Calculation and parallel method and system of geometrically required dislocation density based on reduced integration

Abstract

The present invention discloses a method and system for calculating and parallelizing geometrically required dislocation density based on reduced integration. The present invention obtains the topological structure between mesh units and nodes in a finite element model through pre-processing, and then calculates the values ​​of physical quantities on physical nodes, thereby obtaining more physical information on nodes in the reduced integration unit for calculating gradients, curls, and geometrically required dislocation densities using a shape function method. At the same time, the present invention combines a method for writing external addresses in parallel with multiple processors to achieve communication and calculations in the parallel computing process between multiple processors. The present invention can calculate the values ​​and distribution characteristics of geometrically required dislocation density in a high-quality and accurate manner, which is of great significance for understanding the deformation behavior of materials.

Description

Technical Field

[0001] The invention belongs to the field of crystal plasticity simulation, and in particular relates to a geometrically required dislocation density calculation and parallel method and system based on reduced integration. Background Art

[0002] Crystal plasticity simulation is an important method to study the microscopic deformation behavior of materials such as metals and alloys under external loads. It is of great significance to reveal the basic mechanism of plastic deformation, optimize material properties and guide engineering design. In the process of metal deformation, geometrically necessary dislocations (GNDs) often play an important role. Geometrically necessary dislocations are a type of dislocations caused by lattice rotation and local strain gradients during plastic deformation. Their distribution and evolution directly affect the hardening behavior, size effect and localized deformation phenomenon of the material. By accurately calculating the geometrically necessary dislocations, the dislocation density distribution inside the material can be effectively described and the microscopic strain characteristics can be reflected. This is of great significance for understanding the relationship between material microstructure and mechanical properties and further optimizing material design.

[0003] In crystal plasticity simulation, large deformations are usually accompanied by highly nonlinear stress-strain fields. In order to ensure calculation and avoid numerical ill-posed problems, reduced integration techniques are widely used in the finite element method. Reduced integration reduces the complexity of element stiffness matrix calculation by reducing the number of integration points, thereby effectively avoiding problems such as volume locking and shear locking, and shows extremely high efficiency in engineering applications. However, a significant disadvantage of this technology is that it weakens the unit's ability to respond to local strain gradients. Since the generation of geometrically required dislocations is closely related to strain gradients, reduced integration cannot capture these key local changes, resulting in the inability to calculate geometrically required dislocations.

[0004] Therefore, how to solve the efficient and accurate calculation of geometrically required dislocations in crystal plasticity simulation is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the prior art and provide a method and system for calculating and parallelizing geometrically required dislocation density based on reduced integration.

[0006] The specific technical solutions adopted by the present invention are as follows:

[0007] In a first aspect, the present invention provides a geometrically required dislocation density calculation and parallel method based on reduced integration, which comprises:

[0008] S1. Based on the geometric model of the target material, a crystal plasticity finite element model is established in ABAQUS software, and the mesh units are divided using reduced integration units. After the modeling is completed, a complete description file of the model is output;

[0009] S2. Parse the complete description file of the model, determine the grid unit to which each physical node belongs according to the spatial topological relationship between the physical node and the grid unit, and construct two files, wherein the first file records the grid unit to which each physical node belongs, and the second file records all the physical nodes contained in each grid unit;

[0010] S3. On a multi-processor server, use ABAQUS software to call the user subroutine in parallel, and use each grid unit as an integration node, and assign all integration nodes to different processors for crystal plasticity finite element simulation calculations. Each processor needs to read the model complete description file, the first file, and the second file when calling the user subroutine for the first time. Each processor obtains the plastic deformation gradient tensor assigned to each integration node of itself through simulation calculation and records it in the third file.

[0011] S4, after each processor completes the crystal plasticity finite element simulation calculations of all the integral nodes assigned to itself, it outputs the third file generated by itself to the designated external address;

[0012] S5. After all processors have completed outputting the third file, each processor reads the third file generated by all processors from the specified external address, and integrates the plastic deformation gradient tensors of all integration nodes to generate a crystal plasticity calculation result that records the plastic deformation gradient tensors of all integration nodes;

[0013] S6. Each processor determines all mesh units to which each physical node belongs according to the information recorded in the first file for all integration nodes in the crystal plasticity finite element model, and calculates the average value of the plastic deformation gradient tensors of these mesh units as the plastic deformation gradient tensor corresponding to the physical node;

[0014] S7, each processor determines all physical nodes contained in each grid unit according to the information recorded in the second file for all integration nodes in the crystal plasticity finite element model, and calculates the gradient and curl at the position of the integration node using the shape function method based on the plastic deformation gradient tensor of these physical nodes;

[0015] S8. Each processor calculates the geometrically required dislocation density of each integration node assigned to it based on the calculated curl and uses the Penrose generalized inverse matrix to output the result.

[0016] As a preferred embodiment of the first aspect above, in S1, the geometric model is constructed based on backscattered electron diffraction data of the target material, and after the modeling is completed, the output model complete description file is an INP file.

[0017] As a preferred embodiment of the above-mentioned first aspect, in S2, the topological relationship part of the crystal plasticity finite element model needs to be extracted from the complete description file of the model, and the number of physical nodes and the number of grid units contained in the model need to be determined, and then two first files and second files are established, both of which record information in matrix form; each row in the first file corresponds to a physical node, and the index number of the grid unit to which this physical node belongs is recorded through different columns in the row; each row in the second file corresponds to a grid unit, and the index number of the physical node contained in this grid unit is recorded through different columns in the row.

[0018] As a preferred embodiment of the above-mentioned first aspect, in S3, when the ABAQUS software is run on the server, it is set to parallel mode, and the ABAQUS software uses all the grid units divided in the finite element model as integration nodes, allocates all the integration nodes to different processors, and performs crystal plasticity finite element simulation calculations in parallel; each processor in the server independently calls the VEXTERNALDB subroutine and the VUMAT subroutine, and reads in the complete model description file, the first file and the second file, and performs crystal plasticity finite element simulation calculations on all the integration nodes assigned to itself; after each processor completes the crystal plasticity finite element simulation calculations for all the integration nodes assigned to itself, it obtains the plastic deformation gradient tensor corresponding to each of these integration nodes, and records it in the third file; the third file finally output by each processor records information in the form of a matrix, each row of the matrix corresponds to an integration node, the number of columns of the matrix is ​​equal to the number of gradient components of the plastic deformation gradient tensor, the plastic deformation gradient tensors of all the integration nodes assigned to the processor itself are recorded in the corresponding matrix rows, and the rows corresponding to other integration nodes in the matrix are filled with 0 values.

