Compressible graphene foam compound modeling and heat transfer performance simulation method and system

Abstract

The invention provides a compressible graphene foam compound modeling and heat transfer performance simulation method, and relates to the technical field of graphene heat transfer performance simulation, and the method comprises the following steps: obtaining basic parameters of a graphene sheet, and determining a basic two-dimensional graphene sheet structure; pores are introduced into the two-dimensional graphene sheet structure, carbon atoms in the two-dimensional graphene sheet structure are removed, and foamed structures with different porosities are generated; a rotation matrix is arranged to conduct bending and distortion transformation on the foamed structure of the graphene sheet, and a graphene foam model is generated; doping dopant molecules into the graphene foam model by adopting a random contact method according to the mass fraction, and linking the dopant molecules to carbon atoms to construct a composite graphene foam model; and constructing a potential function model based on a density functional theory, carrying out numerical simulation of heat transfer performance and mechanical performance on the composite graphene foam model, and obtaining a relationship between a stretching rate or a compression rate and stress and the heat transfer performance in a stretching or compression process.

Description

Technical Field

[0001] The present disclosure relates to the technical field of graphene heat transfer performance simulation, and in particular to a compressible graphene foam composite modeling and heat transfer performance simulation method and system. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Due to its unique physical properties in heat and mass transfer, 2D graphene is increasingly widely used in the thermal management of electronic components. Graphene foam has multi-scale structural characteristics. 3D graphene foam assembled from 2D graphene sheets not only maintains the excellent two-dimensional characteristics of graphene to a certain extent, but also has many excellent characteristics such as large-area preparation, large specific surface area, high temperature resistance, and light weight. It can be used in thermal interface materials, energy storage devices, filter devices, sensor devices and other fields, and has gradually become the mainstream. Among the many methods for preparing graphene composites, chemical vapor deposition (CVD) is currently recognized as a method for obtaining large-area, uniform and high-quality two-dimensional graphene. It uses metal foam (such as nickel foam) as a substrate, and chemically decomposes and deposits carbon sources such as CH4 on the substrate surface at a certain temperature or external field, and then removes the metal substrate by corrosion, thereby forming a graphene foam (GF) with a metal foam skeleton shape. As a porous material, graphene foam (GF) prepared by chemical vapor deposition (CVD) shows many excellent properties in heat and mass transfer. GF itself has advantages such as high thermal conductivity, and the compressibility caused by its large porosity makes GF and its composites have a larger variation space in thermal conductivity. The distance between the graphene sheets in the foam structure decreases during compression, resulting in increased thermal conduction in the plane and reduced thermal conduction in the vertical direction, which in turn causes changes in thermal conductivity. The importance of exploring the application of graphene foam in various fields is self-evident, but due to the influence of complex factors such as experimental preparation, experimental exploration of the performance of graphene foam requires a lot of manpower, material resources and financial resources.

[0004] In recent years, numerical simulation has become the mainstream method of research. However, the complex and random structure of graphene foam poses a challenge to the accuracy of numerical simulation. At present, the empirical potential function used in traditional numerical simulation is obtained based on empirical parameters, so it has certain limitations in describing the complex behavior of the system. The calculation often deviates greatly from the actual situation, and the described models are mostly small local models, which are difficult to avoid local contingency. In addition, the traditional empirical potential uses classic two-dimensional graphene parameters to fit the data of the lack of graphene three-dimensional structure, resulting in the existing empirical potential in describing the graphene foam system. The credibility and accuracy are also poor. Moreover, in previous simulation studies, due to limited computing resources, graphene was limited to lamellar shape, making it difficult to perform bending modeling. This limitation has affected the accurate modeling of the complexity of the real system to a certain extent. Summary of the invention

