Dual-phase alloy modeling method and system based on molecular dynamics controllable second phase

Abstract

The application discloses a kind of based on molecular dynamics controllable second phase's dual-phase alloy modeling method and system, it is related to alloy modeling technical field, the method includes: according to the preset three-dimensional model is respectively divided in three-dimensional direction, and multiple unit cubes are divided and three-dimensional array is generated;Each element in three-dimensional array indicates the position in three-dimensional space, and the initial value corresponding to each position is 0;Based on the volume ratio and the number of particles of second phase, the distribution of second phase in three-dimensional model is determined according to random coordinate generation method and breadth-first algorithm, and the distribution of second phase in three-dimensional model is generated as mask file;The value of the unit cube position of second phase in three-dimensional model distribution is 1;Establish the matrix material geometric model and reinforcing body geometric model;Based on matrix material geometric model, reinforcing body geometric model and mask file, the second phase reinforced composite material model is obtained.The model obtained by the application is closer to the microstructure of actual second phase reinforced composite material.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of alloy modeling, in particular to a two-phase alloy modeling method and system based on controllable second phase of molecular dynamics. BACKGROUND

[0002] Particle reinforced composites have excellent performance, such as particle reinforced aluminum matrix composites have high specific strength, high wear resistance and high temperature resistance and other excellent performance, in aerospace, nuclear power and transportation and other fields have a wide performance. Current research shows that the content and size of the reinforcing particles significantly affect the tensile strength, hardness and friction and other properties of the composite material.

[0003] The performance of the material is closely related to its microstructure. For second phase reinforced composites, the proportion and morphology of different second phases greatly affect the performance of the material. For example, the morphology and content of aluminum oxide in aluminum oxide particle reinforced aluminum matrix composites significantly affect the mechanical properties of the composite material, and the size, morphology and distribution of the reinforcing phase in the composite material are random. Therefore, establishing a model of the microstructure of the metal composite material that is more in line with the actual second phase distribution is an important basis for accurately predicting the performance of the material through simulation calculation.

[0004] Molecular dynamics simulation is a very effective computational simulation technique for studying the performance of nanometer metal materials. Commonly used software includes the open source simulation software LAMMPS software, which is based on the Newtonian mechanics theory system and can be used for the study of metal, ceramic and composite material systems. At present, the use of molecular dynamics simulation method to predict the performance of material system has attracted widespread attention from material researchers, and the creation of a material system model is a prerequisite for carrying out molecular dynamics. The quality of the model directly affects the quality of the simulation results. At present, the simulation of second phase reinforced metal materials is mostly based on a composite material model with ideal distribution of the second phase. The content of the second phase is controllable, but the distribution and morphology of the single second phase are difficult to control, and the actual microstructure of the material cannot be well reflected, thereby affecting the accuracy of the simulation results. SUMMARY

[0005] The purpose of the present application is to provide a two-phase alloy modeling method and system based on controllable second phase of molecular dynamics, so that the obtained composite material model is closer to the actual microstructure.

[0006] To achieve the above purpose, the present application provides the following scheme:

[0007] A two-phase alloy modeling method based on controllable second phase of molecular dynamics, comprising:

[0008] The preset three-dimensional model is divided in three-dimensional direction respectively, and a plurality of unit cubes are generated to form a three-dimensional array; each element in the three-dimensional array represents a position in three-dimensional space, and the initial value corresponding to each position is 0;

[0009] determining, according to an average function or a random function, the number of unit cubes corresponding to each particle based on the volume proportion and the particle number of the second phase, the number of unit cubes corresponding to each particle constituting a first set;

[0010] determining the distribution of the second phase in the three-dimensional model according to a random coordinate generation method and a breadth-first algorithm based on the first set, and generating the distribution of the second phase in the three-dimensional model as a mask file, wherein the value corresponding to the position of a unit cube occupied by the second phase in the three-dimensional model is 1;

[0011] establishing a geometric model of a matrix material and a geometric model of a reinforcing body with the same size;

[0012] deleting the unit cubes with the position value of 1 in the geometric model of the matrix material based on the mask file to obtain a geometric model of the matrix material with vacancies, and deleting the unit cubes with the position value of 0 in the geometric model of the reinforcing body to obtain a geometric model of the reinforcing body with vacancies;

[0013] merging the geometric model of the matrix material with vacancies and the geometric model of the reinforcing body with vacancies to obtain a composite material model reinforced by the second phase.

