CN115099081A - Finite element grid dynamic optimization method and device suitable for underground mining - Google Patents
Finite element grid dynamic optimization method and device suitable for underground mining Download PDFInfo
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Abstract
The application discloses a finite element mesh dynamic optimization method and a device suitable for underground mining, wherein the method comprises the following steps: generating a grid optimization range by taking the poor grid as a center by identifying the poor grid in the finite element grid in the underground mining process; determining boundary grids in the optimization range, and subdividing grids in the optimization range except the boundary grids to generate optimization grids in the optimization range; the optimized grid is embedded into the finite element grid, new physical quantity is given to the optimized grid, and dynamic optimization of the grid is realized, so that the average quality of the grid in finite element calculation is dynamically improved, the calculation precision can meet the requirements of stability and robustness in the simulation process, and the accuracy and the reasonability of numerical simulation are improved. Therefore, the problems that poor grids appear in the numerical simulation process and numerical calculation is stopped due to serious deformation of the grids are solved.
Description
Technical Field
The application relates to the technical field of finite element mesh optimization, in particular to a dynamic finite element mesh optimization method and device suitable for underground mining.
Background
Because the underground mining activity often causes large deformation movement of rock strata, when the underground mining problem is analyzed by adopting a finite element method, the serious deformation of the grid can be caused by the large deformation of overlying strata caused by mining, and the poor grid can appear. Whereas finite element method performance depends on mesh quality. The poor grid not only can obviously reduce the calculation precision but also can bring about the numerical calculation problem in the process of participating in the simulation.
In recent years, grid optimization methods have been widely paid attention by scholars, and many optimization methods such as "FE-Meshfree" method, "new spline unit method," "overlapping finite element method," "mixed stress function unit method," "mixed EAS function unit method," and the like have been proposed.
However, in the related art, most grid optimization methods mainly use the calculation accuracy as an optimization target, and the obtained calculation accuracy does not necessarily meet the requirements of stability and robustness of the simulation process. Secondly, the currently proposed grid optimization method has limitations in terms of program portability, cannot be efficiently combined with finite element calculation software, is prone to poor grids and numerical calculation termination caused by serious deformation of the grids in a numerical simulation process, is difficult to improve grid quality, and accordingly influences the analysis of the movement law of the underground mining rock strata, and needs to be solved urgently.
Disclosure of Invention
The application provides a finite element grid dynamic optimization method, a finite element grid dynamic optimization device, electronic equipment and a storage medium, which are suitable for underground mining and are used for solving the problems that inferior grids occur in a numerical simulation process and numerical calculation is stopped due to serious deformation of the grids.
The embodiment of the first aspect of the application provides a method for dynamically optimizing a finite element mesh suitable for underground mining, which comprises the following steps: identifying poor grids in finite element grids in the underground mining process, and generating a grid optimization range by taking the poor grids as a center; determining a boundary grid in the optimization range, and subdividing grids in an internal area of the optimization range except the boundary grid to generate the optimization grid in the optimization range; embedding the optimized mesh into the finite element mesh, and giving new physical quantity to the optimized mesh to realize dynamic optimization of the mesh.
Optionally, in an embodiment of the present application, the generating a grid optimization range centered on the poor grid includes: and taking the body center of the inferior grid as a center, taking the length twice as long as the longest edge of the inferior grid as a radius to form a spherical area, and taking the spherical area as the optimization range of the grid.
Optionally, in an embodiment of the present application, the determining a boundary mesh within the optimization range includes: traversing all the triangular meshes in the optimization range, recording the occurrence frequency of each triangular mesh, and taking the triangular mesh with one occurrence frequency as the boundary mesh.
Optionally, in an embodiment of the present application, said embedding said optimization mesh in said finite element mesh includes: and merging the optimized grids into the finite element grids according to the topological connection relation, deleting repeated nodes in the finite element grids, reordering the rest nodes, changing the node numbers, and merging the mutually independent finite element grids into an integral grid.
