CN114707276A - Periodic composite material elastic constitutive parameter prediction method, equipment and storage medium - Google Patents
Periodic composite material elastic constitutive parameter prediction method, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method, equipment and a storage medium for predicting elastic constitutive parameters of periodic composite materials, wherein the method comprises the following steps: acquiring geometric data of a target representative unit of the periodic composite material, wherein the target representative unit is composed of a first phase material and a second phase material; establishing a double-layer voxelized grid model corresponding to the target representative unit based on the geometric data, wherein the double-layer voxelized grid model comprises a coarse grid model and a fine grid model; determining a boundary voxel corresponding to the junction of the first phase material and the second phase material in the coarse mesh model according to the spatial range of the first phase material and the spatial range of the second phase material in the target representative unit; reconstructing boundary voxels in the coarse mesh model according to the fine mesh model; and carrying out finite element analysis on the new coarse mesh model obtained after the subdivision to obtain the elastic constitutive parameters of the periodic composite material. The method and the device solve the technical problem that the prediction efficiency of the elastic constitutive parameters of the periodic composite material in the prior art is low.
Description
Technical Field
The present disclosure relates to the field of motor control technologies, and in particular, to a method for predicting elastic constitutive parameters of a periodic composite material, an electronic device, and a storage medium.
Background
The periodic composite material integrates the performance advantages of different types of materials, so that the comprehensive mechanical property of the material is greatly improved, the periodic composite material gradually receives wide attention in the fields of biological medicine, aerospace, automobile manufacturing and the like, and the characterization of basic elastic constitutive parameters is the basis of the trend application of the periodic composite material. With the proposal of the application requirement facing complex functions, the structural complexity of the periodic composite material is correspondingly increased, the additive manufacturing technology provides a powerful means for the manufacture of complex function structures, but due to the existence of additive manufacturing errors, the accurate prediction of the elastic constitutive parameters of complex lattice structures often needs to be carried out structural reverse solution, then the elastic constitutive parameters of the periodic composite material are predicted for reversely solved personalized prototypes, and the personalized reverse solution model with high complexity provides higher requirements for the accurate and efficient prediction of the elastic constitutive parameters. The calculation cost is high, and the prediction efficiency of the elastic constitutive parameters of the periodic composite material with complex geometric shape is low.
Disclosure of Invention
The present application mainly aims to provide a method for predicting elastic constitutive parameters of a periodic composite material, an electronic device, and a storage medium, and aims to solve the technical problem of low efficiency in predicting elastic constitutive parameters of a periodic composite material in the prior art.
In order to achieve the above object, the present application provides a method for predicting elastic constitutive parameters of a periodic composite material, including:
acquiring geometric data of a target representative cell of the periodic composite material, wherein the target representative cell is composed of a first phase material and a second phase material;
establishing a double-layer voxelized grid model corresponding to the target representative unit based on the geometric data, wherein the double-layer voxelized grid model comprises a coarse grid model and a fine grid model;
determining boundary voxels corresponding to the junction of the first phase material and the second phase material in the coarse mesh model according to the spatial range of the first phase material and the spatial range of the second phase material in the target representative unit;
reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model;
and carrying out finite element analysis on the new coarse mesh model obtained after the subdivision to obtain the elastic constitutive parameters of the periodic composite material.
Optionally, the step of determining, in the coarse mesh model, boundary voxels corresponding to the intersection of the first phase material and the second phase material according to the spatial range to which the first phase material and the second phase material respectively belong in the target representative cell includes:
determining respective corresponding spatial ranges of the first phase material and the second phase material in the coarse mesh model according to the respective spatial ranges of the first phase material and the second phase material in the target representative cell;
determining the coarse grid voxels with the geometric centers belonging to the spatial range corresponding to the first-phase material as first-phase temporary voxels, and determining the coarse grid voxels with the geometric centers belonging to the spatial range corresponding to the second-phase material as second-phase temporary voxels;
determining a node connected with the first-phase temporary voxel as a first-phase temporary node, and determining a node connected with the second-phase temporary voxel as a second-phase temporary node;
determining the intersection of the first-phase temporary node and the second-phase temporary node as a boundary node;
and determining the voxels connected with the boundary nodes as boundary voxels.
Optionally, the step of reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model includes:
determining other first-phase temporary voxels except the boundary voxel as first-phase coarse grid voxels, and determining other second-phase temporary voxels except the boundary voxel as second-phase coarse grid voxels;
determining other first-phase temporary nodes except the boundary nodes as first-phase nodes, and determining other second-phase temporary nodes except the boundary nodes as second-phase nodes;
determining a spatial extent of the first phase material and the second phase material in the fine mesh model according to a spatial extent to which the first phase material and the second phase material respectively belong in the target representative cell;
determining the fine grid voxels of which the geometric centers belong to the space range corresponding to the first phase material as first-phase fine grid voxels, and determining the fine grid voxels of which the geometric centers belong to the space range corresponding to the second phase material as second-phase fine grid voxels;
acquiring numbers and/or coordinates corresponding to the first-phase coarse grid voxel, the second-phase coarse grid voxel, the boundary voxel, the first-phase node, the second-phase node, the boundary node, the first-phase fine grid voxel and the second-phase fine grid voxel respectively, and a first horizontal value function corresponding to the first-phase fine grid voxel and a second horizontal value function corresponding to the second-phase fine grid voxel;
determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node and the central coordinates of the fine grid voxels;
according to the first level value function, the second level value function and the third level value function, determining a spatial distribution function which takes node coordinates as independent variables and takes level values as dependent variables;
and reconstructing the boundary voxels in the coarse mesh model according to the spatial distribution function.
