Method for predicting heat conductivity of casting powder of melt crystallizer based on molecular dynamics

Abstract

The invention discloses a method for predicting heat conductivity of melt crystallizer casting powder based on molecular dynamics, and belongs to the technical field of heat conductivity calculation of crystallizer casting powder. The method comprises the following steps: establishing an initial model of an amorphous structure of the crystallizer casting powder based on the determined content of each atom in a slag system; setting simulation conditions and parameters, and performing thermal conductivity calculation by adopting a non-equilibrium molecular dynamics method; and post-processing and visualizing thermal conductivity calculation data. According to the method, calculation is carried out from the atomic level of the slag through a molecular dynamics simulation method, interference of external environmental factors is avoided, and therefore the data precision and efficiency are improved, and the cost is greatly reduced.

Description

Technical Field

[0001] The invention relates to the technical field of thermal conductivity of mold powder, and in particular to a method for predicting the thermal conductivity of molten mold powder based on molecular dynamics. Background Art

[0002] As the most important auxiliary material, mold slag plays an important metallurgical function in continuous casting production and is a key factor in the quality control of continuous casting billets. During the casting process, the mold slag melts under the action of the heat of the molten steel and forms a liquid slag layer on the liquid surface of the molten steel, which can play the role of heat insulation and prevent the oxidation of the molten steel. This is the basic function of the mold slag; more importantly, the molten slag flows into the gap between the solidified billet shell and the crystallizer wall (shell wall gap) to form a thin film to control the heat transfer and lubrication in the mold. The thermal conductivity of the liquid slag layer has always been regarded as a key indicator for controlling the heat transfer around the meniscus. Therefore, understanding the influence mechanism of the thermal conductivity of the mold slag is of great significance for controlling the heat transfer of the meniscus.

[0003] So far, the thermal conductivity of mold slag is mainly measured by the transient hot wire method (THW), the laser pulse method (LP) and the pre-heating detection laser flash technology (FFL). Although the transient hot wire method and the laser pulse method are widely used in the measurement of slag thermal conductivity due to their temperature resistance, when the temperature is high, the transient hot wire method has the problem of hot wire leakage, which will significantly affect the measurement of thermal conductivity. Similarly, the transmission and reflection effects will also cause measurement deviations in the laser pulse method. Although the pre-heating detection laser flash technology overcomes the defects of the transient hot wire method and the laser pulse method, it ignores the influence of the interfacial thermal resistance between the melt and the crucible due to the action of the slag composition. Therefore, these methods all have great difficulties (i.e., time-consuming, high cost, and large measurement errors). This has greatly restricted the development of the measurement of thermal conductivity of mold slag.

[0004] Due to the characteristics of low cost, fast cycle and high calculation accuracy, molecular dynamics (MD) simulation has been successfully applied to the estimation of the change of thermal conductivity of materials with temperature. Dasmahapatra et al. predicted the thermal conductivity of amorphous silicon boron nitride (a-SiBN) based on equilibrium molecular dynamics (EMD) simulation, and explored the relationship between thermal conductivity, temperature and composition. Lee et al. used EMD simulation to calculate the thermal conductivity of amorphous silicon with temperature, and the calculation results accurately predicted the thermal conductivity in the low temperature range of 50 to 400K. Therefore, it is an effective, convenient and fast method to explore the thermal conductivity mechanism and thermal conductivity of crystallizer protection slag through molecular dynamics simulation. Summary of the invention

[0005] (I) Purpose of the invention

[0006] In view of the problems existing in the above-mentioned technology, the purpose of the present invention is to provide a method for predicting the thermal conductivity of molten crystallizer protection slag based on molecular dynamics, which can quickly and accurately obtain the thermal conductivity of the crystallizer protection slag under different compositions and different temperature conditions, and can reduce the problems of high cost, poor repeatability and cumbersome experimental process in traditional experiments.

[0007] (II) Technical solution

[0008] In order to achieve the above object, the present invention provides a method for predicting the thermal conductivity of molten crystallizer powder based on molecular dynamics, comprising the following steps:

[0009] S1. First, select the mold protection slag to be tested and measure its density. Then, obtain the atomic content of the slag. Then, obtain the side length of the simulated cubic box by the density and the total number of atoms. Finally, use Materials Studio software to generate an initial structure data file containing the coordinates of each atom of the slag to be tested.

[0010] S2. Import the initial structure data file and potential function of the slag to be tested into the in file that executes the Lammps software running command. In the Lammps software, the non-equilibrium calculation method is used to heat the initial structure of the slag to simulate the heating treatment, and then the temperature is lowered to the specified temperature to continue to relax until the system is fully balanced, and the thermal conductivity calculation data is collected;

[0011] S3. Input the collected thermal conductivity data into the visualization processing software origin, and then calculate the thermal conductivity K of the slag system through the statistical analysis software Matlab.