Optionally, in an embodiment of the present application, the giving of the new physical quantity to the optimization grid includes: the physical quantity of the grid before optimization is equivalent to the grid node before optimization to obtain the equivalent unit physical quantity of the grid node before optimization; taking the equivalent unit physical quantity of the grid node before optimization as a known quantity, and carrying out interpolation calculation according to the position information of the optimization node to obtain the equivalent unit physical quantity of the optimization grid node; and distributing the equivalent unit physical quantities of the nodes of the optimized grid to the optimized grid, and averaging the equivalent unit physical quantities of all the nodes in each grid according to the node equivalent unit physical quantities corresponding to the optimized nodes to obtain new physical quantities of the optimized grid.
The embodiment of the second aspect of the present application provides a finite element mesh dynamic optimization device suitable for underground mining, comprising: the first generation module is used for identifying an inferior grid in a finite element grid in the underground mining process and generating a grid optimization range by taking the inferior grid as a center; the second generation module is used for determining the boundary grid in the optimization range, re-dividing the grid in the area outside the optimization range outside the boundary grid and generating the optimization grid in the optimization range; and the optimization module is used for embedding the optimization mesh into the finite element mesh, giving new physical quantity to the optimization mesh and realizing dynamic optimization of the mesh.
Optionally, in an embodiment of the application, the second generating module is specifically configured to traverse all the triangular meshes in the optimization range, record the occurrence frequency of each triangular mesh, and use a triangular mesh with one occurrence frequency as the boundary mesh.
Optionally, in an embodiment of the application, the optimizing module includes: a merging unit, configured to merge the optimized mesh into the finite element mesh according to a topological connection relationship, delete a duplicate node in the finite element mesh, reorder the remaining nodes, change the node numbers, and merge mutually independent finite element meshes into an integral mesh; the equivalent unit is used for equivalent the physical quantity of the grid before optimization to the grid node before optimization to obtain the physical quantity of the equivalent unit of the grid node before optimization; the computing unit is used for taking the equivalent unit physical quantity of the grid node before optimization as a known quantity, and carrying out interpolation computing to obtain the equivalent unit physical quantity of the optimized grid node according to the position information of the optimized node; and the distribution unit is used for distributing the equivalent unit physical quantities of the nodes of the optimized grid to the optimized grid, and averaging the equivalent unit physical quantities of all the nodes in each grid according to the node equivalent unit physical quantities corresponding to the optimized nodes to obtain new physical quantities of the optimized grid.
An embodiment of a third aspect of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to perform a method of dynamic optimization of a finite element mesh suitable for subterranean mining as described in the embodiments above.
A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to perform a method for dynamic optimization of a finite element mesh for underground mining as described in the previous embodiments.
Therefore, the embodiment of the application has the following beneficial effects:
the method comprises the steps of identifying poor grids in finite element grids in the underground mining process, and generating a grid optimization range by taking the poor grids as a center; determining boundary grids in the optimization range, and subdividing grids in the optimization range except the boundary grids to generate optimization grids in the optimization range; the optimized grid is embedded into the finite element grid, new physical quantity is given to the optimized grid, and dynamic optimization of the grid is realized, so that the average quality of the grid is greatly improved, the quality of inferior grids is improved, and the accuracy and the reasonability of numerical simulation are further improved. Therefore, the problems that poor grids appear in the numerical simulation process, numerical calculation is stopped due to serious deformation of the grids and the like are solved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method for dynamic optimization of a finite element mesh for underground mining according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a grid geometry calculation according to an embodiment of the present application;
FIG. 3 is a logic diagram illustrating an implementation of a method for dynamic optimization of a finite element mesh for underground mining according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a finite element mesh model for underground mining provided in accordance with an embodiment of the present application;
FIG. 5 is a schematic illustration of a during-mine faulty unit and optimization scope according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an optimization scope boundary grid provided in accordance with an embodiment of the present application;
FIG. 7 is a schematic view of a newly formed mesh provided according to an embodiment of the present application;
