Simulation method for strengthening heat storage performance of molten salt by using oxygen vacancy defects of nanoparticles

Abstract

The invention relates to the technical field of heat storage materials, in particular to a simulation method for strengthening the heat storage performance of molten salt by using nano-particle oxygen vacancy defects. The method comprises the following steps: constructing a unit cell structure of silicon dioxide, determining a nanoparticle model containing a surface oxygen vacancy defect based on the unit cell structure of silicon dioxide, and constructing a final silicon dioxide-potassium carbonate nanofluid model based on the nanoparticle model containing the surface oxygen vacancy defect; performing molecular dynamics simulation based on the final silicon dioxide-potassium carbonate nanofluid model to obtain molecular dynamics simulation data; based on molecular dynamics simulation data, the final heat storage performance parameters of the silicon dioxide-potassium carbonate nanofluid model are determined, and by means of the setting mode, results obtained through simulation discover that the heat storage performance of the molten salt can be effectively improved through nano-particle oxygen vacancy defects.

Description

Technical Field

[0001] The present invention relates to the technical field of heat storage materials, and in particular to a simulation method for enhancing the heat storage performance of molten salt by utilizing oxygen vacancy defects in nanoparticles. Background Art

[0002] As a clean and renewable energy source, solar energy is a key focus of energy transition, but its intermittent and unstable nature limits its large-scale application. Concentrated solar power (CSP) combined with thermal energy storage (TES) technology can effectively address this issue and improve energy efficiency by storing heat during the day and releasing it at night. Molten salt, with its low vapor pressure, excellent thermal stability, and wide temperature adaptability, has become a key heat storage material in CSP systems. As CSP systems develop towards high temperatures and high efficiency, especially with the promotion of supercritical carbon dioxide Brayton cycle technology, research focus has gradually shifted to improving the heat transfer and storage properties of molten salts to increase the efficiency of molten salt thermal storage power generation systems.

[0003] Doping molten salts with nanoparticles is considered an effective way to increase the specific heat of molten salt nanofluids. However, there are currently no studies or reports on the impact of nanoparticle oxygen vacancy defects on the thermal storage performance of molten salts and their enhancement.

[0004] Based on this, the present invention proposes a simulation method for enhancing the heat storage performance of molten salt by utilizing oxygen vacancy defects in nanoparticles to solve the above technical problems. Summary of the Invention

[0005] This paper describes a simulation method for enhancing the thermal storage performance of molten salts by utilizing oxygen vacancy defects in nanoparticles. The simulation results show that oxygen vacancy defects in nanoparticles can effectively improve the thermal storage performance of molten salts.

[0006] According to a first aspect, the present invention provides a simulation method for enhancing the thermal storage performance of molten salt using oxygen vacancy defects in nanoparticles, comprising:

[0007] Constructing the unit cell structure of silica;

[0008] Determining a nanoparticle model containing surface oxygen vacancy defects based on the unit cell structure of the silicon dioxide;

[0009] Determining a final silica-potassium carbonate nanofluid model based on the nanoparticle model containing surface oxygen vacancy defects;

[0010] Performing molecular dynamics simulation using the final silica-potassium carbonate nanofluid model to obtain molecular dynamics simulation data;

[0011] Based on the molecular dynamics simulation data, the heat storage performance parameters of the final silica-potassium carbonate nanofluid model are determined, wherein the heat storage performance parameters include specific heat capacity and thermal conductivity.

[0012] According to a second aspect, the present invention provides a simulation device for enhancing the heat storage performance of molten salts by using oxygen vacancy defects of nanoparticles, including:

[0013] A construction unit configured to construct a unit cell structure of silicon dioxide;

[0014] A first data processing unit configured to determine a nanoparticle model containing surface oxygen vacancy defects based on the unit cell structure of silicon dioxide;

[0015] A second data processing unit configured to determine a final silicon dioxide-potassium carbonate nanofluid model based on the nanoparticle model containing surface oxygen vacancy defects;

[0016] A third data processing unit configured to perform molecular dynamics simulation using the final silicon dioxide-potassium carbonate nanofluid model to obtain molecular dynamics simulation data;

[0017] A fourth data processing unit configured to determine heat storage performance parameters of the final silicon dioxide-potassium carbonate nanofluid model based on the molecular dynamics simulation data, wherein the heat storage performance parameters include specific heat capacity and thermal conductivity.