[0012] Furthermore, in step S1, the initial structure of the slag to be tested is established in a simulated cubic box, and the initial coordinates of each atom are located in the simulated cubic box; the density of the slag to be tested is obtained according to experimental tests, and an initial structure data file containing the coordinates of each atom of the slag to be tested is established using the Amorphous Cell Tools module in the MaterialsStudio software.

[0013] Furthermore, in the step S2, the in file of the Lammps software running command executed also includes potential function selection, boundary conditions, equilibrium ensemble, pressure control method, cooling rate, summation method, relaxation time and temperature control method.

[0014] Furthermore, the system potential function adopts the BMH potential function, the boundary conditions adopt periodic boundary conditions, the ensemble is the NVT canonical ensemble, the pressure control method is the Parrinello-Rahman method, the summation method is the Ewald summation method, and the temperature control method is a method of setting cold sources, heat sources and heat transfer zones.

[0015] Furthermore, the cold source, heat source and heat transfer zone are set in the system by dividing the simulation cubic box into 2N blocks along the x-axis. The first block (x=0) is set as the cold end zone, and the N+1th block is set as the heat source zone, thereby obtaining the cold source, heat source and heat transfer zone. Then, the particles with the largest and smallest momentum at the cold end and the hot end are numerically exchanged in momentum, thereby forming a stable heat flux and temperature gradient in the hot end area and the cold end area.

[0016] Furthermore, in step S3, the temperature gradient value of the slag system is obtained according to the calculated data using the visualization software origin, and finally the Fourier heat conduction formula is calculated using the statistical analysis software Matlab to obtain the thermal conductivity of the system.

[0017] (III) Beneficial effects

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention provides a method for predicting the thermal conductivity of molten mold powder based on molecular dynamics. The method can directly analyze the temperature gradient value in the high-temperature mold powder from a microscopic perspective, so as not to be restricted by the conditions of traditional experiments, and at the same time significantly reduces the trial and error cost and time cycle, so as to quickly and accurately obtain the thermal conductivity of the mold powder under different compositions and different temperature conditions, and significantly improve the accuracy and stability of the thermal conductivity of the mold powder. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention is described with the aid of the following drawings:

[0021] Figure 1 This is a flow chart of a method for predicting thermal conductivity of molten crystallizer powder based on molecular dynamics according to Example 1 of the present invention;

[0022] Figure 2 Schematic diagram of the Muller-Plathe exchange method used in Example 1 of the present invention. DETAILED DESCRIPTION

[0023] In order to better understand the technical solution of the present invention, the content of the present invention includes but is not limited to the specific implementation methods described below, and similar technologies and methods should be considered to be within the scope of protection of the present invention. In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0024] It should be clear that the embodiments described in the present invention are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "an" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0026] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0027] Example 1

[0028] (1) According to the main chemical composition of the mold protection slag, the slag in this example is mainly composed of calcium oxide, silicon dioxide, aluminum oxide, sodium oxide and calcium fluoride. The specific composition is shown in Table 1:

[0029] Table 1 Composition of experimental slag of CaO-SiO2-Al2O3-CaF2-Na2O system (mass fraction, %)

[0030]

[0031] The molar ratio is obtained according to the mass ratio of each component of the slag (CaO, SiO2, Al2O3, Na2O and CaF2), and then the number of different atoms (Si, Al, Ca, O, Na and F) is determined based on the total number of atoms of 10,000. Then, the density of the slag is obtained according to the experimental test, and the side length of the box is calculated, as shown in Table 2.

[0032] Table 2 Atomic number, density and box side length of experimental slag of CaO-SiO2-Al2O3-CaF2-Na2O system

[0033]

[0034] Based on the above-mentioned atomic types, numbers and slag densities, a randomly distributed amorphous structure model is constructed by using the Amorphous Cell Tools module in the Materials Studio software to obtain an initial structure data file including the number of atoms, the number of atomic types, the mass of each type of atoms, the atomic ID and the atomic type corresponding to the ID atom, and the coordinate values ​​in the x, y, and z directions.

