Mixed nanofluid heat transfer characteristic analysis method based on molecular dynamics

Abstract

The invention relates to the technical field of nanofluid enhanced heat transfer simulation, in particular to a molecular dynamics-based mixed nanofluid heat transfer characteristic analysis method, which comprises the following steps of: constructing a molecular model comprising copper, graphene nanoparticles and an ionic liquid / water mixed base solution, and calculating the heat transfer characteristic of the mixed nanofluid through a distribution potential function and a periodic boundary condition; and performing energy minimization and molecular dynamic relaxation to achieve thermodynamic equilibrium. A steady-state temperature gradient is established based on the unbalanced molecular dynamics principle, and the thermal conductivity is calculated according to the Fourier law. Microcosmic parameters such as a radial distribution function, mean square displacement and the number of hydrogen bonds are synchronously analyzed, and a coupling mechanism of interaction and heat transfer performance of the nano-particle-base fluid interface is disclosed. According to the method, the heat transfer characteristic of the mixed nanofluid can be clarified on the molecular scale, and an efficient and reliable analysis means is provided for nanofluid screening and design in application of a solar heat collector, a heat management system and the like.

Description

Technical Field

[0001] This invention relates to the field of nanofluid enhanced heat transfer simulation technology, specifically to a method for analyzing the heat transfer characteristics of hybrid nanofluids based on molecular dynamics. Background Technology

[0002] Hybrid nanofluids have wide applications in energy and thermal management systems such as solar collectors, photovoltaic cooling devices, automotive engines, and natural convection equipment. Existing research shows that incorporating hybrid nanoparticles can effectively improve the thermal efficiency of fluids, reduce operating temperatures, and improve overall energy utilization. However, most existing technologies focus on application performance, and research on the microscopic interactions and heat transfer mechanisms between nanoparticles and the base fluid remains limited, especially the impact of the evolution of the base fluid's molecular structure on transport performance.

[0003] For example, hydrogen bond rearrangement in water within a specific temperature range leads to structural reorganization, thereby affecting the intrinsic thermal conductivity of the system. This factor has a significant impact on the overall performance of nanofluids. However, current research on how such microstructural changes interact with nanoparticles and jointly regulate heat transfer and rheological behavior is insufficient. In particular, when ionic liquid / water mixtures are used as base fluids, the coupled regulation mechanism of their "structure-heat transport-rheological behavior" has not been systematically studied.

[0004] Therefore, how to reveal the influence of copper, graphene, and copper-graphene composite nanoparticles on the system structure, energy distribution, and thermal property evolution of ionic liquid / water mixed base liquids at the molecular scale has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a method for analyzing the heat transfer characteristics of hybrid nanofluids based on molecular dynamics. By constructing a molecular model of a mixture of nanoparticles and ionic liquid / water, the thermal conductivity is calculated using non-equilibrium molecular dynamics, and combined with microstructure analysis, the heat transfer mechanism is revealed, providing theoretical guidance for the optimized design of nanofluids.

[0006] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0007] A method for analyzing the heat transfer properties of hybrid nanofluids based on molecular dynamics includes the following steps:

[0008] S1: Construct an initial molecular model including nanoparticles and a mixed base liquid using molecular modeling tools. The mixed base liquid is a binary mixture of ionic liquid and deionized water. The nanoparticles include carbon-based nanomaterials and metal nanoparticles. The nanoparticles are fixed in the model by preset spatial coordinates to ensure that they are spaced at a set distance from each other, thereby suppressing the aggregation effect during the simulation.

[0009] S2: Assign potential functions to each component atom or molecule in the model, and assign potential function parameters determined by the mixing rules to all cross-component interactions, while setting periodic boundary conditions;

[0010] S3: Minimize the energy of the initial molecular model to eliminate non-physical atomic overlap, and then perform molecular dynamics relaxation in an isothermal isochoric ensemble and a microcanonical ensemble in sequence; wherein, in the isothermal isochoric ensemble, a chain-like heat bath is used to stabilize the system temperature at the target value, and in the microcanonical ensemble, it is verified that the total energy fluctuation of the system is lower than the first preset threshold, so that the system reaches thermodynamic equilibrium.

[0011] S4: Based on the principle of non-equilibrium molecular dynamics, a steady-state temperature gradient is established in the relaxed system, and the thermal conductivity of the nanofluid is calculated according to Fourier's law.

[0012] S5: Simultaneously extract and correlate various microstructure dynamic characterization parameters of the system during the non-equilibrium heat transfer simulation process. These parameters include at least the radial distribution function, mean square displacement, number of hydrogen bond topologies, system potential energy trajectory, and spatial number density distribution, in order to synergistically reveal the coupling mechanism between nanoparticle-base liquid interface interaction, base liquid microstructure evolution, and macroscopic heat transport performance at the molecular scale.

