Simulation data processing system for multiple processes, storage medium, and apparatus

Abstract

The multi-process simulation data processing system, storage medium and equipment belong to the finite element simulation technical field of thin-walled complex components. In order to solve the problem that the existing finite element analysis software does not consider the difference of mesh distribution in different process analysis when analyzing the workpiece by finite element analysis, and does not consider the accumulation and transmission of stress and deformation between different meshes, the application first acquires the finite element mesh element data of the thin-walled complex component structure by using a pre-process finite element simulation result effective information extraction module, then performs mesh reconstruction in the post-process by using a mesh reconstruction and node and structure feature transmission module, and transmits the node data and structure feature data of the mesh in the pre-process to the mesh nodes of the mesh in the post-process in the process of mesh reconstruction, and finally performs high-precision finite element calculation of the post-process by using a high-precision operation module of the post-process finite element simulation model.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of finite element simulation of thin-walled complex components, and particularly relates to a multi-process simulation data processing system for thin-walled complex components. BACKGROUND

[0002] In the manufacturing of aerospace precision components, each process will produce different degrees of stress and deformation. The existence of residual stress in the manufacturing process will make the material enter an unstable state, affecting the service life of the structure. Welding deformation will seriously affect the precision of the precision component, change the stress state of the welded structure during service, and even cause the overall failure of the component, seriously affecting the safety of aerospace equipment. Especially for thin-walled complex components such as combustion chambers, the overall stiffness is small, and there are a large number of holes inside, which are more prone to welding deformation. Therefore, in the manufacturing process, it is an important link to accurately predict the stress and deformation caused by each processing step in the manufacturing of aerospace precision components.

[0003] Finite element simulation is a common means of predicting stress and deformation data in the manufacturing process. Under the premise of accurate material parameters and boundary condition settings and reasonable grid quantity, it has very ideal calculation accuracy. In order to save computing resources, the finite element grid is often divided into key parts and non-key parts of the model, and different grid planning is carried out. The key analysis area of the same model in different processes is different, so the grid of the same component in different processes will also have a lot of differences in multi-process analysis. The commonly used finite element simulation software cannot well solve the accumulation and transmission problem of stress and deformation between different grids. In the face of large grid differences between front and rear processes and disordered data distribution, the existing finite element analysis software has poor calculation effect, and even cannot calculate. SUMMARY

[0004] The present application solves the problem that the existing finite element analysis software does not consider the difference in grid distribution in different process analysis when performing finite element analysis on workpieces (especially thin-walled complex components), and does not consider the accumulation and transmission problem of stress and deformation between different grids, resulting in poor calculation effect under the condition of large grid difference between front and rear processes and disordered data distribution.

[0005] The multi-process simulation data processing system includes a grid reconstruction and node and structure feature transmission module. The grid reconstruction and node and structure feature transmission module is based on the finite element grid element data of the thin-walled complex component structure of the front process, reconstructs the grid in the rear process, and transmits the node data and structure feature data of the grid in the front process to the grid node of the grid in the rear process in the process of grid reconstruction. The specific process includes the following steps:

[0006] The finite element model after the pre-process needs to be re-meshed, and new cells are added to the cell region corresponding to the nodes N1-N8 around the center point O and the interpolation point M located between each two nodes of N1-N8 ij , and the subscripts i and j are the serial numbers of the two nodes at the two ends corresponding to the interpolation point; the center points of the four cell faces of the cell corresponding to N1-N8 are H, R, G, and T

[0007] Let P be a new cell node that needs to be added in the new process, and initially select any point in the intended direction in space as P, and determine its initial coordinates, and the three cell face center points closest to P are H, G, and T; at this time, P may be too close or too far from the original node model, which is not conducive to the transmission of cell data, so based on the transmission model, the components corresponding to the P coordinates (g, h, r) are obtained by iterative calculation of each coordinate parameter value;

[0008] The transmission model is as follows:

[0009]

[0010] Where g1-g4, r1-r4, and h1-h4 are the components of the coordinates g, r, and h, respectively; - represents the vector corresponding to the coordinate parameter, such as represents the vector OP corresponding to the coordinate parameter;

