SiC-GNPs synergistically enhanced aluminum matrix composite compression performance prediction method

Abstract

The application provides a SiC-GNPs synergistically reinforced aluminum matrix composite compression performance prediction method, comprising the following steps: 1, constructing a SiC-GNPs / Al synergistically reinforced composite atomic model; 2, selecting an appropriate potential function to describe the interaction between atoms, and calibrating the interface potential function parameters; 3, performing energy minimization processing on the composite material model; 4, performing relaxation processing on the atomic model processed in the step 3 under the isobaric isothermal ensemble; 5, outputting the stress and strain of the composite material in the running process in real time with the simulation running step number; 6, carrying out molecular dynamics equilibrium simulation and mechanical property simulation; 7, repeating the steps 1 to 6, analyzing the influence law of each parameter on the mechanical property of the composite material, and determining the key influence parameter and the optimal value range thereof. The application changes the key parameters to explore the influence law, realizes the rapid and accurate prediction of the mechanical property of the composite material, and provides theoretical support for the design and preparation of high-performance composite materials.

Description

Technical Field

[0001] This invention relates to the field of material mechanical property simulation and calculation technology, and in particular to a method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites. Background Technology

[0002] Aluminum-based composites, with their lightweight, high strength, and excellent thermal and electrical conductivity, have demonstrated significant application value in industries such as aerospace, automotive lightweighting, and electronic packaging. To further enhance the mechanical properties of aluminum-based composites, a synergistic reinforcement method using SiC particles and GNPs is widely adopted. SiC possesses high hardness, high elastic modulus, and a low coefficient of thermal expansion, which theoretically can effectively improve the strength and wear resistance of the matrix. GNPs, with their superior mechanical properties and two-dimensional layered structure, can play a role in interfacial strengthening and load transfer; the synergistic effect of the two achieves a comprehensive reinforcement effect.

[0003] However, the mechanical properties of SiC and GNPs synergistically reinforced aluminum matrix composites are affected by a variety of factors, including the content ratio, size, distribution state, interfacial bonding strength, and preparation process parameters. Traditional experimental preparation and testing methods have problems such as long research and development cycles, high costs, and low efficiency in parameter optimization, making it difficult to achieve precise matching between composite material components and processes.

[0004] Molecular dynamics simulation, a commonly used method for numerical simulation at the microscale, can explore the intrinsic relationship between the atomic structure and properties of materials at the atomic level. It has been widely applied in the performance prediction of single-phase reinforced aluminum matrix composites. Existing techniques have reported the use of molecular dynamics to simulate the mechanical properties of SiC / Al or GNPs / Al composites, such as analyzing the strengthening mechanism of the reinforcing phase relative to the matrix through simulated tensile processes, and optimizing the bonding performance between the reinforcing phase and the matrix based on interfacial atomic interactions. However, these methods all focus on a single reinforcing phase and do not consider the microscopic mechanisms of the synergistic effect of SiC and GNPs, such as their mutual influence, interfacial coupling effects, and synergistic load transfer mechanisms. This leads to significant deviations between the simulation predictions and the actual material properties.

[0005] Meanwhile, existing methods for predicting the performance of synergistically reinforced aluminum matrix composites either ignore existing microscopic problems such as reinforcing phase agglomeration and interface defects, or fail to establish a quantitative correlation between microscopic parameters and macroscopic mechanical properties. This makes it impossible to accurately predict the mechanical properties of composites under different components and process parameters, and thus cannot effectively support material design and optimization needs in engineering practice. Furthermore, current technologies lack systematic optimization methods for key parameters such as the content ratio, size combination, and distribution pattern of the reinforcing phase in SiC and GNPs synergistic reinforcement systems, which limits the practicality and accuracy of simulation prediction methods. Moreover, current research on synergistically reinforced composites mainly focuses on the experimental testing stage, which leads to problems such as high cost and low error tolerance, making the prediction of experimental results extremely important.