[0019] As a preferred embodiment of the first aspect, in S4, the third file generated by each processor is output to different addresses of the internal memory or external memory respectively, and is stored according to a file name with the processor number as an identifier.

[0020] As a preferred embodiment of the above-mentioned first aspect, in S5, after each processor reads the third file generated by all processors from the specified external address, matrix addition operation is performed on the matrices recorded in all the third files in a matrix summation manner to obtain the crystal plasticity calculation result of the plastic deformation gradient tensor of all integration nodes recorded in matrix form.

[0021] As a preferred embodiment of the first aspect, S6, S7 and S8 are all executed in the VEXTERNALDB subroutine of ABAQUS software, and all processors on the server calculate the geometrically required dislocation density for all integration nodes assigned to themselves in parallel.

[0022] In a second aspect, the present invention provides a geometrically required dislocation density calculation and parallel processing system based on reduced integration, which comprises:

[0023] Finite element modeling module, which is used to establish a crystal plasticity finite element model in ABAQUS software based on the geometric model of the target material, and the mesh units are divided using reduced integration units. After the modeling is completed, a complete description file of the model is output;

[0024] A topological relationship parsing module is used to parse the complete description file of the model, determine the grid unit to which each physical node belongs according to the spatial topological relationship between the physical node and the grid unit, and construct two files, wherein the first file records the grid unit to which each physical node belongs, and the second file records all the physical nodes contained in each grid unit;

[0025] A simulation calculation module is used to call the user subroutine in parallel using ABAQUS software on a multi-processor server, and use each grid unit as an integration node, and assign all the integration nodes to different processors for crystal plasticity finite element simulation calculations. Each processor needs to read the model complete description file, the first file, and the second file when calling the user subroutine for the first time, and each processor obtains the plastic deformation gradient tensor assigned to each integration node of itself through simulation calculation and records it in the third file;

[0026] A parallel output module, used for outputting a third file generated by itself to a designated external address after each processor completes the crystal plasticity finite element simulation calculation of all the integral nodes assigned to itself;

[0027] A result integration module, which is used for each processor to read the third file generated by all processors from a specified external address after all processors have completed the output of the third file, and integrate the plastic deformation gradient tensors of all integration nodes to generate a crystal plasticity calculation result recording the plastic deformation gradient tensors of all integration nodes;

[0028] A physical node calculation module, used for each processor to determine all mesh units to which each physical node belongs according to the information recorded in the first file for all integration nodes in the crystal plasticity finite element model, and to calculate the average value of the plastic deformation gradient tensors of these mesh units as the plastic deformation gradient tensor corresponding to the physical node;

[0029] A gradient and curl calculation module, for each processor to determine all physical nodes contained in each grid unit according to the information recorded in the second file for all integration nodes in the crystal plasticity finite element model, and to calculate the gradient and curl at the position of the integration node using a shape function method based on the plastic deformation gradient tensor of these physical nodes;

[0030] The geometrically required dislocation density calculation module is used for each processor to calculate the geometrically required dislocation density of each integration node based on the calculated curl and use the Penrose generalized inverse matrix to output the result for all the integration nodes assigned to it.

[0031] In a third aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, can implement the geometrically required dislocation density calculation and parallel method based on reduced integration as described in any one of the first aspects above.

[0032] In a fourth aspect, the present invention provides a computer electronic device comprising a memory and a processor;

[0033] The memory is used to store computer programs;

[0034] The processor is used to implement the geometrically required dislocation density calculation and parallel method based on reduced integration as described in any one of the first aspects above when executing the computer program.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention achieves efficient parallel calculation of geometrically required dislocation density in crystal plasticity simulation by obtaining the topological results of finite elements and interpolating on physical nodes in combination with shape function methods. Since reduced integration units are often used in large deformation simulations, but the number of integration nodes in the reduced integration units is too small to calculate the gradient, traditional methods cannot calculate the geometrically required dislocation density in large deformation simulations. Geometrically required dislocations have an important influence on the deformation behavior of materials in large deformation problems. This method is of great significance for accurately calculating the numerical value and distribution characteristics of geometrically required dislocation density and understanding the deformation behavior of materials. This method also has the advantages of strong versatility, parallel calculation, and high computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the steps of geometrically required dislocation density calculation and parallel method based on reduced integration;

[0038] Figure 2 Schematic diagram of the module composition of the geometrically required dislocation density calculation and parallel processing system based on reduced integration.

[0039] Figure 3 A schematic diagram of the components of computer electronic equipment;

[0040] Figure 4 It is a flowchart of the implementation of the geometrically required dislocation density calculation and parallel method based on reduced integration in an embodiment of the present invention;

[0041] Figure 5 A schematic diagram of an exemplary topological relationship between physical nodes and grid units and calculation of tensor mean values ​​on physical nodes in an embodiment of the present invention;

[0042] Figure 6 A schematic diagram of communication interaction between multiple processors in a parallel mode according to an embodiment of the present invention;

[0043] Figure 7 A schematic diagram of the result of calculating the nearest neighbor mean using the nearest neighbor averaging method in an embodiment of the present invention;

[0044] Figure 8 This is a graph showing the calculation results of the geometrically required dislocation density in the composite material. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0046] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for the purpose of distinguishing descriptions, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0047] The core inventive concept of the present invention is to obtain the topological structure between units and nodes in the finite element model through pre-processing and then calculate the numerical value of the physical quantity on the physical node, so as to obtain more physical information on the nodes in the reduced integration unit for calculating the derivative by the shape function method. Combined with the method of parallel writing to the external address, parallel communication and calculation can be realized.

[0048] In a preferred embodiment of the present invention, a geometrically required dislocation density calculation and parallel method based on reduced integration is provided, which comprises:

[0049] S1. Based on the geometric model of the target material, a crystal plasticity finite element (CPFE) model is established in ABAQUS software, and the divided grid units adopt reduced integration units. After the modeling is completed, the complete description file of the model is output.