[0035] (2) Import the initial structure data file and potential function of the slag to be tested into the in file that executes the Lammps software running command. In Lammps software, the energy of the initial structure of the slag to be tested is minimized, and periodic boundary conditions are used to form a periodic image. The shape of the simulated cubic box remains unchanged, and the BMH potential function is used. The Parrinello-Rahman and Nose-Hoover heat bath methods are used for pressure and temperature control. In terms of the algorithm, the cutoff radius of the short-range force is set to The cut-off diameter is smaller than the side length of any system box. The Ewald algorithm is used for long-range Coulomb force. The running time step is set to 1fs, and the data is saved every 10 steps through the frog leaping algorithm, for a total of 2220000 steps. In terms of temperature setting, the initial temperature is first set to 5000K, and the NVT ensemble is used for the first 220000 steps. The first 50000 steps allow the system to be fully mixed to eliminate the initial distribution state of the atoms, and then the temperature is reduced to 1873K through 100000 steps, and finally the temperature is further relaxed at 1673K for 70000 steps to obtain the slag equilibrium state. After that, the system uses the NVE ensemble to run at 1873 for 2000000 steps to obtain a stable temperature gradient.

[0036] (3) In the next 2000000 steps, the simulation cubic box is evenly divided into 20 blocks along the x-axis. The first block (x=0) is set as the cold end area, and the 11th block is set as the heat source area, thereby obtaining the cold end, heat source and heat transfer areas. Then, the particles with the largest and smallest momentum at the cold end and the hot end are numerically exchanged in momentum, thereby forming stable heat flux and temperature gradient data in the hot end area and the cold end area, as shown in FIG. Figure 2 shown.

[0037] (4) The visualization software origin is used to obtain the slope value of temperature and distance. Finally, the Fourier heat conduction formula shown in formula (1) is calculated by the statistical analysis software Matlab to obtain the thermal conductivity of the system.

[0038]

[0039] Where Q is the heat flux per unit time in the x direction (J); dT / dx is the temperature gradient (K / m); A is the cross-sectional area of ​​the system box (m 2 ).

[0040] At present, through actual measurement, the thermal conductivity of the five groups of test slags can be obtained as 1.37, 1.44, 1.52, 1.55 and 1.59 W / mK respectively. Comparing the above actual measurement results with the molecular simulation results, it can be seen that the simulation results are highly consistent with the experimental measurements. The specific molecular simulation results are shown in Table 3.

[0041] Table 3 Measured and calculated thermal conductivity of experimental slag in the CaO-SiO2-Al2O3-CaF2-Na2O system

[0042]

[0043] The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the application concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not depart from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for predicting the thermal conductivity of molten crystallizer powder based on molecular dynamics, characterized in that: The steps include: S1. First, select the mold protection slag to be tested and measure its density. Then, obtain the atomic content of the slag. Then, obtain the side length of the simulated cubic box by the density and the total number of atoms. Finally, use Materials Studio software to generate an initial structure data file containing the coordinates of each atom of the slag to be tested. S2. Import the initial structure data file and potential function of the slag to be tested into the in file that executes the Lammps software running command. In the Lammps software, the non-equilibrium calculation method is used to heat the initial structure of the slag to simulate the heating treatment, and then the temperature is lowered to the specified temperature to continue to relax until the system is fully balanced, and the thermal conductivity calculation data is collected; S3. Input the collected thermal conductivity data into the visualization processing software origin, and then calculate the thermal conductivity K of the slag system through the statistical analysis software Matlab.

2. The method according to claim 1, characterized in that In step S1, the initial structure of the slag to be tested is established in a simulated cubic box, and the initial coordinates of each atom are located in the simulated cubic box; the density of the slag to be tested is obtained according to experimental tests, and an initial structure data file containing the coordinates of each atom of the slag to be tested is established using the Amorphous CellTools module in the Materials Studio software.

3. The method according to claim 1, characterized in that: In the step S2, the in file of the Lammps software running command also includes potential function selection, boundary conditions, equilibrium ensemble, pressure control method, cooling rate, summation method, relaxation time and temperature control method.

4. The method according to claim 3, characterized in that The system potential function adopts the BMH potential function, the boundary conditions adopt the periodic boundary conditions, the ensemble is the NVT canonical ensemble, the pressure control method is the Parrinello-Rahman method, the summation method is the Ewald summation method, and the temperature control method is a method of setting a cold source, a heat source and a heat transfer zone.

5. The method according to claim 4, characterized in that The cold source, heat source and heat transfer zone are set in the system by dividing the simulation cubic box into 2N blocks along the x-axis. The first block (x=0) is set as the cold end zone, and the N+1th block is set as the heat source zone, thus obtaining the cold source, heat source and heat transfer zone. Then, the particles with the largest and smallest momentum at the cold end and the hot end are numerically exchanged in momentum, thereby forming a stable heat flux and temperature gradient in the hot end and the cold end.

6. The method according to claim 1, characterized in that In step S3, the temperature gradient value of the slag system is obtained according to the calculated data using the visualization software origin, and finally the Fourier heat conduction formula is calculated using the statistical analysis software Matlab to obtain the thermal conductivity of the system.

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