[0005] In order to solve the above problems, the present disclosure proposes a method and system for modeling and simulating heat transfer performance of compressible graphene foam composites. The established three-dimensional model of graphene foam and its composite is more in line with the actual state of graphene foam prepared by CVD method. A heat transfer performance simulation method based on machine learning is proposed, which can simply realize the automatic process simulation of heat and mass transfer of graphene foam, and has significant technical advantages in the field of molecular dynamics simulation.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] Modeling and heat transfer performance simulation methods of compressible graphene foam composites, including:

[0008] Obtain the set basic parameters of the graphene sheet, define the initial coordinates of each carbon atom, and determine the basic two-dimensional graphene sheet structure;

[0009] Pores are introduced into the two-dimensional graphene sheet structure, and carbon atoms in the two-dimensional graphene sheet structure are removed by random sampling to generate foam structures with different porosities;

[0010] The rotation matrix is ​​set to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; the dopant molecules are doped into the graphene foam model according to the mass fraction using a random contact method, and the dopant molecules are linked to the carbon atoms to construct a composite graphene foam model;

[0011] Based on the density functional theory, a potential function model is constructed to perform numerical simulations on the heat transfer and mechanical properties of the composite graphene foam model to obtain the heat transfer process and the relationship between the elongation or compression rate and stress.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions:

[0013] Compressible graphene foam composite modeling and heat transfer performance simulation system, including:

[0014] A data acquisition module is used to acquire the set basic parameters of the graphene sheet, define the initial coordinates of each carbon atom, and determine the basic two-dimensional graphene sheet structure;

[0015] The model building module is used to introduce pores into the two-dimensional graphene sheet structure, remove carbon atoms in the two-dimensional graphene sheet structure by random sampling, and generate foam structures with different porosities; set the rotation matrix to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; according to the mass fraction, the random contact method is used to add dopant molecules to the graphene foam model, and the dopant molecules are linked to the carbon atoms to construct a composite graphene foam model;

[0016] The numerical simulation module is used to construct a potential function model based on density functional theory, to perform numerical simulation of the heat transfer performance and mechanical properties of the composite graphene foam model, and to obtain the heat transfer process and the relationship between the elongation or compression rate and stress.

[0017] According to some embodiments, the present disclosure adopts the following technical solutions:

[0018] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the compressible graphene foam composite modeling and heat transfer performance simulation method is implemented.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the compressible graphene foam composite modeling and heat transfer performance simulation system.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The compressible graphene foam composite modeling and heat transfer performance simulation method disclosed in the present invention constructs a PDMS-doped graphene foam composite model for the first time through methods such as random sampling, rotation matrix and random contact, which is closer to the physical structure actually prepared and generated, and is a guarantee for exploring and verifying the interaction and action mechanism between dopants and graphene.

[0023] The compressible graphene foam composite modeling and heat transfer performance simulation method disclosed in the present invention constructs the potential function of the PDMS-doped graphene foam composite through a machine learning method, which makes up for the deviation of the traditional empirical potential function in describing the complex behavior of the three-dimensional structure of graphene and its composites, and improves the calculation credibility and accuracy.

[0024] The compressible graphene foam composite modeling and heat transfer performance simulation method disclosed in the present invention uses Python program to perform three-dimensional modeling of graphene foam, but other modeling tools and languages, such as MATLAB and Fortran, can also be considered. After the pure GF modeling is completed, the dopant molecules are added to the GF foam according to the mass fraction. The random contact method is adopted to randomly select carbon atoms in the system, and the dopant molecular chains are linked to the carbon atoms. The number of points where the dopant contacts the GF can be set to 1-multiple according to the conditions. The present invention takes into account contact modes such as point contact, edge contact, and surface contact, and ensures that there is no overlap between atoms. The direction of the dopant molecules in the system is also randomly determined, and this series of methods realizes the interaction between the dopant and the GF. The present invention helps to gain a deeper understanding of the law of changes in the heat transfer performance of the graphene foam composite doped with PDMS under strain, and lays a foundation for the development of devices such as thermal switches with dynamically adjustable thermal resistance based on graphene foam composites. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0026] Figure 1 A modeling diagram of pure graphene foam according to an embodiment of the present disclosure;