[0014] Optionally, the distribution of the second phase in the three-dimensional model is determined according to a random coordinate generation method and a breadth-first algorithm based on the first set, and the distribution of the second phase in the three-dimensional model is generated as a mask file, and specifically includes:

[0015] traversing the number of unit cubes corresponding to each particle in the first set to determine the distribution of each particle, when the i-th particle is traversed:

[0016] randomly selecting a coordinate in the three-dimensional array as a base point coordinate, and the unit cube where the base point coordinate is located is not the outermost unit cube of the three-dimensional model;

[0017] obtaining a set of adjacent point coordinates of the current base point coordinate;

[0018] judging whether there is a coordinate with the value of 1 corresponding to the position of a unit cube in the set of adjacent point coordinates;

[0019] if there is, returning to the step of randomly selecting a coordinate in the three-dimensional array as a base point coordinate, and the unit cube where the base point coordinate is located is not the outermost unit cube of the three-dimensional model;

[0020] if there is not, performing random shape or fixed shape expansion based on the breadth-first algorithm with the current base point coordinate as the center according to the number of unit cubes corresponding to the i-th particle, and setting the value corresponding to the position of the expanded unit cube to 1.

[0021] Optionally, further comprising:

[0022] The composite material model is imported into the LAMMPS software for energy minimization to obtain a stable composite material structure.

[0023] Optionally, the matrix material geometric model and the reinforcing body geometric model are both superlattice structure models.

[0024] Optionally, the matrix material geometric model and the reinforcing body geometric model with the same size are established, and specifically include:

[0025] The crystal structure information of the matrix material is obtained to form a matrix material.cif file;

[0026] The crystal structure information of the reinforcing body material is obtained to form a reinforcing body material.cif file;

[0027] The matrix material.cif file is converted into a first xsf file by using ATOMSK software, and the reinforcing body material.cif file is converted into a second xsf file;

[0028] The matrix material geometric model is generated according to the first xsf file;

[0029] The reinforcing body geometric model is generated according to the second xsf file.

[0030] Optionally, the composite material model includes an alumina reinforced aluminum matrix composite material model and a silicon carbide reinforced aluminum matrix composite material model.

[0031] The application further discloses a two-phase alloy modeling system based on controllable second-phase molecular dynamics, which comprises:

[0032] The three-dimensional array generation module is configured to divide a preset three-dimensional model in three-dimensional directions respectively to generate a three-dimensional array by dividing a plurality of unit cubes; each element in the three-dimensional array represents a position in a three-dimensional space, and an initial value corresponding to each position is 0.

[0033] The unit cube quantity determination module corresponding to the particles is configured to determine the quantity of unit cubes corresponding to each particle based on the volume proportion of the second phase and the quantity of particles according to an average function or a random function, and the quantity of unit cubes corresponding to each particle constitutes a first set.

[0034] The mask file generation module is configured to determine the distribution of the second phase in the three-dimensional model based on the first set according to a random coordinate generation method and a breadth-first algorithm, and generate the distribution of the second phase in the three-dimensional model as a mask file; the value corresponding to the position of the unit cube of the second phase in the three-dimensional model is 1.

[0035] The base material geometry model and the reinforcement geometry model establishing module is used to establish the base material geometry model and the reinforcement geometry model with the same size;

[0036] The vacancy-containing geometry model determining module is used to delete the unit cube with the position value of 1 in the base material geometry model to obtain the vacancy-containing base material geometry model based on the mask file; and delete the unit cube with the position value of 0 in the reinforcement geometry model to obtain the vacancy-containing reinforcement geometry model.