[0013] Furthermore, in step S1, the carbon-based nanomaterial is graphene nanosheets, the metal nanoparticles are copper nanoparticles, the ionic liquid is an imidazole-based ionic liquid, and the mass fraction of water in the mixed base liquid is adjustable within the range of 10% to 90%.

[0014] Furthermore, the set distance between the graphene nanosheets and the copper nanoparticles in the model is not less than 30 Å; the imidazole ionic liquid is 1-butyl-3-methylimidazolium tetrafluoroborate; and the water molecules adopt an extended simple point charge model.

[0015] Furthermore, the potential functions in step S2 include: the AIREBO potential function for graphene carbon atoms, the EAM potential function for copper atoms, the OPLS-AA potential function for ionic liquids, and the SPC / E model for water molecules; the potential function for cross-component interactions is the Lennard-Jones potential function, and the mixing rule is the Lorentz-Berthelot rule.

[0016] Furthermore, the target temperature is 300 K, and the first preset threshold is 0.01% of the total energy of the system; the relaxation time of the isothermal isochoric ensemble is 1 nanosecond, and the relaxation time of the microcanonical ensemble is 1 nanosecond; the integral of the equations of motion uses the Velocity-Verlet algorithm, and the long-range electrostatic interaction is handled using the PPM method with an accuracy set to 10. -4 .

[0017] Furthermore, step S4 specifically includes: uniformly dividing the simulation system into N statistical thin layers along the heat flow direction, selecting two non-adjacent thin layers as a constant heat source and a constant cold source respectively, and applying equal and opposite scalar heat fluxes; continuously monitoring the total energy of the system until its fluctuation standard deviation is lower than a second preset threshold, which is then determined to be a steady state; recording the time-averaged temperature difference between the heat source layer and the cold source layer; and calculating the equivalent thermal conductivity of the nanofluid by dividing the applied scalar heat flux by the product of the time-averaged temperature difference and the cross-sectional area perpendicular to the heat flow direction, according to Fourier's law of thermal conductivity.

[0018] Furthermore, the value of N is 20, the heat source layer is the first layer, the cold source layer is the eleventh layer, and the second preset threshold is 0.5% of the average energy.

[0019] Furthermore, in step S5, the thickness of the ordered layer of base liquid molecules at the nanoparticle interface is quantified by analyzing the position and intensity of the first peak of the radial distribution function; the diffusion coefficient of the base liquid molecules is calculated by the slope of the mean square displacement curve; and the influence of the microstructure of the ionic liquid-water mixed base liquid on the heat transport path under different component ratios is revealed by the correlation between the number of hydrogen bond topological networks and the spatial number density distribution.

[0020] Furthermore, the method also includes a verification step: after the thermophysical property calculation step, the simulated thermal conductivity of the pure ionic liquid-water mixture is compared with the theoretical predictions of the Filippov equation and / or Jamieson equation to verify the accuracy of the molecular model and potential function assignment.

[0021] Furthermore, the molecular dynamics simulations are performed using the LAMMPS software package; the molecular modeling tools include AutoFF for force field parameterization, Packmol for initial structure stacking, and moltemplate for constructing complex molecular topologies.

[0022] The beneficial effects of this invention are:

[0023] This invention, through synergistic analysis of various microscopic characterization parameters such as radial distribution function, mean square displacement, number of hydrogen bonds, system energy, and number density distribution, elucidates for the first time at the molecular level the coupling mechanism between nanoparticles and ionic liquid / water hybrid base fluids. Figure 4 As shown, the radial distribution function curve extracted in step S5 reveals the spatial density oscillation of the base liquid molecules surrounding the nanoparticles, indicating that an ordered molecular layer is formed at the copper-graphene interface. This interface layer significantly improves thermal conductivity by enhancing phonon transport paths; simultaneously, Figure 5 This directly reflects the structural rearrangement behavior of water molecules in an ionic liquid environment. When the water mass fraction is 50%, the hydrogen bond network reaches optimal equilibrium, preserving the inherent high thermal conductivity of water while suppressing hydrogen bond breakage caused by overheating through the steric hindrance effect of the ionic liquid, thus explaining the peak thermal conductivity phenomenon. Furthermore, mean square displacement analysis... Figure 6 The addition of nanoparticles modulates the diffusion ability of the base liquid molecules, and its synergistic effect with the temperature gradient optimizes the heat flow distribution.