FIG. 8 is a diagram illustrating a merged mesh model according to an embodiment of the present application;
fig. 9 is a diagram illustrating Z-direction displacement results of a new mesh after assignment according to an embodiment of the present application;
FIG. 10 is an exemplary diagram of a finite element mesh dynamic optimization device suitable for underground mining according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Description of reference numerals: the device comprises a first generation module-100, a second generation module-200, an optimization module-300, a memory-1101, a processor-1102 and a communication interface-1103.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The method, the device, the electronic equipment and the storage medium for dynamically optimizing the finite element mesh suitable for underground mining according to the embodiment of the application are described below with reference to the accompanying drawings. In order to solve the problems mentioned in the background technology, the application provides a dynamic optimization method of a finite element mesh suitable for underground mining, wherein in the method, a mesh optimization range is generated by identifying poor meshes in the finite element mesh in the underground mining process and taking the poor meshes as centers; determining boundary grids in the optimization range, and subdividing grids in the optimization range except the boundary grids to generate optimization grids in the optimization range; the optimized grid is embedded into the finite element grid, new physical quantity is given to the optimized grid, and dynamic optimization of the grid is realized, so that the average quality of the grid is greatly improved, the quality of the inferior grid is improved, the accuracy and the rationality of numerical simulation are further improved, and the method has important significance for analyzing the movement rule of the underground mining rock stratum. Therefore, the problems that poor grids appear in the numerical simulation process and numerical calculation is stopped due to serious deformation of the grids are solved.
Specifically, fig. 1 is a flowchart of a method for dynamically optimizing a finite element mesh suitable for underground mining according to an embodiment of the present application.
As shown in fig. 1, the method for dynamically optimizing a finite element mesh suitable for underground mining comprises the following steps:
in step S101, a poor grid in the finite element grid of the underground mining process is identified, and a grid optimization range is generated with the poor grid as a center.
In the embodiment of the application, the optimization object of the finite element mesh dynamic optimization method suitable for underground mining is an inferior mesh which appears in the calculation process, the inferior mesh is likely to appear in an area with larger deformation of an overlying rock layer, and whether the inferior mesh is the inferior mesh can be judged by calculating the mesh quality because the inferior mesh is defined as the inferior mesh. Firstly, establishing an underground mining finite element grid calculation model, judging the grid quality in the excavation iterative calculation process, and determining the inferior grid by continuously calculating the grid quality in the iterative process.
It will be appreciated that the mesh quality is determined based on the mesh geometry and that the mesh quality does not change with changes in size, i.e. two differently sized, identically shaped meshes have the same mesh quality. Generally, grid quality values are normalized to 0-1, where "1" represents the best grid quality and "0" represents the worst grid quality. The grid quality as an execution condition of the dynamic optimization method can be determined by an existing grid quality calculation formula, and the calculation of the grid geometric size is shown in fig. 2.
Generally, there are three grid quality calculation formulas, and in practical applications, the minimum value of the three grid qualities is selected as the quality value of the grid.
Wherein V is the unit volume, S i Is the area of each side of the cell, /) ij Length of edge connecting vertices i and j, C d 1832.8208 is the proportionality constant used to maximize the cell quality metric.
Optionally, in an embodiment of the present application, generating a grid optimization range centered on the poor grid includes: the body center of the inferior grid is used as the center, the length twice as long as the longest edge of the inferior grid is used as the radius to form a spherical area, and the spherical area is used as the grid optimization range.
Specifically, after the poor-quality mesh is determined, aiming at a data structure form of a mesh model in a finite element method, the embodiment of the application takes a body center O of the poor-quality mesh as a center, 2 times of the length of the longest edge d of the mesh as a radius, a spherical region omega is formed, and the mesh inside the region omega is an optimization range.
It should be noted that in the specific implementation, the computational model in the finite element method can be represented by a conventional. ele mesh file and a. node file, all of which are stored in digital form by those skilled in the art.
In step S102, a boundary mesh in the optimization range is determined, and meshes are re-subdivided for an area inside the optimization range other than the boundary mesh, so as to generate an optimized mesh in the optimization range.
After identifying the poor meshes in the finite element meshes and generating the mesh optimization range, further, the boundary meshes in the optimization range need to be determined in the embodiment of the present application.