[0037] The composite material model determining module is used to combine the vacancy-containing base material geometry model and the vacancy-containing reinforcement geometry model to obtain the second-phase-reinforced composite material model.

[0038] The application further discloses an electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the second-phase-based dual-phase alloy modeling method based on molecular dynamics.

[0039] According to the specific embodiments of the application, the following technical effects are achieved:

[0040] The application is based on the three-dimensional array generated by three-dimensional model division, the first set composed of the number of unit cubes corresponding to each second-phase particle, the distribution of the second phase in the three-dimensional model is determined according to the random coordinate generation method and the breadth-first algorithm, and the distribution of the second phase in the three-dimensional model is generated as a mask file, so as to obtain the composite material model with controllable content, quantity, distribution and morphology of the second phase, and the obtained composite material model is closer to the actual microstructure. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0042] Figure 1 A second-phase-based dual-phase alloy modeling method based on molecular dynamics is provided for the embodiments of the present application Figure 1 ;

[0043] Figure 2 A second-phase-based dual-phase alloy modeling method based on molecular dynamics is provided for the embodiments of the present application Figure 2 ;

[0044] Figure 3A part of a 100*100*100 array file provided by the embodiment of the present application is shown in the figure;

[0045] Figure 4 An alumina phase diagram provided by the embodiment of the present application is shown in the figure;

[0046] Figure 5 A schematic diagram of an alumina reinforced aluminum matrix composite material model provided by the embodiment of the present application is shown in the figure;

[0047] Figure 6 A silicon carbide phase diagram provided by the embodiment of the present application is shown in the figure;

[0048] Figure 7 A schematic diagram of a silicon carbide reinforced aluminum matrix composite material model provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0050] The purpose of the present application is to provide a dual-phase alloy modeling method and system based on molecular dynamics controllable second phase, so that the obtained composite material model is closer to the actual microstructure.

[0051] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Embodiment 1

[0053] As shown in Figure 1 and Figure 2 The embodiment provides a dual-phase alloy modeling method based on molecular dynamics controllable second phase, which specifically comprises the following steps:

[0054] Step 101: According to the preset three-dimensional model, respectively divide in three-dimensional direction, divide a plurality of unit cubes to generate three-dimensional array; each element in the three-dimensional array represents a position in three-dimensional space, and the initial value corresponding to each position is 0.

[0055] The three-dimensional array is a controllable three-dimensional array, and the position values corresponding to the elements in the array include two numbers, 1 and 0, which correspond to an actual three-dimensional model. Each number 0 and 1 represents a unit volume size of a cube (unit cube). The number, position and distribution of the number 1 in the array correspond to the number, position and distribution of the second phase in the composite material model. Therefore, a complex second phase reinforced metal composite material model can be generated by controlling the distribution of the number 1 in the array.

[0056] In step 101, the three-dimensional space (space where the three-dimensional model is located) is divided into different equal parts in X, Y and Z directions according to requirements. The equal parts in the three directions can be the same or different. Cubes of unit volume are divided, so that the length, width and height of the three-dimensional array can be input to generate the required unit cubes. The total content of the second phase is controlled by counting the number of 1s in the array, the number of the second phase is controlled by counting the number of 1 blocks, and the average function, random function and breadth optimization algorithm are used for control. An initial three-dimensional array is generated according to the input length, width, height, total content and number information. The initial value of each member is 0, and a part of the values corresponding to the three-dimensional array is as shown in Figure 3

[0057] Step 102: Based on the volume ratio and the number of particles of the second phase, the number of unit cubes corresponding to each particle is determined according to the average function or the random function. The number of unit cubes corresponding to each particle constitutes a first set.

[0058] In step 102, the following steps are included:

[0059] Step 103: Based on the first set, the distribution of the second phase in the three-dimensional model is determined according to the random coordinate generation method and the breadth priority algorithm, and the distribution of the second phase in the three-dimensional model is generated as a mask file. The value corresponding to the position of the unit cube of the second phase in the three-dimensional model is 1.