[0006] The key challenge currently facing research on aluminum matrix composites lies in how to accurately reveal the synergistic mechanism between SiC and GNPs through molecular dynamics simulations, establish a quantitative relationship between microstructural parameters and macroscopic mechanical properties, and at the same time ensure that the simulation methods are both engineering-practical and computationally accurate. Summary of the Invention

[0007] The purpose of this invention is to provide a method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites. This method solves the technical problems of existing prediction methods that ignore the microscopic mechanism of synergistic effects, have low prediction accuracy, and poor practicality. Through full-process optimization and multi-dimensional analysis of molecular dynamics simulation, it achieves rapid and accurate prediction of the mechanical properties of composite materials under different components and process parameters, providing solid theoretical support for the design and preparation of high-performance SiC-GNPs / Al composite materials.

[0008] The technical solution adopted by this invention to solve its technical problem is: This invention provides a method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites, comprising the following steps: Step 1: Construct an atomic model of SiC-GNPs / Al synergistic reinforced composite material, and achieve the distribution of the reinforcing phase by batch embedding and uniform dispersion, and then perform energy minimization treatment to achieve a stable state; Step 2: Set the molecular dynamics simulation parameters and boundary conditions, select a suitable potential function to describe the interaction between atoms, and calibrate the interface potential function parameters; Step 3: Perform energy minimization on the composite material model to eliminate unreasonable interatomic forces; Step 4: Perform isobaric and isothermal relaxation on the atomic model processed in Step 3; Step 5: Based on Step 4, conduct mechanical property simulations such as tensile and compressive stresses, and output the stress and strain of the composite material in real time as the number of simulation steps increases. Step 6: Conduct molecular dynamics equilibrium simulation and mechanical property simulation to reveal and analyze the synergistic enhancement mechanism of SiC and GNPs, perform sensitivity analysis on each parameter, and clarify the influence of each microscopic parameter on the mechanical properties of aluminum matrix composites. Step 7: Change the volume fraction ratio of SiC to GNPs, change the particle size of SiC and the size of GNPs, and repeat steps 1 to 6 to analyze the influence of each parameter on the mechanical properties of the composite material and determine the key influencing parameters and their optimal value range.

[0009] Furthermore, in step 1, the atomic model of the composite material includes atomic models of the aluminum matrix, SiC particles, and GNPs.

[0010] Furthermore, in step 1, the aluminum matrix atomic model adopts a face-centered cubic structure with dimensions of 20 nm × 20 nm × 20 nm and approximately 10 atoms. 6 The microstructure consists of 1,000 particles, each with a periodic boundary condition; SiC particles with a cubic (3C-SiC) structure and a particle size of 7-20 Å; GNPs with a 3-layer graphene structure and a size of 20-40 Å; the volume fraction ratio of SiC to GNPs is (0.5-10):(0.5-1.5), with a spacing of 5-20 Å; and the microdefect concentration is 0.01-0.1 at.%.

[0011] Furthermore, in step 2, the molecular dynamics simulation parameters and boundary conditions include setting the simulation temperature, pressure, time step, and boundary conditions.

[0012] Furthermore, in step 2, the potential functions are selected as follows: the embedded atom method (EAM) potential function is used between Al-Al atoms, the Tersoff potential function is used inside SiC particles, the Airebo potential function is used inside GNPs, and the Lennard-Jones (LJ) potential function is used at the interfaces between Al and SiC, and between Al and GNPs. The selected LJ potential function parameters need to be calculated and calibrated using first-principles calculations; the simulation temperature range is 300-900K, and the pressure is 0 MPa; periodic boundaries are set in the X and Y directions, and free boundary conditions are used in the Z direction.

[0013] Furthermore, in step 3, the conjugate gradient method is used to iterate until the maximum interatomic force is <1×10⁻⁶. -4 The eV / Å energy is minimized for the composite material model to bring it to an initial stable state and avoid the influence of initial stress on subsequent simulation results.

[0014] Furthermore, in step 4, the temperature is initialized to 300K, the pressure is set to 0 MPa, the time step is 0.001ps, and the atomic model processed in step 3 is subjected to isobaric and isothermal ensemble relaxation treatment with a relaxation time of 300 ps, ​​so that the temperature, pressure and volume of the atomic model in the system reach stability.