[0018] According to a third aspect, the present invention provides an electronic device including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method of the first aspect is implemented.

[0019] According to a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed on a computer, the computer is made to execute the method of the first aspect.

[0020] According to the simulation method, device, equipment and medium for the heat storage performance of molten salts provided by the present invention, first, a unit cell structure of silicon dioxide is built. Based on the constructed unit cell structure of silicon dioxide, a nanoparticle model with surface oxygen vacancy defects is further established. Subsequently, according to the nanoparticle model containing surface oxygen vacancy defects, a final silicon dioxide-potassium carbonate nanofluid model is successfully constructed. After completing the model construction step, molecular dynamics simulation calculations are carried out by means of the finally established silicon dioxide-potassium carbonate nanofluid model, so as to obtain corresponding molecular dynamics simulation data. Based on the obtained kinetic simulation data, the heat storage performance parameters of the final silicon dioxide-potassium carbonate nanofluid model are determined, wherein the heat storage performance parameters include specific heat capacity and thermal conductivity. In summary, through the above technical means, the present invention discovers that nanoparticles carrying oxygen vacancies play an efficient role in enhancing the heat storage performance of molten salts. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 The flowchart of the simulation method for the molten salt heat storage performance according to an embodiment is shown;

[0023] Figure 2 The schematic block diagram of the simulation device for the molten salt heat storage performance according to an embodiment is shown;

[0024] Figure 3 The schematic diagram of the final silica-potassium carbonate nanofluid model according to an embodiment is shown;

[0025] Figure 4 The charge distribution diagram of the O-vacancy-containing SiO2 nanoparticles in different charge states according to an embodiment is shown;

[0026] Figure 5 The schematic diagram of the variation relationship between the number of vacancies and the specific heat capacity of the nanoparticle model containing surface oxygen vacancy defects according to an embodiment is shown;

[0027] Figure 6 The schematic diagram of the molten salt ion distribution near the oxygen-vacancy-defect-containing SiO2 nanoparticles according to an embodiment is shown;

[0028] Figure 7 The schematic diagram of the variation relationship between the number of vacancies and the thermal conductivity of the nanoparticle model containing surface oxygen vacancy defects according to an embodiment is shown. Detailed implementation manners

[0029] The following describes the solution provided by the present invention in conjunction with the drawings.

[0030] Figure 1 The flowchart of the simulation method for the molten salt heat storage performance according to an embodiment is shown. It can be understood that this method can be executed by any device, equipment, platform, or equipment cluster with computing and processing capabilities. As Figure 1 shown, this method includes:

[0031] Step 100: Construct the unit cell structure of silica;

[0032] Step 102: Based on the unit cell structure of silica, determine the nanoparticle model containing surface oxygen vacancy defects;

[0033] Step 104: Based on the nanoparticle model containing surface oxygen vacancy defects, determine the final silica-potassium carbonate nanofluid model;

[0034] Step 106: Use the final silica-potassium carbonate nanofluid model to perform molecular dynamics simulations to obtain molecular dynamics simulation data;

[0035] Step 108: Based on the molecular dynamics simulation data, determine the heat storage performance parameters of the final silica-potassium carbonate nanofluid model, where the heat storage performance parameters include specific heat capacity and thermal conductivity.

[0036] In this embodiment, first, a silica unit cell structure is built. Based on the constructed silica unit cell structure, a nanoparticle model with surface oxygen vacancy defects is further established. Subsequently, according to the nanoparticle model containing surface oxygen vacancy defects, the final silica-potassium carbonate nanofluid model is successfully constructed. After completing the model construction step, molecular dynamics simulation calculations are carried out using the finally established silica-potassium carbonate nanofluid model to obtain the corresponding molecular dynamics simulation data. Based on the obtained molecular dynamics simulation data, the heat storage performance parameters of the final silica-potassium carbonate nanofluid model are determined, where the heat storage performance parameters include specific heat capacity and thermal conductivity. In summary, through the above technical means, the present invention discovers that nanoparticles carrying oxygen vacancies can efficiently enhance the heat storage performance of molten salts.