[0027] Figure 2 Taking PDMS (dimethylsiloxane) as an example, the GF / PDMS composite foam in the simulation modeling is used in the embodiment of the present disclosure;

[0028] in, Figure 2 (a) is a PDMS doping rate of 0wt% in the system; (b) is a PDMS doping rate of 2.5wt% in the system; (c) is a PDMS doping rate of 5wt% in the system; (d) is a PDMS doping rate of 7.5wt% in the system; (e) is a PDMS doping rate of 10wt% in the system. DETAILED DESCRIPTION

[0029] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0030] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0031] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0032] Example 1

[0033] In one embodiment of the present disclosure, a compressible graphene foam composite modeling and heat transfer performance simulation method is provided. A three-dimensional graphene foam model is established through a Python program, so that the modeling is more consistent with the state of real CVD graphene foam. A method for simulating heat transfer performance based on machine learning is also proposed, which can simply realize the automatic process simulation of graphene heat and mass transfer, and has significant technical advantages in the field of molecular dynamics simulation, including:

[0034] Step 1: Obtain the set basic parameters of the graphene sheet, define the initial coordinates of each carbon atom, and determine the basic two-dimensional graphene sheet structure;

[0035] Step 2: introducing pores into the two-dimensional graphene sheet structure, removing carbon atoms in the two-dimensional graphene sheet structure by random sampling to generate a foam structure with different porosities;

[0036] Step 3: Setting a rotation matrix to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; adding dopant molecules to the graphene foam model using a random contact method according to the mass fraction, linking the dopant molecules to the carbon atoms, and constructing a composite graphene foam model;

[0037] Step 4: Construct a potential function model based on density functional theory, perform numerical simulation of the heat transfer performance and mechanical properties of the composite graphene foam model, and obtain the heat transfer process and the relationship between the elongation or compression rate and stress.

[0038] As an example, in previous simulation studies, due to limited computing resources, graphene was limited to a lamellar shape and difficult to perform bending modeling. This limitation affected the accurate modeling of the complexity of the real system to a certain extent. The present disclosure proposes a compressible graphene foam composite modeling and heat transfer performance simulation method to achieve diversified modeling of CVD graphene. The specific implementation process is as follows:

[0039] Step 1: Obtain the set basic parameters of the graphene sheet, define the initial coordinates of each carbon atom, and determine the basic two-dimensional graphene sheet structure;

[0040] The specific steps include:

[0041] 1) First, the basic parameters are set, including the size parameters of the graphene sheet, length L, width W and height H, and the unit atomic bond length range a;

[0042] 2) Define the initial coordinates r of each carbon atom i , to determine the basic two-dimensional graphene sheet structure.

[0043] Specifically, the lattice constant a of graphene is (Angstroms), the initial coordinates r of each carbon atom are determined based on the lattice constant a and the geometric properties of graphene i The coordinates of the two carbon atoms in the basic unit cell can be expressed as (0,0) and (1.23,2.13). By replicating the unit cell on a plane, a complete graphene structure can be generated. A carbon atom on the plane is randomly selected, and a straight line passing through the carbon atom is randomly made as the rotation axis. Graphene sheets in different directions are constructed by rotating at random angles.

[0044] Step 2: introducing pores into the two-dimensional graphene sheet structure, removing carbon atoms in the two-dimensional graphene sheet structure by random sampling to generate a foam structure with different porosities;

[0045] Specifically, in order to form a foam-like structure, pores need to be introduced into the two-dimensional graphene sheet structure, and the porosity P determines the size and distribution of the voids in the foam structure.

[0046] By randomly sampling, some atoms or atomic clusters are removed from the graphene sheet to generate foam-like structures with different porosities. The porosity P can be controlled by adjusting the number and position of the removed carbon atoms.