[0060] In step 103, the following steps are included:

[0061] The number of unit cubes corresponding to each particle in the first set is traversed to determine the distribution of each particle. When the i-th particle is traversed:

[0062] A coordinate in the three-dimensional array is randomly selected as a base point coordinate (x, y, z).

[0063] According to the judge function, it is judged whether the unit cube where the base point coordinate is located is the outermost unit cube of the three-dimensional model.

[0064] If yes, return to the step of randomly selecting a coordinate in the three-dimensional array as a base point coordinate.

[0065] ​If not, it indicates that the seed node position is reasonable, and the adjacent point coordinate set B along the X, Y, Z axis direction is obtained as the center of the current base point coordinate, that is, the adjacent point coordinate (x1, y1, z1) of the current base point coordinate is:

[0066] x1 = x + dx, y1 = y + dy, z1 = z + dz;

[0067] In the formula, dx, dy, and dz are unit change amounts in the X, Y, and Z axis directions, respectively.

[0068] According to the judge function, it is judged whether there is a coordinate with a value of 1 corresponding to the position of the unit cube in the current adjacent point coordinate set.

[0069] If there is, the step of randomly selecting a coordinate as the base point coordinate in the three-dimensional array is returned, and the unit cube where the base point coordinate is located is not the outermost unit cube of the three-dimensional model.

[0070] If not, according to the number of unit cubes corresponding to the i-th particle, the random shape or fixed shape is expanded based on the breadth-first algorithm with the current base point coordinate as the center, and the value corresponding to the position of the unit cube after expansion is set to 1.

[0071] The total number of 1 in the three-dimensional array, the number and shape of the particles composed of 1, and other information are output to the file "information.txt"; the constructed three-dimensional array is output to the file "array.txt"; the total number of 1 in the actual generated three-dimensional array is verified using the loop counting method, and the result is output to "checkout.txt". Among them, the file "array.txt" will be used as a mask file.

[0072] Step 104: Establishing a base material geometric model and a reinforcing body geometric model with the same size.

[0073] The base material geometric model and the reinforcing body geometric model are both superlattice structure models.

[0074] The step 104 specifically includes:

[0075] Obtaining the crystal structure information of the base material to form a base material.cif file.

[0076] Obtaining the crystal structure information of the reinforcing body material to form a reinforcing body material.cif file.

[0077] Converting the base material.cif file into a first xsf file and the reinforcing body material.cif file into a second xsf file by using ATOMSK software.

[0078] Generating the matrix material geometric model according to the first xsf file.

[0079] Generating the reinforcement geometric model according to the second xsf file.

[0080] The composite material model includes but is not limited to an alumina reinforced aluminum matrix composite material model and a silicon carbide reinforced aluminum matrix composite material model, and a model of any second phase reinforced metal matrix composite material can be created.

[0081] Generating the matrix material geometric model according to the first xsf file; generating the reinforcement geometric model according to the second xsf file, specifically including:

[0082] Creating an initial model three-dimensional box (BOX).

[0083] According to the need, the length, width and height of the initial model three-dimensional BOX space to be created are set, the length, width and height of the BOX are the size of the required simulation system, which can be 10nm*10nm*10nm, or 150nm*100nm*50nm, and the specific size can be set according to the need, and the length, width and height of the two three-dimensional BOXes are the same.

[0084] The matrix material xsf file is used to fill the model three-dimensional BOX to obtain the matrix material initial model, and the reinforcement material xsf file is used to fill the model three-dimensional BOX to obtain the reinforcement material initial model.

[0085] The three-dimensional BOX space filled by the crystal structure cif files of different matrix materials and reinforcement materials can obtain different matrix and reinforcement initial models, but it should be noted that the length, width and height of the matrix model and the reinforcement model need to be exactly the same.

[0086] Step 105: deleting the unit cube with a position value of 1 in the matrix material geometric model based on the mask file to obtain a matrix material geometric model with vacancies; deleting the unit cube with a position value of 0 in the reinforcement geometric model to obtain a reinforcement geometric model with vacancies.

