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

Abstract

The present invention provides a compressible graphene foam composite modeling and heat transfer performance simulation method, which relates to the technical field of graphene heat transfer performance simulation, including: obtaining basic parameters of graphene sheets and determining a basic two-dimensional graphene sheet structure; introducing pores into the two-dimensional graphene sheet structure and removing carbon atoms in the two-dimensional graphene sheet structure to generate a foam structure with different porosities; setting a rotation matrix to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; using a random contact method to introduce dopant molecules into the graphene foam model according to the mass fraction, linking the dopant molecules to carbon atoms to construct a composite graphene foam model; constructing a potential function model based on density functional theory, numerically simulating the heat transfer performance and mechanical properties of the composite graphene foam model, and obtaining 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.

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] 2D graphene, due to its unique physical properties in heat and mass transfer, is increasingly being used in thermal management applications in electronic components. Graphene foam exhibits multi-scale structural characteristics. 3D graphene foam, assembled from 2D graphene sheets, retains graphene's excellent two-dimensional properties to a certain extent while also offering numerous advantages, such as large-scale fabrication, high surface area, high-temperature resistance, and lightweight properties. It is finding applications in thermal interface materials, energy storage devices, filters, sensors, and other fields, and is gradually becoming mainstream. Among the numerous methods for preparing graphene composites, chemical vapor deposition (CVD) is currently recognized as the most effective method for obtaining large-scale, uniform, and high-quality 2D graphene. Using a metal foam (such as nickel foam) as a substrate, a carbon source, such as CH4, is chemically decomposed and deposited onto the substrate surface under a specific temperature or external field. The metal substrate is then removed by etching, resulting in a graphene foam (GF) that mimics the metal foam skeleton. As a porous material, graphene foam (GF) prepared by CVD exhibits numerous outstanding properties in terms of heat and mass transfer. GF itself has advantages such as high thermal conductivity, and the compressibility caused by its large porosity allows GF and its composites to have greater variability in thermal conductivity. During compression, the distance between the graphene sheets in the foam structure decreases, resulting in increased in-plane heat conduction and decreased vertical heat conduction, which in turn causes changes in thermal conductivity. The importance of exploring graphene foam in various applications is self-evident, but due to complex factors such as experimental preparation, experimental exploration of the properties of graphene foam requires a lot of manpower, material and financial resources.

[0004] In recent years, numerical simulation has become the mainstream research method. 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 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 randomness. In addition, the traditional empirical potential uses classic two-dimensional graphene parameters to fit the lack of data on the three-dimensional structure of graphene, resulting in the existing empirical potential having poor credibility and accuracy in describing the graphene foam system. Moreover, in previous simulation studies, due to limited computing resources, graphene was confined to a 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 the 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] A rotation matrix is ​​set to bend and twist the foam-like structure of the graphene sheet to generate a graphene foam model. Dopant molecules are added to the graphene foam model using a random contact method according to the mass fraction, and the dopant molecules are linked to the carbon atoms to construct a composite graphene foam model.

[0011] Based on density functional theory, a potential function model is constructed to perform numerical simulations of 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] The data acquisition module is used to 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;

[0015] The model construction module is used to introduce pores into the two-dimensional graphene sheet structure and remove carbon atoms from the two-dimensional graphene sheet structure through random sampling to generate foam structures with different porosities; a rotation matrix is ​​set to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; dopant molecules are added to the graphene foam model using a random contact method according to mass fraction, and the dopant molecules are linked to 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, perform numerical simulation of the heat transfer and mechanical properties of the composite graphene foam model, and obtain the heat transfer process and the relationship between the stretch rate 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 includes: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement 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. It 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 disclosed method for modeling and simulating the heat transfer performance of compressible graphene foam composites constructs a potential function of the PDMS-doped graphene foam composite through a machine learning method, which compensates 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 a Python program to perform three-dimensional modeling of the graphene foam, but other modeling tools and languages, such as MATLAB and Fortran, can also be considered. After the pure GF modeling is completed, dopant molecules are added to the GF foam according to the mass fraction. Using a random contact method, carbon atoms in the system are randomly selected, and the dopant molecular chains are linked to the carbon atoms. The number of contact points between the dopant and the GF can be set to 1 or more according to the conditions. The present disclosure 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. This series of methods realizes the interaction between the dopant and the GF. The present disclosure helps to gain a deeper understanding of the change law of the heat transfer performance of the PDMS-doped graphene foam composite under strain, and lays the 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, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

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

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

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

[0029] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0030] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as 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 intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0032] Example 1