Optionally, the step of determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, and the coordinates of the center of the fine mesh voxel includes:
determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, the center coordinates of the fine grid voxels, and a preset boundary node horizontal value algorithm, wherein the boundary node horizontal value algorithm is as follows:
wherein x isi coarseIs the boundary node level value, yj fineIs the fine grid voxel level value of the fine grid model, i is the number of the boundary node, j is the number of the fine grid voxel, NiIs a spherical spatial range with the boundary node as the center and l as the radius, omegaijIs preset with rijAs a function of the argument, rijIs the distance, r, from the boundary node i to the center of the fine-grid voxel jijAnd determining according to the coordinates of the boundary node i and the center coordinates of the fine grid voxel j.
Optionally, the radius of the spherical spatial range is 0.1-3 times the maximum side length of the coarse mesh voxel.
Optionally, the step of reconstructing the boundary voxels in the coarse mesh model according to the spatial distribution function includes:
setting the horizontal value in the spatial distribution function as a preset horizontal value to obtain a spatial curved function;
and dividing the boundary voxel according to the intersection point of the space surface function and the edge of the boundary voxel.
Optionally, the step of performing finite element analysis on the new coarse mesh model obtained after the subdivision to obtain the elastic constitutive parameters of the periodic composite material includes:
establishing an extended finite element model based on a new coarse mesh model obtained after subdivision;
applying periodic boundary conditions to the extended finite element model, applying strain loads, and solving a finite element equation to obtain a displacement vector;
establishing an energy homogenization formula of the target representative unit;
and bringing the displacement vector into the energy homogenization column to obtain the elastic constitutive parameters of the periodic composite material.
Optionally, the number of voxels in any one of the three orthogonal spatial directions in the fine mesh model is 2-32 times the number of voxels in the corresponding spatial direction in the coarse mesh model.
The present application further provides an electronic device, the electronic device is an entity device, the electronic device includes: the method comprises a memory, a processor and a program of the periodic composite elastic constitutive parameter prediction method stored on the memory and capable of running on the processor, wherein the program of the periodic composite elastic constitutive parameter prediction method can realize the steps of the periodic composite elastic constitutive parameter prediction method when being executed by the processor.
The present application further provides a storage medium, which is a computer-readable storage medium, and a program for implementing a method for predicting elastic constitutive parameters of periodic composite materials is stored on the computer-readable storage medium, and when the program is executed by a processor, the method for predicting elastic constitutive parameters of periodic composite materials implements the steps of the method for predicting elastic constitutive parameters of periodic composite materials as described above.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the periodic composite elastic constitutive parameter prediction method as described above.
The application provides a method for predicting elastic constitutive parameters of a periodic composite material, an electronic device and a storage medium, which are used for determining distribution position information of a first phase material and a second phase material in a target representative unit of the periodic composite material by acquiring geometric data of the target representative unit, wherein the target representative unit is composed of the first phase material and the second phase material, and then establishing a double-layer voxelized grid model corresponding to the target representative unit based on the geometric data, wherein the double-layer voxelized grid model comprises a coarse grid model and a fine grid model, so that voxelized division of the target representative unit with two different precisions is realized, and further, according to the respective space ranges of the first phase material and the second phase material in the target representative unit, in the coarse mesh model, determining a boundary voxel corresponding to the junction of the first phase material and the second phase material, reconstructing the boundary voxel in the coarse mesh model according to the fine mesh model, realizing high-precision voxel division of the boundary voxel in the coarse mesh model, enabling the boundary of the coarse mesh to approach a structural real boundary, further performing finite element analysis on a new coarse mesh model obtained after division to obtain elastic constitutive parameters of the periodic composite material, realizing finite element analysis based on the coarse mesh model, and because two-phase boundaries in the coarse mesh model subjected to the finite element analysis are accurately constructed based on the fine mesh model with higher precision, the geometric accuracy of the boundary can be effectively ensured, and because the coarse mesh model contains fewer meshes and the fine mesh model does not perform finite element calculation, therefore, on the basis of ensuring the accuracy of the two-phase boundary, the efficiency of finite element calculation is effectively improved, and the technical problem of low efficiency of predicting the elastic constitutive parameters of the periodic composite material in the prior art is solved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart illustrating an embodiment of a method for predicting elastic constitutive parameters of a periodic composite material according to the present application;
FIG. 2 is a scene schematic diagram of a coarse mesh layer and a fine mesh layer in the periodic composite elastic constitutive parameter prediction method of the present application;
FIG. 3 is a schematic view of a scenario of an embodiment of the method for predicting elastic constitutive parameters of periodic composite material according to the present application;
fig. 4 is a schematic structural diagram of a hardware operating environment related to the method for predicting the elastic constitutive parameters of the periodic composite material according to the present application.
