Simulation Method for Float Distribution in DC Casting of Aluminum Alloy

Abstract

This invention discloses a method for simulating the distribution of floating crystals in semi-continuous / DC casting of aluminum alloys. The simulation method includes the following steps: S1, establishing a geometric model of the ingot based on the product's geometric modeling parameters, casting parameters, thermophysical parameters, boundary conditions, and initial conditions, and obtaining a casting model based on the flow field, temperature field, and concentration field during the ingot solidification process; S2, establishing a grain motion capture model, setting the initial grain generation method and its effective criteria based on the casting model, simulating the grain nucleation region during solidification, and obtaining the grain distribution result; S3, using the grain distribution result as the first initial condition, and combining the temperature field and concentration field, introducing grain growth / remelting factors to obtain the distribution state of floating crystals during the casting process. This invention, by combining with a macroscopic segregation model and considering the influence of grain nucleation position, growth, and remelting, can simulate the movement and distribution characteristics of floating crystals during the casting process, which helps to improve casting quality.

Description

Technical Field

[0001] This invention relates to computer simulation technology, and in particular to a method for simulating the distribution of floating crystals in semi-continuous / DC casting of aluminum alloys. Background Technology

[0002] The deposition of free float crystals at the ingot center is the main mechanism leading to negative segregation at the center of semi-continuous aluminum alloy ingots. Controlling free float crystal deposition at the ingot center is key to improving negative segregation, and understanding the movement and distribution characteristics of free float crystals within the ingot is a prerequisite for achieving free float crystal control. Because it involves multi-scale, multi-phase processes such as flow, solidification, and grain movement, the movement and distribution information of free float crystals within the ingot remains unknown, hindering the prediction of the performance of cast products during production and R&D.

[0003] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a method for simulating the distribution of floating crystals in semi-continuous / DC casting of aluminum alloys. By combining it with a macrosegregation model and considering the influence of grain nucleation location, growth, and remelting, the method can simulate the movement and distribution characteristics of floating crystals during the casting process. The prediction results can be used to reveal the mechanism by which floating crystals affect the negative segregation at the center of the ingot, and then to propose targeted improvement measures to regulate the movement and distribution of floating crystals, so as to improve the negative segregation at the center of the aluminum alloy DC ingot and obtain a high-quality ingot.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for simulating the distribution of floating crystals in DC casting of aluminum alloys, comprising the following steps: S1, establishing a geometric model of the ingot based on the geometric modeling parameters, casting parameters, thermophysical property parameters, boundary conditions, and initial conditions of the product, and obtaining a casting model based on the flow field, temperature field, and concentration field during the solidification process of the ingot; S2, establishing a grain motion capture model, setting the initial grain generation method and its effective criteria based on the casting model, simulating the grain nucleation region during the solidification process, and obtaining the grain distribution result; S3, using the grain distribution result as the first initial condition, and combining the temperature field and concentration field, introducing the size change caused by the growth / remelting of floating crystal grains during the casting process, to obtain the distribution state of floating crystals during the casting process.

[0006] The preferred method is: “S1. Based on the ingot size and casting method, establish an ingot geometric model, combine the actual casting alloy and parameters, set relevant thermophysical parameters, boundary conditions and initial conditions, and calculate the flow field, temperature field and concentration field of the ingot solidification process” as the macroscopic segregation model of the casting process. The flow field, temperature field and concentration field results obtained based on this model will be coupled with the subsequent grain movement to provide macroscopic solidification results.

[0007] In one or more embodiments of the present invention, the geometric modeling parameters in S1 are at least selected from dimensions (including but not limited to external dimensions, thickness dimensions, and angles) and shapes.

[0008] In one or more embodiments of the present invention, the casting parameters in S1 are selected from the pouring method and the alloy type.

[0009] In one or more embodiments of the present invention, the thermophysical parameters in S1 are selected from density, viscosity, specific heat capacity, thermal conductivity, latent heat of solidification, solidus temperature, liquidus temperature, eutectic temperature, and solute element diffusion coefficient.