Optionally, in an embodiment of the present application, determining a boundary mesh within the optimization range includes: traversing all the triangular meshes in the optimization range, recording the occurrence frequency of each triangular mesh, and taking the triangular mesh with one occurrence frequency as a boundary mesh.
Specifically, the boundary mesh of the optimization range may be obtained from the triangle units constituting the mesh. The basic principle of acquisition is as follows: traversing all the triangular meshes in the optimization range, recording the occurrence frequency of each triangular mesh, if a certain triangular mesh only appears once, the triangular surface is a boundary surface, and after all the triangular meshes are circulated, obtaining the boundary mesh in the optimization range.
As an implementation manner, according to the principle, the embodiment of the application can write a relevant program by using open source software TetGen, so that a common outer boundary grid of a finite element grid model can be obtained.
The open source software TetGen is a C + + program for generating any three-dimensional polyhedral mesh and can be used for generating a high-quality mesh suitable for finite element numerical simulation. In the open source software TetGen interface, a related command line can be called, and the generated off file is imported into a TetGen program, so that a high-quality computing grid is generated, and the aims of eliminating the poor grid and improving the average quality of the grid are fulfilled.
In step S103, the optimization mesh is embedded in the finite element mesh, and new physical quantities are given to the optimization mesh, thereby implementing mesh dynamic optimization.
It can be understood that, in order to merge the original independent finite element meshes into an integral mesh, after the high-quality mesh is generated, the new mesh needs to be merged with the original mesh according to the topological relation.
Specifically, in one embodiment of the present application, embedding an optimization mesh in a finite element mesh comprises: the optimized mesh is merged into the finite element mesh according to the topological connection relation, nodes at the boundary mesh are repeated in the merging process, and the existence of the repeated nodes can cause errors in solving the stiffness matrix. Therefore, the repeated nodes in the model are judged and deleted, the rest nodes are reordered, the numbers of the nodes are changed, and the original mutually independent finite element grids are combined into a whole grid.
After the mesh merging is completed, the position and the topological relation of partial meshes are changed. Therefore, the new mesh must be given physical quantities.
Optionally, in an embodiment of the present application, the assigning of the new physical quantity to the optimization grid includes: the physical quantity of the grid before optimization is equivalent to the grid node before optimization to obtain the equivalent unit physical quantity of the grid node before optimization; taking the equivalent unit physical quantity of the grid node before optimization as a known quantity, and carrying out interpolation calculation according to the position information of the optimization node to obtain the equivalent unit physical quantity of the optimization grid node; and distributing the equivalent unit physical quantities of the nodes of the optimization grid to the optimization grid, and averaging the equivalent unit physical quantities of all the nodes in each grid according to the node equivalent unit physical quantities corresponding to the optimization nodes to obtain new physical quantities of the optimization grid.
Wherein the physical quantities comprise: nodal velocity (X, Y, Z direction), nodal displacement (X, Y, Z direction), cell stress (XX, YY, ZZ, XY, XZ, YZ directions). The assignment of the new grid physical quantity needs to be carried out in 3 steps:
1) and (5) equating the old grid physical quantity to the old grid node to obtain the equivalent unit physical quantity of the old node. Specifically, a volume weighted average method is adopted, and assuming that M units surround a node i, the equivalent unit physical quantity of the node can be obtained by equation (4).
Wherein,
the volume of the j cell is shown,
is a physical quantity of cells surrounding the jth cell of the inode,
are equivalent unit physical quantities of the inode.
2) And taking the equivalent unit physical quantity of the old grid node as a known quantity, and carrying out interpolation calculation according to the position information of the new node to obtain the equivalent unit physical quantity of the new grid node.
3) And distributing the equivalent unit physical quantity of the new grid node to the new grid unit. And according to the node equivalent unit physical quantity corresponding to the new node, averaging the equivalent unit physical quantities of all the nodes in each grid to obtain the new physical quantity of the grid. Assuming that a unit i is composed of M nodes, a new physical quantity of the unit can be obtained by equation (5).
Wherein,
is a new physical quantity of the unit i,
is the equivalent unit physical quantity of the j node.