The objectives, features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
In a first embodiment of the method for predicting the elastic constitutive parameters of the periodic composite material, referring to fig. 1, the method for predicting the elastic constitutive parameters of the periodic composite material includes:
step S10, acquiring geometric data of a target representative unit of the periodic composite material, wherein the target representative unit is composed of a first phase material and a second phase material;
in this embodiment, it should be noted that the periodic composite material is a composite material with periodic arrangement, the periodic composite material is a two-phase composite material formed by compounding a first-phase material and a second-phase material, the target representative unit is a representative volume unit established based on the periodic composite material, that is, the target representative cell is composed of a first phase material and a second phase material, the target representative cell having a sufficiently large size with respect to the microstructure of the periodic composite material, such that the target representative unit is capable of reflecting a compositional property of the material, the target representative unit having a sufficiently small size relative to the periodic composite material, such that the target representative unit can be treated as a continuum in the periodic composite.
Specifically, geometric data of a target representative unit of the periodic composite material is obtained, where the geometric data includes image data, point cloud data, structural solid geometric data, boundary expression geometric data, parameter expression geometric data, and/or unit expression geometric data, and the geometric data may be acquired by a geometric data acquisition device or may be acquired by calculation, which is not limited in this embodiment.
It is easily understood that, since the target representative cell is composed of the first phase material and the second phase material, the geometric data of the target representative cell includes the spatial range to which the first phase material and the second phase material respectively belong in the target representative cell.
Step S20, establishing a double-layer voxelized grid model corresponding to the target representative unit based on the geometric data, wherein the double-layer voxelized grid model comprises a coarse grid model and a fine grid model;
in this embodiment, specifically, based on the geometric data, the target representative unit is divided into a first number of coarse grid voxels to obtain a coarse grid model, and the target representative unit is divided into a second number of fine grid voxels to obtain a fine grid model, where the coarse grid model and the fine grid model form a double-layer voxelized grid model corresponding to the target representative unit, where the first number is smaller than the second number, that is, the coarse grid voxels in the coarse grid model are fewer in number and lower in precision, and the fine grid voxels in the fine grid model are more in number and higher in precision.
Optionally, the number of voxels in any one of the three orthogonal spatial directions in the fine mesh model is 2-32 times the number of voxels in the corresponding spatial direction in the coarse mesh model.
In a practical manner, the voxel is a cuboid, and the length ratio of the side lengths of the cuboid in different spatial directions is in a range of 0.25-4.
It is easy to understand that the double-layer voxelized grid model may include voxelized grid information, where the voxelized grid information includes numbers and coordinate information of voxels and voxel nodes, and corresponding relations of voxels and voxel nodes, and the like, where a voxel node is a vertex, a voxel number and a voxel node number may be numbered according to an actual situation, a voxel coordinate and a voxel node coordinate may be determined after a corresponding coordinate system is established according to an actual situation, and a corresponding relation of voxels and voxel nodes may be associated according to an actual situation.
Step S30, determining boundary voxels corresponding to the junctions of the first phase material and the second phase material in the coarse mesh model according to the spatial ranges of the first phase material and the second phase material in the target representative cell;
in this embodiment, specifically, the spatial ranges of the first phase material and the second phase material in the coarse mesh model respectively corresponding to the first phase material and the second phase material in the target representative unit are determined according to the spatial ranges of the first phase material and the second phase material in the target representative unit, and then one or more boundary voxels corresponding to the boundary are determined according to the position of the boundary between the spatial range corresponding to the first phase material and the spatial range corresponding to the second phase material, where the determining of the one or more boundary voxels corresponding to the boundary may be performed by determining one or more voxels belonging to the boundary as a boundary voxel, determining voxels within a certain preset range from the boundary as a boundary voxel, or using other determining manners.
Optionally, the step of determining, in the coarse mesh model, boundary voxels corresponding to the intersection of the first phase material and the second phase material according to the spatial range to which the first phase material and the second phase material respectively belong in the target representative cell includes:
step S31, determining the corresponding spatial range of the first phase material and the second phase material in the coarse mesh model according to the spatial range of the first phase material and the second phase material in the target representative cell;
step S32, determining the coarse grid voxels whose geometric centers belong to the spatial range corresponding to the first-phase material as first-phase temporary voxels, and determining the coarse grid voxels whose geometric centers belong to the spatial range corresponding to the second-phase material as second-phase temporary voxels;
step S33, determining a node connected to the first-phase temporary voxel as a first-phase temporary node, and determining a node connected to the second-phase temporary voxel as a second-phase temporary node;
step S34, determining the intersection of the first-phase temporary node and the second-phase temporary node as a boundary node;
in step S35, the voxels connected to the boundary node are determined as boundary voxels.