[0024] This invention employs a non-equilibrium molecular dynamics method combined with a rigorous thermodynamic equilibrium process, ensuring the physical accuracy and numerical reliability of the predicted thermal properties. In step S4, by dividing the system into 20 uniform thin layers along the heat flow direction and applying equal amounts of reverse heat flow between the heat source (layer 1) and the cold source (layer 11), a steady-state temperature gradient conforming to Fourier's law is established. The achievement of steady-state is confirmed by monitoring the system's energy fluctuations (standard deviation <0.5%), effectively avoiding the interference of transient effects on the thermal conductivity calculation. This invention directly simulates the microscopic processes of macroscopic heat transport, and its accuracy is verified by the thermal conductivity of the base liquid and the Filippov and Jamieson equations. Figure 3 As shown in the figure. Simultaneously, the energy minimization and step-by-step relaxation strategy employed in step S3 specifically involves stabilizing the NVT ensemble to 300K, verifying energy conservation ΔE / E < 0.01% in the NVE ensemble, eliminating non-physical states caused by initial atomic overlap, and ensuring the computational stability of the integrals of the equations of motion and long-range interactions using the Velocity-Verlet algorithm and the PPPM electrostatic solver. This results in simulation results that are highly consistent with experimental observations, making it particularly suitable for complex systems sensitive to hydrogen bonding and electrostatic interactions, such as ionic liquid / water systems.

[0025] The modeling framework of this invention possesses high flexibility and versatility, enabling rapid design and optimization of multi-component nanofluid systems. In step S1, nanofluid models with different water mass fractions of 25%, 50%, and 75% were constructed using tools such as AutoFF, Packmol, and moltemplate. The evolution of structure-thermal properties was systematically studied by adjusting the component ratios, as shown in Table 1. The spatial arrangement of nanoparticles, such as placing graphene at Z=15Å and 105Å, and copper at Z=45Å and 75Å, effectively prevented agglomeration effects and ensured consistency between the simulation conditions and the actual dispersion state. Number density distribution analysis ( Figure 7 This further demonstrates that this configuration induces the directional alignment of base fluid molecules at the nanoparticle interface, forming a hierarchical structure that enhances thermal transport. Furthermore, as shown in Examples 4 and 5, the method of this invention can be extended to pure copper or pure graphene nanofluid systems. Comparison reveals the synergistic effect of the mixed nanoparticles: copper provides high phonon transport efficiency, while graphene constructs a continuous thermally conductive network. This provides an efficient tool for screening nanofluids for specific applications such as solar collectors and photovoltaic cooling.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A schematic diagram illustrating the steps for analyzing the heat transfer characteristics of hybrid nanofluids;

[0029] Figure 2 This is a schematic diagram of a hybrid nanofluid simulation box and heat transfer.

[0030] Figure 3 A schematic diagram comparing the thermal conductivity of the mixed nanofluids at different mass fractions;

[0031] Figure 4 A schematic diagram comparing the radial distribution function of the mixed nanofluids at different mass fractions;

[0032] Figure 5 A schematic diagram comparing the number of hydrogen bonds in mixed nanofluids at different mass fractions;

[0033] Figure 6 A schematic diagram comparing the mean square displacement of the mixed nanofluids at different mass fractions;

[0034] Figure 7 A schematic diagram comparing the number density of the mixed nanofluid in the Z direction under different mass fractions;

[0035] Figure 8 This diagram illustrates the potential energy, kinetic energy, parallel potential energy, and molecular potential energy of the mixed nanofluids at different mass fractions. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1

[0038] The method for analyzing the heat transfer characteristics of hybrid nanofluids based on molecular dynamics described in this embodiment includes the following steps:

[0039] The first step involves constructing a hybrid nanofluid model. Specifically, this includes: First, using the AutoFF open-source tool, individual models of graphene, copper, [BMIM]BF4, and water are built, generating a moltemplate template. Then, using the packmol software package and the moltemplate toolbox, a graphene / copper hybrid nanofluid is constructed, ensuring that graphene, copper, [BMIM]BF4, and water are uniformly and orderly arranged within a box, considering spatial dimensions. This allows for the construction of hybrid nanofluid models with different mass fractions. Next, based on different water molecule mass fractions, the number of water molecules, the mass of each water molecule, and the number of [BMIM]BF4 ion pairs are adjusted accordingly to establish nanofluids with different mass fractions.

[0040] The second step involves selecting potential functions and boundary conditions: the Airebo potential function is chosen to describe the interactions between C atoms in graphene within the hybrid nanofluid; the EAM potential function is chosen to describe the interactions between copper atoms; the OPLS-AA potential function is chosen to describe the intermolecular interactions of [BMIM]BF4; the SPC / E model is used for water molecules; and the Lennard-Jones potential function is used to describe the interactions between other molecules (graphene and copper, [BMIM]BF4 and water, copper and [BMIM]BF4 and water, and [BMIM]BF4 and water). The potential energy parameters of different atoms are calculated using the Lorentz-Berthelot mixing rule. Periodic boundary conditions are selected; in the direction of the periodic boundary conditions, the boundary can be considered as an infinitely large interval, making the simulated micro / nano system more closely resemble the macroscopic environment.

[0041] The third step is relaxation and initial parameter determination. First, the energy of the simulation box is minimized, then the system is relaxed under the NVT ensemble, and then energy is conserved under the NVE ensemble.

