A design method for thermal conductivity modification of copper-based composite materials based on graphene doping
Through molecular dynamics simulation of graphene-doped copper-based composite material model, the ablation problem of guide rail material during electromagnetic launch was solved, the thermal conductivity was optimized, the launch efficiency was improved and the experimental cost was reduced.
Patent Information
- Application Number
- CN202310023541.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-01-09
- Publication Date
- 2025-09-26
- Estimated Expiration
- 2043-01-09
AI Technical Summary
During electromagnetic launch, existing guide rail materials suffer from ablation and sliding due to high-energy arc erosion, affecting launch efficiency. Traditional experimental testing is costly and difficult to optimize thermal conductivity.
By establishing a graphene-doped copper-based composite material model and using the molecular dynamics simulation software LAMMPS and potential function optimization, the surface temperature changes under different graphene contents and distribution patterns are calculated to guide material modification design.
It has achieved accurate prediction of the thermal conductivity of the guide rail material under extreme working conditions, guided the experiment to manufacture the guide rail material with better thermal conductivity, reduced costs and improved launch efficiency.
Smart Images
Figure CN116030918B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a thermal conductivity modification design method for a copper-based composite material based on graphene doping, and belongs to the field of electromagnetic catapult guide rail material design. Background Art
[0002] With the advancement of science and technology, the pace of innovation in military equipment is accelerating. Electromagnetic launch technology, as a new concept weapon technology, has the advantages of higher speed and higher efficiency compared to traditional gunpowder launch technology. Electromagnetic launch devices use electromagnetic force to launch objects. However, the operating conditions of the guide rail, which is a key part of the electromagnetic launch device, are very harsh. During launch, the exit velocity of the projectile will reach several thousand meters per second. The material of the guide rail will be subjected to a huge current shock, resulting in serious ablation problems. Studies have shown that after the ammunition passes through the guide rail, the surface of the guide rail will show a lot of gouging and sliding due to erosion by the high-energy arc. The heat and momentum generated by the sliding arc causes the guide rail material to melt and splash. The damaged guide rail will result in poor subsequent launch results. Therefore, improving the thermal conductivity of the guide rail material is one of the important goals to improve launch efficiency.
[0003] Graphene has excellent electrical, thermal, and mechanical properties, and its incorporation improves the performance of copper-based composites. Directly observing surface temperature variations under extreme operating conditions through experiments is prohibitively expensive, while molecular dynamics simulations offer a better microscopic view of material performance, making it easier to observe microscopic mechanisms than through experiments. Therefore, a design method for thermally modifying copper-based composites doped with graphene is needed to improve the thermal conductivity of the rails and enhance launch efficiency. Summary of the Invention
[0004] The purpose of the present invention is to propose a thermal conductivity modification design method for copper-based composite materials based on graphene doping, to find the optimal graphene content and distribution of the material, and to predict and assist in the manufacture of graphene-copper-based composite rail materials with better thermal conductivity.
[0005] The objectives of the present invention are achieved through the following technical solutions.
[0006] A design method for improving the thermal conductivity of copper-based composite materials based on graphene doping is characterized by model establishment, selection and combination of potential functions, setting of simulation environment variables, and temperature distribution calculation method. The method is implemented by the following steps:
[0007] Step 1: Selecting model parameters based on the graphene content of the actual graphene-copper matrix composite material and the mortar-brick masonry structural parameters, wherein the model parameters include the three-dimensional side length of the copper substrate of the model, the shape, position and mass fraction of the graphene;
[0008] Step 2: Based on the parameters, a pure copper model was established using the ATOMSK software, and graphene was generated at a specified position within the copper model using the LAMMPS software, and overlapping copper atoms were deleted to establish a graphene-copper alloy molecular model. The graphene mass fractions of the models were 1wt%, 0.75wt%, 0.5wt%, and 0.25wt%, respectively. There were two graphene distribution forms: six layers and two layers with the same mass fraction, and each graphene sheet was spaced 1000x1000.