In this embodiment, specifically, according to the spatial range to which the first phase material and the second phase material belong in the target representative unit, determining the spatial range to which the first phase material and the second phase material correspond in the coarse grid model, obtaining the geometric center coordinates of each coarse grid voxel in the coarse grid model, determining at least one coarse grid voxel of which the geometric center coordinates belong to the spatial range to which the first phase material corresponds as a first-phase temporary voxel, determining at least one coarse grid voxel of which the geometric center coordinates belong to the spatial range to which the second phase material corresponds as a second-phase temporary voxel, determining a node in the coarse grid model connected to the first-phase temporary voxel as a first-phase temporary node, and determining a node in the coarse grid model connected to the second-phase temporary voxel as a second-phase temporary node, and determining nodes belonging to the first-phase temporary nodes and the second-phase temporary nodes as boundary nodes, and determining voxels connected with the boundary nodes as boundary voxels.
In a practical manner, referring to the upper coarse grid layer diagram in fig. 2, according to the determination manner of the boundary voxels in the above steps S31-S35, it can be determined that the coarse grid voxels are boundary voxels except for four first-phase voxels in the lower left bold border and five second-phase voxels in the upper right bold border.
Step S40, reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model;
in this embodiment, specifically, according to the voxelized grid information in the double-layer voxelized grid model, a first-phase coarse grid voxel, a second-phase coarse grid voxel, a boundary voxel, a first-phase node, a second-phase node, and a boundary node in the coarse grid model are determined, and numbers and/or coordinate information corresponding to the first-phase coarse grid voxel, the second-phase coarse grid voxel, the boundary voxel, the first-phase node, the second-phase node, and the boundary node, respectively, are determined, and according to the voxelized grid information in the double-layer voxelized grid model, a first-phase fine grid voxel and a second-phase fine grid voxel in the fine grid model are determined, and numbers and/or coordinate information corresponding to the first-phase fine grid voxel and the second-phase fine grid voxel, respectively, are obtained, and a preset first horizontal value function corresponding to the first-phase fine grid voxel and a preset number and/or coordinate information corresponding to the second-phase fine grid voxel are obtained And determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node and the central coordinates of the fine grid voxels, determining a spatial distribution function with the node coordinates as an independent variable and the horizontal values as a dependent variable according to the first horizontal value function, the second horizontal value function and the third horizontal value function, solving the spatial distribution function to obtain a spatial surface function corresponding to the interface between the first phase material and the second phase material, and performing MarchingCubes (mobile cube) subdivision on the boundary voxels according to the intersection points of the spatial surface function and the edges of the boundary voxels in the coarse grid model to obtain the boundary voxels with higher boundary accuracy and precision.
Optionally, the step of reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model includes:
step S41, determining other first-phase temporary voxels except the boundary voxel as first-phase coarse grid voxels, and determining other second-phase temporary voxels except the boundary voxel as second-phase coarse grid voxels;
step S42, determining other first-phase temporary nodes except the boundary node as first-phase nodes, and determining other second-phase temporary nodes except the boundary node as second-phase nodes;
step S43, determining the spatial range of the first phase material and the second phase material in the fine mesh model according to the spatial range of the first phase material and the second phase material in the target representative cell;
step S44, determining the fine grid voxels with the geometric centers belonging to the spatial range corresponding to the first phase material as first-phase fine grid voxels, and determining the fine grid voxels with the geometric centers belonging to the spatial range corresponding to the second phase material as second-phase fine grid voxels;
step S45, obtaining numbers and/or coordinates corresponding to the first-phase coarse grid voxel, the second-phase coarse grid voxel, the boundary voxel, the first-phase node, the second-phase node, the boundary node, the first-phase fine grid voxel, and the second-phase fine grid voxel, and a first horizontal value function corresponding to the first-phase fine grid voxel and a second horizontal value function corresponding to the second-phase fine grid voxel;
step S46, determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, and the center coordinates of the fine-grid voxels;
step S47, determining a spatial distribution function using node coordinates as an independent variable and using level values as a dependent variable according to the first level value function, the second level value function, and the third level value function;
step S48, reconstructing the boundary voxels in the coarse mesh model according to the spatial distribution function.
In this embodiment, specifically, the other first-phase temporary voxels except the boundary voxel are determined as first-phase coarse grid voxels, the other second-phase temporary voxels except the boundary voxel are determined as second-phase coarse grid voxels, the other first-phase temporary nodes except the boundary node are determined as first-phase nodes, the other second-phase temporary nodes except the boundary node are determined as second-phase nodes, the spatial range of the first-phase material and the second-phase material in the fine grid model is determined according to the spatial range to which the first-phase material and the second-phase material respectively belong in the target representative cell, the fine grid voxel whose geometric center belongs to the spatial range corresponding to the first-phase material is determined as a first-phase fine grid voxel, and the fine grid voxel whose geometric center belongs to the spatial range corresponding to the second-phase material is determined as a second-phase fine grid voxel, acquiring numbers and/or coordinates corresponding to the first-phase coarse grid voxel, the second-phase coarse grid voxel, the boundary voxel, the first-phase node, the second-phase node, the boundary node, the first-phase fine grid voxel, and the second-phase fine grid voxel, acquiring a preset first horizontal value function corresponding to the first-phase fine grid voxel and a preset second horizontal value function corresponding to the second-phase fine grid voxel, determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, and the center coordinate of the fine grid voxel, and determining a spatial distribution function with node coordinates as an argument and horizontal values as a dependent variable according to the first horizontal value function, the second horizontal value function, and the third horizontal value function, and solving the spatial distribution function to obtain a spatial surface function corresponding to the interface of the first phase material and the second phase material, and performing MarchingCubes (mobile cubes) subdivision on the boundary voxel according to the spatial surface function and a section formed by the boundary voxel in the coarse mesh model to obtain the boundary voxel with higher boundary accuracy and accuracy.