[0015] Furthermore, in step 5, a progressive loading method is used to simulate tensile and compressive mechanical properties, with a strain rate of 10. 9 s -1 The simulated temperature was between 300-900K, and the stress-strain, atomic displacement, and energy change data of the system were recorded in real time during the loading process.

[0016] Furthermore, in step 6, molecular dynamics equilibrium simulation and mechanical property simulation are carried out by using OVITO software for atomic trajectory analysis, DXA algorithm for dislocation extraction, interface energy calculation, and other methods.

[0017] Furthermore, in step 6, the synergistic enhancement mechanism of SiC and GNPs is revealed and analyzed, including the dispersion effect of GNPs on SiC particles, the load transfer effect at the interface between the two, the proliferation and inhibition law of dislocations around the reinforcing phase, and the influence of interface bonding strength on the synergistic enhancement effect. The regulation mechanism of different reinforcing phase contents, sizes and distributions on the synergistic enhancement effect is clarified, filling the gap in the research on the microscopic mechanism of synergistic enhancement in the existing technology.

[0018] The present invention provides a method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites. Based on LAMMPS software, an atomic model of SiC-GNPs / Al composites with microscopic defects and different reinforcement ratios is constructed. An appropriate potential function is selected and the interatomic potential functions are calibrated. After energy minimization and NPT ensemble relaxation, compressive performance simulations are carried out under different conditions. Combined with OVITO software and other methods, the synergistic reinforcement mechanism is analyzed. By changing key parameters, the influence law is explored, and the mechanical properties of composite materials can be predicted rapidly and accurately. This provides theoretical support for the design and preparation of high-performance SiC-GNPs / Al composite materials. Attached Figure Description

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Figure 1 This is a flowchart of the method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites based on molecular dynamics, as described in this invention. Figure 2 This is a schematic diagram of the atomic model of the SiC-GNPs / Al composite material constructed in this invention; Figure 3This is a schematic diagram of the mechanical simulation model of synergistic enhancement of SiC and GNPs in this invention; Figure 4 The image shows the morphology of the 0.5SiC-0.5GNPs / Al composite material after compression simulation in this invention. Figure 5 This is a dislocation diagram of the 0.5SiC-0.5GNPs / Al composite material after compression simulation in this invention; Figure 6 The stress-strain curves from the compression simulation of the 0.5SiC-0.5GNPs / Al composite material in this invention are shown. Figure 7 This is a dislocation-strain diagram during the compression simulation of the 0.5SiC-0.5GNPs / Al composite material in this invention; Figure 8 This is a stress-strain curve from the compression simulation of the nSiC-mGNPs / Al composite material in this invention. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0022] Please see Figure 1-8 This invention provides a method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites, comprising the following steps: Step 1: Construct an atomic model of SiC-GNPs / Al synergistic reinforced composite material, and achieve the distribution of the reinforcing phase by batch embedding and uniform dispersion, and then perform energy minimization treatment to achieve a stable state; Step 2: Set the molecular dynamics simulation parameters and boundary conditions, select a suitable potential function to describe the interaction between atoms, and calibrate the interface potential function parameters; Step 3: Perform energy minimization on the composite material model to eliminate unreasonable interatomic forces; Step 4: Perform isobaric and isothermal relaxation on the atomic model processed in Step 3; Step 5: Based on Step 4, conduct mechanical property simulations such as tensile and compressive stresses, and output the stress and strain of the composite material in real time as the number of simulation steps increases. Step 6: Conduct molecular dynamics equilibrium simulation and mechanical property simulation to reveal and analyze the synergistic enhancement mechanism of SiC and GNPs, perform sensitivity analysis on each parameter, and clarify the influence of each microscopic parameter on the mechanical properties of aluminum matrix composites. Step 7: Change the volume fraction ratio of SiC to GNPs, change the particle size of SiC and the size of GNPs, and repeat steps 1 to 6 to analyze the influence of each parameter on the mechanical properties of the composite material and determine the key influencing parameters and their optimal value range.