[0009] Step 3: Based on the atomic types involved in the simulation, select the MEAM potential function corresponding to copper atoms, the AIREBO potential function corresponding to carbon atoms, and the MORSE potential function corresponding to the relationship between copper and carbon atoms. According to the performance of graphene carbon bonds in the simulation, modify the initial cutoff distance of the AIREBO potential function, rcmin_cc, from 1.7 to 2.005, to ensure that the molecular simulation process is close to the real situation;
[0010] Step 4: Use the steepest descent algorithm to minimize the energy of the model to optimize the model structure through LAMMPS. The time interval is set to 1 fs, the temperature is 300 K, the ensemble is the NVT ensemble, and the relaxation time is 3 ps.
[0011] Step 5: Calculate the surface temperature variation of the composite material under different working conditions, different graphene contents, and different graphene distribution simulations. Specifically:
[0012] The first step is to set the model x, y, and z directions to periodic boundary conditions through the LAMMPS command, and in the z direction, the model base thickness is set to The atomic group is set as a fixed layer, which remains fixed during the simulation, with a thickness of The atomic group is set as the isothermal layer, which is kept at 300K during the simulation, and the remaining atoms form the moving layer;
[0013] The second step is to use the creat_atoms command in LAMMPS above the model Generate a radius of The copper ball was used to hit the model substrate along the z-axis at 4km / s, 6km / s, and 8km / s respectively using the velocity command to simulate different working conditions. After the impact, the simulation was continued for 10ps to ensure the free transfer of energy.
[0014] The third step is to use the region and group commands to move the model base The atomic group is set as the thermometric layer;
[0015] The fourth step is to set up the impact simulation to be performed under the NVE ensemble. During the simulation, the kinetic energy of each atom per femtosecond is calculated, and the temperature is calculated based on the kinetic energy of the entire atomic group. The thermometer command is used to output the temperature data of the thermometer layer atoms in the simulation, and the corresponding temperature change curve is plotted.
[0016] Step 6: Compare the temperature variation patterns to select the graphene mass fraction and distribution pattern with the best thermal conductivity.
[0017] The beneficial effects of the present invention are as follows: Traditional experimental testing of the temperature variation pattern of the guide rail surface under extreme working conditions is costly and difficult to achieve. The present invention provides a thermal conductivity modification design method for copper-based composite materials based on graphene doping, which can calculate the surface temperature variation of graphene-copper-based composite materials under extreme working conditions through molecular simulation software, and determine the optimal thermal conductivity modification design method for graphene-doped copper-based composite materials based on the variation pattern of the temperature curve, thereby guiding experiments, predicting and assisting in the manufacture of guide rail materials with better thermal conductivity. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 Flowchart of the present invention.
[0019] Figure 2 This is a diagram showing the construction process and results of the graphene-copper alloy molecular model of the present invention.
[0020] Figure 3 This is the atomic component partitioning of the model of the present invention.
[0021] Figure 4 It is a side view of the impact simulation of the present invention.
[0022] Figure 5 This is a temperature change diagram of the model temperature measuring layer of pure copper and four different graphene contents under 8 km / s working conditions.
[0023] Figure 6 This is a temperature change diagram of the model temperature measuring layer of pure copper and four different graphene contents under 6 km / s working conditions.
[0024] Figure 7 This is a temperature change diagram of the model temperature measuring layer of pure copper and four different graphene contents under 4 km / s working conditions.
[0025] Figure 8 This is a temperature change diagram of the model temperature measuring layer of the present invention when two and six graphene layers are distributed under the working condition of 8 km / s. DETAILED DESCRIPTION
[0026] The present invention will be further described below with reference to the accompanying drawings and examples.
[0027] The present invention provides a method for designing thermal conductivity modification of copper-based composite materials based on graphene doping. The overall steps are as follows: Figure 1 As shown, the following is a detailed description:
[0028] Step 1: Model construction is based on the graphene content of the real graphene-copper matrix composite material and the structural parameters of the mortar-brick masonry. The model parameters include the three-dimensional side length of the copper substrate of the model, the shape, position and mass fraction of the graphene.