[0042] The fourth step is the calculation of thermal conductivity coefficient. A steady-state temperature gradient is established based on Fourier's law, and non-equilibrium molecular dynamics (NEMD) is used to calculate the thermal conductivity of the hybrid nanofluid.

[0043] The fifth step is the analysis and visualization of heat transfer characteristics. This involves a combined analysis of the radial distribution function (RDF), mean square displacement (MSD), number of hydrogen bonds, system energy, and number density distribution of the nanofluid heat transfer process.

[0044] In this embodiment, the first step of constructing the hybrid nanofluid model further includes:

[0045] In the simulation, graphene and copper were placed at fixed positions, with graphene placed at Z=15 and 105 and copper placed at Z=45 and 75, respectively, to ensure that the spacing between each nanoparticle was at least 30 Å, so as to prevent agglomeration during relaxation and heat transfer.

[0046] In this embodiment, in the second step, the simulation box is a three-dimensional box. In order to reduce the influence of the "size effect" on the simulation results, the present invention adopts periodic boundary conditions.

[0047] In this embodiment, in the second step, the expression for the Airebo potential function between C atoms in graphene is as follows:

[0048]

[0049] In the formula, It is the overall potential energy of the entire system. It is the covalent interaction of the REBO potential. It's Leonard Jones' LJ item. It is a compensating torsional interaction, where i and j represent any two different atoms in the system.

[0050] In this embodiment, the EAM potential function between copper atoms in the second step is described as follows:

[0051]

[0052] In the formula, It is the total potential energy. Representing the type of embedding energy, atomic electron density function, and atomic... and atoms The short-range pair potential interaction between them, where i and j represent any two different atoms in the system. This represents the distance between atoms i and j. This represents the electron density generated by atom j at the location of atom i.

[0053] In this embodiment, the second step, the OPLS-AA potential function between [BMIM]BF4 atoms, is described as follows:

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, Indicates bond stretch energy. Represents the bond angle bending energy. This represents the dihedral torsional barrier. Represents non-bonded interaction energy. These represent the parameters for bond stretching, angular bending, and dihedral angle terms, respectively. C represents the energy conversion parameter. It is the charge of the particle, and These are the size and energy parameters of each atom. Indicates instantaneous bond length, Represents average bond length, The coefficients representing the first, second, and third harmonic terms in the dihedral potential energy function.

[0059] The interactions between graphene and copper, [BMIM]BF4, and water; between copper and [BMIM]BF4 and water; and between [BMIM]BF4 and water are described by the Lennard-Jones potential as follows:

[0060]

[0061] In the formula, where and rc represents the interaction strength, atomic length scale, cutoff radius (rc = 10 Å for all LJ potential functions), and distance between the two atoms i and j, respectively.

[0062] The potential energy parameters between different atoms were calculated using the Lorentz-Berthelot mixing rule, and are described as follows:

[0063]

[0064] In the formula, i and j represent different atoms.

[0065] In this embodiment, in the third step, a PPPM solver with an accuracy of 10 is selected. -4 The Velocity-Verlet algorithm is used to solve the equations of motion.

[0066] In this embodiment, in the fourth step, before calculating the thermal conductivity coefficient of the hybrid nanofluid, the system is first balanced in the NVT ensemble for 1 ns using a Nose-Hoover thermostat, and then run in the NVE ensemble for 1 ns to ensure energy conservation and that the system is in a steady state during the simulation process. The overall kinetic energy, potential energy, total energy, and temperature of the system are monitored. Figure 2 As shown. The thermal conductivity of the [BMIM]BF4-water mixed base liquid was verified using the Filippov and Jamieson equations, as follows: Figure 3 As shown.

[0067] In this embodiment, in the fourth step, under the premise of system stability, non-equilibrium molecular dynamics (NEMD) is used to calculate the thermal conductivity of the hybrid nanofluid. The calculation formula is as follows:

[0068]

[0069] Where κ represents the thermal conductivity of the nanofluid system, A is the cross-sectional area of ​​the nanofluid system perpendicular to the heat flow direction, and J... z It is the heat flux density within the nanofluid system. It is the temperature gradient along the Z-axis.

[0070] In this embodiment, in the fifth step, the radial distribution function is described as follows: It is used to quantify the spatial density variation of atoms or particles relative to a reference atom in a condensed phase system. Formally, it describes the probability density of finding other atoms at radial distances r to r+dr from the reference atom, and is normalized by the system's average number density. The description is as follows:

[0071]

[0072] in, , , ,and These represent the number of nanoparticles within the distance interval r to r+∇r, the counts of atomic types α and β in the system, and the number of β atoms located in the region between two adjacent spherical shells, respectively.