It can be understood that the grid within the optimization range is regenerated, and the grid can be effectively combined with numerical simulation software, so that the problem of poor grid in the numerical simulation process is solved, and the condition of numerical calculation termination caused by serious deformation of the grid can be avoided; by dynamically optimizing the poor grid in the calculation process, the average quality of the grid is greatly improved, the quality of the poor grid is improved, and the accuracy and the reasonability of numerical simulation are further improved.
A method for dynamically optimizing a finite element mesh suitable for underground mining according to the present application is described in detail below with reference to an exemplary embodiment.
FIG. 3 is a logic diagram of an implementation of a method for dynamic optimization of a finite element mesh for underground mining. As shown in fig. 3, the main steps of the grid dynamic optimization process in the underground mining process are as follows:
s1: according to geological data of a mining area, an underground mining three-dimensional geological model is established, the modeling process can be completed in three-dimensional modeling software Midas GTS, grids are divided into grid type files which can be identified by numerical simulation software, and the three-dimensional geological model is shown in figure 4.
S2: and importing the three-dimensional geological model into numerical simulation software, giving physical and mechanical parameters to the stratum, selecting a proper material constitutive model, and setting mining boundary conditions. And simulating the step mining of the underground working face by compiling the mining command stream. Dynamic identification of grid quality is realized by embedding the compiled grid quality judgment program into an iterative loop of numerical simulation software.
S3: when the mine reaches 140m, the grid with the number of 60524 is judged to be a poor grid through a grid quality calculation program, the quality value is calculated by the formulas (1), (2) and (3), and the results are 0.174639, 0.411635 and 0.445706, and the grid needs to be optimized.
S31: and (3) taking the body center O of the poor grid as the center, taking the length of 2 times of the longest edge d of the grid as the radius, making a spherical region omega, and taking the grid in the region omega as an optimization range. An optimized range mesh model centered on a poor mesh is shown in fig. 5.
S32: and extracting the outer surface mesh of the optimization range as a boundary mesh gamma. The optimization range boundary grid is shown in fig. 6.
S33: based on the boundary mesh Γ of the optimization range, an internal mesh is generated by the TetGen program. The newly generated mesh is shown in fig. 7.
S34: the newly generated mesh needs to be merged with the original old mesh, and the merged mesh model is shown in fig. 8.
S35: after the grids are merged, physical quantities need to be assigned to the new grid, and the assigned physical quantities mainly include node velocity (X, Y, Z direction), node displacement (X, Y, Z direction), and cell stress (XX, YY, ZZ, XY, XZ, YZ directions). The Z-direction displacement result of the new grid after assignment is shown in fig. 9.
S4: after the grid dynamic optimization scheme is executed, iterative computation is carried out in numerical simulation software, next mining is simulated, and the cycle termination condition is the end of the mining process. And analyzing and outputting the calculation result after the mining process is finished.
According to the method for dynamically optimizing the finite element grid suitable for underground mining, which is provided by the embodiment of the application, the grid optimization range is generated by identifying the inferior grid in the finite element grid in the underground mining process and taking the inferior grid as the center; determining boundary grids in the optimization range, and subdividing grids in the optimization range except the boundary grids to generate optimization grids in the optimization range; the optimized grid is embedded into the finite element grid, new physical quantity is given to the optimized grid, and dynamic optimization of the grid is realized, so that the average quality of the grid is greatly improved, the quality of the inferior grid is improved, the accuracy and the rationality of numerical simulation are further improved, and the method has important significance for analyzing the movement rule of the underground mining rock stratum.
Next, a finite element mesh dynamic optimization device suitable for underground mining according to an embodiment of the present application will be described with reference to the accompanying drawings.
FIG. 10 is a block diagram of a finite element mesh dynamic optimization apparatus suitable for underground mining according to an embodiment of the present application.
As shown in fig. 10, the finite element mesh dynamic optimization device 10 for underground mining comprises: a first generation module 100, a second generation module 200, and an optimization module 300.