[0037] In an embodiment of the present invention, based on the unit cell structure of silica, determining a nanoparticle model containing surface oxygen vacancy defects includes:

[0038] Perform a supercell expansion operation on the unit cell structure of silica to obtain a supercell-expanded supercell structure;

[0039] Cut the supercell-expanded unit cell structure with the center of the supercell as the center of the sphere to obtain an initial model of silica nanoparticles; wherein, the initial model of nanoparticles includes multiple oxygen atoms and multiple silicon atoms;

[0040] Remove a preset proportion of oxygen atoms in the initial model of silica nanoparticles to obtain a nanoparticle model containing surface oxygen vacancy defects.

[0041] In this embodiment, a nanoparticle model containing surface oxygen vacancy defects is determined based on the unit cell structure of silicon dioxide. The specific process is as follows: First, the Atomsk tool is used to construct a SiO2 unit cell and perform a supercell expansion operation on it to obtain a supercell structure with supercell expansion, which has a sufficient size to meet the requirements of subsequent research. Then, the supercell structure with supercell expansion is cut with the center of the supercell as the center of the sphere, and a spherical nanoparticle with a diameter of 2 nm is constructed by spherical interception. The initial model of this nanoparticle consists of 40 Si atoms and 80 O atoms, a total of 120 atoms. Subsequently, the oxygen vacancy introduction step is entered. The initial model of the constructed SiO2 nanoparticle is imported into the Materials Studio software, and under the visual interface, different numbers of oxygen atoms are selectively removed from the nanoparticle surface using the structure selection function to simulate the formation process of surface oxygen vacancy defects. The specific operation is to remove 5, 10, 15, and 20 O atoms respectively to obtain nanoparticle structure models with different oxygen vacancy concentrations, that is, the final required nanoparticle models containing surface oxygen vacancy defects.

[0042] In an embodiment of the present invention, based on the nanoparticle model containing surface oxygen vacancy defects, the final silica-potassium carbonate nanofluid model is determined, including:

[0043] The nanoparticle model containing surface oxygen vacancy defects is optimized to obtain an optimized nanoparticle model;

[0044] The electrostatic charge of each atom in the nanoparticle model containing surface oxygen vacancy defects is calculated to obtain the ESP charge value of each atom;

[0045] The optimized nanoparticle model is placed at the center of the simulation box, and potassium carbonate molecules are randomly filled into the optimized nanoparticle model to obtain an initial silica-potassium carbonate nanofluid model;

[0046] The ESP charge value of each atom is imported into the silica-potassium carbonate nanofluid model to obtain the final silica-potassium carbonate nanofluid model.

[0047] In this embodiment, the final silica-potassium carbonate nanofluid model is determined based on the nanoparticle model containing surface oxygen vacancy defects. The specific steps are as follows: First, an advanced structural optimization algorithm is used to optimize the structure of the nanoparticle model containing surface oxygen vacancy defects. Through optimization, the atomic coordinates in the model reach the stable state with the lowest energy, reducing the lattice distortion and internal stress of the model, and obtaining an optimized nanoparticle model with good structural stability and rationality. Subsequently, electrostatic charge calculations at the quantum chemistry level are carried out for each atom in the nanoparticle model containing surface oxygen vacancy defects, and the electrostatic potential (ESP) charge value of each atom is obtained using an accurate calculation method. This calculation process can accurately characterize the charge distribution characteristics of atoms in a specific chemical environment. Then, the optimized nanoparticle model is placed at the geometric center of the simulation box to ensure the symmetry and rationality of the model in the simulation space. After that, according to the random filling strategy of molecular dynamics simulation, potassium carbonate molecules are randomly filled around the optimized nanoparticle model. During this process, the intermolecular interaction and steric hindrance effect are considered to ensure that the distribution of potassium carbonate molecules around the nanoparticles conforms to the actual physicochemical environment, thereby constructing an initial silica-potassium carbonate nanofluid model. Finally, the ESP charge value of each atom accurately calculated previously is accurately imported into the initial silica-potassium carbonate nanofluid model. Through the accurate assignment of charges, the model can more realistically reflect the electrostatic interaction between atoms in the system, and then determine the final silica-potassium carbonate nanofluid model, which can be used for subsequent in-depth molecular dynamics simulations and performance studies.