[0033] One embodiment of the present disclosure provides a method for modeling and simulating the heat transfer properties of a compressible graphene foam composite. Using a Python program, a three-dimensional graphene foam model is established, ensuring that the modeling more closely matches the behavior of actual CVD graphene foam. A machine learning-based heat transfer performance simulation method is also proposed, enabling simplified, automated, and streamlined simulation of graphene heat and mass transfer. This method offers 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 and removing carbon atoms from the two-dimensional graphene sheet structure by random sampling to generate foam structures with different porosities;

[0036] Step 3: Setting a rotation matrix to bend and twist the foam-like structure of the graphene sheet to generate a graphene foam model; using a random contact method to dope dopant molecules into the graphene foam model according to the mass fraction, linking the dopant molecules to the carbon atoms to construct 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 elongation or compression rate and stress.

[0038] As an example, in previous simulation studies, due to limited computing resources, graphene was confined to a lamellar shape, making it difficult to perform curved modeling. This limitation, to a certain extent, affected the accurate modeling of the complexity of real systems. This 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, set the basic parameters, 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 two carbon atoms in a 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. By randomly selecting a carbon atom on the plane and drawing a line through it as the axis of rotation, graphene sheets with different orientations can be constructed by rotating them at random angles.

[0044] Step 2: Introducing pores into the two-dimensional graphene sheet structure and removing carbon atoms from the two-dimensional graphene sheet structure by random sampling to generate foam structures 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] Furthermore, 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, the rotation angle θ and the spherical angle φ, to rotate and bend the graphene sheet in three-dimensional space. These transformations can be achieved using 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 Equation 1). The matrix maps the original coordinates to new coordinates to achieve the bending and twisting of the graphene sheet (as shown in Equation 2). The specific degree and direction of bending are input as adjustable parameters into the program to generate graphene sheets with different degrees of bending and twisting. To ensure that the generated structures are physically reasonable, molecular dynamics relaxation is used to adjust the distances and angles 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 to generate pores, 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, to stabilize the model, energy minimization is first performed for 0.1ns, followed by 0.2ns of NPT ensemble relaxation and 0.2ns of 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 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;

[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 incorporation of dopants uses Python code. After the pure GF (graphene mass fraction accounts for 100%) is modeled, the dopant mass is calculated based on the target mass fraction of the dopant (total mass of graphene foam × target mass fraction ratio). And based on the mass of a single molecular chain of the dopant, the number of dopant molecular chains required to be doped, n (n = total mass of graphene foam × target mass fraction ratio / mass of a single molecular chain of the dopant). Through Python code, carbon atoms in the GF are randomly selected as doping points, and the dopant molecular chains are linked to the carbon atoms. By setting the number and sum of contact points, point contact, line contact, and surface contact are formed, and the uniform distribution of dopants in the composite is ensured.

[0055] As an example, random contact between the dopant and the GF can be achieved through point, edge, and surface contact, ensuring that atoms do not overlap. The orientation of the dopant molecules in the system is also randomly determined. This series of methods enables 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] GFs differ from traditional crystals in that their morphologies within a system are complex, varied, and irregular. The empirical potential functions used in traditional numerical simulations are derived from empirical parameters, and therefore have limitations in describing complex system behavior. Calculations often deviate significantly from reality, and the models they describe are mostly small, local models, making it difficult to avoid local contingencies. In contrast, machine learning potential functions, by learning from a large number of model features, can more accurately capture system behavior. They are particularly adept at handling highly nonlinear and complex large systems, significantly improving predictive performance.

[0059] Therefore, in the model selection of this 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, with a step size of 1 fs. The temperature was gradually increased from 100K to 500K within a simulation time of about 2000 ps. A structure was extracted every 100 ps, ​​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 graphene and graphene foam were perturbed to enrich the dataset. In addition, DFT-based MD simulations of graphene and graphene foam were performed at higher temperatures for 20 ps and random sampling of structures was performed to expand the training and test sets.

[0060] This paper uses the currently most accurate quantum mechanics DFT calculation to obtain the force, energy and potential data of the structure. The VASP package is selected and combined with single-point energy calculation to perform high-precision DFT calculations on all data sets and the calculation results are added to the data set.