[0050] It should be noted that the establishment of a CPFE model in ABAQUS software belongs to the prior art, which can be achieved by importing a pre-built geometric model for the target material, and then by defining material parameters, assembling, loading, dividing the mesh and other operations. For this, please refer to the practice in the prior art and will not be repeated. Based on the established CPFE model, combined with the powerful pre- and post-processing functions and efficient solver of ABAQUS, users can easily establish complex polycrystalline models, perform crystal plastic finite element analysis, and accurately simulate the plastic deformation behavior of materials. In an embodiment of the present invention, the above-mentioned geometric model is constructed based on the backscattered electron diffraction (Electron Back Scatter Diffraction, EBSD) data of the target material using a script to extract the contour. In addition, according to the general settings of ABAQUS software, the complete description file of the model output after modeling can use an INP file. The INP file is an analysis input file that contains a complete description of the entire model, which enables users to flexibly transfer and modify the data of the finite element model between the preprocessor and the solver.

[0051] S2. Parse the complete description file of the model, determine the grid unit to which each physical node belongs according to the spatial topological relationship between the physical nodes and the grid units, and construct two files, wherein the first file records the grid unit to which each physical node belongs, and the second file records all the physical nodes contained in each grid unit.

[0052] In an embodiment of the present invention, in the process of constructing the first file and the second file, it is necessary to first locate and extract the topological relationship part of the crystal plasticity finite element model from the model complete description file (INP file), and determine the number of physical nodes and the number of grid units contained in the model, and then establish two first files and second files that both record information in the form of matrices. The number of rows in the above-mentioned first file is equal to the number of physical nodes contained in the model, each row corresponds to a physical node, and the index number of the grid unit to which this physical node belongs is recorded in different columns in the row; the number of rows in the above-mentioned second file is equal to the number of grid units contained in the model, each row corresponds to a grid unit, and the index number of the physical node contained in this grid unit is recorded in different columns in the row.

[0053] In an embodiment of the present invention, referring to the general output format of ABAQUS software, the first file and the second file can be output as DAT files for subsequent reading by user subroutines.

[0054] S3. On a multi-processor server, use ABAQUS software to call the user subroutine in parallel, and use each grid unit as an integration node. All integration nodes are assigned to different processors for crystal plasticity finite element simulation calculations. Each processor needs to read the model complete description file, the first file, and the second file when calling the user subroutine for the first time. Each processor obtains the plastic deformation gradient tensor assigned to each integration node of itself through simulation calculation and records it in the third file.

[0055] It should be noted that, since the present invention uses reduced integral units to divide the grid when constructing the CPFE model. The present invention is directed to the VEXTERNALDB explicit dynamics module of ABAQUS software (which is suitable for the simulation of complex interactions, large deformations and failure processes). Since the explicit dynamics module generally does not support high-order units, and the grid is reasonably divided by adjusting the size, the CPS4R unit (plane stress reduction unit), CPE4R unit (plane strain reduction unit) and C3D8R unit (three-dimensional hexahedral reduction unit) are sufficient to simulate most situations, so the present invention only needs to consider the above three units. The above three units each contain only one integral node, so the integral nodes in the present invention correspond one to one with the grid units, and the index number of the integral node can be regarded as the index number of the grid unit, and the integral node and the unit can be directly indexed by the serial number.

[0056] It should be noted that the parallelization of the ABAQUS software to call the user subroutine on a multi-processor server can be achieved by selecting the parallel option in the function interface of the ABAQUS software itself. This is a function of the ABAQUS software itself, and the software will automatically allocate all integration nodes to different processors. However, the parallel processing operation directly performed by the ABAQUS software cannot obtain the plastic deformation gradient tensor of all integration nodes in the global scope on a single processor, so the present invention needs to optimize it through subsequent steps.

[0057] In an embodiment of the present invention, the process of performing crystal plasticity finite element simulation calculation in parallel and generating the third file in the above-mentioned step S3 is as follows: running ABAQUS software on a multi-processor server and setting the software to parallel mode, ABAQUS software uses all mesh units divided in the finite element model as integration nodes, distributes all integration nodes to different processors, and performs crystal plasticity finite element simulation calculation in parallel; each processor in the server independently calls the VEXTERNALDB subroutine and the VUMAT subroutine, and reads in the complete model description file, the first file and the second file (which can be executed by the VEXTERNALDB subroutine), and performs crystal plasticity finite element simulation calculation on all integration nodes assigned to itself (which can be executed by the VUMAT subroutine); after each processor completes the crystal plasticity finite element simulation calculation for all integration nodes assigned to itself, it obtains the plastic deformation gradient tensor F corresponding to each of these integration nodes p , and record it in the third file. The third file finally output by each processor records information in the form of a matrix, each row of the matrix corresponds to an integral node, the number of columns of the matrix is ​​equal to the number of gradient components of the plastic deformation gradient tensor, and the plastic deformation gradient tensors of all integral nodes assigned to the processor itself are recorded in the corresponding matrix rows, while the rows corresponding to other integral nodes in the matrix are filled with 0 values.

[0058] In an embodiment of the present invention, the plastic deformation gradient tensor F p It is a 3×3 tensor, so the number of columns of the matrix is ​​equal to 9. The 3×3 plastic deformation gradient tensor is flattened into a one-dimensional vector and recorded in one row of the matrix.

[0059] S4. After each processor completes the crystal plasticity finite element simulation calculations of all the integral nodes assigned to itself, it outputs the third file generated by itself to the designated external address.

[0060] It should be noted that the third file generated by each processor can be executed by the VUMAT subroutine when output, and its output position can be output to either the memory address or the external memory address. However, since different processors will output corresponding third files, in order to avoid conflicts, these third files need to be output to different addresses of the internal memory or external memory, and stored according to the file name with the processor number as the identifier. Later, when it is necessary to read these third files, it can also be convenient to identify which processor it is generated and output by according to the identifier in the file name.

[0061] S5. After all processors have completed the output of the third file, each processor reads the third file generated by all processors from the specified external address (which can be executed by the VUMAT subroutine), and integrates the plastic deformation gradient tensors of all integration nodes to generate a crystal plasticity calculation result that records the plastic deformation gradient tensors of all integration nodes.