[0087] Specifically, step 105 includes:

[0088] ATOMSK software is used for mask processing, and the mask file (mask file) is the array.txt file generated in the above steps. For mask processing of the matrix material initial model, the atoms in the region corresponding to the "1" block in the array are deleted to obtain a matrix material model with vacancies.

[0089] For the reinforcement material, the atoms in the region corresponding to the "0" block in the array are deleted to obtain a reinforcement model with vacancies.

[0090] Step 106: merging the base material model with vacancies and the reinforcing material model with vacancies to obtain a second-phase reinforced composite material model.

[0091] The step 106 specifically comprises: establishing the base material model and the reinforcing material model with the same coordinates and length, width and height dimensions, and automatically aligning the origins and coordinate axes of the two models when merging the base material model with vacancies and the reinforcing material model with vacancies; performing mask processing on the base material model and the reinforcing material model by using the same array.txt file, so that the vacancies of the base material model with vacancies are exactly the atomic regions of the reinforcing material; and filling the reinforcing atoms into the vacancies in the base material model after merging to obtain the composite material model.

[0092] The composite material model generated in the step 106 may have the problems of too close, coincident or too far atomic distances, and is in an unstable state, so that energy minimization processing is required.

[0093] Therefore, the method further comprises: importing the composite material model into the LAMMPS software to perform energy minimization, obtaining a stable composite material model, and outputting a composite material model data file, specifically comprising:

[0094] Importing the composite material model into the LAMMPS.

[0095] Setting a potential function, and the specific potential function needs to be selected according to the specific material type of the composite material model.

[0096] Performing structure and energy minimization by using a conjugate gradient algorithm; setting the temperature to 300K and the system pressure to 0.1bar by using an isothermal-isobaric (NPT) system, relaxing for 50ps to balance the atomic positions and energy in the model, and then outputting the relaxed composite material model.

[0097] The method further comprises:

[0098] Importing the generated stable composite material model file into a visualization software OVITO (Open Visualization Tool) to process, observe and statistically analyze whether the base material atoms and the reinforcing material atoms in the composite material are coincident, too close or too far, so as to analyze and verify the accuracy of the composite material model.

[0099] The generated structure and energy stable composite material model can be used for molecular dynamics simulation calculation, which can predict the mechanical properties of the composite material, the simulation method includes tensile, compression, indentation and friction and wear simulation, etc. The performance such as elastic modulus, tensile strength, compression strength, nano indentation and friction, and the deformation behavior and deformation mechanism of the composite material can be obtained, so as to guide the research on the deformation behavior of the actual composite material.

[0100] The present application generates a controllable array, and then applies the mask technology of Atomsk (The Swiss-army knife of atomic simulations) software to generate a composite material initial model with controllable second phase distribution, and then uses LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator) simulation software to minimize the energy of the initial model to make it stable, and the stable alloy model can be used for subsequent simulation calculation. The total content, number, size and shape of the second phase in the model can be controlled by the controllable array. This method can also be used to create composite material models of different matrix materials and different reinforcing phases.

[0101] The technical effects of the present application are as follows:

[0102] (1) The number and distribution of 1 and 0 in the array can be controlled to obtain an array of controllable distribution of 1 and 0 blocks, so as to obtain a composite material model with controllable content, number, distribution and morphology of the second phase, and the model is consistent with the microstructure of the actual second phase reinforced composite material.

[0103] (2) Different models constructed by the present application can be compared and analyzed quantitatively and qualitatively, for example, a composite material model with the same second phase content but different second phase number and morphology can be constructed to study the influence of the second phase number and morphology on the performance of the composite material.

[0104] Example 3

[0105] This embodiment applies the modeling method of the controllable second phase of the dual-phase alloy based on molecular dynamics provided in Example 1 to specifically realize the geometric modeling of the aluminum oxide reinforced aluminum matrix composite material with controllable Al2O3 phase, and specifically includes the following steps.

