockwise by 10° around the leading edge of the inlet to simulate the variable angle of attack situation. Figure 5 (a) and Figure 5 (b) show the mesh of the aircraft before deformation. Figure 5(c) and Figure 5 (d) show the deformed aircraft grid and give the distribution of the grid distortion; Figure 5 (e) and Figure 5 (f) show the variation laws of the average distortion of the grid and boundary and the calculation time with the number of groups.

[0081] Table 2 gives the calculation time-consuming of the multi-scale method and the method proposed in the present invention. It can be seen that the method proposed in the present invention has high efficiency. Without considering the preprocessing time, the deformation of the large-scale grid can be completed within one minute.

[0082] Table 2 Efficiency comparison of large-scale aircraft grid deformation methods

[0083]

[0084] In summary, the present invention groups the boundary nodes into equal-sized parts by the Knuth-Durstenfeld random shuffling algorithm, so that each group can represent the boundary shape. The construction complexity of the linear algebraic equations is reduced from to , and the solution complexity is approximately reduced from to , significantly improving the calculation efficiency, where represents the number of boundary nodes. The processes of constructing, solving, and interpolating the linear algebraic equations after grouping by this method have high parallelism. The hybrid MPI and OpenMP parallel technologies are used to independently construct the RBF interpolation function based on each group, significantly improving the construction and solution efficiency of the linear equations.

[0085] The volume nodes are classified into near-wall volume nodes, intermediate volume nodes, and far-wall volume nodes according to the wall distance. Different types of nodes are calculated using different numbers of support points and different grouped interpolation functions, further reducing the calculation overhead; using trigonometric function weighting ensures the smooth propagation of the displacement field from the boundary to the space, and the volume point grouping process further improves the efficiency of the interpolation process.

[0086] In addition, since the grouping process and the wall distance calculation provided by the present invention do not depend on the specific deformation, they can be completed in the preprocessing. In actual calculation, only the linear equations and the volume point interpolation need to be solved. While reducing the calculation overhead, the processes of solving the linear equations and the volume point interpolation are significantly accelerated by the way of boundary node grouping.

[0087] The method proposed by the present invention takes into account efficiency, accuracy, and flexibility, enabling the RBF method to be applicable not only to the deformation of complex large-scale grids but also to the multi-physics field coupling calculation of the integrated internal and external flows of an engine. This is because the present invention groups nodes, significantly reducing the computational amount, and by using parallel computing, the computing speed is significantly improved.

[0088] Although each step in this embodiment Figure 1 is shown in sequence according to the indication of the arrow, these steps do not necessarily have to be executed in the order indicated by the arrow. Unless clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in this embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be completed at the same moment but can be executed at different moments. The execution order of these sub-steps or stages does not necessarily have to be sequential either, but can be executed alternately or in rotation with at least a part of other steps or sub-steps or stages of other steps.

[0089] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0090] The above-described embodiments merely represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. An engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation, characterized in that: The method comprises: Step 1, generating an engine flow field grid, obtaining boundary nodes and volume nodes; randomly shuffling and grouping the boundary nodes to obtain a number of boundary node groups of the same size; Step 2, constructing a parallel architecture, the parallel architecture including more than two processes, and evenly distributing the boundary node group and the volume node to each process; Step 3, each process calculates the wall distance from the volume node to all boundary node groups, and classifies the volume nodes according to the wall distance to obtain classification results; Step 4, each process establishes an interpolation function based on the assigned boundary node group, and then solves the linear algebraic equations to obtain a radial basis interpolation function based on the boundary node group; Step 5: The radial basis interpolation function of each boundary node group is broadcast to all processes; then different group interpolation functions are used to perform interpolation calculations according to the classification results of the volume nodes to obtain interpolation displacement results; displacement superposition is performed according to the interpolation displacement results to output the final mesh deformation result.

2. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to claim 1 is characterized in that: In step 1, the engine flow field grid is generated, and boundary nodes and volume nodes are obtained, including: Generate the engine flow field grid, obtain the coordinates and boundary displacements of the boundary nodes, and obtain the coordinates of the volume nodes.

3. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to claim 1 is characterized in that: Step 1 also includes: presetting the number of boundary node groups , number of parallel cores and support radius .

4. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to any one of claims 1 to 3, characterized in that: In step 1, the boundary nodes are randomly shuffled and grouped to obtain a number of boundary node groups of the same size, including: Step 101, establish a size of The index array arr, is the total number of boundary nodes; the index array arr stores numbers 1, 2, ..., , ;set up ; Step 102, generate 1 to A random integer ; Step 103, obtain the value of arr[r], and swap the value of arr[r] with the value of the index array arr[n+1]; Step 104, n = n - 1; Step 105, repeat steps 102, 103, and 104 until n = 1, complete the random shuffle of boundary nodes; Step 106, based on the index array arr, group the boundary nodes into groups of the same size A group of boundary nodes.

5. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to any one of claims 1 to 3, characterized in that: In step 3, each process calculates the wall distance from the volume node to all boundary node groups, and classifies the volume nodes according to the wall distance to obtain classification results, including: In each process, the nearest neighbor search algorithm is used to calculate the minimum wall distance from the volume node to all boundary node groups; The volume nodes are classified according to the minimum wall distance, and the classification rules are as follows: ； In the formula, Indicates that the volume node belongs to the near-wall region; Indicates that the volume node belongs to the intermediate transition area; Indicates that the volume node belongs to the area far away from the wall; Indicates The minimum distance between a volume point and the wall; represents an adjustable characteristic length parameter; Indicates another adjustable coefficient.

6. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to claim 5 is characterized in that: In step 4, each process establishes an RBF interpolation function based on the assigned boundary node group. The interpolation function expression is: ； In the formula, represents the position vector of any position in space, Indicates The position vector of the RBF support points; express Displacement field vector at position; Indicates The weight coefficient vector of RBF support points; Indicates the number of border nodes contained in the border node group; Indicates the support radius.

7. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to claim 6 is characterized in that: In step 4, each process establishes an interpolation function based on the assigned boundary node group, and then solves the linear algebraic equations to obtain a radial basis interpolation function based on the boundary node group, including: Each process establishes an RBF interpolation function based on the assigned boundary node group, so that the interpolation function is exactly equal to the node displacement on the boundary node, thereby constructing a linear algebraic equation system, the expression of which is: ； In the formula, Indicates support point For support point The impact of Indicates support point Corresponding to unknown coefficients of dimension; For support point The displacement field Dimension values; Solving the unknown coefficients of interpolation functions based on linear algebraic equations , the radial basis interpolation function based on the boundary node group is obtained, and then the displacement field vector is obtained.

8. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to claim 5 is characterized in that: In step 5, different grouping interpolation functions are used to perform interpolation calculations according to the classification results of the volume nodes to obtain interpolation displacement results, including: For volume nodes , if the minimum wall distance , indicating that the node is not affected and the interpolation displacement is 0; like , then select one of the radial basis interpolation functions of all boundary node groups for interpolation calculation to obtain the interpolation displacement result of the far wall volume node; like ,from Select the closest wall node from the group of boundary nodes A group of boundary nodes, and use The interpolation results of the boundary node groups are weighted to obtain the interpolation displacement results of the near-wall volume nodes; like , using trigonometric functions to perform weighted calculations on the interpolation displacement results of the near-wall volume nodes and the far-wall volume nodes to obtain the interpolation displacement results of the intermediate nodes; If the minimum wall distance , indicating that the node is located on the surface, find the boundary node group containing the node to calculate the interpolation displacement result.

9. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to claim 8 is characterized in that: like ,from Select the closest boundary node from the group A group of boundary nodes, and use The weighted interpolation results of the boundary node groups are used as the final interpolation displacement result. The expression of the weight coefficient is: ； In the formula, Indicates the current volume node and the The distance between the boundary node groups; Indicates the current volume node and the The distance between the boundary node groups.

10. The engine multi-field coupling simulation method based on multi-domain radial basis function grid deformation according to claim 8, characterized in that: like , the interpolation displacement results of the near-wall volume nodes and the far-wall volume nodes are weighted by trigonometric functions to obtain the interpolation displacement results of the intermediate nodes. The calculation expression is: ； In the formula, Indicates the interpolation displacement result of the intermediate node; It represents the interpolation displacement result of the volume node near the wall; Represents the interpolated displacement result of the far wall volume node; , represents the weight coefficient; Represents an adjustable characteristic length parameter.

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