[0061] A machine learning method is used to calculate the configurational force and energy data 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, the data was imported into the Python environment, the data file was read, and 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.) were extracted. Then, the data was divided into training and test sets using train_test_split. The XGBoost regression model was defined, and the hyperparameter range was set, including the number of estimators nestimators, the learning rate η, and the maximum depth d. Hyperparameters were tuned on the training set using grid search cross validation (GridSearchCV) to find the best model. After training the best model, predictions were made on the training and test sets, and the mean square error (MSE) and coefficient of determination (R2) of the model on the training and test sets were calculated and output. Matplotlib was used to visualize the results, and a scatter plot was used to show the relationship between the actual target value and the predicted value. The coefficient of determination and root mean square error (RMSE) were added to the plot to evaluate the model performance. The configurations of the training set are perturbed and relaxed through the NPT ensemble to generate more diverse configuration samples, and configurations that cannot be described by the potential function are screened out and added to the training and test sets 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 and test sets. Through active learning methods, new configuration data are 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 for 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, we assume that heat transfer is one-dimensional conduction perpendicular to the system, meaning that heat flows from the hot end (high-temperature end) of the model to the cold end (low-temperature end). The hot and cold ends refer to the two ends of a material or system with different temperature regions, with the hot end referring to the end with the higher temperature in the system or material. In heat conduction or heat exchange, the hot end is the source of heat, transferring heat to the material. The cold end refers to the end of the system or material with the lower temperature. The cold end receives heat transferred from the hot end, causing heat in the material to flow from the hot end to the cold end. Using the Langevin method, the model is divided into 15 heat sink layers along the z-axis, and a 2-ns heat transfer simulation is performed on the system to ensure steady-state heat transfer. Heat flows along the heat transfer axis from the hot end to the cold end of the box. 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, and the dynamic change law of the system pressure is obtained through this volume change. Since the model adopts a periodic repetitive structure, the repeating 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 process, 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] The data acquisition module is used to 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;

[0075] The model construction module is used to introduce pores into the two-dimensional graphene sheet structure and remove carbon atoms from the two-dimensional graphene sheet structure through random sampling to generate foam structures with different porosities; a rotation matrix is ​​set to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; dopant molecules are added to the graphene foam model using a random contact method according to mass fraction, and the dopant molecules are linked to 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, perform numerical simulation of the heat transfer and mechanical properties of the composite graphene foam model, and obtain the heat transfer process and the relationship between the stretch rate 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, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements 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 produce 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 the instructions executed on the computer or other programmable device 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. Those skilled in the art 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. A rotation matrix is ​​set to bend and twist the foam-like structure of the graphene sheet to generate a graphene foam model. Dopant molecules are added to the graphene foam model using a random contact method according to the mass fraction, and the dopant molecules are linked to the carbon atoms to construct a composite graphene foam model. Constructing a composite graphene foam model includes: after a pure graphene sheet is modeled, doping dopant molecules into the foam structure of the graphene sheet according to a mass fraction, randomly selecting carbon atoms in the system using point contact, edge contact, and surface contact methods, and linking the dopant molecules to the carbon atoms, ensuring that the carbon atoms do not overlap, and randomly determining the direction of the dopant molecules. The number of contact points between the dopant and the graphene sheet is set to one or more according to conditions; Based on density functional theory, a potential function model is constructed to perform numerical simulations of 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-like structure of the graphene sheet to generate a graphene foam model, including: the bending and twisting of the foam-like structure are achieved by adjusting the bending parameter rotation angle and 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: 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. This includes: 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 configuration of the material system, extracting the feature matrix and target vector, defining the XGBoost regression model, and setting the hyperparameter range to construct the potential function model.

6. The compressible graphene foam composite modeling and heat transfer performance simulation method according to claim 5, characterized in that: The composite graphene foam model was placed at a pressure of 0 Pa and a temperature of 300 K. The conjugate gradient algorithm (GGA) was 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 was in a stable state. The temperatures of the hot and cold ends were adjusted, and the model was divided into 15 layers of heat sinks along the z axis using the Langevin method. The system was then subjected to a 2 ns heat transfer simulation.

7. A compressible graphene foam composite modeling and heat transfer performance simulation system, specifically implementing the compressible graphene foam composite modeling and heat transfer performance simulation method according to any one of claims 1 to 6, characterized in that: include: The data acquisition module is used to 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; The model construction module is used to introduce pores into the two-dimensional graphene sheet structure and remove carbon atoms from the two-dimensional graphene sheet structure through random sampling to generate foam structures with different porosities; a rotation matrix is ​​set to bend and twist the foam structure of the graphene sheet to generate a graphene foam model; dopant molecules are added to the graphene foam model using a random contact method according to mass fraction, and the dopant molecules are linked to 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, perform numerical simulation of the heat transfer and mechanical properties of the composite graphene foam model, and obtain the heat transfer process and the relationship between the stretch rate or compression rate and stress.

8. 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 according to any one of claims 1 to 6 is implemented.

9. 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 to enable the electronic device to implement the compressible graphene foam composite modeling and heat transfer performance simulation method according to any one of claims 1 to 6.

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