[0073] In this embodiment, in step five, the mean square displacement (MSD) is a fundamental indicator for quantifying atomic mobility, defined as the ensemble mean square displacement of an atom over a period of time. For a system comprising N atoms, the mean square displacement at time t is described as follows:

[0074]

[0075] In the formula, This represents the position vector of atom i at time t. This represents the ensemble mean for statistically independent configurations.

[0076] In this embodiment, the number density calculation process in the fifth step is as follows: Figure 4 As shown.

[0077] Example 2

[0078] As described in this embodiment, a method for analyzing the heat transfer characteristics of hybrid nanofluids based on molecular dynamics is used. Figure 1 As shown, it includes the following steps:

[0079] The first step is to construct a graphene / copper hybrid nanofluid model. Specifically, this involves: firstly, using the AutoFF open-source tool to construct individual models of graphene, copper, [BMIM]BF4, and water, and generating a moltemplate template; then, using the packmol software package and the moltemplate toolbox package to construct a graphene / copper hybrid nanofluid, ensuring that graphene, copper, [BMIM]BF4, and water are uniformly and orderly arranged in the box while considering the spatial portion, thereby constructing hybrid nanofluid models with different mass fractions.

[0080] The base fluid for the hybrid nanofluids was prepared by mixing water with 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM]BF4) at different mass ratios. The mixing ratios were defined by the mass fraction of water and were divided into three levels: 25%, 50%, and 75%. To maintain consistent thermodynamic conditions (especially density and pressure) in the simulation setup, the number of water molecules, the mass of each water molecule, and the number of [BMIM]BF4 ion pairs were adjusted accordingly for each mass fraction, as shown in Table 1.

[0081] Table 1. Components of the Hybrid Nanofluid

[0082] Quality score (%) <![CDATA[[BMIM]BF4 number of molecules]]> number of water molecules 25 441 1848 50 281 3543 75 134 5057

[0083] This hybrid nanofluidic system consists of graphene nanosheets (lateral dimensions: 21 Å × 21 Å) and spherical copper nanoparticles (radius: 1.0 nm), dispersed in a binary fluid of [BMIM]BF4 ionic liquid and water. The graphene sheets are fixed at z = 15 Å and z = 105 Å; the copper nanoparticles are fixed at z = 45 Å and z = 75 Å. Figure 2 As shown.

[0084] The second step involves selecting potential functions and boundary conditions: the Airebo potential function is chosen to describe the interactions between C atoms in graphene within the hybrid nanofluid; the EAM potential function is chosen to describe the interactions between copper atoms; the OPLS-AA potential function is chosen to describe the intermolecular interactions of [BMIM]BF4; the SPC / E model is used for water molecules; and the Lennard-Jones potential function is used to describe the interactions between other molecules (graphene and copper, [BMIM]BF4 and water, copper and [BMIM]BF4 and water, and [BMIM]BF4 and water). The potential energy parameters of different atoms are calculated using the Lorentz-Berthelot mixing rule. Periodic boundary conditions are selected; in the direction of the periodic boundary conditions, the boundary can be considered as an infinitely large interval, making the simulated micro / nano system more closely resemble the macroscopic environment.

[0085] The third step is to determine the relaxation and initial parameters. Molecular dynamics simulations are performed in 1 femtosecond time steps. The equilibrium scheme consists of two consecutive stages: (1) Energy minimization: eliminating non-physical atomic overlap and reaching a local potential minimum; using the PPPM solver with an accuracy of 10 -4 The Velocity-Verlet algorithm was used to solve the equations of motion. (2) Relaxation stage: The Nose-Hoover thermostat was used to perform 1ns of NVT ensemble equilibration to stabilize the temperature at 300K, followed by 1ns of NVE ensemble operation to ensure energy conservation (ΔE / E < 0.01%). The initial atomic velocities were allocated according to the Maxwell-Boltzmann distribution at the target temperature (300K) to ensure physically representative kinetic energy initialization.

[0086] The fourth step is the calculation of thermal conductivity coefficients: Non-equilibrium molecular dynamics (NEMD) is used to calculate the thermal conductivity of the hybrid nanofluid. Based on Fourier's law, the heat flux is constrained along the z-axis. To establish a steady-state temperature gradient, energy exchange is performed using the fixehex command in LAMMPS: a temperature gradient is established by applying heat fluxes of equal magnitude but opposite direction (+δQ and -δQ) to designated hot and cold storage regions, injecting heat energy into layer 1 (designated as the hot storage) at the same rate and extracting heat energy from layer 11 (the cold storage), thus creating a continuous thermal imbalance. The simulation chamber is discretized along the z-axis into 20 uniform slices, each 6 Å thick. Periodic boundary conditions are applied in all directions, which requires correction for the net heat flux across the boundaries to avoid artifacts in the temperature gradient calculation. The system is considered to have reached steady state when the energy fluctuation is below a certain threshold (standard deviation < 0.5% of the average energy). The thermal conductivity (κ) is then derived from the time-averaged temperature difference (ΔT) between the hot and cold storage regions and the applied heat flux.