[0048] In this embodiment, the DMol 3 quantum chemistry module in the Materials Studio software is used to perform geometric structure optimization and charge distribution calculation on the above defect structure. During the optimization process, the density functional theory (DFT) method is adopted, the B3LYP functional and the DNP basis set are selected, the energy convergence threshold is set to 2×10 -5 eV, and the maximum atomic force is The maximum displacement is The spin polarization is set to unconstrained, and at the same time, the total system charge is set to +5, +10, +15, and +20 e respectively according to the number of missing oxygen atoms.

[0049] In this embodiment, Figure 4 shows the charge distribution of SiO2 nanoparticles containing oxygen vacancies in different charge states (120, 115, 110, 105, and 100 represent the total number of atoms in the nanoparticles, and their carried charges are 0, +5, +10, +15, and +20 respectively). Figure 4In it, the sub - figure reveals the local charge aggregation caused by oxygen vacancies (such as the positively charged silicon dangling bonds) through the charge density distribution (such as the color shade). In addition, the main figure shows the distribution law of relative charge with radius under different oxygen vacancy defects, indicating that the charge migrates towards the surface or near the vacancy at high charge states. This figure emphasizes the regulatory effect of oxygen vacancies on the electronic structure of SiO2 nanoparticles, such as the formation of defect states and charge redistribution.

[0050] As Figure 3 shown, in this embodiment, Figure 3 shows the final silica - potassium carbonate nanofluid model, Figure 3 in which, the yellow spheres represent oxygen atoms, the purple spheres represent potassium atoms, the gray spheres represent carbon atoms, and the red spheres represent oxygen atoms. Figure 3 Sub - figure a in it is a SiO2 nanoparticle without O vacancies on the surface, Figure 3 Sub - figure b in it is a SiO2 nanoparticle with O vacancies on the surface, Figure 3 Sub - figure c in it is the final silica - potassium carbonate nanofluid model.

[0051] In an embodiment of the present invention, after optimizing the nanoparticle model with surface oxygen vacancy defects to obtain an optimized nanoparticle model, it further includes:

[0052] Conducting optimization verification on the optimized nanoparticle model to obtain a verified nanoparticle model;

[0053] Among them, the optimization verification includes total energy convergence verification, atomic force convergence verification, and structural configuration rationality verification.

[0054] In this embodiment, the optimization verification covers multiple key aspects. First is the total energy convergence verification. Continuously adjust the model parameters until the total energy of the system reaches a convergent state to ensure the accuracy and stability of the energy calculation results, which is crucial for reflecting the true energy characteristics of the model. Secondly, conduct atomic force convergence verification. Use precise atomic force calculation methods to repeatedly calculate the forces exerted on the atoms in the model. When the atomic force converges to a very small value range, it indicates that the atoms are in a stable force - bearing state and the model structure has mechanical stability. Finally, carry out structural configuration rationality verification. Starting from multiple dimensions such as geometric structure, bond lengths and angles between atoms, and spatial packing patterns, and based on relevant chemical theories and comparison and analysis with actual experimental data, judge whether the structural configuration of the model conforms to chemical principles and objective facts, so as to comprehensively ensure the scientificity and reliability of the optimized model.

[0055] In one embodiment of the present invention, the molecular dynamics simulation parameters include the system temperature, the fluctuation of the total energy of the system, the atomic coordinates of the system, the velocities of the atoms in the cold region of the system, the velocities of the atoms in the hot region of the system, the simulation time, the length of the simulation system in the x-axis direction, the length of the simulation system in the y-axis direction, and the temperature gradient of the simulation system in the z-axis direction.

[0056] In this embodiment, the LAMMPS molecular dynamics simulation software is used to simulate the thermodynamic and transport behaviors of the constructed SiO2-K2CO3 nanofluid system (the final silica-potassium carbonate nanofluid model). 1. Potential function setting: The interactions within the system are described by the Buckingham potential function including the Coulomb term. The parameters of the K2CO3 molten salt part are from the verified BMH potential function, and the parameters of the SiO2 nanoparticle part are from relevant literature. The interaction terms between different elements are calculated using the Lorentz-Berthelot mixing rule, and the long-range Coulomb interaction is solved using the Ewald method.

