Modeling prediction method for thermal conductivity of graphene-based phase change energy storage composite material

Abstract

The invention discloses a modeling prediction method for the thermal conductivity of a graphene-based phase change energy storage composite material, which comprises the following steps: firstly, determining the thermal conductivity and interface thermal resistance of graphene by adopting a molecular dynamics simulation method; secondly, generating a random Thiessen polygon skeleton structure through an Abaqus plug-in developed by Python, and exporting the random Thiessen polygon skeleton structure as a geometric model; secondly, importing the geometric model into COMSOL software, setting boundary conditions, and performing thermal conductivity calculation in combination with a molecular dynamics simulation result; and finally, repeatedly generating a plurality of geometric models for numerical simulation, calculating the thermal conductivity, and averaging until the numerical value converges. By combining molecular dynamics simulation and finite element analysis, the thermal conductivity of the graphene filled paraffin composite phase change material can be accurately predicted, and theoretical guidance is provided for material design and performance optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management of functional materials, and in particular to a modeling and prediction method for thermal conductivity of a graphene-based phase-change energy storage composite material. Background Art

[0002] With the rapid development of modern science and technology, thermal management and energy storage technologies have become increasingly important in the fields of electronic equipment, aerospace, energy, etc. Phase change materials (PCMs) are widely used in temperature control and energy management systems because they can absorb and release a large amount of latent heat during the phase change process. Paraffin, as a common phase change material, has attracted widespread attention due to its high latent heat and stable chemical properties. However, the low thermal conductivity of paraffin limits its further promotion in efficient thermal management applications.

[0003] Graphene is a two-dimensional material composed of a single layer of carbon atoms with extremely high thermal conductivity, excellent mechanical properties and good electrical conductivity. Phase change materials filled into the graphene skeleton can significantly improve the thermal conductivity of the composite material, thereby enhancing its thermal management performance. However, the interfacial thermal resistance between graphene and paraffin and the microstructure of the composite material have a complex effect on the overall thermal conductivity of the material, and traditional experimental methods and simple theoretical models are difficult to accurately predict the thermal conductivity of this composite material.

[0004] At present, the research on the thermal conductivity of graphene-filled paraffin composite phase change materials mainly focuses on experimental measurements and simple theoretical models. Although experimental measurements can provide real thermal conductivity data, the process is complicated, time-consuming and costly. Simple theoretical models often cannot accurately describe the microstructure and interface effects of the material, and there is a large deviation between the predicted results and the actual situation. Numerical simulation methods such as finite element analysis (FEA) and molecular dynamics simulation (MD) have gradually become important tools for studying the thermal conductivity of composite materials. However, a single method is difficult to fully consider the multi-scale effects and interface complexity of graphene / paraffin composites, and a comprehensive modeling method combining multiple methods is urgently needed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a modeling and prediction method for the thermal conductivity of a graphene-based phase change energy storage composite material in view of the defects involved in the background technology.