[0087] The fifth step is the analysis and visualization of heat transfer characteristics. This involves a combined analysis of the radial distribution function (RDF), mean square displacement (MSD), number of hydrogen bonds, system energy, and number density distribution of the nanofluid heat transfer process.

[0088] Finally, it should be specifically noted that the above-described embodiments are merely examples of implementations of the present invention using Graphene-Cu / [BMIM]BF4-H2O mixed nanofluids as an example. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be pointed out that those skilled in the art can make various modifications, equivalent substitutions, and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

[0089] Example 3

[0090] The simulation chamber measures 40 Å × 40 Å × 120 Å and uses graphene sheets with a lateral dimension of 21 Å × 21 Å and spherical copper nanoparticles with a radius of 10 Å. Two nanoparticles of each type are placed inside the simulation chamber, while the remaining portion of the chamber is filled with [BMIM]BF4 and water at mass fractions of 25%, 50%, and 75%, respectively. To contrast with the graphene-copper / [BMIM]BF4-water mixed nanofluid, a mixed nanofluid of pure graphene and pure copper with [BMIM]BF4-water as the base fluid was also constructed.

[0091] In this embodiment, the following potential functions are used to describe the components and their interactions in the hybrid nanofluid: the C atoms of graphene, the copper atoms, the [BMIM]BF4 ionic liquid, and the water molecules are described by the AIREBO, EAM, OPLS-AA, and SPC / E potential functions, respectively; the interactions between different components are described by the Lennard-Jones potential function, the parameters of which are determined by the Lorentz-Berthelot mixing rule.

[0092] The relaxation process was performed in two steps with a step size of 1 fs. First, energy minimization was performed to eliminate atomic overlap and reach a local potential minimum. Then, relaxation was performed: the system was stabilized at 300 K for 1 ns in the NVT ensemble (Nose-Hoover thermostat), and then in the NVE ensemble for 1 ns to verify energy conservation (ΔE / E < 0.01%). The initial atomic velocities were set according to the Maxwell-Boltzmann distribution at 300 K. Throughout the process, the equations of motion were solved using the Velocity-Verlet algorithm, and electrostatic interactions were solved with an accuracy of 10... -4 The PPPM method is used for processing.

[0093] The thermal conductivity of the hybrid nanofluid was calculated using nonequilibrium molecular dynamics (NEMD). The simulation process was as follows: the system was divided into 20 thin layers with a thickness of 6 Å along the z-axis, and three-dimensional periodic boundary conditions were applied. To establish a temperature gradient, heat (+JQ) was continuously injected into the first layer, while an equal amount of heat (-JQ) was extracted from the eleventh layer in the opposite direction. When the system energy fluctuation reached a steady state (standard deviation < 0.5%), the average temperature difference (ΔT) between the heat source layer and the cold source layer was recorded. Finally, based on Fourier's law, the thermal conductivity (κ) was calculated from the heat flux density JQ and ΔT. To elucidate the heat transfer mechanism of the hybrid nanofluid, the radial distribution function (RDF), mean square displacement (MSD), number of hydrogen bonds, system energy, and number density distribution during the simulation were also calculated. Figures 4-8 As shown.

[0094] Example 4

[0095] The simulated box measures 40 Å × 40 Å × 120 Å. Two spherical copper nanoparticles with a radius of 10 Å are placed at z = 30 and 60 Å, respectively, centered in the x and y directions. The rest of the box is filled with [BMIM]BF4 and water, with mass fractions of 25%, 50%, and 75%, respectively.

[0096] In this embodiment, copper, [BMIM]BF4, and water molecules are represented by the EAM, OPLS-AA, and SPC / E potential functions, respectively, while all intercomponent interactions are described by the Lennard-Jones potential function, with parameters determined by the Lorentz-Berthelot mixing rule. System relaxation is performed using a 1 fs time step. Energy minimization is performed first, followed by sequential 1 ns NVT (300 K) and NVE ensemble relaxation to ensure temperature stability and energy conservation (ΔE / E < 0.01%). The initial velocity is set according to the Maxwell-Boltzmann distribution, and the Velocity-Verlet algorithm and PPPM solver are used.

[0097] Thermal conductivity was calculated using nonequilibrium molecular dynamics (NEMD). A steady-state temperature gradient was established by placing a heat source (layer 1) and a cold source (layer 11) in the z-direction and applying equal and opposite heat flows. The thermal conductivity (κ) was ultimately calculated using Fourier's law based on the steady-state heat flux density and temperature difference. The simulation system was divided into 20 layers and periodic boundary conditions were employed. The radial distribution function (RDF), mean square displacement (MSD), number of hydrogen bonds, system energy, and number density distribution of the nanofluid heat transfer process were analyzed in conjunction.