[0011] Using the transmission model for iterative calculation, the relative error of the coordinate calculation of the P point in the two iterations is not greater than the error threshold X θ as the stopping iteration condition, and the iteration transmission is stopped when the stopping iteration condition is met, and the final determined P point coordinates are obtained as the node of the newly generated cell;

[0012] After obtaining the P point coordinates, the mesh is re-meshed, the coordinates and structural feature data of each node of the mesh corresponding to each point P are determined according to the coordinate relationship between the P point and each node of the mesh, the mesh is re-meshed with the P point as the interpolation point to be interpolated, and the structural feature data of the interpolation point is transmitted based on the structural feature data of each node of the mesh corresponding to the P point, i.e., the structural feature data of the P point is obtained by using the linear difference extrapolation method based on the node structural feature data of the mesh corresponding to the P point.

[0013] Further, the stopping iteration condition has the following specific form:

[0014]

[0015] Where X N+1 , X N , and XN-1 The coordinates of the P points obtained in the N+1th iteration, the Nth iteration and the N-1th iteration, respectively.

[0016] Further, the simulation data processing system for multiple processes further comprises a pre-process finite element simulation result effective information extraction module, which acquires finite element grid element data of the thin-walled complex component structure based on a Python data processing method.

[0017] Further, the process in which the pre-process finite element simulation result effective information extraction module acquires finite element grid element data of the thin-walled complex component structure based on the Python data processing method comprises the following steps:

[0018] In an object-oriented manner, the FEniCS library in Python is used to load the model file by using the Mesh function to acquire the grid element data of the thin-walled complex component structure, and the data of any grid element E k includes node data and structure characteristic data, the node data includes node number data N ki and node coordinate data, and the structure characteristic data includes temperature field data T ki , node displacement data D ki , and stress data sigma ki ; k represents a certain element, and i represents a certain node in the element.

[0019] Further, the simulation data processing system for multiple processes further comprises a high-precision operation module of a post-process finite element simulation model, which takes the processing result of the grid reconstruction and node and structure characteristic transmission module as the initial value of the post-process, sets the initial condition of the post-process finite element model, sets the boundary condition based on the finite element simulation model, and then performs post-process finite element calculation.

[0020] A computer storage medium, the storage medium stores at least one instruction, the at least one instruction is loaded and run by the processor The simulation data processing system for multiple processes.

[0021] A simulation data processing device for multiple processes, the device comprises a processor and a memory, the memory stores at least one instruction, the at least one instruction is loaded and run by the processor The simulation data processing system for multiple processes.

[0022] Advantages:

[0023] The system considers the difference of mesh distribution in different process analysis, and transmits stress and other structure characteristic data based on the difference mesh, so that the simulation of deformation accumulation and the like can be effectively carried out. Therefore, through the present application, reliable data transmission between different process difference meshes of thin-walled complex components (such as combustion chamber thin-walled structure) can be realized, the accuracy of finite element model mesh node data in subsequent processes is effectively improved, and the accuracy of finite element analysis is improved, so that the problem of poor calculation effect under the condition of large mesh difference and disordered data distribution between front and rear processes can be effectively solved. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 Data structure diagram of a typical three-dimensional hexahedral mesh.

[0025] Figure 2 Schematic diagram of element partitioning method.

[0026] Figure 3 Schematic diagram of combustion chamber component thin-walled panel structure.

[0027] Figure 4 Modeling for combustion chamber component thin-walled panel structure.

[0028] Figure 5 Schematic diagram of mesh difference for welding and heat treatment.

[0029] Figure 6 Calculation result (temperature field after welding) of finite element model. DETAILED DESCRIPTION

[0030] The present application provides a simulation data processing system for combustion chamber thin-walled complex components in multiple processes, realizes the processing algorithm and transmission method design of model mesh information, stress and deformation data and the like in multiple processes, and completes the data mapping calculation and data transmission between difference meshes. The present application can solve the accumulation and transmission problems of stress and deformation in each process of such thin-walled components, and effectively improve the accuracy of calculation results.