[0023] Furthermore, in step 1, the atomic model of the composite material includes atomic models of the aluminum matrix, SiC particles, and GNPs.

[0024] Furthermore, in step 1, the aluminum matrix atomic model adopts a face-centered cubic structure with dimensions of 20 nm × 20 nm × 20 nm and approximately 10 atoms. 6 The microstructure consists of 1,000 particles, each with a periodic boundary condition; SiC particles with a cubic (3C-SiC) structure and a particle size of 7-20 Å; GNPs with a 3-layer graphene structure and a size of 20-40 Å; the volume fraction ratio of SiC to GNPs is (0.5-10):(0.5-1.5), with a spacing of 5-20 Å; and the microdefect concentration is 0.01-0.1 at.%.

[0025] Furthermore, in step 2, the molecular dynamics simulation parameters and boundary conditions include setting the simulation temperature, pressure, time step, and boundary conditions.

[0026] Furthermore, in step 2, the potential functions are selected as follows: the embedded atom method (EAM) potential function is used between Al-Al atoms, the Tersoff potential function is used inside SiC particles, the Airebo potential function is used inside GNPs, and the Lennard-Jones (LJ) potential function is used at the interfaces between Al and SiC, and between Al and GNPs. The selected LJ potential function parameters need to be calculated and calibrated using first-principles calculations; the simulation temperature range is 300-900K, and the pressure is 0 MPa; periodic boundaries are set in the X and Y directions, and free boundary conditions are used in the Z direction.

[0027] Furthermore, in step 3, the conjugate gradient method is used to iterate until the maximum interatomic force is <1×10⁻⁶. -4 The eV / Å energy is minimized for the composite material model to bring it to an initial stable state and avoid the influence of initial stress on subsequent simulation results.

[0028] Furthermore, in step 4, the temperature is initialized to 300K, the pressure is set to 0 MPa, the time step is 0.001ps, and the atomic model processed in step 3 is subjected to isobaric and isothermal ensemble relaxation treatment with a relaxation time of 300 ps, ​​so that the temperature, pressure and volume of the atomic model in the system reach stability.

[0029] Furthermore, in step 5, a progressive loading method is used to simulate tensile and compressive mechanical properties, with a strain rate of 10. 9 s -1 The simulated temperature was between 300-900K, and the stress-strain, atomic displacement, and energy change data of the system were recorded in real time during the loading process.

[0030] Furthermore, in step 6, molecular dynamics equilibrium simulation and mechanical property simulation are carried out by using OVITO software for atomic trajectory analysis, DXA algorithm for dislocation extraction, interface energy calculation, and other methods.

[0031] Furthermore, in step 6, the synergistic enhancement mechanism of SiC and GNPs is revealed and analyzed, including the dispersion effect of GNPs on SiC particles, the load transfer effect at the interface between the two, the proliferation and inhibition law of dislocations around the reinforcing phase, and the influence of interface bonding strength on the synergistic enhancement effect. The regulation mechanism of different reinforcing phase contents, sizes and distributions on the synergistic enhancement effect is clarified, filling the gap in the research on the microscopic mechanism of synergistic enhancement in the existing technology.

[0032] Specifically, the method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites provided by this invention includes the following steps: Step 1: Construct an aluminum matrix atomic model using the molecular dynamics simulation software LAMMPS. A face-centered cubic Al atom structure is selected, and the model dimensions are set to Lx × Ly × Lz = 20 nm × 20 nm × 20 nm, with 10 atoms. 6 Approximately 1000 atomic structures were constructed. Periodic boundary conditions were used in the X and Y directions to simulate an infinitely extended aluminum matrix, while non-periodic boundary conditions were used in the Z direction for simulations of compression and tension. Atomic models of SiC particles and GNPs were constructed: SiC particles adopted a cubic phase (β-SiC) structure, with particle size set according to volume fraction and adjusted based on actual application requirements; GNPs adopted a 3-layer graphene structure, with dimensions set according to volume fraction requirements. SiC particles and GNPs were embedded into the aluminum matrix atomic models, controlling the volume fraction ratio of SiC to GNPs to be (0.5-10):(0.5-1.5), ensuring that SiC particles were uniformly embedded within the aluminum matrix, while GNPs were embedded parallel to the aluminum matrix and maintained a distance of 5-20 Å or more from the SiC particles, to simulate the distribution of the reinforcing phase in actual aluminum matrix composites as closely as possible. Microscopic defects were introduced: vacancy defects were randomly introduced at a concentration of 0.01–0.1 at.%, preferably 0.05 at.%.