[0062] It should be noted that the third files of different formats need to be integrated according to their respective formats when integrated to ensure that the final calculation result contains the plastic deformation gradient tensors of all integration nodes. In an embodiment of the present invention, since the third file records information in the form of a matrix, each row of the matrix corresponds to an integration node, but only the plastic deformation gradient tensors of all integration nodes assigned to the processor itself are recorded in the corresponding matrix row, and the rows corresponding to other integration nodes in the matrix are filled with 0 values. Therefore, when integrating such third files, it can be directly implemented by matrix summation. The specific method is: after each processor can read the third files generated by all processors from the specified external address, it performs matrix addition operations on the matrices recorded in all third files in the matrix summation method to obtain the crystal plasticity calculation results that record the plastic deformation gradient tensors of all integration nodes in the form of a matrix.

[0063] S6. Each processor determines all mesh units to which each physical node belongs according to the information recorded in the first file for all integration nodes in the crystal plasticity finite element model, and calculates the average value of the plastic deformation gradient tensors of these mesh units as the plastic deformation gradient tensor corresponding to this physical node.

[0064] It should be noted that, since a physical node may belong to multiple mesh units, when calculating the plastic deformation gradient tensor of each physical node, the above S6 of the present invention adopts a neighbor averaging method, that is, it is necessary to first search for all mesh units described in the physical node according to the first file, and these mesh units are regarded as neighbor units of this physical node, and then the plastic deformation gradient tensors of these neighbor units are averaged, and this average value is the plastic deformation gradient tensor of the physical node. Since the present invention adopts reduced integration units and targets the CPS4R unit (plane stress reduction unit), CPE4R unit (plane strain reduction unit) and C3D8R unit (three-dimensional hexahedral reduction unit) in ABAQUS explicit dynamics, the plastic deformation gradient tensor of the mesh unit is the plastic deformation gradient tensor of the only integral node contained in the unit. The calculation formula of the plastic deformation gradient tensor of the physical node can be expressed as:

[0065]

[0066] in Represents the value of one of the components of the plastic deformation gradient tensor of the i-th physical node, Represents one of the component values ​​of the plastic deformation gradient tensor corresponding to the only integral node in the j-th grid unit (i.e., the neighboring unit) to which the i-th physical node belongs, Represents the total number of units belonging to the i-th physical node.

[0067] It should be noted that the above formula is for the 3×3 plastic deformation gradient tensor F p The single component of is calculated, so the plastic deformation gradient tensor F at the jth integration node is p Each of the 9 components needs to be calculated , which can be finally combined to obtain The average plastic deformation gradient tensor of the grid cells is the plastic deformation gradient tensor of the i-th physical node.

[0068] S7. Each processor determines all the physical nodes contained in each grid unit according to the information recorded in the second file for all the integration nodes in the crystal plasticity finite element model, and calculates the gradient and curl at the integration node position using the shape function method based on the plastic deformation gradient tensor of these physical nodes.

[0069] It should be noted that the process of using the shape function method to calculate the gradient and curl at the position of the integral node belongs to the prior art and can be implemented with reference to the prior art. When calculating the gradient of an integral node, the corresponding grid unit index number can be determined according to the index number of the integral node. Each grid unit is composed of a series of physical nodes. The physical nodes contained in this grid unit can be determined from the information recorded in the second file. Each physical node has calculated a 3×3 plastic deformation gradient tensor. Therefore, assuming that the total number of physical nodes of the grid unit is n, the dimension of the plastic deformation gradient tensor on all physical nodes constituting the grid unit is n×3×3. The shape function method can be used to calculate the gradient of the plastic deformation gradient tensor at the position of the integral node inside the grid unit (the gradient is a 3×3×3 tensor), and the curl corresponding to this integral node can be further calculated based on the Levi-Civita symbol (a 3×3 tensor).

[0070] S8. Each processor calculates the geometrically required dislocation density of each integration node assigned to it based on the calculated curl and uses the Penrose generalized inverse matrix to output the result.

[0071] It should be noted that using the Penrose generalized inverse matrix to calculate the geometrically required dislocation density of each integration node also belongs to the prior art. In the aforementioned step S7, each processor has already calculated the curl in the form of a 3×3 tensor for all the integration nodes assigned to itself, so it can obtain the direction matrix passed by the VUMAT subroutine on all the integration nodes assigned to itself, and calculate its Penrose generalized inverse matrix, and then use this 3×3 tensor curl and the Penrose generalized inverse matrix to calculate the geometrically required dislocation density of the integration node.

[0072] It should be noted that the above three steps S6, S7 and S8 can all be executed in the VEXTERNALDB subroutine of ABAQUS software, and the execution of these three steps is also parallel for all processors on the server, that is, different processors calculate the geometric required dislocation density for all integral nodes assigned to themselves in parallel and output the results, thereby speeding up the processing efficiency. However, the parallel processing methods in these three steps are slightly different. Since the geometric required dislocation density of a single integral node may need to rely on the information of other nodes and units, in step S6, each processor needs to calculate the plastic deformation gradient tensor corresponding to all physical nodes in the crystal plasticity finite element model; in step S7, each processor needs to calculate the gradient corresponding to all integral nodes in the crystal plasticity finite element model, and the curl only needs to be calculated for all integral nodes assigned to itself (of course, the curl of all integral nodes in the global calculation model is also possible, but it will reduce the calculation efficiency); but in step S8, each processor only needs to calculate the geometric required dislocation density for all integral nodes assigned to itself and output the results, without calculating the geometric required dislocation density for integral nodes not assigned to itself.

[0073] In addition, it should be noted that in the geometrically required dislocation density calculation and parallel method based on reduced integration described in the above embodiment, each step S1 to S8 can be implemented in the form of program code.