Optionally, the step of determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, and the coordinates of the center of the fine mesh voxel includes:
determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, the center coordinates of the fine grid voxels, and a preset boundary node horizontal value algorithm, wherein the boundary node horizontal value algorithm is as follows:
wherein x isi coarseIs the boundary node level value, yj fineIs the fine grid voxel level value of the fine grid model, i is the number of the boundary node, j is the number of the fine grid voxel, NiIs a spherical spatial range with the boundary node as the center and l as the radius, omegaijIs preset with rijAs a function of the argument, rijIs the distance, r, from the boundary node i to the center of the fine-grid voxel jijAnd determining according to the coordinates of the boundary node i and the center coordinates of the fine grid voxel j.
In this embodiment, specifically, the third horizontal value function of the boundary node is determined according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, the center coordinates of the fine mesh voxels, and a preset boundary node horizontal value algorithm, where the boundary node horizontal value algorithm is:
wherein x isi coarseAs node level values, y, in the coarse mesh modelj fineIs the fine grid voxel level value of the fine grid model, i is the number of the boundary node, j is the number of the fine grid voxel, NiIs a spherical spatial range with the boundary node as the center and l as the radius, omegaijIs preset with rijAs a function of the argument, rijIs the distance, r, from the boundary node i to the center of the fine-grid voxel jijAccording to the coordinates of the boundary node i and the central coordinates of the fine grid voxel j, wherein y isj fineThe evaluation may be performed according to the first level function and the second level function, for example, if the first level function is a constant 1 and the second level function is a constant-1, the level value of the first phase fine grid voxel is 1 and the level value of the second phase fine grid voxel is-1.
Optionally, the radius/of the spherical spatial range is 0.1-3 times the maximum side length of the coarse mesh voxel.
In a practical manner, the ω isijHas the functional form of ωij(rij) 1, or ωij(rij)＝k(l-rij)a,(k>0,a>0) Either the first or the second substrate is, alternatively,
optionally, the step of reconstructing the boundary voxels in the coarse mesh model according to the spatial distribution function includes:
step S481, setting a horizontal value in the spatial distribution function as a preset horizontal value, to obtain a spatial curved function;
step S482, subdividing the boundary voxel according to an intersection point of the spatial surface function and an edge of the boundary voxel.
In this embodiment, specifically, if the level value of the first-phase fine-mesh voxel is 1, the level value of the second-phase fine-mesh voxel is-1, the level value of the first-phase node is 1, and the level value of the second-phase node is-1, for example, the preset level value of the first-phase material and the second-phase material interface is preset to 0, and then the level value in the spatial distribution function is the preset level value, the spatial distribution function is solved to obtain a spatial curved function corresponding to the first-phase material and the second-phase material interface, and marching cubes are performed on the boundary voxel according to the intersection point of the spatial curved function and the edge of the boundary voxel in the rough-mesh model, boundary voxels with higher boundary accuracy and precision are obtained.
And step S50, carrying out finite element analysis on the new coarse mesh model obtained after the splitting to obtain the elastic constitutive parameters of the periodic composite material.
In this embodiment, specifically, a finite element analysis is performed on a new coarse mesh model obtained after the splitting to obtain elastic constitutive parameters of the periodic composite material, where the elastic constitutive parameters are macro equivalent elastic constitutive parameters and/or equivalent elastic matrices of the periodic composite material.
Optionally, the step of performing finite element analysis on the new coarse mesh model obtained after the subdivision to obtain the elastic constitutive parameters of the periodic composite material includes:
step S51, establishing an expanded finite element model based on the new coarse mesh model obtained after the subdivision;
step S52, applying periodic boundary conditions and strain loads to the extended finite element model, and solving a finite element equation to obtain a displacement vector;
step S53, establishing an energy homogenization formula of the target representative unit;
and step S54, bringing the displacement vector into the energy homogenization column to obtain the elastic constitutive parameters of the periodic composite material.