[0033] Step 2: Accurate selection of potential functions to describe interatomic interactions: The embedded atom method (EAM) potential function, which can accurately describe the bonding between metal atoms, is used for Al-Al atoms. The Tersoff potential function is used to describe the Si-Si, Si-C, and CC atoms inside SiC particles. The Tersoff potential function is suitable for covalent bonded systems. The Airebo potential function is used for CC atoms inside GNPs, which can accurately simulate the interlayer interaction and in-plane covalent bond interaction of graphene. The Morse potential function is used for interatomic interactions at the Al / SiC interface, and the Lennard-Jones (LJ) potential function is used for other interatomic interactions. The cutoff radius is 10 Å. The potential function parameters are calibrated by first-principles calculation to ensure the simulation accuracy of interface bonding and solve the prediction deviation problem caused by unreasonable interface potential function parameters in the prior art. Step 3: Perform energy minimization on the composite material model, using the conjugate gradient method to iterate until the maximum interatomic force is <1×10⁻⁶. -4 eV / Å is used to eliminate unreasonable interatomic forces, bring the model to an initial stable state, and avoid the influence of initial stress on subsequent simulation results; Step 4: Initialize the temperature to 300K, the pressure to 0 MPa, and the time step to 0.001 ps. Perform isobaric isothermal (NPT) ensemble relaxation on the atomic model processed in Step 3 for 300 ps to stabilize the temperature, pressure, and volume of the atomic model within the system. Step 5: Based on Step 4, conduct simulations of tensile and compressive mechanical properties using a progressive loading method with a strain rate of 10. 9 s -1 The simulation temperature is between 300-900K, and the stress-strain, atomic displacement, energy change and other data of the system are recorded in real time during the loading process. The stress and strain of the composite material during operation are output in real time with the number of simulation steps. Step 6: Using OVITO software for atomic trajectory analysis, DXA algorithm for dislocation extraction, and interface energy calculation, the synergistic enhancement mechanism of SiC and GNPs is revealed. This includes the dispersion effect of GNPs on SiC particles, the load transfer effect at the interface, the proliferation and inhibition of dislocations around the reinforcing phase, and the influence of interface bonding strength on the synergistic enhancement effect. The mechanism by which different reinforcing phase contents, sizes, and distributions affect the synergistic enhancement effect is clarified, filling the gap in the study of the microscopic mechanism of synergistic enhancement in existing technologies.

[0034] Step 7: Change the volume fraction ratio of SiC to GNPs, change the particle size of SiC and the size of GNPs, and repeat steps 1 to 6 to analyze the influence of each parameter on the mechanical properties of the composite material and determine the key influencing parameters and their optimal value range.