The first generation module 100 is configured to identify a poor grid in a finite element grid in an underground mining process, and generate a grid optimization range centered on the poor grid. And a second generating module 200, configured to determine a boundary mesh within the optimization range, and re-divide the mesh in an area inside the optimization range except the boundary mesh to generate the optimization mesh within the optimization range. And the optimization module 300 is used for embedding the optimization mesh into the finite element mesh, giving new physical quantity to the optimization mesh and realizing dynamic optimization of the mesh.
Optionally, in an embodiment of the present application, generating a grid optimization range centered on a poor grid includes: the body center of the inferior grid is used as the center, the length which is twice as long as the longest edge of the inferior grid is used as the radius to be used as a spherical area, and the spherical area is used as the grid optimization range.
Optionally, in an embodiment of the present application, the second generating module 200 is specifically configured to traverse all triangle meshes within the optimization range, record the occurrence frequency of each triangle mesh, and use a triangle mesh with one occurrence frequency as a boundary mesh.
Optionally, in an embodiment of the present application, embedding the optimization mesh in the finite element mesh includes: and merging the optimized grids into the finite element grids according to the topological connection relation, deleting repeated nodes in the finite element grids, reordering the rest nodes, changing node numbers, and merging the mutually independent finite element grids into an integral grid.
Optionally, in an embodiment of the present application, the optimization module 300 includes: the equivalent unit is used for equivalent the physical quantity of the grid before optimization to the grid node before optimization to obtain the physical quantity of the equivalent unit of the grid node before optimization; the computing unit is used for taking the equivalent unit physical quantity of the grid node before optimization as a known quantity, and carrying out interpolation computing to obtain the equivalent unit physical quantity of the optimized grid node according to the position information of the optimized node; and the distribution unit is used for distributing the equivalent unit physical quantities of the nodes of the optimization grid to the optimization grid, and averaging the equivalent unit physical quantities of all the nodes in each grid according to the node equivalent unit physical quantities corresponding to the optimization nodes to obtain new physical quantities of the optimization grid.
It should be noted that the foregoing explanation of the embodiment of the method for dynamically optimizing a finite element mesh suitable for underground mining also applies to the apparatus for dynamically optimizing a finite element mesh suitable for underground mining of this embodiment, and details thereof are not repeated herein.
According to the finite element mesh dynamic optimization device suitable for underground mining provided by the embodiment of the application, firstly, an underground mining finite element mesh calculation model is established. Judging the grid quality in the excavation iterative computation process, and defining the low-quality grid as a poor grid; then, the poor-quality grids are used as centers, grids in a certain area are defined as an optimization range according to topological connection relations among the grids, and the internal area is re-subdivided on the basis of boundary grids of the optimization range to obtain high-quality optimization grids; and finally, embedding the optimized grid into the original computational grid, replacing the grid in the original optimization range, giving a new grid a computational physical quantity, and realizing dynamic optimization of the computational grid, thereby greatly improving the average quality of the grid, improving the quality of the inferior grid, further improving the accuracy and rationality of numerical simulation, and having important significance for analyzing the movement rule of the underground mining rock stratum.
Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
a memory 1101, a processor 1102, and a computer program stored on the memory 1101 and executable on the processor 1102.
The processor 1102, when executing the program, implements the method for dynamic optimization of a finite element mesh suitable for underground mining provided in the above embodiments.
Further, the electronic device further includes:
a communication interface 1103 for communicating between the memory 1101 and the processor 1102.
A memory 1101 for storing computer programs that are executable on the processor 1102.
The memory 1101 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 1101, the processor 1102 and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101 and the processor 1102 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 11, but it is not intended that there be only one bus or one type of bus.
Optionally, in a specific implementation, if the memory 1101, the processor 1102 and the communication interface 1103 are integrated on one chip, the memory 1101, the processor 1102 and the communication interface 1103 may complete communication with each other through an internal interface.