[0074] Therefore, based on the same inventive concept, another preferred embodiment of the present invention also provides a geometrically required dislocation density calculation and parallel processing system based on reduced integration corresponding to the geometrically required dislocation density calculation and parallel method based on reduced integration provided in the above embodiment, such as Figure 2 As shown, the system includes:

[0075] Finite element modeling module, which is used to establish a crystal plasticity finite element model in ABAQUS software based on the geometric model of the target material, and the mesh units are divided using reduced integration units. After the modeling is completed, a complete description file of the model is output;

[0076] A topological relationship parsing module is used to parse the complete description file of the model, determine the grid unit to which each physical node belongs according to the spatial topological relationship between the physical node and the grid unit, and construct two files, wherein the first file records the grid unit to which each physical node belongs, and the second file records all the physical nodes contained in each grid unit;

[0077] A simulation calculation module is used to call the user subroutine in parallel using ABAQUS software on a multi-processor server, and use each grid unit as an integration node, and assign all the integration nodes to different processors for crystal plasticity finite element simulation calculations. Each processor needs to read the model complete description file, the first file, and the second file when calling the user subroutine for the first time, and each processor obtains the plastic deformation gradient tensor assigned to each integration node of itself through simulation calculation and records it in the third file;

[0078] A parallel output module, used for outputting the third file generated by the VUMAT subroutine to a designated external address after each processor completes the crystal plasticity finite element simulation calculations of all the integral nodes assigned to itself;

[0079] A result integration module, which is used for each processor to read the third file generated by all processors from a specified external address after all processors have completed the output of the third file, and integrate the plastic deformation gradient tensors of all integration nodes to generate a crystal plasticity calculation result recording the plastic deformation gradient tensors of all integration nodes;

[0080] A physical node calculation module, used for each processor to determine all mesh units to which each physical node belongs according to the information recorded in the first file for all integration nodes in the crystal plasticity finite element model, and to calculate the average value of the plastic deformation gradient tensors of these mesh units as the plastic deformation gradient tensor corresponding to the physical node;

[0081] A gradient and curl calculation module, for each processor to determine all physical nodes contained in each grid unit according to the information recorded in the second file for all integration nodes in the crystal plasticity finite element model, and to calculate the gradient and curl at the position of the integration node using a shape function method based on the plastic deformation gradient tensor of these physical nodes;

[0082] The geometrically required dislocation density calculation module is used for each processor to calculate the geometrically required dislocation density of each integration node based on the calculated curl and use the Penrose generalized inverse matrix to output the result for all the integration nodes assigned to it.

[0083] The specific implementation method of each functional module in the above system is the same as the corresponding method steps in the above embodiment, and will not be repeated here.

[0084] Similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer electronic device corresponding to the geometrically required dislocation density calculation and parallel method based on reduced integration provided in the above embodiment, such as Figure 3 As shown, the computer electronic device includes a memory and a processor;

[0085] The memory is used to store computer programs;

[0086] The processor is used to implement the geometrically required dislocation density calculation and parallel method based on reduced integration as described above when executing the computer program.

[0087] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0088] Therefore, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer-readable storage medium corresponding to the geometrically required dislocation density calculation and parallel method based on reduced integration provided in the above embodiment, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it can implement the geometrically required dislocation density calculation and parallel method based on reduced integration as described above.

[0089] It is understandable that the above storage medium and memory may be random access memory (RAM) or non-volatile memory (NVM), such as at least one disk memory. The storage medium may also be a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc., which can store program codes.

[0090] It can be understood that the above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0091] It should also be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In each embodiment provided in this application, the division of steps or modules in the device and method is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or steps can be combined or integrated together, and a module or step can also be split.

[0092] The present invention will further demonstrate the detailed implementation process and technical effects of the geometrically required dislocation density calculation and parallel method based on reduced integration shown in the above steps S1 to S8 in a specific example through a specific embodiment, so as to facilitate understanding of the essence of the present invention.

[0093] Example

[0094] In the embodiment, the specific implementation process of the geometrically required dislocation density calculation and parallel method based on reduced integration is as follows: Figure 4 As shown (part of the information can be stored in the global variable common block during the calculation process), the specific steps are as follows:

[0095] Step (1): Based on the geometric model of the target material, a crystal plasticity finite element model is established in ABAQUS software, and the mesh units are divided using reduced integration units. After the modeling is completed, a complete model description file in INP format is output, which specifically includes the following sub-steps:

[0096] 1.a) Establish a geometric model in ABAQUS, where the geometric model can be realized by extracting the contour using a PYTHON script based on the EBSD data obtained from the EBSD experiment of the target material;

[0097] 1.b) Assign material parameters, assemble, load, and divide mesh units in ABAQUS software, select "reduced integration" as the mesh type, and output an INP file containing a complete description of the model;

[0098] Step (2), calculate the relationship between physical nodes and mesh units: read the INP file through MATLAB, traverse each physical node in the crystal plasticity finite element simulation, calculate the mesh unit containing the physical node and output it as a DAT file. The DAT file includes two matrix files, namely, matrix PhyNode2Element and matrix Element2PhyNode. The matrix PhyNode2Element is used to store the mesh unit to which each physical node belongs, and the matrix Element2PhyNode is used to store the physical nodes contained in each mesh unit. Specifically, it includes the following sub-steps:

[0099] 2.a) Read the INP file generated by ABAQUS based on MATLAB, locate the topological relationship part of the finite element model, and obtain the number of mesh elements numel and the number of physical nodes nPhyPoint contained in the finite element model;

[0100] 2.b) Save the physical node information contained in each grid unit read to the matrix Element2PhyNode. The number of rows of the matrix Element2PhyNode is the number of grid units numel contained in the finite element model. Each row corresponds to a grid unit and records the index number of the physical node contained in this grid unit. For the matrix PhyNode2Element, the number of rows is the number of physical nodes nPhyPoint contained in the finite element model, and the number of columns is the maximum number of grid units maxNbr to which a single physical node belongs. In this embodiment, maxNbr is set to 20. Each row in the matrix PhyNode2Element corresponds to a physical node. For the i-th physical node, all grid units are traversed. If the grid unit contains this physical node, the corresponding grid unit index number is added to the i-th row of the matrix PhyNode2Element. As a result, the grid unit to which each physical node belongs is recorded row by row in the finally generated matrix PhyNode2Element, and all physical nodes contained in each grid unit are recorded row by row in the matrix Element2PhyNode.

[0101] 2.c) Output the matrices Element2PhyNode and PhyNode2Element as DAT files for subsequent user subroutines to read.