[0106] (1) Generate an array of 100*100*100 by a controllable array generation program, which contains 1000000 elements. In this example, two arrays txt files are generated, one of which contains 10 blocks of "1" with a total of 100000 "1"s, and the other contains 5 blocks of "1" with a total of 100000 "1"s. These two files are used for subsequent mask files. The total volume content of the second phase is the same, but the number of particles in the second phase is different.

[0107] (2) Create a geometric model of pure aluminum with a size of 20*20*20 nm, containing about 500000 atoms; create a geometric model of Al2O3 phase with a size of 20*20*20 nm, containing about 498000 atoms. The model construction software is Atomsk.

[0108] (3) Process the pure aluminum geometric model and the Al2O3 phase geometric model with the mask file generated in step (1).

[0109] Wherein: the mask file 1 processing process is: for the pure aluminum geometric model, the region of "1" in the mask file is the region to be deleted, and the region of "0" is the region to be preserved. After processing, the pure aluminum geometric model deletes 18900 atoms; for the Al2O3 phase geometric model, the region of "1" in the mask file is the region to be preserved, and the region of "0" is the region to be deleted. After processing, the number of atoms in the Al2O3 phase is about 19300, and the number of Al2O3 phases is 10, as shown in (a) of Figure 4

[0110] The mask file 2 processing process is the same as the mask file 1 processing process. After processing, the number of atoms in the Al2O3 phase is also 19300, and the number of Al2O3 phases is 5, as shown in (b) of Figure 4 It can be found that the total volume content of the Al2O3 phase in the two models is the same, but the number of particles is different, resulting in different volumes of single second phase in the two models.

[0111] (4) Combine the pure aluminum geometric model and the Al2O3 phase geometric model processed by the mask file in step (3) together. After merging, the Al2O3 phase is just in the empty position of the pure aluminum geometric model, thereby obtaining the model of the composite material. The composite material model processed by the mask file 1 is shown in (a) of Figure 5 The composite material model processed by the mask file 2 is shown in (b) of Figure 5

[0112] ​​(5) The composite material model generated in step (4) is imported into LAMMPS software for energy minimization, so as to stabilize the structure of the composite material, and a model data file is outputted, which can be used for simulation and calculation of the performance of the composite material.

[0113] Example 4

[0114] This example applies the modeling method of the controllable second phase of the dual-phase alloy based on molecular dynamics provided in Example 1 to specifically realize the modeling of a geometric model of a controllable SiC phase silicon carbide reinforced aluminum matrix composite material, which includes the following steps:

[0115] (1) A controllable array generation program is written, and a 100*100*100 array containing 1,000,000 elements is generated by using the program. In this example, two array txt files are generated in total, one of which contains 10 blocks of the number "1" with a total "1" content of 100,000, and the other contains 10 blocks of the number "1" with a total "1" content of 200,000. The two files are used for subsequent mask files. The number of second phase particles is controlled to be the same, but the total volume content of the second phase is different.

[0116] (2) A geometric model of pure aluminum is created, with a model size of 20*20*20 nm and an aluminum atom number of about 500,000; a geometric model of the SiC phase is created, with a model size of 20*20*20 nm and a total atom number of about 512,000. The model construction software is Atomsk.

[0117] (3) The mask files generated in step 1 are used to process the pure aluminum geometric model and the SiC phase geometric model; wherein: the mask file 1 processing process is as follows: for the pure aluminum geometric model, the region of the number "1" in the mask file is the region where the atoms are to be deleted, and the region of the number "0" is the region where the atoms are to be retained. After processing, 18,900 atoms are deleted from the pure aluminum geometric model; for the SiC phase geometric model, the region of the number "1" in the mask file is the region where the atoms are to be retained, and the region of the number "0" is the region where the atoms are to be deleted. After processing, the number of atoms of the SiC phase is about 21,000, and the number of SiC phases is 10, as shown in (a) of Figure 6 ; the mask file 2 processing process is the same as that of the mask file 1, and the number of atoms of the SiC phase after processing is also 42,000, and the number of SiC phases is 10, as shown in (b) of Figure 6 . It can be found that the number of SiC phase particles of the two models is the same, but the total volume content is different, resulting in different volumes of each phase.