[0031] The system can be used as a separate finite element data processing system, that is, the data transmission and processing are realized by interfacing with the finite element analysis tool, or it can be used as a processing module or component in the finite element analysis tool. The present application will be described in detail in conjunction with the specific embodiments. Specific embodiment one:

[0033] The present embodiment is a simulation data processing system for multiple processes, which comprises an extraction module of effective information of finite element simulation results of pre-process, a mesh reconstruction and node and structure characteristic transmission module, and a high-precision operation module of finite element simulation model of post-process; wherein,

[0034] The pre-process finite element simulation result effective information extraction module: based on a Python data processing method, obtains finite element grid element data of the thin-walled complex component structure;

[0035] The thin-walled structure of the combustion chamber undergoes various processing procedures, including rolling, welding, heat treatment, etc. The grid distribution in different process analyses is different. The existing finite element processing software cannot well solve the accumulation and transmission problems of stress and deformation between different grids. Under the condition of large grid difference between the pre-process and the post-process and disordered data distribution, the calculation effect is poor. The present embodiment processes the thin-walled structure model of the combustion chamber. All the elements in the thin-walled structure model of the combustion chamber are hexahedral elements. The element characteristics in the finite element model are extracted to form a data structure diagram of a typical three-dimensional hexahedral grid as shown in Figure 1 The object-oriented way is adopted, the FEniCS library in Python is used, and the Mesh function in the library is used to load the model file to obtain the grid element data of the thin-walled complex component structure. The data of any grid element E k includes node data and structure characteristic data. The node data includes node number data N ki and node coordinate data. The structure characteristic data includes temperature field data T ki , node displacement data D ki , and stress data σ ki ; k refers to an element (element number), and i refers to a node in the element (element node number).

[0036] The grid reconstruction and node and structure characteristic transmission module: based on the finite element grid element data of the thin-walled complex component structure in the pre-process, the grid is reconstructed in the post-process, and the node data and structure characteristic data of the grid in the pre-process are transmitted to the grid nodes of the grid in the post-process in the process of grid reconstruction. The specific process includes the following steps:

[0037] Taking the data transmission between the welding process and the heat treatment process as an example, the structure of the thin-walled plate of the combustion chamber part is shown in Figure 3 , and the thin-walled plate structure modeling diagram of the combustion chamber part is shown in Figure 4 After welding, due to the small stiffness, a large deformation will be generated. At the same time, the heat treatment and welding finite element analysis positions have different emphases, which leads to the difference in grid distribution between the pre-process and the post-process. Therefore, for the subsequent heat treatment process, grid reconstruction is needed to add new element cells and delete redundant element cells. In order to solve the model reconstruction and combination problem caused by the grid difference in the multi-process calculation of the present combustion chamber structure, the present application uses formula (1) to complete the grid ID sorting, node coordinate superposition, and combined grid joint surface in the grid model reconstruction and combination process, and further achieves the purpose of transmitting the result data.

[0038] like Figure 5 As shown, Figure 5 This diagram illustrates the mesh differences between welding (left) and heat treatment (right). The finite element model after welding requires mesh reconstruction. During reconstruction, nodes that need to be deleted can be deleted directly after determining their location without changing the cell type. New cells need to be added to certain structural areas during reconstruction. The mesh cells before adding new cells are shown below. Figure 2 As shown, the cell region corresponding to nodes N1-N8 around node O is divided, and the interpolation point located between any two nodes in node N1-N8 is denoted as M. ij The subscripts i and j are the indexes of the two nodes at the two ends of the interpolation point, and the distribution of points located in the middle of each node is named M. 12 (Located between N1 and N2), M 13 (Located between N1 and N3), M 23 (Located between N2 and N3), ..., and so on; the center points of the four element faces corresponding to the elements N1-N8 are denoted as H, R, G, and T;

[0039] Let point P be the new cell node added in the new process where a new cell grid needs to be added. Initially, any point in the intended direction within space is selected as P. After determining its initial coordinates, the three center points of the cell faces closest to P are H, G, and T. At this time, point P may be too close or too far from the original node model, which is not conducive to the transfer of cell data. Therefore, based on the transfer model, the components corresponding to the coordinates (g, h, r) of P are obtained by iterative calculation of the coordinate parameter values.