[0006] The present invention adopts the following technical solutions to solve the above technical problems: A modeling and prediction method for thermal conductivity of a graphene-based phase-change energy storage composite material comprises the following steps: Step 1), using molecular dynamics simulation method to determine the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material; Step 1.1), define the system parameters in LAMMPS software, set the unit system to metal units, set the dimension to three dimensions, set the x and y directions to periodic boundary conditions, and the z direction to fixed boundary conditions; Step 1.2), perform energy minimization, use the conjugate gradient method, set the maximum force convergence criterion and the maximum number of iterations; Step 1.3), initialize the system temperature, set the initial velocity of Gaussian distribution, and run to equilibrium state; Step 1.4), divide the system into hot and cold source areas in the z direction, set the hot source temperature and cold source temperature respectively, and set the remaining areas as fixed ends; Step 1.5), use Langevin thermostat to maintain the temperature of heat source and cold source, and run until stable temperature distribution; Step 1.6), record the heat flux density and temperature gradient of the system, and calculate the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material by Fourier's thermal conductivity law; Step 2), generate the geometric structure of the graphene skeleton through the Abaqus subroutine developed in Python; Step 2.1), place the Voronoi generation script in the Abaqus plugin folder, start Abaqus, select the plugin and run it; Step 2.2), select the generated structure type as uniform in the parameter setting, set the wall thickness to the thickness of the graphene skeleton sheet, and insert at least M random points to generate the geometric structure of the graphene skeleton, where M is the preset first constant threshold; Step 2.3), the generated Voronoi structure, i.e. the geometric structure of the graphene skeleton, is exported as a STEP format file; Step 3), calculate the thermal conductivity of the graphene-based phase change energy storage composite material model in COMSOL; Step 3.1), import the geometric structure of the graphene skeleton in the STEP file format into COMSOL to check the integrity of the geometric structure of the graphene skeleton; Step 3.2), construct a cube with the same size as the imported graphene skeleton geometry in COMSOL, and perform Boolean operations on the two to generate a geometric structure model of the graphene-based phase change energy storage composite material; Step 3.3), compressing the geometric structure model of the graphene-based phase change energy storage composite material according to the pore structure of the actual material, and adjusting the compression ratio to match the porosity of the actual material; Step 3.4), perform meshing and use a refined mesh at the interface; Step 3.5), set boundary conditions: apply a fixed heat flux at one end of the geometric structure model of the graphene-based phase change energy storage composite material, apply a fixed temperature at the other end, set the remaining four faces as periodic boundary conditions, and use the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material as simulation input parameters; Step 3.6), run the numerical simulation, record the steady-state temperature distribution and heat flow data, and calculate the effective thermal conductivity of the geometric structure model of the graphene-based phase change energy storage composite material; Step 4), repeat step 2) and step 3) N times, with N preset second constant thresholds, generate a geometric structure model of the graphene-based phase change energy storage composite material with N convergent average values ​​of effective thermal conductivities under the same setting parameters, and use the converged average value as the thermal conductivity of the graphene-based phase change energy storage composite material.

[0007] As a further optimization scheme of the modeling and prediction method of thermal conductivity of a graphene-based phase change energy storage composite material of the present invention, in the step 1.2), the maximum force convergence standard is set to 1.0e-8, and the maximum number of iterations is set to 1 million steps.

[0008] As a further optimization scheme of the modeling and prediction method of thermal conductivity of a graphene-based phase change energy storage composite material of the present invention, the system temperature is initialized to 300K in step 1.3).

[0009] As a further optimization scheme of the modeling prediction method of thermal conductivity of a graphene-based phase change energy storage composite material of the present invention, in the step 1.4), the heat source temperature is set to 350K and the cold source temperature is set to 250K.

[0010] As a further optimization scheme of the modeling and prediction method of thermal conductivity of a graphene-based phase change energy storage composite material according to the present invention, the M is taken as 30.

[0011] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects: The present invention combines molecular dynamics simulation and finite element analysis, comprehensively considers the intrinsic thermal conductivity of graphene, interfacial thermal resistance and the microstructure of random Thiessen polygon skeleton, and can more accurately predict the thermal conductivity of graphene-based phase change energy storage composite materials. By generating geometric models multiple times and calculating the average value of their effective thermal conductivity, the convergence and stability of the prediction results are ensured. This method significantly reduces the experimental workload, provides efficient and accurate theoretical guidance for the design and performance optimization of materials, and helps to improve the overall performance and efficiency of thermal management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of a modeling and prediction method for thermal conductivity of a graphene-based phase-change energy storage composite material of the present invention; Figure 2 It is a model for calculating the thermal conductivity and interfacial thermal resistance of the graphene composite material of the present invention; Figure 3 The core python code for generating random Voronoi polygon structures for the present invention; Figure 4 Abaqus user interface for generating Voronoi polygon plug-in for the present invention; Figure 5 A Voronoi polygonal graphene skeleton model generated for the present invention; Figure 6 A geometric model of the graphene phase change energy storage composite material of the present invention; Figure 7 It is a schematic diagram for comparing the calculated results of the present invention with the experimental measured values. DETAILED DESCRIPTION

[0013] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings: The present invention can be implemented in many different forms and should not be considered to be limited to the embodiments described herein. On the contrary, these embodiments are provided to make this disclosure thorough and complete, and will fully express the scope of the present invention to those skilled in the art. In the accompanying drawings, components are enlarged for clarity.