[0047] Further, a rotation matrix is ​​set to bend and twist the foam-like structure of the graphene sheet to generate a graphene foam model;

[0048] Specifically, the bending and twisting of the foam-like structure requires adjusting the bending parameters rotation angle θ and spherical angle φ, and the graphene sheet can be rotated and bent in three-dimensional space. These transformations can be achieved through the rotation matrix R(θ,φ), where the rotation angle θ controls the rotation angle of the graphene sheet around a certain axis (usually the x, y or z axis). The spherical angle φ is used to specify the rotation direction of the graphene sheet in three-dimensional space. The rotation matrix R(θ,φ) is a 3×3 matrix (as shown in Formula 1). The matrix maps the original coordinates to new coordinates to achieve the bending and twisting of the graphene sheet (as shown in Formula 2). The specific bending degree and direction are input into the program as adjustable parameters to generate graphene sheets with different bending and twisting degrees. In order to ensure that the generated structure is physically reasonable, molecular dynamics relaxation is used to adjust the distance and angle between atoms.

[0049]

[0050] r i-after =R(θ,φ)·r i (2)

[0051] When this process is implemented, the basic graphene sheet and the initial atomic coordinates r are first defined. i . Then, the porosity P is applied for pore generation, and some atoms are removed by random or regular algorithms. The graphene sheet is bent and twisted using the rotation matrix R(θ,φ). During the simulation, in order to stabilize the model, energy minimization is first performed for 0.1ns, followed by 0.2ns NPT ensemble relaxation and 0.2ns NVE ensemble relaxation to ensure that the system enters a stable state. The generated graphene foam model is displayed using MS, Ovito or other 3D visualization tools.

[0052] Step 3: doping the graphene foam model with dopant molecules by random contact method according to the mass fraction, linking the dopant molecules to the carbon atoms, and constructing a composite graphene foam model;

[0053] Specifically, after the pure graphene sheet is modeled, dopant molecules are added to the foam structure of the graphene sheet according to the mass fraction. The carbon atoms in the system are randomly selected using point contact, edge contact and surface contact methods, and the dopant molecules are linked to the carbon atoms, ensuring that there is no overlap between the carbon atoms. The direction of the dopant molecules is also randomly determined, and the number of contact points between the dopant and the graphene sheet is set to one or more according to the conditions.

[0054] Among them, the doping of dopants uses Python code. After the pure GF (graphene mass fraction accounts for 100%) modeling is completed, the dopant mass (total mass of graphene foam × target mass fraction ratio) is calculated according to the target mass fraction ratio of the dopant, and the number of dopant molecular chains required for doping n (n = total mass of graphene foam × target mass fraction ratio / mass of single molecular chain of dopant) is calculated according to the mass of a single molecular chain of the dopant. Through Python code, carbon atoms in GF are randomly selected as doping points, and the dopant molecular chains are linked to carbon atoms. By setting the number and sum of contact points, point contact, line contact and surface contact are formed, and the dopant is ensured to be evenly distributed in the composite.

[0055] As an embodiment, the random contact between the dopant and the GF includes point contact, edge contact, surface contact, etc., and ensures that there is no overlap between atoms. The direction of the dopant molecules in the system is also randomly determined. This series of methods realizes the interaction between the dopant and the GF.

[0056] Step 4: Construct a potential function model based on density functional theory, perform numerical simulation of the heat transfer performance and mechanical properties of the composite graphene foam model, and obtain the relationship between the stretching rate or compression rate and stress as well as the heat transfer performance during the stretching or compression process.

[0057] First, 1) construct a potential function model based on density functional theory, including:

[0058] GF is different from traditional crystals. Its morphology in the system is complex, changeable and irregular. The empirical potential function used in traditional numerical simulation is obtained based on empirical parameters, so it has certain limitations in describing the complex behavior of the system. The calculation often deviates greatly from the actual situation, and the described models are mostly small local models, which are difficult to avoid local contingency. In contrast, machine learning potential functions can more accurately capture the behavior of the system by learning a large number of model features, and are particularly good at dealing with highly nonlinear and complex large systems, thereby significantly improving the prediction performance.