[0010] In one or more embodiments of the present invention, the boundary conditions in S1 are selected from the thermal boundary conditions of the hot top, the velocity boundary conditions of the hot top, the thermal boundary conditions of water cooling, the velocity boundary conditions of water cooling, the thermal boundary conditions of air cooling, and the velocity boundary conditions of air cooling.

[0011] In one or more embodiments of the present invention, step S2 includes: (1) combining the casting model, setting the initial grain generation mode in the ingot to enter with the aluminum liquid, and setting the number of grains to N0, the equivalent diameter to d0, and the density to ρ0; (2) based on the liquid phase fraction fl of the ingot calculated in S1, setting the grain validity criterion as: fl > 0.9, then the grain is valid; calculating the first grain distribution result; (3) based on the first grain distribution result, resetting the grain validity criterion as: 0.9 < fl < 1.0, then the grain is valid; calculating the second grain distribution result, and using it as the first initial condition in S3.

[0012] In one or more embodiments of the present invention, the grain growth / remelting factor in step S3 is selected to drive the spherical solid / liquid interface, and the grain state of the floating crystals is determined based on the temperature field and the concentration field.

[0013] In one or more embodiments of the present invention, step S3 includes: (1) According to the second grain distribution result obtained in S2, the grains are distributed in the initial solidification interval: 0.9 < fl < 1.0, which is close to the grain nucleation region in the actual solidification process, and this is used as the initial condition for the simulation of the floating crystal distribution, making the result more accurate; (2) Considering the influence of grain growth / remelting factors: Regarding grain growth / remelting as the movement of a spherical solid / liquid interface, based on the temperature field T and concentration field C l , , l , ,

[0018] ,

[0020] ,

[0019] , , C l *, the grain growth rate formula is expressed as: .

[0014] Where C l is the solute concentration in the liquid phase, C l * represents the solute concentration at the grain growth interface, D l is the liquid phase diffusion coefficient, d is the grain radius, and k is the solute distribution coefficient.

[0015] In one or more embodiments of the present invention, the grain state includes the growth rate and the distribution pattern.

[0016] Compared with the prior art, the method for simulating the floating crystal distribution in semi - continuous / DC casting of aluminum alloys according to the embodiments of the present invention can more accurately give the movement, growth and final distribution law of grains in the ingot after nucleation in the two - phase region of the casting process. The simulation results can not only reveal the growth and movement process of floating crystals and their distribution law in the solidified ingot, but also provide technical support for applying measures to control the deposition of floating crystals in the center of the ingot, thereby improving the central negative segregation of the ingot.

[0017] In another aspect, the simulation method of the present invention considers the influence of the grain nucleation region in the actual casting solidification process (the nucleation position is the initial generation position of grains, which has a significant impact on their subsequent movement and final distribution law), making the prediction result of the floating crystal distribution more reasonable.

[0018] In another aspect, by combining the temperature field T and concentration field C l , C l * calculated by the segregation model, considering the influence of the growth / remelting mechanism of floating grains during the movement of molten aluminum in the ingot, makes the prediction result of the floating crystal distribution more accurate.

[0019] In another aspect, it can more accurately predict the movement and distribution law of floating crystals during the solidification process and after solidification of the ingot.

[0020] In another aspect, the prediction results obtained by the simulation method of the present invention can be used to adjust the DC casting process of aluminum alloys, such as adjusting casting parameters, optimizing the flow divider bag, applying an external field, etc., to control the deposition of floating grains in the central region of the ingot during the casting process, thereby suppressing negative segregation in the center of the ingot and optimizing the internal quality of the ingot. Attached Figure Description

[0021] Figure 1 The calculated distribution of solute elements in the ingot is the result of one embodiment of the present invention.

[0022] Figure 2 The calculated liquid phase fraction in the ingot is the result of one embodiment of the present invention.

[0023] Figure 3 This is the calculated initial distribution result of floating grains in the ingot according to one embodiment of the present invention;

[0024] Figure 4 The calculated distribution of floating grains after ingot solidification is obtained according to one embodiment of the present invention.

[0025] Figure 5 The results of calculations on the movement and distribution of floating crystals during the casting process of large-size flat ingots are shown in one embodiment of the present invention.