[0098] Example 5

[0099] The simulated box measures 40 Å × 40 Å × 120 Å. Two graphene sheets with a transverse dimension of 21 Å × 21 Å are placed at z = 30 and 60 Å, respectively, and centered in the x and y directions. The rest of the box is filled with [BMIM]BF4 and water, with mass fractions of 25%, 50%, and 75%, respectively.

[0100] In this embodiment, graphene, [BMIM]BF4 and water molecules are represented by AIREBO, OPLS-AA and SPC / E potential functions, respectively, while all intercomponent interactions are described by Lennard-Jones potential functions, with parameters determined by the Lorentz-Berthelot mixing rule.

[0101] The system relaxation uses a 1 fs time step. Energy minimization is performed first, followed by sequential 1 ns NVT (300 K) and NVE ensemble relaxation to ensure temperature stability and energy conservation (ΔE / E < 0.01%). The initial velocity is set according to the Maxwell-Boltzmann distribution, and the Velocity-Verlet algorithm and PPPM solver are used.

[0102] Thermal conductivity was calculated using nonequilibrium molecular dynamics (NEMD). A steady-state temperature gradient was established by placing a heat source (layer 1) and a cold source (layer 11) in the z-direction and applying equal and opposite heat flows. The thermal conductivity (κ) was ultimately calculated using Fourier's law based on the steady-state heat flux density and temperature difference. The system was divided into 20 layers in the simulation, and periodic boundary conditions were employed. The radial distribution function (RDF), mean square displacement (MSD), number of hydrogen bonds, system energy, and number density distribution of the nanofluid heat transfer process were analyzed in conjunction.

[0103] In summary, this invention proposes a molecular dynamics-based method for analyzing the heat transfer properties of hybrid nanofluids. A molecular model is constructed comprising copper and graphene nanoparticles and an ionic liquid / water mixture as the base fluid. Energy minimization and molecular dynamics relaxation are performed using a distribution potential function and periodic boundary conditions to achieve thermodynamic equilibrium. A steady-state temperature gradient is established based on non-equilibrium molecular dynamics principles, and thermal conductivity is calculated according to Fourier's law. Simultaneous analysis of microscopic parameters such as radial distribution function, mean square displacement, and number of hydrogen bonds reveals the coupling mechanism between the nanoparticle-base fluid interface and heat transfer performance. This invention can elucidate the heat transfer characteristics of hybrid nanofluids at the molecular scale, providing an efficient and reliable analytical method for the screening and design of nanofluids in applications such as solar collectors and thermal management systems.

[0104] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for analyzing the heat transfer characteristics of hybrid nanofluids based on molecular dynamics, characterized in that, Includes the following steps: S1: Construct an initial molecular model including nanoparticles and a mixed base liquid using molecular modeling tools. The mixed base liquid is a binary mixture of ionic liquid and deionized water. The nanoparticles include carbon-based nanomaterials and metal nanoparticles. The nanoparticles are fixed in the model by preset spatial coordinates to ensure that they are spaced at a set distance from each other, thereby suppressing the aggregation effect during the simulation. S2: Assign potential functions to each component atom or molecule in the model, and assign potential function parameters determined by the mixing rules to all cross-component interactions, while setting periodic boundary conditions; S3: Minimize the energy of the initial molecular model to eliminate non-physical atomic overlap, and then perform molecular dynamics relaxation in an isothermal isochoric ensemble and a microcanonical ensemble in sequence; wherein, in the isothermal isochoric ensemble, a chain-like heat bath is used to stabilize the system temperature at the target value, and in the microcanonical ensemble, it is verified that the total energy fluctuation of the system is lower than the first preset threshold, so that the system reaches thermodynamic equilibrium. S4: Based on the principle of non-equilibrium molecular dynamics, a steady-state temperature gradient is established in the relaxed system, and the thermal conductivity of the nanofluid is calculated according to Fourier's law. S5: Simultaneously extract and correlate various microstructure dynamic characterization parameters of the system during the non-equilibrium heat transfer simulation process. These parameters include at least the radial distribution function, mean square displacement, number of hydrogen bond topologies, system potential energy trajectory, and spatial number density distribution, in order to synergistically reveal the coupling mechanism between nanoparticle-base liquid interface interaction, base liquid microstructure evolution, and macroscopic heat transport performance at the molecular scale.

2. The method as described in claim 1, characterized in that, In step S1, the carbon-based nanomaterial is graphene nanosheets, the metal nanoparticles are copper nanoparticles, the ionic liquid is an imidazole ionic liquid, and the mass fraction of water in the mixed base liquid is adjustable within the range of 10% to 90%.

3. The method as described in claim 2, characterized in that, The distance between the graphene nanosheets and the copper nanoparticles in the model is not less than 30 Å; the imidazole ionic liquid is 1-butyl-3-methylimidazolium tetrafluoroborate; and the water molecules adopt an extended simple point charge model.