[0035] For example, in one embodiment, a method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites is disclosed, comprising the following steps: Step 1: Using LAMMPS software, construct an atomic model of the aluminum matrix. The Al atoms adopt a face-centered cubic (FCC) structure with a lattice constant of 4.05 Å. The model size is set to 20 nm × 20 nm × 20 nm, and the number of atoms is approximately 4.85 × 10⁻⁶. 5 Periodic boundary conditions were applied in the X and Y directions, while free boundary conditions were applied in the Z direction to facilitate the simulation of compressibility performance. An atomic model of SiC particles and an atomic model of GNPs were constructed: both reinforcements were selected at 0.5 vol%, with SiC particles adopting a β-SiC structure and a particle size of 1.414 nm; GNPs adopted a 3-layer graphene structure with a size of 21.32 nm × 21.32 nm. Using atomic substitution, SiC particles and GNPs were embedded in an aluminum matrix, with SiC particles uniformly distributed within the aluminum matrix and GNPs embedded parallel to it at a distance of 10 Å from the SiC particles. Simultaneously, 0.05 at.% vacancy defects were introduced to simulate microscopic defects encountered during actual fabrication. Step 2: Select potential functions: The EAM potential function is used for Al-Al atoms; the Tersoff potential function is used for all atomic interactions within SiC particles; the Airebo potential function is used for the CC atoms inside GNPs, which can accurately simulate the interlayer interactions and in-plane covalent bond interactions of graphene; the Morse potential function is used to describe the interactions between Al and SiC interface atoms; the Lennard-Jones (LJ) potential function is used to describe the interactions between other atoms, and the potential function parameters are calibrated by first-principles calculations to ensure the simulation accuracy of interface bonding interactions and solve the prediction bias problem caused by unreasonable interface potential function parameters in the existing technology. Step 3: Minimize the energy of the initial aluminum-based composite atomic model using the conjugate gradient method until the energy is minimized to < 1 × 10⁻⁶. -4 eV / Å eliminates unreasonable interatomic forces, allowing the model to reach an initial stable state.

[0036] Step 4: Simulate a temperature of 300K, a pressure of 0MPa, and a time step of 0.001ps; perform NPT ensemble relaxation on the atomic model for 300 ps to allow the system temperature, pressure, and volume to stabilize, and record the atomic coordinates and energy distribution after equilibrium. Step 5: Use a progressive compression method with a strain rate of 1×10⁻⁶. 9 s -1The model after equilibrium is subjected to compression simulation. The temperature is kept constant at 300K during the compression process. Stress-strain data is recorded in real time and output as a Strain-Stress file. The compression state is output as a dump file in real time. Step 6: Import the Strain-Stress file into Origin for plotting and analysis. At the same time, import the dump file into OVITO software for analysis. Using the DXA algorithm to extract dislocation trajectories, it was found that SiC particles can hinder dislocation movement, GNPs can disperse SiC particles and reduce agglomeration, and the interface between the two can achieve efficient load transfer, forming a synergistic enhancement effect. Step 7: Change the volume fraction ratio of SiC to GNPs (n=0.5, 1.0, 5.0, 10.0, m=0.5, 1.0, 1.5), the SiC particle size is 1.414nm, 1.78nm, 2.98nm, 3.7nm, and the number of GNPs layers is 3 square nanolayers with sizes of 2.132nm, 3.162nm, 3.873nm. Repeat steps 1 to 6 to analyze the influence of each parameter on mechanical properties. It was found that when the volume fraction ratio of SiC to GNPs is 0.5:1.0, the SiC particle size is 1.414nm, and the GNPs size is 2.132nm, the composite material has the best compressive strength.

[0037] The present invention provides a method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites. Based on LAMMPS software, an atomic model of SiC-GNPs / Al composites with microscopic defects and different reinforcement ratios is constructed. An appropriate potential function is selected and the interatomic potential functions are calibrated. After energy minimization and NPT ensemble relaxation, compressive performance simulations are carried out under different conditions. Combined with OVITO software and other methods, the synergistic reinforcement mechanism is analyzed. By changing key parameters, the influence law is explored, and the mechanical properties of composite materials can be predicted rapidly and accurately. This provides theoretical support for the design and preparation of high-performance SiC-GNPs / Al composite materials.