In this embodiment, specifically, based on a new coarse mesh model obtained after subdivision, an extended finite element model is established, a stiffness matrix is assembled according to the extended finite element model, periodic boundary conditions are applied to the extended finite element model, a preset number of different uniform unit strain loads are set, a finite element equation is solved according to the stiffness matrix obtained by assembly to obtain respective corresponding displacement vectors of the strain loads, an energy homogenization matrix of the target representative unit is established, the displacement vectors are substituted into the energy homogenization matrix to solve an equivalent elastic constitutive matrix to obtain elastic constitutive parameters of the periodic composite material, where the elastic constitutive parameters are macroscopic equivalent elastic constitutive parameters and/or equivalent elastic matrices of the periodic composite material, the number of said strain loads may be 6, for example { 100000 }T，{0 1 0 0 0 0}T，{0 0 1 0 0 0}T，{0 0 0 1 0 0}T，{0 0 0 0 1 0}T，{0 0 0 0 0 1}T。
In one implementation, referring to fig. 3, fig. 3 is a schematic diagram of a target representative cell of a 3D printed Ti6Al4V-PEEK periodic composite material, the method for predicting elastic constitutive parameters of the periodic composite material includes:
for Ti based on 3D printing6Al4V-PEEK periodic composite material is subjected to CT scanning, Ti6Al4V is determined as a first-phase material, PEEK is determined as a second-phase material, point cloud data of a target representative unit of the periodic composite material are collected by using a CT image, as shown in fig. 3A, wherein the space range occupied by the first-phase material is a point cloud part shown in fig. 3A, the space range occupied by the second-phase material in the representative unit is the residual space of the space except the first-phase material, double-layer voxelization mesh model building is carried out on the target representative unit, the first-layer mesh is a coarse mesh model (divided into coarse mesh voxels of 30 x 30), the second-layer mesh is a fine mesh model (divided into fine mesh voxels of 180 x), the shape of the voxels is a cube, and the numbers, the positions and the positions of the voxels in the coarse mesh model and the fine mesh model are stored, Coordinate information and a corresponding relationship between voxels and voxel nodes, dividing coarse grid voxels in the coarse grid model into first-phase temporary voxels and second-phase temporary voxels according to a spatial range (point cloud range) of the first-phase material and the second-phase material and a central coordinate position of the coarse grid voxels in the coarse grid model, as shown in fig. 3B, a grid part is a first-phase temporary voxel, the remaining blank space is a second-phase temporary voxel, dividing fine grid voxels in the fine grid model into first-phase fine grid voxels and second-phase fine grid voxels according to the spatial range (point cloud range) of the first-phase material and the second-phase material and a central coordinate position of the fine grid voxels in the fine grid model, determining nodes connected with the first-phase temporary voxels as first-phase temporary nodes in the coarse grid model, determining a node connected with the second-phase temporary voxel as a second-phase temporary node, determining an intersection of the first-phase temporary node and the second-phase temporary node as a boundary node, determining a residual node from which the boundary node is removed in the first-phase temporary node as a first-phase node, determining a residual node from which the boundary node is removed in the second-phase temporary node as a second-phase node, determining a voxel connected with the boundary node as a boundary voxel, determining a residual voxel from which the boundary voxel is removed in the first-phase temporary voxel as a first-phase coarse grid voxel, and removing edges in the second-phase temporary voxelDetermining the rest voxels of the boundary voxels as second-phase coarse grid voxels, and defining the node level value in the coarse grid model as xi coarseI is a node number, a horizontal value of 1 is given to a first phase node in the coarse grid model, a horizontal value of-1 is given to a second phase node in the coarse grid model, and a voxel horizontal value in the fine grid model is defined as yj fineJ is a voxel number, a horizontal value of 1 is given to a first phase fine grid voxel in the fine grid model, a horizontal value of-1 is given to a second phase fine grid voxel in the fine grid model, and the horizontal value of the boundary node of the coarse grid layer is calculated as follows:
wherein i is the number corresponding to the boundary node in the coarse mesh model, j is the number corresponding to the fine mesh voxel in the fine mesh model, NiIs a spherical space range with a boundary node as the center and l as the radius, wherein l is 1.1 times of the side length of the coarse grid voxel, and omega isij(rij)＝1。
According to the level value of the nodes in the coarse mesh model, determining a spatial distribution function phi with the coordinates of the nodes as independent variables and the level value as dependent variables, solving phi to be 0 to obtain a spatial surface function, determining the intersection points of the spatial surface function and the edges of boundary voxels in the coarse mesh model, performing Marchang Cubes subdivision on the boundary voxels in the coarse mesh model by using the intersection points, establishing a weakly discontinuous extended finite element model for the coarse mesh according to a new boundary voxel obtained by subdivision and an original first-phase coarse mesh voxel and a second-phase coarse mesh voxel, assembling a stiffness matrix according to the division result of the extended finite element mesh as shown in figures 3C and 3D to obtain an integral stiffness matrix K, setting periodic boundary conditions for the extended finite element model, and setting 6 different uniform unit strain loads, i.e., { 100000 }T,{0 1 0 0 0 0}T,{0 0 1 0 0 0}T,{0 0 0 1 0 0}T,{0 0 0 0 1 0}T, {0 0 0 0 0 1}TRespectively solving displacement vectors χ under 6 different loads for the overall stiffness matrix Ki(i ═ 1,2,3,4,5,6), establishing an array of energy homogenizing moments of the target representative units, and translating the displacement vector χiSubstituting the equivalent elastic constitutive matrix into the energy homogenization elastic moment array to obtain a macroscopic equivalent elastic constitutive parameter, and outputting the equivalent elastic matrix as follows:
in this embodiment, by obtaining geometric data of a target representative unit of the periodic composite material, where the target representative unit is composed of a first phase material and a second phase material, determining distribution position information of the first phase material and the second phase material in the target representative unit of the periodic composite material is achieved, and then establishing a two-layer voxelized grid model corresponding to the target representative unit based on the geometric data, where the two-layer voxelized grid model includes a coarse grid model and a fine grid model, achieving voxelized division of the target representative unit with two different accuracies, and then determining a boundary voxel corresponding to an intersection of the first phase material and the second phase material in the coarse grid model according to a spatial range to which the first phase material and the second phase material respectively belong in the target representative unit, reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model, so as to realize high-precision voxel subdivision of the boundary voxels in the coarse mesh model, enable the boundary of the coarse mesh to approach the structural real boundary, further perform finite element analysis on a new coarse mesh model obtained after subdivision, obtain elastic constitutive parameters of the periodic composite material, and realize finite element analysis based on the coarse mesh model .