[0102] Step (3), crystal plasticity simulation: On a multi-processor server, use ABAQUS software to call user subroutines (mainly VEXTERNALDB subroutines and VUMAT subroutines) in parallel, and use each grid unit as an integration node. ABAQUS software automatically distributes all integration nodes to different processors for crystal plasticity finite element simulation calculations. Each processor needs to read the INP file when calling the user subroutine for the first time, and also needs to read the DAT file to obtain the matrix PhyNode2Element and the matrix Element2PhyNode. Each processor obtains the plastic deformation gradient tensor F assigned to each integration node of itself through simulation calculation. p Information is stored in the matrix IntPnt_Fp. The process executed in each processor specifically includes the following:

[0103] 3.a) Read the DAT file generated by MATLAB in the VEXTERNALDB subroutine to obtain the matrix PhyNode2Element and the matrix Element2PhyNode;

[0104] 3.b) In the VUMAT subroutine, define numel as the total number of units (as mentioned above, the present invention adopts reduced integration and for the CPS4R / CPE4R / C3D8R units involved, the number of grid units is equal to the number of integration nodes), define a matrix IntPnt_Fp with numel rows and 9 columns, obtain the current integration node number, and convert the 3×3 plastic deformation gradient tensor F on the current integration node into p The component values ​​are stored row by row in the matrix IntPnt_Fp. For example, Fp(1,1) is stored in the first column of IntPnt_Fp, Fp(1,2) is stored in the second column of IntPnt_Fp, Fp(1,3) is stored in the third column of IntPnt_Fp, ... Fp(3,3) is stored in the ninth column of IntPnt_Fp. In the final matrix IntPnt_Fp of a processor, only the rows corresponding to the integration nodes allocated to the current processor store the plastic deformation gradient tensor F row by row. p The 9 component values ​​of , and the rows corresponding to the other integration nodes are filled with 0 values.

[0105] Step (4), output of crystal plasticity calculation results: In each processor, in the VUMAT subroutine, each outputs the matrix IntPnt_Fp to a different external address. The matrices output by different processors are represented by IntPnt_Fp_i, where i represents the i-th processor, and specifically includes the following contents:

[0106] 4.a) In the VUMAT subroutine, the IntPnt_Fp matrix is ​​output as a DAT file. The IntPnt_Fp matrix file output by each processor is named with the processor number as the identifier, and the file name is "IntPnt_Fp_i.dat", where i represents the i-th processor.

[0107] 4.b) During the parallel execution of the subroutines, each processor only processes the calculations on the integral nodes assigned to the processor. As mentioned above, the IntPnt_Fp matrix is ​​defined in each processor, so the matrix IntPnt_Fp in the processor has only the row elements of the corresponding integral nodes that are not 0, and the other elements are all 0. Therefore, after the IntPnt_Fp assignment in each processor is completed, the matrix IntPnt_Fp is output row by row to different external addresses for storage.

[0108] Step (5), read and merge the results: Wait for all processors to finish outputting. The VUMAT subroutine in each processor needs to read the output matrices of all processors and sum them up, and store them in the matrix IntPnt_Fp_AllReduce. Specifically, it includes the following sub-steps:

[0109] 5.a) In the VEXTERNALDB subroutine, read the "IntPnt_Fp_i.dat" file and define the matrix IntPnt_Fp_AllReduce with numel rows and 9 columns;

[0110] 5.b) During the parallel execution of the subroutines, each processor will read the contents of the "IntPnt_Fp_i.dat" file and sum them up to obtain the matrix IntPnt_Fp_AllReduce. Since only the row elements of the corresponding integration node in the matrix IntPnt_Fp in each processor are not 0, and all other elements are 0, the matrix IntPnt_Fp_AllReduce stores the plastic velocity gradient information on all integration nodes. So far, the plastic velocity gradient information on all integration nodes is obtained in each processor, and the communication between different processors when executing subroutines in parallel is realized.

[0111] Step (6), calculate the neighbor average: In each processor, in the VEXTERNALDB subroutine, at a specified time, the unit number of the corresponding physical node is obtained based on the matrix PhyNode2Element, so as to calculate the average value of a physical quantity in all units containing the physical node on the physical node and store it in the matrix PhyNodes_Fp_byNbrAvg_AllReduce. The specific process of this step is as follows:

[0112] 6.a) Calculate the average value of a physical quantity in all units containing the physical node at the physical node (the value in the unit is the average of the values ​​on all integral nodes inside the unit. For the three types of units involved in the present invention, namely, CPS4R unit (plane stress reduction unit), CPE4R unit (plane strain reduction unit) and C3D8R unit (three-dimensional hexahedral reduction unit), the value in the unit is the value of the only integral node contained in the unit), and the formula is:

[0113]

[0114] in Represents the value on the i-th physical node, represents the component value at the jth integration node, Represents the number of units to which the ith physical node belongs. The plastic deformation gradient tensor F at the jth integration node p Each of the 9 components needs to be calculated .

[0115] 6.b) Traverse each physical node and store the average value calculated on each physical node into the matrix PhyNodes_Fp_byNbrAvg_AllReduce.

[0116] Figure 5 An exemplary schematic diagram is shown for obtaining the grid unit to which a physical node belongs through the topological structure between units and nodes and calculating the mean value on the physical node. Figure 5 (a) shows the topological relationship between the finite element unit and the physical node, where the units containing the i-th physical node are unit j1 to unit j5; (b) shows the process of calculating the neighbor average value on the i-th physical node: Taking the CPS4R unit as an example, there is only one integration node inside each unit, and units j1 to unit j5 correspond to integration nodes j1 to integration nodes j5 respectively. Based on the matrix PhyNode2Element, the plastic deformation gradient tensor F on each integration node can be indexed p Value and calculate F p The average value of F is shown in (c) which shows the principle process of calculating the gradient through the shape function inside the unit: traversing all physical nodes to calculate F p After averaging, in each unit, based on the F p The numerical and shape function interpolation methods can be used to calculate the gradient. The following describes in detail how to calculate the gradient and curl in the grid cell.

[0117] Step (7), calculate the gradient and curl: In each processor, in the VEXTERNALDB subroutine, obtain the physical node number contained in each unit based on the matrix Element2PhyNode, index the average value of the corresponding physical node in PhyNodes_Fp_byNbrAvg_AllReduce, and calculate the gradient and curl at the integral node position in the unit based on the method of using shape functions. The specific steps of this method are as follows:

[0118] 7.a) Calculate the gradient inside the unit and set the plastic deformation gradient tensor One component of ,variable The gradient is calculated as:

[0119]

[0120] in Represents one of the component values ​​of the plastic deformation gradient tensor of the i-th physical node contained in the grid unit, Represents the shape function corresponding to the i-th physical node contained in the grid unit, , , They represent the partial derivatives in the x, y, and z dimensions respectively, and M represents the number of physical nodes contained in the grid unit.