[0040] The transmission model is as follows:

[0041]

[0042] Where g1-g4, r1-r4, and h1-h4 are the components of coordinates g, r, and h, respectively; - represents the vector corresponding to the coordinate parameter, such as... This represents the vector OP corresponding to the coordinate parameter;

[0043] Using a transitive model for iterative calculations, the relative error between the first and second iterations of calculating the coordinates of point P is no greater than the error threshold X. θ At that time, that is, the value X of the (N+1)th iteration. N+1 With the Nth iteration value X N The absolute value of the difference and the value X of the Nth iteration N With the value X of the N-1th iteration N-1 The ratio of the absolute values ​​of the differences is less than or equal to the error threshold X. θ The calculation is terminated, as shown in formula (2):

[0044]

[0045] Thus, the final determined P point coordinates best suited for data transmission can be obtained as the nodes of the subsequently newly generated cells.

[0046] After obtaining the P point coordinates, the reconstruction of the grid is performed, that is, based on the coordinate relationship between the thin-walled plate structure finite element grid P and each node of the original grid proposed in the above formula, the difference grid (the grid of the newly added cell) before and after reconstruction corresponding to the same coordinate point (that is, the grid to which the newly added cell is needed) is found, and the coordinates and structural feature data of each node corresponding to the grid of each point P are determined.

[0047] The grid reconstruction is performed with the P point as the interpolation point to be interpolated, and in the reconstruction process, the structural feature data of each node of the grid corresponding to the interpolation point is transmitted to the interpolation point, that is, based on the structural feature data of the node corresponding to the grid where the P point is located, the structural feature data of the P point is obtained by using the linear difference extrapolation method.

[0048] The high-precision operation module of the post-process finite element simulation model: taking the processing results of the grid reconstruction and node and structural feature transmission module as the initial value of the post-process, the initial condition setting of the post-process finite element model can be completed, and then the boundary condition setting and the like is continued based on the finite element simulation model, and then the post-process high-precision finite element calculation is performed.

[0049] The result of the finite element model processed by the scheme of the embodiment for the combustion chamber thin-walled structure model is shown in Figure 6 . Specific embodiment two:

[0051] The embodiment is a computer storage medium, and the storage medium stores at least one instruction. The at least one instruction is loaded and run by a processor, and the at least one instruction is for the simulation data processing system of multiple processes.

[0052] It should be understood that the instructions include a computer program product, software or computerized method corresponding to any method described in the present application; the instructions can be used to program a computer system or other electronic device. The computer storage medium can include a readable medium having instructions stored thereon, and can include but is not limited to a magnetic storage medium, an optical storage medium, a magneto-optical storage medium, a read-only memory (ROM), a random access memory (RAM), an erasable programmable memory (such as an EPROM and an EEPROM), and a flash memory layer, or other types of media suitable for storing electronic instructions. Specific embodiment three:

[0054] The embodiment is a simulation data processing device for multi-processes, the device comprises a processor and a memory, it should be understood that any device comprising the processor and the memory described in the application comprises other units, modules, which display, interact, process, control and the like through signals or instructions, and other functions;

[0055] The memory stores at least one instruction, the at least one instruction is loaded by the processor and runs the simulation data processing system for multi-processes.

[0056] The above calculation examples of the application are only to illustrate the calculation model and calculation process of the application, and are not limited to the embodiments of the application. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above description, and here it is impossible to enumerate all the embodiments, and any obvious changes or variations derived from the technical solutions of the application still fall within the protection scope of the application.