Further, an embodiment of the present invention provides an electronic device, where the electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the periodic composite elastic constitutive parameter prediction method in the above embodiments.
Referring now to FIG. 4, shown is a schematic diagram of a structure suitable for use in an electronic device implementing an embodiment of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
Generally, the following systems may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device with various systems, it is to be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
The electronic device provided by the invention adopts the method for predicting the elastic constitutive parameters of the periodic composite material in the embodiment, and solves the technical problem of low prediction efficiency of the elastic constitutive parameters of the periodic composite material in the prior art. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the invention are the same as the beneficial effects of the method for predicting the elastic constitutive parameters of the periodic composite material provided by the embodiment, and other technical features of the electronic device are the same as those disclosed in the embodiment method, which are not repeated herein.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Further, the present embodiments provide a computer-readable storage medium having computer-readable program instructions stored thereon for performing the periodic composite elastic constitutive parameter prediction method of the above embodiments.
The computer readable storage medium provided by the embodiments of the present invention may be, for example, a USB flash disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination thereof. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer-readable storage medium may be embodied in an electronic device; or may be present alone without being incorporated into the electronic device.
The computer readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring geometric data of a target representative cell of the periodic composite material, wherein the target representative cell is composed of a first phase material and a second phase material; establishing a double-layer voxelized grid model corresponding to the target representative unit based on the geometric data, wherein the double-layer voxelized grid model comprises a coarse grid model and a fine grid model; determining boundary voxels corresponding to the junction of the first phase material and the second phase material in the coarse mesh model according to the spatial range of the first phase material and the spatial range of the second phase material in the target representative unit; reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model; and carrying out finite element analysis on the new coarse mesh model obtained after the subdivision to obtain the elastic constitutive parameters of the periodic composite material.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.
The computer readable storage medium provided by the invention stores the computer readable program instructions for executing the method for predicting the elastic constitutive parameters of the periodic composite material, and solves the technical problem that the prediction efficiency of the elastic constitutive parameters of the periodic composite material is low in the prior art. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the invention are the same as the beneficial effects of the method for predicting the elastic constitutive parameters of the periodic composite material provided by the embodiment, and are not repeated herein.
Further, the present application also provides a computer program product comprising a computer program which, when being executed by a processor, implements the steps of the periodic composite elastic constitutive parameter prediction method as described above.
The computer program product provided by the application solves the technical problem that the prediction efficiency of the elastic constitutive parameters of the periodic composite material is low in the prior art. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present invention are the same as the beneficial effects of the method for predicting the elastic constitutive parameters of the periodic composite material provided by the above embodiment, and are not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A method for predicting elastic constitutive parameters of a periodic composite material is characterized by comprising the following steps:
acquiring geometric data of a target representative cell of the periodic composite material, wherein the target representative cell is composed of a first phase material and a second phase material;
establishing a double-layer voxelized grid model corresponding to the target representative unit based on the geometric data, wherein the double-layer voxelized grid model comprises a coarse grid model and a fine grid model;
determining boundary voxels corresponding to the junction of the first phase material and the second phase material in the coarse mesh model according to the spatial range of the first phase material and the spatial range of the second phase material in the target representative unit;
reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model;
and carrying out finite element analysis on the new coarse mesh model obtained after the subdivision to obtain the elastic constitutive parameters of the periodic composite material.
2. The method according to claim 1, wherein the step of determining boundary voxels corresponding to the intersection of the first phase material and the second phase material in the coarse mesh model according to the spatial range of the first phase material and the second phase material in the target representative cell comprises:
determining respective corresponding spatial ranges of the first phase material and the second phase material in the coarse mesh model according to the respective spatial ranges of the first phase material and the second phase material in the target representative cell;
determining the coarse grid voxels with the geometric centers belonging to the spatial range corresponding to the first-phase material as first-phase temporary voxels, and determining the coarse grid voxels with the geometric centers belonging to the spatial range corresponding to the second-phase material as second-phase temporary voxels;
determining a node connected with the first-phase temporary voxel as a first-phase temporary node, and determining a node connected with the second-phase temporary voxel as a second-phase temporary node;
determining the intersection of the first-phase temporary node and the second-phase temporary node as a boundary node;
and determining the voxels connected with the boundary nodes as boundary voxels.