[0014] Please refer to Figure 1 The embodiment of the present invention provides a modeling and prediction method for the thermal conductivity of a graphene-based phase change energy storage composite material. The method is based on a combination of molecular dynamics simulation and numerical simulation, and fully considers the influence of various factors such as the number of graphene layers, defects, size, and interface thermal resistance, so as to achieve an accurate prediction of the effective thermal conductivity of the graphene-based phase change energy storage composite material. The method specifically includes the following steps: Step 1), using molecular dynamics simulation method to determine the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material; Step 1.1), use LAMMPS software to define system parameters, the unit system is metal units, and the dimension is three-dimensional. Set the x and y directions as periodic boundary conditions, and the z direction as fixed boundary conditions.

[0015] Step 1.2) Perform energy minimization using the conjugate gradient method, set the maximum force convergence criterion to 1.0e-8, and the maximum number of iterations to 1 million steps.

[0016] Step 1.3), initialize the system temperature to 300K, set the initial velocity to Gaussian distribution, and run to equilibrium.

[0017] Step 1.4), divide the system into hot and cold source areas in the z direction, set the hot source temperature to 350K and the cold source temperature to 250K respectively, and set the rest of the area as fixed ends.

[0018] Step 1.5) Use a Langevin thermostat to maintain the temperature of the heat source and the cold source until a stable temperature distribution is achieved.

[0019] Step 1.6), record the heat flux density and temperature gradient of the system, and calculate the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material by Fourier's thermal conductivity law, as shown in Figure 2 shown.

[0020] Step 2), the geometric structure of the graphene skeleton is generated by the Abaqus subroutine developed in Python.

[0021] Step 2.1), place the Voronoi generation script in the Abaqus plugin folder, start Abaqus, select the plugin and run it.

[0022] Step 2.2), select the generated structure type as uniform in the parameter settings, set the wall thickness to the thickness of the graphene skeleton layer, and insert at least 30 random points to generate the geometric structure of the graphene skeleton, such as Figure 3 and Figure 4 shown.

[0023] Step 2.3), the generated Voronoi structure is the geometric structure of the graphene skeleton, which is exported as a STEP format file, such as Figure 5 shown.

[0024] Step 3) Calculate the thermal conductivity of the graphene-based phase change energy storage composite material model in COMSOL.

[0025] Step 3.1), import the geometric structure of the graphene skeleton in STEP file format into COMSOL and check the integrity of the geometric structure.

[0026] In step 3.2), a cube with the same size as the imported graphene skeleton geometry is constructed in COMSOL, and a Boolean operation is performed on the two to generate a geometric structure model of the graphene-based phase change energy storage composite material, such as Figure 6 shown.

[0027] In step 3.3, the geometric model is compressed according to the pore structure of the actual material, and the compression ratio is adjusted to match the porosity of the actual material.

[0028] Step 3.4), perform meshing and use a refined mesh at the interface.

[0029] Step 3.5), set the boundary conditions: apply a fixed heat flux at one end of the geometric structure model, a fixed temperature at the other end, set the remaining four faces as periodic boundary conditions, and use the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material as simulation input parameters.

[0030] Step 3.6), run the numerical simulation, record the steady-state temperature distribution and heat flow data, and calculate the effective thermal conductivity of the geometric model, such as Figure 7 shown.

[0031] Step 4), repeat steps 2) and 3), generate multiple models under the same setting parameters, calculate their effective thermal conductivity and take the average value.

[0032] In step 4.1), a loop is set up in the Python script to generate the Voronoi geometry structure and the numerical simulation is repeated until the average value converges.