[0057] The expression of the Buckingham potential function is as follows:

[0058]

[0059] In the formula, U ij represents the interaction energy between atoms i and j, r is the distance from site i to site j. q is the charge of the atom at site i or j. A ij represents the repulsive parameter, ρ represents the hardness parameter; C ij represents the van der Waals parameter, so this term is used to describe the dissipative force. The potential parameters of the Buckingham potential are shown in Table 1

[0060] Table 1 Potential function parameters of carbonate-based SiO2

[0061]

[0062] 2. Simulation box and boundary conditions: A three-dimensional cubic simulation box is used, and periodic boundary conditions are set to eliminate boundary effects. The cutoff radius is set to The initial velocities of the atoms in the system follow a Gaussian distribution. The time integration uses the Verlet algorithm, and the time step is set to 1 fs.

[0063] 3. Relaxation process: The simulation process is carried out in stages, including three stages: heating, annealing, and equilibration. The system is heated from 300 K to 1600 K under the NPT ensemble, and then slowly cooled to 1200 K under the NPT condition. Finally, at 1200 K, first NPT and then NVT equilibration are carried out.

[0064] 4. Performance simulation process: (1) The specific heat capacity is calculated based on the total energy fluctuation relationship under the NVT ensemble, running 1,000,000 steps. (2) The thermal conductivity is calculated using the Reverse Non-Equilibrium Molecular Dynamics (RNEMD) method, running 1,000,000 steps under the NVE ensemble. The simulation box is divided into 20 equally thick spatial layers along the z-axis direction. By periodically exchanging the kinetic energy of some atoms between the first layer (cold region) and the eleventh layer (hot region) during the stable operation of the system, a heat flux is forced in the system. As the simulation time progresses, the system gradually reaches a steady-state heat conduction state, forming a stable temperature gradient along the z-axis direction. (3) Output parameters. During the simulation process, the LAMMPS molecular dynamics software is used for data collection, and the output includes the spatial coordinates, velocities, heat fluxes, temperature gradients, temperatures, and total system energy of each atom in each calculation step for subsequent calculations.

[0065] As Figure 6 shown, in this embodiment, as Figure 6 is a schematic diagram obtained by extracting atomic coordinates and performing post-processing. In this model system, potassium carbonate, as the molten salt medium, forms ion compression layers in an alternating distribution state. Compared with conventional liquid molten salts, this ion compression layer exhibits higher structural stability and compactness, and is more similar to the crystal structure in terms of structural characteristics. This special structure enables it to have the ability to store additional vibrational energy, thereby significantly enhancing the energy fluctuation of the system under unit temperature conditions, and finally intuitively showing an optimized improvement in heat storage performance.

[0066] In one embodiment of the present invention, the specific heat capacity is determined by the following formula:

[0067]

[0068] In the formula, C is the specific heat capacity, k B is the Boltzmann constant, is the fluctuation of the total system energy, and T is the system temperature.

[0069] As Figure 5 shown, in this embodiment, compared with the molten salt nanofluid without oxygen vacancy defects in traditional nanoparticles, the molten salt nanofluid containing SiO2 nanoparticles with oxygen vacancy defects (the final silica-potassium carbonate nanofluid model) has a significant increase in specific heat capacity.

[0070] In one embodiment of the present invention, the thermal conductivity is determined by the following formula:

[0071]

[0072] In the formula, λ is the thermal conductivity, m is the mass of the atom, υ hotis the velocity of atoms in the cold region of the system, υ cold is the velocity of atoms in the hot region of the system, t is the simulation time, L x is the length of the simulation system along the x-axis, L y is the length of the simulation system along the y-axis, is the temperature gradient of the simulation system in the z-axis direction.

[0073] As Figure 7 shown, in this embodiment, compared with the molten salt nanofluid without oxygen vacancy defects in nanoparticles of the traditional one, the molten salt nanofluid containing SiO2 nanoparticles with oxygen vacancy defects (the final silica-potassium carbonate nanofluid model) has a significantly improved thermal conductivity.