[0059] Therefore, in the model selection of the present disclosure, in different simulations, pressures of -1, 0, and 1 GPa were randomly applied to the x, y, and z directions of the composite foam model, and the step size was selected as 1 fs. The temperature was gradually increased from 100K to 500K within a simulation time of about 2000ps. A structure was extracted every 100ps, and a total of 200 structures were obtained. After that, each structure was subjected to a 2% to 4% unit cell deformation and The atomic coordinates of the samples were perturbed to enrich the number of data sets. In addition, DFT-based MD was used to simulate graphene and graphene foam for 20 ps at higher temperatures and randomly sampled structures to expand the number of training and test sets.

[0060] The present invention adopts the currently most accurate quantum mechanics DFT calculation to obtain the force, energy and potential data of the structure, selects the VASP package and combines it with single-point energy calculation to perform high-precision DFT calculations on all data sets and adds the calculation results to the data set.

[0061] A machine learning method is used to calculate the configurational force and energy of the material system through XGBoost using the VASP software package based on density functional theory (DFT). These data are used as the data set for training potential functions.

[0062] Then, import the data into the Python environment, read the data file, extract the configuration feature matrix X (the feature matrix is ​​the input parameter for training, including the atomic positions of the model) and the target vector y (including force, energy, potential, etc.). Then, use train_test_split to divide the data into training and test sets. Define the XGBoost regression model and set the hyperparameter range, including the number of estimators nestimators, the learning rate η, and the maximum depth d. Use grid search cross validation (GridSearchCV) to tune the hyperparameters on the training set to find the best model. After training the best model, predict the training set and test set, calculate and output the mean square error (MSE) and determination coefficient (R2) of the model on the training set and test set. Use Matplotlib to visualize the results, show the relationship between the actual target value and the predicted value through a scatter plot, and add the determination coefficient and root mean square error (RMSE) to the figure to evaluate the model performance. The configuration of the training set is perturbed and relaxed through the NPT ensemble to generate more diverse configuration samples, and the configurations that cannot be described by the potential function are screened out and added to the training set and test set for retraining. This process is repeated continuously, and the pressure and applied force of each NPT ensemble relaxation increase to ensure that extreme configurations can be added to the training set and test set. Through active learning methods, new configuration data is continuously added, and the NEP-4 potential function neural network is retrained using active learning methods to obtain a more comprehensive potential function and improve its descriptive ability in different configurations. This method combines DFT calculations and machine learning to ensure that the model has good predictive performance and generalization capabilities in a variety of material configurations.

[0063] 3) Heat transfer performance simulation calculation

[0064] Before the calculation, an arbitrary modeling model is first selected and placed in an environment with a pressure of 0 Pa and a temperature of 300 K. The conjugate gradient algorithm (GGA) is used to perform energy minimization for 0.1 ns, followed by 0.2 ns of NPT ensemble relaxation and then 0.2 ns of NVE ensemble relaxation to ensure that the system is in a stable state.

[0065] Secondly, it is assumed that heat transfer is one-dimensional heat conduction perpendicular to the system, that is, the heat flow is transferred from the hot end face (high temperature end face) of the model to the cold end face (low temperature end face). The hot end and the cold end refer to the two ends of the material or system with different temperature zones. The hot end refers to the end with higher temperature in the system or material. In the process of heat conduction or heat exchange, the hot end is the source of heat, which transfers heat to the material. The cold end refers to the end with lower temperature in the system or material. The cold end receives the heat transferred from the hot end, so that the heat flow in the material flows from the hot end to the cold end. The Langevin method is used to divide the model into 15 layers of heat sinks along the z-axis, and the system is simulated by heat transfer for 2ns to ensure that the heat transfer reaches a stable state. The heat flow flows from the hot end of the box to the cold end along the heat transfer axis. The temperature gradient of the sample is generated by the exchange of particle kinetic energy between the hot zone and the cold zone. The energy subtracted from the hot zone is equal to the energy added to the cold zone, and increases linearly with time. When the heat reduction at the hot end is equal to the heat increase at the cold end, it means that the heat exchange has been stably established in the system. The formula for stable heat exchange is as follows:

[0066]

[0067] Where J is the heat flux along the heat transfer axis, represents the temperature gradient. The heat flux can be determined from the energy transfer rate, where E represents the accumulated energy, S is the cross-sectional area of ​​the supercell, and k is the thermal conductivity.