[0029] Step 2: Using the structural parameters obtained in step 1, a pure copper model is established using ATOMSK. Graphene is generated at a specified position within the copper model using LAMMPS and overlapping copper atoms are deleted to establish a graphene-copper alloy molecular model. Figure 2 As shown, the base copper model in the x, y, and z directions is [1 _ 1 0],[1 _ 1 _ 2], [1 1 1], both with length At present, the mass fraction of graphene in graphene-copper matrix composites is mostly below 1wt%, so the mass fraction of graphene in the model is set to 1wt%, 0.75wt%, 0.5wt%, and 0.25wt% respectively. Referring to the mortar-brick masonry structure with excellent impact resistance, the graphene is designed to be distributed parallel to the impact loading surface in two forms: six layers with the same mass fraction and two layers, with the first layer at a distance from the surface. Each layer of graphene is spaced
[0030] Step 3: According to the atomic categories involved in the simulation, select the MEAM potential function corresponding to copper atoms, the AIREBO potential function corresponding to carbon atoms, and the MORSE potential function corresponding to the relationship between copper and carbon atoms. In the simulation, the carbon bond in graphene collapsed when the AIREBO cutoff distance parameter rcmin_cc was 1.7. Therefore, the initial cutoff distance of the AIREBO potential function was modified, and rcmin_cc was changed from 1.7 to 2.005 to ensure that the molecular simulation process is close to the actual situation.
[0031] Step 4: Use the steepest descent algorithm in LAMMPS to minimize the energy of the model in step 2 to optimize the model structure. The energy tolerance etol and force tolerance ftol are set to 1.0e-9, the number of iterations maxiter is 10,000 steps, the number of force calculations maxeval is 100,000 steps, the time interval is set to 1 fs, the temperature is room temperature 300 K, the ensemble is the NVT ensemble, and the relaxation time is 3 ps.
[0032] Step 5: Use the relaxed graphene-copper composite molecular model to calculate the surface temperature of the composite material under different working conditions, different graphene contents, and different graphene distribution simulations.
[0033] In step 5, the calculation of the effects of different working conditions, different graphene contents, and different graphene distributions on the thermal conductivity of the material can be divided into the following detailed steps:
[0034] 1) Set the x, y, and z directions as periodic boundary conditions using the LAMMPS command, such as Figure 3 As shown, in the z direction, the model base thickness is set to The atomic group is set as a fixed layer, which remains fixed during the simulation, with a thickness of The atomic group is set as the isothermal layer, which maintains a temperature of 300K during the simulation, and the remaining atoms form the moving layer.
[0035] 2) If Figure 4 As shown, in LAMMPS, use the creat_atoms command above the model Generate a radius of To simulate the high momentum and heat generated when the arc ablates the track surface, the velocity command is used to make the copper ball hit the model substrate along the z-axis at speeds of 4km / s, 6km / s, and 8km / s respectively to simulate different working conditions. After the impact, the simulation is continued for 10ps to ensure the free transfer of energy.
[0036] 3) To observe the temperature change of the near-surface material more clearly, use the region and group commands to group the model above the base. The atomic group is set as the thermometric layer,
[0037] 4) Set the impact simulation to be performed under the NVE ensemble. During the simulation, the kinetic energy of each atom per femtosecond is counted, and the temperature is calculated based on the kinetic energy of the entire atomic group.
[0038] The kinetic energy of each atom is calculated as
[0039]
[0040] ke—kinetic energy of the atom, ev;
[0041] m—mass of the atom, g / mol;
[0042] v—the speed of the atom
[0043] The temperature of the thermometric layer is calculated as follows:
[0044]
[0045] KE=∑ke
[0046] KE is the total kinetic energy of the group of atoms, that is, the sum of the kinetic energy ke of each atom, ev;
[0047] dim: the dimension of the simulation, set to 3;
[0048] N: the number of atoms in the group;
[0049] k: Boltzmann constant, 1.380649×10 -23 J / K;
[0050] T: temperature, K.