[0074] The specific embodiments of the present invention have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] According to an embodiment of another aspect, the present invention provides a simulation device for the heat storage performance of molten salt. Figure 2 shows a schematic block diagram of a simulation device for the heat storage performance of molten salt according to an embodiment. It can be understood that the device can be implemented by any device, equipment, platform, and cluster of devices with computing and processing capabilities. As Figure 2 shown, the device includes: a construction unit 200, a first data processing unit 202, a second data processing unit 204, a third data processing unit 206, and a fourth data processing unit 208. The main functions of each component unit are as follows:

[0076] The construction unit 200 is configured to construct the unit cell structure of silicon dioxide;

[0077] The first data processing unit 202 is configured to determine a nanoparticle model containing surface oxygen vacancy defects based on the unit cell structure of the silicon dioxide;

[0078] The second data processing unit 204 is configured to determine the final silica-potassium carbonate nanofluid model based on the nanoparticle model containing surface oxygen vacancy defects;

[0079] The third data processing unit 206 is configured to perform molecular dynamics simulation using the final silica-potassium carbonate nanofluid model to obtain molecular dynamics simulation data;

[0080] The fourth data processing unit 208 is configured to determine the heat storage performance parameters of the final silica-potassium carbonate nanofluid model based on the molecular dynamics simulation data, where the heat storage performance parameters include specific heat capacity and thermal conductivity.

[0081] As a preferred embodiment, the determining the final silica-potassium carbonate nanofluid model based on the nanoparticle model containing surface oxygen vacancy defects includes:

[0082] Performing an optimization process on the nanoparticle model containing surface oxygen vacancy defects to obtain an optimized nanoparticle model;

[0083] Calculating the electrostatic charge of each atom in the optimized nanoparticle model to obtain the ESP charge value of each atom;

[0084] Placing the optimized nanoparticle model at the center of the simulation box and randomly filling potassium carbonate molecules into the optimized nanoparticle model to obtain an initial silica-potassium carbonate nanofluid model;

[0085] Importing the ESP charge value of each atom into the silica-potassium carbonate nanofluid model to obtain the final silica-potassium carbonate nanofluid model.

[0086] As a preferred embodiment, after performing the optimization process on the nanoparticle model containing surface oxygen vacancy defects to obtain an optimized nanoparticle model, it further includes:

[0087] Performing an optimization verification on the optimized nanoparticle model to obtain a verified nanoparticle model;

[0088] Wherein, the optimization verification includes total energy convergence verification, atomic force convergence verification and structural configuration rationality verification.

[0089] As a preferred embodiment, the molecular dynamics simulation parameters include system temperature, fluctuation of the total system energy, system atomic coordinates, velocities of atoms in the cold region of the system, velocities of atoms in the hot region of the system, simulation time, length of the simulation system in the x-axis direction, length of the simulation system in the y-axis direction, and temperature gradient of the simulation system in the z-axis direction.

[0090] As a preferred embodiment, the specific heat capacity is determined by the following formula:

[0091]

[0092] In the formula, C is the specific heat capacity, k B is the Boltzmann constant, is the fluctuation of the total system energy, and T is the system temperature.

[0093] As a preferred embodiment, the thermal conductivity is determined by the following formula:

[0094]

[0095] In the formula, λ is the thermal conductivity, m is the mass of the atom, υ hot is the velocity of the atoms in the cold region of the system, υ cold is the velocity of the atoms in the hot region of the system, t is the simulation time, L x is the length of the simulation system along the x-axis, L y is the length of the simulation system along the y-axis, is the temperature gradient of the simulation system in the z-axis direction.

[0096] According to an embodiment of another aspect, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the method described in combination with Figure 1 herein.

[0097] According to an embodiment of still another aspect, an electronic device is further provided, including a memory and a processor. An executable code is stored in the memory. When the processor executes the executable code, the method described in combination with Figure 1 herein is implemented.

[0098] The embodiments of the present invention are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0099] Those skilled in the art should be able to realize that, in one or more of the above examples, the functions described in the present invention can be implemented by hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0100] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the present invention should be included within the protection scope of the present invention.