[0033] Step 4.2), the converged average value is the thermal conductivity of the graphene-based phase change energy storage composite material. The simulation data and experimental measurement data under different porosities are shown in Figure 7 shown.

[0034] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0035] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A modeling and prediction method for thermal conductivity of graphene-based phase change energy storage composite materials, characterized in that: The following steps are involved: Step 1), using molecular dynamics simulation method to determine the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material; Step 1.1), define the system parameters in LAMMPS software, set the unit system to metal units, set the dimension to three dimensions, set the x and y directions to periodic boundary conditions, and the z direction to fixed boundary conditions; Step 1.2), perform energy minimization, use the conjugate gradient method, set the maximum force convergence criterion and the maximum number of iterations; Step 1.3), initialize the system temperature, set the initial velocity of Gaussian distribution, and run to equilibrium state; Step 1.4), divide the system into hot and cold source areas in the z direction, set the hot source temperature and cold source temperature respectively, and set the remaining areas as fixed ends; Step 1.5), use Langevin thermostat to maintain the temperature of heat source and cold source, and run until stable temperature distribution; Step 1.6), record the heat flux density and temperature gradient of the system, and calculate the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material by Fourier's thermal conductivity law; Step 2), generate the geometric structure of the graphene skeleton through the Abaqus subroutine developed in Python; Step 2.1), place the Voronoi generation script in the Abaqus plugin folder, start Abaqus, select the plugin and run it; Step 2.2), select the generated structure type as uniform in the parameter setting, set the wall thickness to the thickness of the graphene skeleton sheet, and insert at least M random points to generate the geometric structure of the graphene skeleton, where M is the preset first constant threshold; Step 2.3), the generated Voronoi structure, i.e. the geometric structure of the graphene skeleton, is exported as a STEP format file; Step 3), calculate the thermal conductivity of the graphene-based phase change energy storage composite material model in COMSOL; Step 3.1), import the geometric structure of the graphene skeleton in the STEP file format into COMSOL to check the integrity of the geometric structure of the graphene skeleton; Step 3.2), construct a cube with the same size as the imported graphene skeleton geometry in COMSOL, and perform Boolean operations on the two to generate a geometric structure model of the graphene-based phase change energy storage composite material; Step 3.3), compressing the geometric structure model of the graphene-based phase change energy storage composite material according to the pore structure of the actual material, and adjusting the compression ratio to match the porosity of the actual material; Step 3.4), perform meshing and use a refined mesh at the interface; Step 3.5), set boundary conditions: apply a fixed heat flux at one end of the geometric structure model of the graphene-based phase change energy storage composite material, apply a fixed temperature at the other end, set the remaining four faces as periodic boundary conditions, and use the thermal conductivity and interface thermal resistance of the graphene-based phase change energy storage composite material as simulation input parameters; Step 3.6), run the numerical simulation, record the steady-state temperature distribution and heat flow data, and calculate the effective thermal conductivity of the geometric structure model of the graphene-based phase change energy storage composite material; Step 4), repeat step 2) and step 3) N times, with N preset second constant thresholds, generate a geometric structure model of the graphene-based phase change energy storage composite material with N convergent average values ​​of effective thermal conductivities under the same setting parameters, and use the converged average value as the thermal conductivity of the graphene-based phase change energy storage composite material.

2. The modeling and prediction method for thermal conductivity of graphene-based phase change energy storage composite materials according to claim 1, characterized in that: In step 1.2), the maximum force convergence criterion is set to 1.0e-8, and the maximum number of iterations is set to 1 million steps.

3. The modeling and prediction method for thermal conductivity of graphene-based phase change energy storage composite materials according to claim 1, characterized in that: In step 1.3), the system temperature is initialized to 300K.

4. The modeling and prediction method for thermal conductivity of graphene-based phase change energy storage composite materials according to claim 1, characterized in that: In step 1.4), the heat source temperature is set to 350K and the cold source temperature is set to 250K.

5. The modeling and prediction method for thermal conductivity of graphene-based phase change energy storage composite materials according to claim 1, characterized in that: The M is set to 30.

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