[0118] (4) The pure aluminum geometric model and the SiC phase geometric model processed by the mask file in step 3 are combined together, and the SiC phase is just in the empty position of the pure aluminum geometric model after the combination, so as to obtain the model of the composite material. The composite material model processed by the mask file 1 is shown in Fig. 1(a), and the composite material model processed by the mask file 2 is shown in Fig. 1(b). Figure 7 Figure 7

[0119] (5) The composite material model generated in step (4) is imported into the LAMMPS software for energy minimization, so as to stabilize the structure of the composite material, and then a model data file is output. The model data file can be used for simulation and calculation research on the performance of the composite material.

[0120] Example 5

[0121] The embodiment provides a two-phase alloy modeling system based on molecular dynamics controllable second phase, and the system comprises:

[0122] A three-dimensional array generation module is configured to divide a preset three-dimensional model in three-dimensional directions respectively, and generate a three-dimensional array by dividing a plurality of unit cubes. Each element in the three-dimensional array represents a position in a three-dimensional space, and an initial value corresponding to each position is 0.

[0123] A unit cube quantity determination module is configured to determine, based on a volume proportion of the second phase and a particle quantity, a quantity of unit cubes corresponding to each particle according to an average function or a random function, and the quantity of unit cubes corresponding to each particle constitutes a first set.

[0124] A mask file generation module is configured to determine a distribution of the second phase in the three-dimensional model based on the first set according to a random coordinate generation method and a breadth-first algorithm, and generate the distribution of the second phase in the three-dimensional model as a mask file. A value corresponding to a unit cube position in which the second phase is distributed in the three-dimensional model is 1.

[0125] A matrix material geometric model and a reinforcement geometric model establishment module is configured to establish a matrix material geometric model and a reinforcement geometric model with the same size.

[0126] A geometric model with vacancies determination module is configured to delete, based on the mask file, a unit cube with a position value of 1 in the matrix material geometric model, to obtain a matrix material geometric model with vacancies, and delete a unit cube with a position value of 0 in the reinforcement geometric model, to obtain a reinforcement geometric model with vacancies.

[0127] A composite material model determination module is configured to combine the matrix material geometric model with vacancies and the reinforcement geometric model with vacancies to obtain a second phase reinforced composite material model.

[0128] ​​Example 6

[0129] The electronic device includes a memory for storing a computer program and a processor for running the computer program to make the electronic device perform the method for modeling dual-phase alloy based on controllable second phase of molecular dynamics according to the embodiment 1.

[0130] The embodiments are described in a progressive manner in the specification, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other.

[0131] The principles and implementation manners of the present application are described by using specific examples in the specification. The above description of the examples is only used to help understand the method and core idea of the present application. Meanwhile, for the general skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for modeling two-phase alloys based on a controllable second phase using molecular dynamics, characterized in that, include: Based on the preset 3D model, the model is divided into multiple unit cubes in three dimensions to generate a 3D array. Each element in the three-dimensional array represents a position in three-dimensional space, and the initial value for each position is 0. Based on the volume ratio and particle number of the second phase, the number of unit cubes corresponding to each particle is determined according to an average function or a random function, and the number of unit cubes corresponding to each particle constitutes the first set. Based on the first set, the distribution of the second phase in the 3D model is determined according to the random coordinate generation method and the breadth-first search algorithm, and the distribution of the second phase in the 3D model is generated as a mask file; the value corresponding to the unit cube position of the second phase in the 3D model is 1; Establish geometric models of the matrix material and the reinforcement with the same dimensions; Based on the mask file, delete the unit cubes with a position value of 1 in the matrix material geometry model to obtain a matrix material geometry model with empty positions; delete the unit cubes with a position value of 0 in the reinforcement geometry model to obtain a reinforcement geometry model with empty positions. The geometric model of the matrix material with vacancies and the geometric model of the reinforcement with vacancies are combined to obtain the composite material model with second-phase reinforcement.