Claims

1. A simulation method for enhancing the heat storage performance of molten salts by using oxygen vacancy defects of nanoparticles, characterized in that including: Constructing the unit cell structure of silica; Based on the unit cell structure of the silica, determining a nanoparticle model containing surface oxygen vacancy defects; Based on the nanoparticle model containing surface oxygen vacancy defects, determining a final silica-potassium carbonate nanofluid model; Performing molecular dynamics simulations using the final silica-potassium carbonate nanofluid model to obtain molecular dynamics simulation data; Based on the molecular dynamics simulation data, determining the heat storage performance parameters of the final silica-potassium carbonate nanofluid model, where the heat storage performance parameters include specific heat capacity and thermal conductivity.

2. The method according to claim 1, wherein The determining a nanoparticle model containing surface oxygen vacancy defects based on the unit cell structure of the silica includes: Performing a supercell expansion operation on the unit cell structure of the silica to obtain a supercell-expanded supercell structure; Performing a cutting process on the supercell-expanded supercell structure with the center of the supercell as the center of the sphere to obtain an initial model of the silica nanoparticles; wherein, the initial model of the nanoparticles includes a plurality of oxygen atoms and a plurality of silicon atoms; Removing a preset proportion of oxygen atoms in the initial model of the silica nanoparticles to obtain a nanoparticle model containing surface oxygen vacancy defects.

3. The method according to claim 1, wherein The determining a final silica-potassium carbonate nanofluid model based on the nanoparticle model containing surface oxygen vacancy defects includes: Performing an optimization process on the nanoparticle model containing surface oxygen vacancy defects to obtain an optimized nanoparticle model; Performing electrostatic charge calculations on each atom in the optimized nanoparticle model to obtain the ESP charge value of each atom; Placing the optimized nanoparticle model at the center of the simulation box and randomly filling potassium carbonate molecules into the optimized nanoparticle model to obtain an initial silica-potassium carbonate nanofluid model; Importing the ESP charge value of each atom into the silica-potassium carbonate nanofluid model to obtain the final silica-potassium carbonate nanofluid model.

4. The method according to claim 3, wherein After performing an optimization process on the nanoparticle model containing surface oxygen vacancy defects to obtain an optimized nanoparticle model, it further includes: Performing optimization verification on the optimized nanoparticle model to obtain a verified nanoparticle model; wherein, the optimization verification includes total energy convergence verification, atomic force convergence verification, and structural configuration rationality verification.

5. The method according to claim 1, wherein The molecular dynamics simulation data includes system temperature, fluctuations in the total energy of the system, system atomic coordinates, velocities of atoms in the cold region of the system, velocities of atoms in the hot region of the system, simulation time, length of the simulation system in the x-axis, length of the simulation system in the y-axis, and temperature gradient of the simulation system in the z-axis direction.

6. The method according to claim 5, wherein The specific heat capacity is determined by the following formula: where C is the specific heat capacity, k B is the Boltzmann constant, is the fluctuation of the total energy of the system, and T is the temperature of the system.

7. The method according to claim 5, characterized in that, The thermal conductivity is determined by the following formula: where λ is the thermal conductivity, m is the mass of the atom, υ hot is the velocity of the atoms in the cold region of the system, υ cold is the velocity of the atoms in the hot region of the system, t is the simulation time, L x is the length of the simulation system along the x-axis, L y is the length of the simulation system along the y-axis, is the temperature gradient of the simulation system in the z-axis direction.

8. A simulation device for enhancing the heat storage performance of molten salts by using the oxygen vacancy defects of nanoparticles, characterized in that, including: A construction unit configured to construct the unit cell structure of silica; A first data processing unit configured to determine a nanoparticle model containing surface oxygen vacancy defects based on the unit cell structure of the silica; A second data processing unit, configured to determine a final silica-potassium carbonate nanofluid model based on the nanoparticle model containing surface oxygen vacancy defects; A third data processing unit, configured to perform molecular dynamics simulations using the final silica-potassium carbonate nanofluid model to obtain molecular dynamics simulation data; A fourth data processing unit, configured to determine the heat storage performance parameters of the final silica-potassium carbonate nanofluid model based on the molecular dynamics simulation data, wherein the heat storage performance parameters include specific heat capacity and thermal conductivity.

9. An electronic device, characterized in that, Comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method according to any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed on a computer, the computer is made to execute the method according to any one of claims 1-7.

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