Claims

1. A simulation data processing system for a plurality of processes, characterized by, The grid reconstruction and node and structure feature transmission module includes a grid reconstruction and node and structure feature transmission module, which is based on the finite element grid element data of the thin-walled complex component structure of the previous process, and performs grid reconstruction in the subsequent process, and transmits the node data and structure feature data of the grid in the previous process to the grid nodes of the grid in the subsequent process during the grid reconstruction. The specific process includes the following steps: The finite element model after the pre-process needs to be re-meshed, in the re-meshing, new element cells are added to some structure areas, the element area corresponding to the center point O and the nodes N1-N8 around O is divided, and the interpolation point between each two nodes of N1-N8 is recorded as M ij , the subscripts i and j are the serial numbers of the two nodes at the two ends corresponding to the interpolation point; the center points of the four element faces of the element corresponding to N1-N8 are recorded as H, R, G and T; The P point is a new unit node that needs to be added in the new process, and an arbitrary point in the intended direction in space is selected as P at the initial stage, and the initial coordinates are determined, and the three unit face center points closest to P are H, G and T; at this time, the P point may be too close or too far from the original node model, which is not conducive to the transmission of unit data, so based on the transmission model, the components corresponding to the P coordinate (g, h, r) are obtained through iterative calculation of each coordinate parameter value; The transmission model is as follows: Wherein, g1-g4, r1-r4, h1-h4 are the components of coordinates g, r and h respectively; and the coordinate parameters correspond to vectors. The relative error of the coordinates of the P point calculated by the two iterations is not greater than the error threshold X θ As a stop iteration condition, when the stop iteration condition is met, the iterative transmission is stopped, and the final determined P point coordinates of the data transmission are obtained as the nodes of the newly generated cells in the subsequent. After obtaining the P point coordinates, the grid is reconstructed, the coordinates and structure feature data of each node of the grid corresponding to the P point are determined according to the coordinate relationship between the P point and each node of the grid, the P point is taken as the interpolation point to be interpolated, and the grid is reconstructed, and in the reconstruction process, the structure feature data of the interpolation point is transmitted based on the structure feature data of each node of the grid corresponding to the interpolation point, that is, the structure feature data of the P point is obtained by using the linear difference extrapolation method based on the node structure feature data corresponding to the grid where the P point is located.

2. The simulation data processing system for multiple processes according to claim 1, characterized by, The specific form of the stop iteration condition is as follows: wherein X N+1 , X N , X N-1 are the coordinates of the P point obtained in the N+1th iteration, Nth iteration, N-1th iteration, respectively.

3. The simulation data processing system for multiple processes according to claim 1 or 2, characterized by, The system further includes a previous process finite element simulation result effective information extraction module, which acquires the finite element grid element data of the thin-walled complex component structure based on the Python data processing method.

4. The simulation data processing system for multiple processes according to claim 3, characterized by, The process of acquiring the finite element grid element data of the thin-walled complex component structure based on the Python data processing method by the previous process finite element simulation result effective information extraction module includes the following steps: In the object-oriented way, using the FEniCS library in python, using the Mesh function to load the model file, get the grid element data of the thin-walled complex component structure, any grid element E k The data includes node data and structure characteristic data, the node data includes node number data N ki and node coordinate data, the structure characteristic data includes temperature field data T ki , node displacement data D ki , stress data σ ki ; k refers to a certain element, and i refers to a certain node in the element.

5. The simulation data processing system for multiple processes according to claim 3, wherein, The system further includes a high-precision operation module of the finite element simulation model of the subsequent process, which takes the processing result of the grid reconstruction and node and structure feature transmission module as the initial value of the subsequent process, that is, the initial condition setting of the finite element model of the subsequent process is completed, and then the boundary condition setting is continued based on the finite element simulation model, and then the finite element calculation of the subsequent process is performed.

6. A computer storage medium, characterized in that The storage medium stores at least one instruction, and the at least one instruction is loaded and run by the processor to execute the simulation data processing system for multiple processes according to any one of claims 1 to 5.

7. A simulation data processing apparatus for a plurality of processes, characterized by comprising: The device includes a processor and a memory, and the memory stores at least one instruction, and the at least one instruction is loaded and run by the processor to execute the simulation data processing system for multiple processes according to any one of claims 1 to 5.

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