3. The method of predicting elastic constitutive parameters of the periodic composite material as claimed in claim 2, wherein the step of reconstructing the boundary voxels in the coarse mesh model according to the fine mesh model comprises:
determining other first-phase temporary voxels except the boundary voxel as first-phase coarse grid voxels, and determining other second-phase temporary voxels except the boundary voxel as second-phase coarse grid voxels;
determining other first-phase temporary nodes except the boundary nodes as first-phase nodes, and determining other second-phase temporary nodes except the boundary nodes as second-phase nodes;
determining a spatial extent of the first phase material and the second phase material in the fine mesh model according to a spatial extent to which the first phase material and the second phase material respectively belong in the target representative cell;
determining the fine grid voxels of which the geometric centers belong to the space range corresponding to the first phase material as first-phase fine grid voxels, and determining the fine grid voxels of which the geometric centers belong to the space range corresponding to the second phase material as second-phase fine grid voxels;
acquiring numbers and/or coordinates corresponding to the first-phase coarse grid voxel, the second-phase coarse grid voxel, the boundary voxel, the first-phase node, the second-phase node, the boundary node, the first-phase fine grid voxel and the second-phase fine grid voxel respectively, and a first horizontal value function corresponding to the first-phase fine grid voxel and a second horizontal value function corresponding to the second-phase fine grid voxel;
determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node and the central coordinates of the fine grid voxels;
according to the first level value function, the second level value function and the third level value function, determining a spatial distribution function which takes node coordinates as independent variables and takes level values as dependent variables;
and reconstructing the boundary voxels in the coarse mesh model according to the spatial distribution function.
4. The method for predicting elastic constitutive parameters of periodic composite materials according to claim 3, wherein the step of determining the third horizontal value function of the boundary nodes according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary nodes and the center coordinates of the fine grid voxels comprises:
determining a third horizontal value function of the boundary node according to the first horizontal value function, the second horizontal value function, the coordinates of the boundary node, the center coordinates of the fine grid voxels, and a preset boundary node horizontal value algorithm, wherein the boundary node horizontal value algorithm is as follows:
wherein x isi coarseIs the boundary node level value, yj fineIs the fine grid voxel level value of the fine grid model, i is the number of the boundary node, j is the number of the fine grid voxel, NiIs a spherical spatial range with the boundary node as the center and l as the radius, omegaijIs preset with rijAs a function of the argument rijIs an edgeDistance r from the boundary node i to the center of the fine-grid voxel jijAnd determining according to the coordinates of the boundary node i and the center coordinates of the fine grid voxels j.
5. The method for predicting the elastic constitutive parameter of the periodic composite material as claimed in claim 4, wherein the radius of the spherical space range is 0.1-3 times the maximum side length of the voxel of the coarse grid.
6. The method according to claim 3, wherein the step of reconstructing the boundary voxels in the coarse mesh model according to the spatial distribution function comprises:
setting the horizontal value in the spatial distribution function as a preset horizontal value to obtain a spatial curved function;
and dividing the boundary voxel according to the intersection point of the space surface function and the edge of the boundary voxel.
7. The method for predicting the elastic constitutive parameters of the periodic composite material as claimed in claim 1, wherein the step of performing finite element analysis on the new coarse mesh model obtained after the subdivision to obtain the elastic constitutive parameters of the periodic composite material comprises:
establishing an extended finite element model based on a new coarse mesh model obtained after subdivision;
applying periodic boundary conditions to the extended finite element model, applying strain loads, and solving a finite element equation to obtain a displacement vector;
establishing an energy homogenization formula of the target representative unit;
and bringing the displacement vector into the energy homogenization column to obtain the elastic constitutive parameters of the periodic composite material.
8. The method for predicting the elastic constitutive parameters of the periodic composite material as claimed in claim 1, wherein the number of voxels in any one of three orthogonal spatial directions in the fine mesh model is 2-32 times the number of voxels in the corresponding spatial direction in the coarse mesh model.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the periodic composite elastic constitutive parameter prediction method of any one of claims 1 to 8.
10. A storage medium, characterized in that the storage medium is a computer-readable storage medium having a program stored thereon to implement a periodic composite elastic constitutive parameter prediction method, the program being executed by a processor to implement the steps of the periodic composite elastic constitutive parameter prediction method according to any one of claims 1 to 8.
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| CN115798654A (en) * | 2023-02-03 | 2023-03-14 | 国家超级计算天津中心 | Model material processing method, device, equipment and medium |
| CN115995279A (en) * | 2023-03-22 | 2023-04-21 | 中国空气动力研究与发展中心计算空气动力研究所 | Material mechanical property evaluation method, device, equipment and readable storage medium |
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| CN115798654A (en) * | 2023-02-03 | 2023-03-14 | 国家超级计算天津中心 | Model material processing method, device, equipment and medium |
| CN115995279A (en) * | 2023-03-22 | 2023-04-21 | 中国空气动力研究与发展中心计算空气动力研究所 | Material mechanical property evaluation method, device, equipment and readable storage medium |
| CN115995279B (en) * | 2023-03-22 | 2023-06-02 | 中国空气动力研究与发展中心计算空气动力研究所 | Material mechanical property evaluation method, device, equipment and readable storage medium |
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