[0038] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or equivalent variations to the above-disclosed technical content and apply them to other fields. However, any simple modifications, equivalent variations and alterations made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites, characterized in that, Includes the following steps: Step 1: Construct an atomic model of SiC-GNPs / Al synergistic reinforced composite material, and achieve the distribution of the reinforcing phase by batch embedding and uniform dispersion, and then perform energy minimization treatment to achieve a stable state; Step 2: Set the molecular dynamics simulation parameters and boundary conditions, select a suitable potential function to describe the interaction between atoms, and calibrate the interface potential function parameters; Step 3: Perform energy minimization on the composite material model to eliminate unreasonable interatomic forces; Step 4: Perform isobaric and isothermal relaxation on the atomic model processed in Step 3; Step 5: Based on Step 4, conduct mechanical property simulations such as tensile and compressive stresses, and output the stress and strain of the composite material in real time as the number of simulation steps increases. Step 6: Conduct molecular dynamics equilibrium simulation and mechanical property simulation to reveal and analyze the synergistic enhancement mechanism of SiC and GNPs, perform sensitivity analysis on each parameter, and clarify the influence of each microscopic parameter on the mechanical properties of aluminum matrix composites. Step 7: Change the volume fraction ratio of SiC to GNPs, change the particle size of SiC and the size of GNPs, and repeat steps 1 to 6 to analyze the influence of each parameter on the mechanical properties of the composite material and determine the key influencing parameters and their optimal value range.

2. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that, In step 1, the atomic model of the composite material includes atomic models of the aluminum matrix, SiC particles, and GNPs.

3. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that, In step 1, the aluminum matrix atomic model adopts a face-centered cubic structure with dimensions of 20 nm × 20 nm × 20 nm and approximately 10 atoms. 6 The microstructure consists of 1,000 particles, each with a periodic boundary condition; SiC particles with a cubic (3C-SiC) structure and a particle size of 7-20 Å; GNPs with a 3-layer graphene structure and a size of 20-40 Å; the volume fraction ratio of SiC to GNPs is (0.5-10):(0.5-1.5), with a spacing of 5-20 Å; and the microdefect concentration is 0.01-0.1 at.%.

4. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that: In step 2, the molecular dynamics simulation parameters and boundary conditions include setting the simulation temperature, pressure, time step, and boundary conditions.

5. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that: In step 2, the potential functions are selected as follows: the embedded atom method (EAM) potential function is used between Al-Al atoms, the Tersoff potential function is used inside SiC particles, the Airebo potential function is used inside GNPs, and the Lennard-Jones (LJ) potential function is used at the interfaces between Al and SiC and between Al and GNPs. The selected LJ potential function parameters need to be calculated and calibrated using first-principles calculations. The simulation temperature range is 300-900K, and the pressure is 0 MPa. Periodic boundaries are set in the X and Y directions, and free boundary conditions are used in the Z direction.

6. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that: In step 3, the conjugate gradient method is used to iterate until the maximum interatomic force is <1×10. -4 The eV / Å energy is minimized for the composite material model to bring it to an initial stable state and avoid the influence of initial stress on subsequent simulation results.

7. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that: In step 4, the temperature is initialized to 300K, the pressure is set to 0 MPa, and the time step is 0.001 ps. The atomic model processed in step 3 is subjected to isobaric and isothermal ensemble relaxation treatment with a relaxation time of 300 ps to stabilize the temperature, pressure, and volume of the atomic model in the system.

8. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that, In step 5, a progressive loading method is used to simulate tensile and compressive mechanical properties, with a strain rate of 10. 9 s -1 The simulated temperature was between 300-900K, and the stress-strain, atomic displacement, and energy change data of the system were recorded in real time during the loading process.

9. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that, In step 6, molecular dynamics equilibrium simulation and mechanical property simulation are carried out by using OVITO software for atomic trajectory analysis, DXA algorithm for dislocation extraction, interface energy calculation, and other methods.

10. The method for predicting the compressive properties of SiC-GNPs synergistically reinforced aluminum matrix composites as described in claim 1, characterized in that, In step 6, the synergistic enhancement mechanism of SiC and GNPs is revealed and analyzed, including the dispersion effect of GNPs on SiC particles, the load transfer effect at the interface between the two, the proliferation and inhibition law of dislocations around the reinforcing phase, and the influence of interface bonding strength on the synergistic enhancement effect. The regulation mechanism of different reinforcing phase contents, sizes and distributions on the synergistic enhancement effect is clarified, filling the gap in the research on the microscopic mechanism of synergistic enhancement in the existing technology.

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