The processor 1102 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements a method for dynamic optimization of a finite element mesh suitable for underground mining as described above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
Claims (10)
1. A finite element mesh dynamic optimization method suitable for underground mining is characterized by comprising the following steps:
identifying poor grids in finite element grids in the underground mining process, and generating a grid optimization range by taking the poor grids as a center;
determining a boundary grid in the optimization range, and subdividing grids in an internal area of the optimization range except the boundary grid to generate the optimization grid in the optimization range;
embedding the optimized mesh into the finite element mesh, and giving new physical quantity to the optimized mesh to realize dynamic optimization of the mesh.
2. The method of claim 1, wherein generating a grid optimization range centered on the poor quality grid comprises:
and taking the body center of the inferior grid as a center, taking the length twice as long as the longest edge of the inferior grid as a radius to form a spherical area, and taking the spherical area as the optimization range of the grid.
3. The method of claim 1 or 2, wherein the determining the boundary grid within the optimization scope comprises:
traversing all the triangular meshes in the optimization range, recording the occurrence frequency of each triangular mesh, and taking the triangular mesh with one occurrence frequency as the boundary mesh.
4. The method of claim 1, wherein embedding the optimization mesh in the finite element mesh comprises:
and merging the optimized grids into the finite element grids according to the topological connection relation, deleting repeated nodes in the finite element grids, reordering the rest nodes, changing the node numbers, and merging the mutually independent finite element grids into an integral grid.
5. The method of claim 1, wherein said assigning new physical quantities to said optimization grid comprises:
the physical quantity of the grid before optimization is equivalent to the grid node before optimization to obtain the equivalent unit physical quantity of the grid node before optimization;
taking the equivalent unit physical quantity of the grid node before optimization as a known quantity, and carrying out interpolation calculation according to the position information of the optimization node to obtain the equivalent unit physical quantity of the optimization grid node;
and distributing the equivalent unit physical quantities of the nodes of the optimized grid to the optimized grid, and averaging the equivalent unit physical quantities of all the nodes in each grid according to the node equivalent unit physical quantities corresponding to the optimized nodes to obtain new physical quantities of the optimized grid.
6. A finite element mesh dynamic optimization device adapted for underground mining, comprising:
the first generation module is used for identifying the inferior grid in the finite element grid in the underground mining process and generating a grid optimization range by taking the inferior grid as a center;
the second generation module is used for determining the boundary grid in the optimization range, re-dividing the grid in the area outside the optimization range outside the boundary grid and generating the optimization grid in the optimization range;
and the optimization module is used for embedding the optimization mesh into the finite element mesh, giving new physical quantity to the optimization mesh and realizing dynamic optimization of the mesh.
7. The apparatus according to claim 6, characterized in that the second generation module is specifically configured to,
traversing all the triangular meshes in the optimization range, recording the occurrence frequency of each triangular mesh, and taking the triangular mesh with one occurrence frequency as the boundary mesh.
8. The apparatus of claim 6, wherein the optimization module comprises:
the merging unit is used for merging the optimized grids into the finite element grids according to the topological connection relation, deleting repeated nodes in the finite element grids, reordering the rest nodes, changing the node numbers and merging the finite element grids which are independent into an integral grid;
the equivalent unit is used for enabling the grid physical quantity before optimization to be equivalent to the grid node before optimization to obtain the equivalent unit physical quantity of the grid node before optimization;
the computing unit is used for taking the equivalent unit physical quantity of the grid node before optimization as a known quantity, and carrying out interpolation computing to obtain the equivalent unit physical quantity of the optimized grid node according to the position information of the optimized node;
and the distribution unit is used for distributing the equivalent unit physical quantity of the optimized grid node to the optimized grid, and averaging the equivalent unit physical quantities of all nodes in each grid according to the node equivalent unit physical quantity corresponding to the optimized node to obtain a new physical quantity of the optimized grid.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method for dynamic optimization of a finite element mesh for underground mining of any of claims 1-5.
10. A computer-readable storage medium having stored thereon a computer program for execution by a processor for implementing a method for dynamic optimization of a finite element mesh for underground mining according to any of claims 1-5.
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| CN117195827B (en) * | 2023-11-07 | 2024-02-20 | 巨霖科技(上海)有限公司 | Grid boundary surface identification method, finite element calculation method and device |
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