[0051] 5) Use the thermo command to output the temperature data of the thermometric layer atoms in the simulation and draw the corresponding temperature change curve, such as Figure 5 As shown in the figure, under the working condition of 8km / s, the peak temperature of the thermometric layer of pure copper reached 1429K, and the peak temperatures of the thermometric layers of composite materials with graphene mass fractions of 1wt%, 0.75wt%, 0.5wt% and 0.25wt% were 1233K, 1322K, 1293K and 1388K. Under the working condition of 6km / s, the peak temperatures of the thermometric layers of pure copper, 1wt%, 0.75wt%, 0.5wt% and 0.25wt% graphene-copper composite materials were 780K, 712K, 712K, 771K and 814K, while under the working condition of 4km / s, the data were 487K, 449K, 454K, 484K and 499K. When the graphene mass fraction of the model was 1wt%, the thermal conductivity performance of the model under the three working conditions was the best, which was 196K, 102K and 50K lower than the highest temperatures of other models respectively. Figure 6 As shown, the temperature of the composite material with two-layer distribution of graphene is generally lower than that of the composite material with six-layer distribution, with the peak temperature difference being 50-100K.
[0052] Step 6: Compare the changing patterns of the temperature curves to select the graphene mass fraction and distribution pattern with the best thermal conductivity.
[0053] In summary, graphene is arranged in two horizontal layers with a spacing of The graphene-copper composite material model with a mass fraction of 1wt% has the best thermal conductivity.
Claims
1. A method for improving the thermal conductivity of copper-based composite materials based on graphene doping, characterized in that The establishment of the model, the combination and modification of potential functions, the setting of simulation environment variables and the temperature distribution calculation method specifically include the following steps: Step 1: Selecting model parameters based on the graphene content of the actual graphene-copper matrix composite material and the mortar-brick masonry structural parameters, wherein the model parameters include the three-dimensional side length of the copper substrate of the model, the shape, position and mass fraction of the graphene; Step 2: Based on the parameters, a pure copper model was established using the ATOMSK software, and graphene was generated at a specified position within the copper model using the LAMMPS software, and overlapping copper atoms were deleted to establish a graphene-copper alloy molecular model. The graphene mass fractions of the models were 1wt%, 0.75wt%, 0.5wt%, and 0.25wt%, respectively. There were two graphene distribution forms: six layers and two layers with the same mass fraction, and each graphene sheet was spaced 1000x1000. Step 3: Based on the atomic types involved in the simulation, select the MEAM potential function corresponding to copper atoms, the AIREBO potential function corresponding to carbon atoms, and the MORSE potential function corresponding to the relationship between copper and carbon atoms. According to the performance of graphene carbon bonds in the simulation, modify the initial cutoff distance of the AIREBO potential function, rcmin_cc, from 1.7 to 2.005, to ensure that the molecular simulation process is close to the real situation; Step 4: Use the steepest descent algorithm to minimize the energy of the model to optimize the model structure through LAMMPS. The time interval is set to 1 fs, the temperature is 300 K, the ensemble is the NVT ensemble, and the relaxation time is 3 ps. Step 5: Calculate the surface temperature variation of the composite material under different working conditions, different graphene contents, and different graphene distribution simulations. Specifically: The first step is to set the model x, y, and z directions to periodic boundary conditions through the LAMMPS command, and in the z direction, the model base thickness is set to The atomic group is set as a fixed layer, which remains fixed during the simulation, with a thickness of The atomic group is set as the isothermal layer, which is kept at 300K during the simulation, and the remaining atoms form the moving layer; The second step is to use the creat_atoms command in LAMMPS above the model Generate a radius of The copper ball was used to hit the model substrate along the z-axis at 4km / s, 6km / s, and 8km / s respectively using the velocity command to simulate different working conditions. After the impact, the simulation was continued for 10ps to ensure the free transfer of energy. The third step is to use the region and group commands to move the model base The atomic group is set as the thermometric layer; The fourth step is to set up the impact simulation to be performed under the NVE ensemble. During the simulation, the kinetic energy of each atom per femtosecond is calculated, and the temperature is calculated based on the kinetic energy of the entire atomic group. The thermometer command is used to output the temperature data of the thermometer layer atoms in the simulation, and the corresponding temperature change curve is plotted. Step 6: Compare the temperature variation patterns to select the graphene mass fraction and distribution pattern with the best thermal conductivity.