[0026] Figure 6 The results of the calculated movement and distribution of floating crystals in the ingot at different casting speeds according to one embodiment of the present invention are shown, where a is 200 mm / min; b is 250 mm / min; c is 300 mm / min; and d is 350 mm / min. Detailed Implementation

[0027] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0028] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0029] The DC casting process of aluminum alloys involves multiple field couplings such as flow, solidification, and solute redistribution. All of these macroscopic and microscopic physical fields affect the movement, growth, and sedimentation of floating grains. Therefore, it is necessary to establish a macroscopic segregation model for aluminum alloy DC casting to reflect the formation law and distribution state of floating grains during the casting process, and to predict the processes and states of aluminum melt flow, solidification, solute distribution, and solute redistribution at the solidification front.

[0030] The following example illustrates the implementation process of the present invention using the simulation of preparing a rectangular trapezoidal ingot of Al-4.5%Cu alloy. It should be noted that this is only one embodiment of the present invention and is not intended to limit the alloy composition of the present invention. The method proposed in this invention is applicable to the DC casting process of all aluminum alloys to meet the requirements of preparing ultra-wide aluminum alloy flat ingots, round ingots, rectangular trapezoidal ingots, and other ingots, as well as casting related aluminum alloy products such as shells, cylinders, boxes, and frames.

[0031] A method for simulating the distribution of floating crystals in DC casting of aluminum alloys includes the following steps:

[0032] S1. Based on the ingot size and casting method, establish the ingot geometric model, combine the actual casting alloy and parameters, set the relevant thermophysical parameters, boundary conditions and initial conditions, and calculate the flow field, temperature field and concentration field of the ingot solidification process.

[0033] S2. Establish a grain motion capture model, combine the casting model results established in S1, set the initial grain generation method and its effective criteria, and simulate the grain nucleation region during the solidification process.

[0034] S3. Using the grain distribution results of S2 as initial conditions, and combining the temperature field and concentration field calculated by the segregation model, the grain growth and remelting factors are introduced to obtain the final distribution results of floating crystals in the casting process.

[0035] Specifically, step S1 may include:

[0036] Establish a DC casting geometric model: Based on the actual working conditions of DC ingot geometry, shape, pouring method, etc., and considering the symmetry of the ingot geometry, determine the structural dimensions of the geometric model;

[0037] Meshing: The geometric model is constructed and meshed using Gambit or ICEM software. To improve convergence, hexahedral meshes are preferred during meshing to ensure the quality of all meshes in the geometric model.

[0038] Thermodynamic calculations: Based on the elemental composition of aluminum alloys under actual working conditions, thermodynamic software such as Thermocalc or JmatPro is used to calculate the required thermophysical parameters, mainly including: density, viscosity, specific heat capacity, thermal conductivity, latent heat of solidification, solidus temperature, liquidus temperature, eutectic temperature, and solute element diffusion coefficients that can be obtained from literature.

[0039] Boundary and initial conditions settings: Based on the ingot geometric model established above, set the thermal and velocity boundary conditions of the corresponding boundaries (hot top, water cooling, air cooling), as well as the initial values ​​of each physical field (including temperature field, velocity field, concentration field, etc.) in the calculation area.

[0040] The flow field, temperature field, and concentration field of the ingot solidification process in DC casting of aluminum alloy were obtained by coupled calculation using flow model, solidification model, and solute transport model. Figure 1 The figure shows the calculated Cu element distribution during the casting process of Al-4.5%Cu alloy. Figure 2 The result shown is the calculated liquid phase fraction distribution.

[0041] Flow model:

[0042] 1) Continuity equation

[0043]

[0044] 2) Momentum conservation equation

[0045]

[0046] Among them, momentum source term This can be expressed as:

[0047]

[0048] In the formula For density, Let t be the velocity and t be the time. Where p is the effective viscosity, T is the pressure, and T is the temperature. Gravitational acceleration, Coefficient of thermal expansion The solute expansion coefficient of element i, A very small positive number is introduced to ensure that the denominator is not zero.