[0068] 4) Mechanical properties simulation calculation

[0069] The system is set to stretch or compress in the z-axis direction. Through this volume change, the dynamic change law of the system pressure is obtained. Since the model adopts a periodic repetitive structure, the repetitive units in the system adopt periodic boundary conditions to ensure that the size of the simulation box can accommodate the expected deformation. During the simulation, energy minimization is first performed for 0.1ns, followed by 0.2ns NPT ensemble relaxation and 0.2ns NVE ensemble relaxation to ensure that the system enters a stable state. Then, it is stretched or compressed in the strain direction for 0.5ns at a specific strain rate. The molecular dynamics software GPUMD is used to calculate the forces between atoms at different time steps, and the Virial theorem (such as formula 5) is used to calculate the stress tensor during the stretching or compression process to observe the relationship between the stretching rate or compression rate and the stress.

[0070]

[0071] where σ is the stress tensor, N is the number of atoms in the simulation, V is the simulation volume, and F i and F j It is the force between atoms.

[0072] Example 2

[0073] In one embodiment of the present disclosure, a compressible graphene foam composite modeling and heat transfer performance simulation system is provided, comprising:

[0074] A data acquisition module is used to acquire the set basic parameters of the graphene sheet, define the initial coordinates of each carbon atom, and determine the basic two-dimensional graphene sheet structure;

[0075] The model building module is used to introduce pores into the two-dimensional graphene sheet structure, remove carbon atoms in the two-dimensional graphene sheet structure by random sampling, and generate foam structures with different porosities; set the rotation matrix to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; according to the mass fraction, the random contact method is used to add dopant molecules to the graphene foam model, and the dopant molecules are linked to the carbon atoms to construct a composite graphene foam model;

[0076] The numerical simulation module is used to construct a potential function model based on density functional theory, to perform numerical simulation of the heat transfer performance and mechanical properties of the composite graphene foam model, and to obtain the heat transfer process and the relationship between the elongation or compression rate and stress.

[0077] Example 3

[0078] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the compressible graphene foam composite modeling and heat transfer performance simulation method is implemented.

[0079] Example 4

[0080] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the compressible graphene foam composite modeling and heat transfer performance simulation method.

[0081] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0083] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for modeling and simulating heat transfer performance of a compressible graphene foam composite, characterized in that: include: Obtain the set basic parameters of the graphene sheet, define the initial coordinates of each carbon atom, and determine the basic two-dimensional graphene sheet structure; Pores are introduced into the two-dimensional graphene sheet structure, and carbon atoms in the two-dimensional graphene sheet structure are removed by random sampling to generate foam structures with different porosities; The rotation matrix is ​​set to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; the dopant molecules are doped into the graphene foam model according to the mass fraction using a random contact method, and the dopant molecules are linked to the carbon atoms to construct a composite graphene foam model; Based on the density functional theory, a potential function model is constructed to perform numerical simulations on the heat transfer and mechanical properties of the composite graphene foam model to obtain the heat transfer process and the relationship between the elongation or compression rate and stress.

2. The compressible graphene foam composite modeling and heat transfer performance simulation method according to claim 1, characterized in that: The set basic parameters of the graphene sheet include: the size parameters of the graphene sheet, length, width, height, and the unit atomic bond length range.