2. The method for modeling two-phase alloys based on a molecular dynamics-controlled second phase according to claim 1, characterized in that, Based on the first set, the distribution of the second phase in the 3D model is determined according to the random coordinate generation method and the breadth-first search algorithm, and the distribution of the second phase in the 3D model is generated as a mask file, specifically including: Iterate through the number of unit cubes corresponding to each particle in the first set to determine the distribution of each particle. When iterating to the i-th particle: Randomly select a coordinate in the 3D array as the base point coordinate, and the unit cube where the base point coordinate is located is not the outermost unit cube of the 3D model; Obtain the set of coordinates of adjacent points of the current base point; Determine if there exists a coordinate with a value of 1 corresponding to the position of a unit cube in the current set of adjacent point coordinates; If it exists, return the step of randomly selecting a coordinate in the 3D array as the base point coordinate, and the unit cube where the base point coordinate is located is not the outermost unit cube of the 3D model. If it does not exist, then based on the number of unit cubes corresponding to the i-th particle, the expansion is performed with the current base point coordinates as the center, using a breadth-first search algorithm to expand the shape into a random or fixed shape. After expansion, the value corresponding to the position of the unit cube is set to 1.

3. The method for modeling two-phase alloys based on a molecular dynamics-controlled second phase according to claim 1, characterized in that, Also includes: The composite material model was imported into LAMMPS software for energy minimization to obtain a stable composite material structure.

4. The method for modeling two-phase alloys based on a molecular dynamics-controllable second phase according to claim 1, characterized in that, Both the matrix material geometric model and the reinforcement geometric model are superlattice structure models.

5. The method for modeling two-phase alloys based on a molecular dynamics-controlled second phase according to claim 1, characterized in that, Establish geometric models of the matrix material and reinforcement body with the same dimensions, specifically including: Obtain the crystal structure information of the matrix material to form a matrix material .cif file; Obtain the crystal structure information of the reinforcing material to form the reinforcing material .cif file; The matrix material .cif file was converted into a first xsf file using ATOMSK software, and the reinforcement material .cif file was converted into a second xsf file. Generate a geometric model of the matrix material based on the first xsf file; Generate an enhanced geometry model based on the second xsf file.

6. The method for modeling two-phase alloys based on a molecular dynamics-controlled second phase according to claim 1, characterized in that, The composite material models include an alumina-reinforced aluminum-based composite material model and a silicon carbide-reinforced aluminum-based composite material model.

7. A two-phase alloy modeling system based on a molecular dynamics-controllable second phase, characterized in that, include: The 3D array generation module is used to divide a preset 3D model into multiple unit cubes in three dimensions to generate a 3D array. Each element in the three-dimensional array represents a position in three-dimensional space, and the initial value for each position is 0. The module for determining the number of unit cubes corresponding to each particle is used to determine the number of unit cubes corresponding to each particle based on the volume ratio of the second phase and the number of particles, according to an average function or a random function. The number of unit cubes corresponding to each particle constitutes the first set. The mask file generation module is used to determine the distribution of the second phase in the 3D model based on the first set, according to the random coordinate generation method and the breadth-first search algorithm, and generate the distribution of the second phase in the 3D model as a mask file; the value corresponding to the unit cube position of the second phase in the 3D model is 1; The module for creating matrix material geometric models and reinforcement geometric models is used to create matrix material geometric models and reinforcement geometric models of the same size. The module for determining the geometric model with vacancies is used to, based on the mask file, delete the unit cubes with a position value of 1 in the matrix material geometric model to obtain the matrix material geometric model with vacancies; and delete the unit cubes with a position value of 0 in the reinforcement geometric model to obtain the reinforcement geometric model with vacancies. The composite material model determination module is used to merge the geometric model of the matrix material with vacancies and the geometric model of the reinforcement with vacancies to obtain a composite material model with second-phase reinforcement.

8. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the method for modeling a two-phase alloy based on a molecularly dynamically controllable second phase according to any one of claims 1 to 7.

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