[0121] 7.b) Calculate the plastic deformation gradient tensor The curl of is given by:

[0122]

[0123] in is a curvature matrix with 3 rows and 3 columns, is the Nabla operator. The component form of the curl calculation formula is:

[0124]

[0125] in is the Levi-Civita symbol, Represents a two-dimensional tensor (i.e., a matrix) Middle Line Components ( and The value range of is [1,2,3], corresponding to the 3×3 plastic deformation gradient tensor F p 9 servings), . The matrix is ​​calculated by the method described in 7.a). The derivative of each component of Each component in is treated as a variable , through the above variables The gradient calculation formula is derived) and substituted into the component form of the above curl calculation formula to finally calculate the matrix The curl and curvature matrices of

[0126] Step (8), calculate the geometrically required dislocation density: In each processor, in the VEXTERNALDB subroutine, based on the calculated curl, use the Penrose generalized inverse matrix to calculate the geometrically required dislocation density, and output the result to the integration node. The specific process of this step is as follows:

[0127] 8.a) Calculate the curl of the plastic velocity gradient and correlate it with the dislocation density:

[0128]

[0129] in represents the plastic deformation gradient tensor, obtained from the crystal plasticity finite element simulation, represents the negative of the curl transpose of the plastic velocity gradient, represents the geometric required dislocation density of the screw component, tangential edge component, and normal edge component on the i-th slip system, represents the burger vector on the i-th slip system, The unit vectors representing the slip direction, tangent direction and normal direction of the dislocation in the i-th slip system. Represents the total number of slip systems.

[0130] 8.b) Vectorize the plastic velocity gradient curl matrix:

[0131]

[0132] in is a 3×3 matrix, for 9×1 vector formed by row expansion. To calculate the direction matrix of the geometrically required dislocation density, it can be calculated from the crystal orientation and Euler angles. It belongs to the existing method and can be directly calculated by the VUMAT subroutine and then passed to the subsequent geometrically required dislocation calculation module. is the dislocation density vector.

[0133] 8.c) Calculate the geometrically required dislocation density based on the Penrose generalized inverse matrix method:

[0134]

[0135] Figure 6The figure shows an exemplary diagram of the communication between subroutines, the communication between subroutines and external data, and the parallelization method in the process of calculating geometrically required dislocation density. During the first run, the VEXTERNALDB subroutine reads the finite element topology and neighbor relationship (i.e., the relationship between physical nodes and grid cells) calculated by MATLAB, and then performs crystal plasticity calculations. The VUMAT subroutine is executed in parallel, and tensors are calculated in each processor. The calculation results are stored in different memory areas. After all processors have completed the calculation, the VEXTERNALDB subroutine reads all the calculation results and integrates them to obtain the crystal plasticity calculation results that record the plastic deformation gradient tensors of all integral nodes, thereby realizing data communication between different processors. After obtaining this complete crystal plasticity calculation result, the VEXTERNALDB subroutine and the VUMAT subroutine also need to perform calculations on information such as gradient, curl, and geometric required dislocation density.

[0136] In order to demonstrate the technical effect of the method described in steps (1) to (8), the method was implemented on a machine equipped with a Gen Intel(R) Core(TM) i7-12700K CPU, and experiments were conducted on a variety of configurations and unit types. Figure 7 , Figure 8 The calculation results are shown.

[0137] Figure 7 According to the neighbor average method in step (6), the neighbor average is first calculated on the physical node, and then the neighbor average is calculated on the integration node according to the value on the physical node. The two-dimensional model and CPS4R unit are used. Figure 7 (a), (b), and (c) show the horizontal axis X, the vertical axis Y, and the plastic velocity gradient component respectively. The calculation results after the "integration node-physical node-integration node" method, (d), (e), and (f) show the horizontal coordinate X, vertical coordinate Y, and plastic velocity gradient components, respectively. Direct results on the integration nodes show that the nearest neighbor averaging method performs with good accuracy.

[0138] Figure 8 The calculation result diagram of the gradient in the composite material. This embodiment adopts a two-dimensional model, CPS4R unit, Figure 8 (a)-(h) show the plastic velocity gradient The distribution cloud diagram of the 9 components of the derivative shows good consistency between the calculated results and the literature and theory.

[0139] It can be seen that the results of this embodiment show that this method can be well applied to different crystal plasticity finite element models, and all show good efficiency, and can accurately calculate the derivative of the plastic deformation gradient tensor Fp and the geometrically required dislocation density .

[0140] In summary, the present invention realizes the parallel calculation of geometrically required dislocation density in the reduced integration unit, effectively describes the dislocation density distribution inside the material, reflects the microscopic strain characteristics and the interaction between grains, and is of great significance for understanding the relationship between material microstructure and mechanical properties and further optimizing material design.

[0141] The above-described embodiments are only some preferred implementations of the present invention, but are not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A geometrically required dislocation density calculation and parallel method based on reduced integration, characterized in that: include: S1. Based on the geometric model of the target material, a crystal plasticity finite element model is established in ABAQUS software, and the mesh units are divided using reduced integration units. After the modeling is completed, a complete description file of the model is output; S2. Parse the complete description file of the model, determine the grid unit to which each physical node belongs according to the spatial topological relationship between the physical node and the grid unit, and construct two files, wherein the first file records the grid unit to which each physical node belongs, and the second file records all the physical nodes contained in each grid unit; S3. On a multi-processor server, use ABAQUS software to call the user subroutine in parallel, and use each grid unit as an integration node, and assign all integration nodes to different processors for crystal plasticity finite element simulation calculations. Each processor needs to read the model complete description file, the first file, and the second file when calling the user subroutine for the first time. Each processor obtains the plastic deformation gradient tensor assigned to each integration node of itself through simulation calculation and records it in the third file. S4, after each processor completes the crystal plasticity finite element simulation calculations of all the integral nodes assigned to itself, it outputs the third file generated by itself to the designated external address; S5. After all processors have completed outputting the third file, each processor reads the third file generated by all processors from the specified external address, and integrates the plastic deformation gradient tensors of all integration nodes to generate a crystal plasticity calculation result that records the plastic deformation gradient tensors of all integration nodes; S6. Each processor determines all mesh units to which each physical node belongs according to the information recorded in the first file for all integration nodes in the crystal plasticity finite element model, and calculates the average value of the plastic deformation gradient tensors of these mesh units as the plastic deformation gradient tensor corresponding to the physical node; S7, each processor determines all physical nodes contained in each grid unit according to the information recorded in the second file for all integration nodes in the crystal plasticity finite element model, and calculates the gradient and curl at the position of the integration node using the shape function method based on the plastic deformation gradient tensor of these physical nodes; S8. Each processor calculates the geometrically required dislocation density of each integration node assigned to it based on the calculated curl and uses the Penrose generalized inverse matrix to output the result.