4. The method as described in claim 1, characterized in that, The potential functions in step S2 include: the AIREBO potential function for graphene carbon atoms, the EAM potential function for copper atoms, the OPLS-AA potential function for ionic liquids, and the SPC / E model for water molecules; the potential function for cross-component interactions is the Lennard-Jones potential function, and the mixing rule is the Lorentz-Berthelot rule. The expression for the Airebo potential function between C atoms in graphene is as follows: In the formula, It is the covalent interaction of the REBO potential. It's Leonard Jones' LJ item. It is a compensatory torsional interaction; The EAM potential function between copper atoms is described as follows: In the formula, Representing the type of embedding energy, atomic electron density function, and atomic... and atoms Short-range potential interactions between them; The OPLS-AA potential function between [BMIM]BF4 atoms is described as follows: In the formula, These represent the parameters for bond stretching, angular bending, and dihedral angle terms, respectively; C represents the energy conversion parameter. It is the charge of the particle, and These are the size and energy parameters of each atom; The interactions between graphene and copper, [BMIM]BF4, and water; between copper and [BMIM]BF4 and water; and between [BMIM]BF4 and water are described by the Lennard-Jones potential as follows: In the formula, where and These represent the interaction strength, atomic length scale, cutoff radius, and distance between the two atoms i and j, respectively. The potential energy parameters between different atoms are calculated using the Lorentz-Berthelot mixing rule and are described as follows: In the formula, i and j represent different atoms.

5. The method as described in claim 1, characterized in that, The target temperature is 300 K, and the first preset threshold is 0.01% of the total energy of the system; the relaxation time of the isothermal isochoric ensemble is 1 nanosecond, and the relaxation time of the microcanonical ensemble is 1 nanosecond; the integral of the equations of motion uses the Velocity-Verlet algorithm, and the long-range electrostatic interaction is handled using the PPM method with an accuracy set to 10. -4 .

6. The method as described in claim 1, characterized in that, Step S4 specifically includes: uniformly dividing the simulation system into N statistical thin layers along the heat flow direction, selecting two non-adjacent thin layers as a constant heat source and a constant cold source respectively, and applying equal and opposite scalar heat fluxes; continuously monitoring the total energy of the system until its fluctuation standard deviation is lower than a second preset threshold, which is then determined to be a steady state; recording the time-averaged temperature difference between the heat source layer and the cold source layer; and calculating the equivalent thermal conductivity of the nanofluid by dividing the applied scalar heat flux by the product of the time-averaged temperature difference and the cross-sectional area perpendicular to the heat flow direction, according to Fourier's law of thermal conductivity. The thermal conductivity of the hybrid nanofluid was calculated using nonequilibrium molecular dynamics (NEMD), and the formula is as follows: Where κ represents the thermal conductivity of the nanofluid system, A is the cross-sectional area of ​​the nanofluid system perpendicular to the heat flow direction, and J... z It is the heat flux density within the nanofluid system. It is the temperature gradient along the Z-axis.

7. The method as described in claim 6, characterized in that, The value of N is 20, the heat source layer is the first layer, the cold source layer is the eleventh layer, and the second preset threshold is 0.5% of the average energy.

8. The method as described in claim 1, characterized in that, In step S5, the thickness of the ordered layer of base liquid molecules at the nanoparticle interface is quantified by analyzing the position and intensity of the first peak of the radial distribution function; the diffusion coefficient of the base liquid molecules is calculated by the slope of the mean square displacement curve; and the influence of the microstructure of the ionic liquid-water mixed base liquid on the heat transport path under different component ratios is revealed by the correlation between the number of hydrogen bond topological networks and the spatial number density distribution. The radial distribution function is used to quantify the spatial density variation of atoms or particles relative to a reference atom in a condensed phase system. It describes the probability density of finding other atoms at radial distances r to r+dr from the reference atom and is normalized by the system's average density. The description is as follows: in, , , ,and These represent the number of nanoparticles within the distance interval r to r+∇r, the counts of atomic types α and β in the system, and the number of β atoms located in the region between two adjacent spherical shells, respectively. Mean square displacement (MSD) is a fundamental metric for quantifying atomic mobility. It is defined as the ensemble mean square displacement of atoms over a period of time. For a system containing N atoms, the mean square displacement at time t is described as follows: In the formula, This represents the position vector of atom i at time t. This represents the ensemble mean for statistically independent configurations.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes a verification step: after the thermophysical property calculation step, the simulated thermal conductivity of the pure ionic liquid-water mixture is compared with the theoretical predictions of the Filippov equation and / or Jamieson equation to verify the accuracy of the molecular model and potential function assignment.

10. The method according to any one of claims 1 to 8, characterized in that, The molecular dynamics simulations were performed using the LAMMPS software package; the molecular modeling tools included AutoFF for force field parameterization, Packmol for initial structure stacking, and moltemplate for constructing complex molecular topologies.

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