3. The compressible graphene foam composite modeling and heat transfer performance simulation method according to claim 1, characterized in that: Pores are introduced into a two-dimensional graphene sheet structure, and carbon atoms in the two-dimensional graphene sheet structure are removed by random sampling to generate foam structures with different porosities, including: the porosity determines the size and distribution of voids in the foam structure, and some atoms or atomic clusters are removed from the graphene sheet by random sampling to generate foam structures with different porosities, wherein the porosity is controlled by adjusting the number and position of the removed carbon atoms.

4. The compressible graphene foam composite modeling and heat transfer performance simulation method according to claim 1, characterized in that: A rotation matrix is ​​set to bend and twist the foam structure of the graphene sheet to generate a graphene foam model, including: the bending and twisting of the foam structure are achieved by adjusting the bending parameter rotation angle and the spherical angle, and the graphene sheet can be rotated and bent in three-dimensional space. The rotation and bending transformation is achieved through the rotation matrix, and the specific bending degree and direction are input as adjustable parameters to generate graphene sheets with different bending and twisting degrees.

5. The compressible graphene foam composite modeling and heat transfer performance simulation method according to claim 1, characterized in that: The composite graphene foam model is constructed, including: after the pure graphene sheet is modeled, dopant molecules are doped into the foam structure of the graphene sheet according to the mass fraction, and carbon atoms in the system are randomly selected by point contact, edge contact and surface contact methods, and the dopant molecules are linked to the carbon atoms, and it is ensured that there is no overlap between the carbon atoms, the direction of the dopant molecules is also randomly determined, and the number of contact points between the dopant and the graphene sheet is set to one or more according to conditions.

6. The compressible graphene foam composite modeling and heat transfer performance simulation method according to claim 1, characterized in that: Based on density functional theory, a potential function model is constructed, and the heat transfer performance and mechanical properties of the composite graphene foam model are numerically simulated to obtain the relationship between the stretching rate or compression rate and the stress, as well as the heat transfer performance during the stretching or compression process, including: using quantum mechanics DFT calculations to obtain the force, energy and potential data of the structure, forming a data set, selecting the VASP package and combining it with single-point energy calculations to perform high-precision DFT calculations on the data set and adding the calculation results to the data set, using machine learning methods, through XGBoost, using density functional theory to calculate the force and energy data of the material system, extracting the feature matrix and target vector, defining the XGBoost regression model, and setting the hyperparameter range to construct a potential function model.

7. The compressible graphene foam composite modeling and heat transfer performance simulation method according to claim 6, characterized in that: The composite graphene foam model was placed at a pressure of 0Pa and a temperature of 300K, and the conjugate gradient algorithm GGA was used to perform energy minimization for 0.1ns. After 0.2ns of NPT ensemble relaxation, 0.2ns of NVE ensemble relaxation was performed to ensure that the system was in a stable state. The temperatures of the hot and cold ends were adjusted, and the Langevin method was used to divide the model into 15 layers of heat sinks along the z-axis, and the system was subjected to a 2ns heat transfer simulation.

8. Compressible graphene foam composite modeling and heat transfer performance simulation system, characterized in that: include: A data acquisition module is used to acquire the set basic parameters of the graphene sheet, define the initial coordinates of each carbon atom, and determine the basic two-dimensional graphene sheet structure; The model building module is used to introduce pores into the two-dimensional graphene sheet structure, remove carbon atoms in the two-dimensional graphene sheet structure by random sampling, and generate foam structures with different porosities; set the rotation matrix to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; according to the mass fraction, the random contact method is used to add dopant molecules to the graphene foam model, and the dopant molecules are linked to the carbon atoms to construct a composite graphene foam model; The numerical simulation module is used to construct a potential function model based on density functional theory, to perform numerical simulation of the heat transfer performance and mechanical properties of the composite graphene foam model, and to obtain the heat transfer process and the relationship between the elongation or compression rate and stress.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the compressible graphene foam composite modeling and heat transfer performance simulation method as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the compressible graphene foam composite modeling and heat transfer performance simulation method as described in any one of claims 1 to 7.

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