2. The geometrically required dislocation density calculation and parallel method based on reduced integration as claimed in claim 1, characterized in that: In S1, the geometric model is constructed based on backscattered electron diffraction data of the target material, and after the modeling is completed, the output model complete description file is an INP file.

3. The geometrically required dislocation density calculation and parallel method based on reduced integration as claimed in claim 1, characterized in that: In S2, it is necessary to extract the topological relationship part of the crystal plasticity finite element model from the complete description file of the model, and determine the number of physical nodes and the number of grid units contained in the model, and then establish two first files and second files that both record information in matrix form; each row in the first file corresponds to a physical node, and the index number of the grid unit to which this physical node belongs is recorded through different columns in the row; each row in the second file corresponds to a grid unit, and the index number of the physical node contained in this grid unit is recorded through different columns in the row.

4. The geometrically required dislocation density calculation and parallel method based on reduced integration as claimed in claim 1, characterized in that: In S3, when the ABAQUS software is run on the server, it is set to parallel mode. The ABAQUS software uses all the grid units divided in the finite element model as integration nodes, allocates all the integration nodes to different processors, and performs crystal plasticity finite element simulation calculations in parallel; each processor in the server independently calls the VEXTERNALDB subroutine and the VUMAT subroutine, and reads in the complete model description file, the first file and the second file, and performs crystal plasticity finite element simulation calculations on all the integration nodes assigned to itself; after each processor completes the crystal plasticity finite element simulation calculations for all the integration nodes assigned to itself, it obtains the plastic deformation gradient tensor corresponding to each of these integration nodes, and records it in the third file; the third file finally output by each processor records information in the form of a matrix, each row of the matrix corresponds to an integration node, the number of columns of the matrix is ​​equal to the number of gradient components of the plastic deformation gradient tensor, the plastic deformation gradient tensors of all the integration nodes assigned to the processor itself are recorded in the corresponding matrix rows, and the rows corresponding to other integration nodes in the matrix are filled with 0 values.

5. The geometrically required dislocation density calculation and parallel method based on reduced integration as claimed in claim 4, characterized in that: In S4, the third file generated by each processor is output to different addresses of the internal memory or external memory respectively, and is stored according to a file name with the processor number as an identifier.

6. The geometrically required dislocation density calculation and parallel method based on reduced integration as claimed in claim 5, characterized in that: In S5, after each processor reads the third file generated by all processors from the specified external address, matrix addition operation is performed on the matrices recorded in all the third files in a matrix summation manner to obtain the crystal plasticity calculation result of the plastic deformation gradient tensor of all integration nodes recorded in matrix form.

7. The geometrically required dislocation density calculation and parallel method based on reduced integration as claimed in claim 1, characterized in that: S6, S7 and S8 are all executed in the VEXTERNALDB subroutine of the ABAQUS software, and all processors on the server calculate the geometrically required dislocation density for all the integral nodes assigned to themselves in parallel.

8. A geometrically required dislocation density calculation and parallel processing system based on reduced integration, characterized in that: include: Finite element modeling module, which is used to establish a crystal plasticity finite element model in ABAQUS software based on the geometric model of the target material, and the mesh units are divided using reduced integration units. After the modeling is completed, a complete description file of the model is output; A topological relationship parsing module is used to parse the complete description file of the model, determine the grid unit to which each physical node belongs according to the spatial topological relationship between the physical node and the grid unit, and construct two files, wherein the first file records the grid unit to which each physical node belongs, and the second file records all the physical nodes contained in each grid unit; A simulation calculation module is used to call the user subroutine in parallel using ABAQUS software on a multi-processor server, and use each grid unit as an integration node, and assign all the integration nodes to different processors for crystal plasticity finite element simulation calculations. Each processor needs to read the model complete description file, the first file, and the second file when calling the user subroutine for the first time, and each processor obtains the plastic deformation gradient tensor assigned to each integration node of itself through simulation calculation and records it in the third file; A parallel output module, used for outputting a third file generated by itself to a designated external address after each processor completes the crystal plasticity finite element simulation calculation of all the integral nodes assigned to itself; A result integration module, which is used for each processor to read the third file generated by all processors from a specified external address after all processors have completed the output of the third file, and integrate the plastic deformation gradient tensors of all integration nodes to generate a crystal plasticity calculation result recording the plastic deformation gradient tensors of all integration nodes; A physical node calculation module, used for each processor to determine all mesh units to which each physical node belongs according to the information recorded in the first file for all integration nodes in the crystal plasticity finite element model, and to calculate the average value of the plastic deformation gradient tensors of these mesh units as the plastic deformation gradient tensor corresponding to the physical node; A gradient and curl calculation module, for each processor to determine all physical nodes contained in each grid unit according to the information recorded in the second file for all integration nodes in the crystal plasticity finite element model, and to calculate the gradient and curl at the position of the integration node using a shape function method based on the plastic deformation gradient tensor of these physical nodes; The geometrically required dislocation density calculation module is used for each processor to calculate the geometrically required dislocation density of each integration node based on the calculated curl and use the Penrose generalized inverse matrix to output the result for all the integration nodes assigned to it.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, it can implement the geometrically required dislocation density calculation and parallel method based on reduced integration as described in any one of claims 1 to 7.

10. A computer electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the geometrically required dislocation density calculation and parallel method based on reduced integration as described in any one of claims 1 to 7 when executing the computer program.

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