[0049] Solidification model:

[0050] The energy conservation equation is expressed as:

[0051]

[0052] In the formula, For effective thermal conductivity, enthalpy It can be expressed as a function of temperature:

[0053]

[0054] Ingot liquid phase fraction It can be calculated using the following formula:

[0055]

[0056] In the formula, and These are the liquidus temperature and the solidus temperature, respectively.

[0057] Solute transport model:

[0058] Solute conservation equation:

[0059]

[0060] For turbulent Schmidt number, solid phase velocity Set to pulling speed.

[0061] Mass fraction of solute elements in the liquid phase:

[0062]

[0063] is the solute reverse diffusion coefficient.

[0064] In step S2:

[0065] Based on the casting model established above, a grain motion capture model is established by coupling it with the Lagrange model:

[0066]

[0067] In the formula, For particle density, The particle diameter, For particle velocity, The contact force between particle i and particle j. This refers to the drag force exerted by the fluid on the particles. The pressure gradient force acting on the particle. The resultant force of buoyancy and gravity of the particle. The virtual mass force acting on the particle. For Saffman force.

[0068] The above formula is a force model of floating grains during the solidification process, which is used to obtain the motion behavior of floating grains in the ingot.

[0069] The initial formation method of the target grains in the ingot is set as follows: they enter with the molten aluminum, with a grain number of N0, an equivalent diameter of d0, and a density of ρ0. Based on the liquid phase fraction fl calculated by the macrosegregation model, the effective grain criterion is set as fl > 0.9, and the grains are retained. After calculation, the first grain distribution result is obtained. Based on the first grain distribution result, the grain failure criterion is reset as 0.9 < fl < 1.0, and the grains are retained. The second grain distribution result is calculated (see...). Figure 3 This serves as the initial condition for subsequent grain distribution calculations.

[0070] In step S3:

[0071] Using the second grain distribution result as the initial condition for this step (the first initial condition), the grain movement, growth, and distribution during the casting process are predicted. Specifically, the influence of grain growth and remelting factors is considered: grain growth / remelting is treated as the movement of a spherical solid / liquid interface, and the temperature field T and concentration field C are calculated based on S1. l C l * The formula for grain growth rate is expressed as: .

[0072] Grain settling criterion: During the growth / remelting process, some floating grains within the ingot move to the non-free-flowing solid phase region and settle, ceasing further movement. Therefore, the settling criterion determines whether grains move or settle. The settling criterion can be set based on the critical solid fraction of the free-flowing and non-free-flowing solid phase regions of the ingot (typically between 0.2 and 0.4): that is, when the solid fraction of the region where the grain is located is lower than the critical solid fraction value, the grain can move freely; otherwise, it is considered that the grain is trapped and settles. In this embodiment, the critical solid fraction is set to 0.3.

[0073] Through the above steps, simulation calculations can be performed to obtain the floating grain movement, growth, and sedimentation results of Al-4.5%Cu aluminum alloy during DC casting. The simulation also yields the floating grain distribution results after the ingot solidifies, such as... Figure 4 , Figure 5 , Figure 6 As shown.

[0074] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for simulating the distribution of floating crystals in DC casting of aluminum alloys, comprising the following steps: S1. Based on the product's geometric modeling parameters, casting parameters, thermal property parameters, boundary conditions and initial conditions, establish the ingot geometric model, and obtain the casting model based on the flow field, temperature field and concentration field during the ingot solidification process. S2. Establish a grain motion capture model. Based on the casting model, set the initial grain generation method and its effective criteria, simulate the grain nucleation region during solidification, obtain the grain distribution results, and establish a grain motion capture model based on the casting model and coupled with the Lagrange model. ； In the formula, For particle density, The particle diameter, For particle velocity, This refers to the drag force exerted by the fluid on the particles. The pressure gradient force acting on the particle. The resultant force of buoyancy and gravity of the particle. The virtual mass force acting on the particle. For Saffman force; S3. Using the grain distribution result as the first initial condition, and combining the temperature field and concentration field, introduce the floating crystal grain growth / remelting factor during the casting process to obtain the distribution state of floating crystals during the casting process; Step S2 includes: (1) Based on the casting model, the initial grain generation method in the ingot is set to enter with the aluminum liquid, and the number of grains is set to N0, the equivalent diameter is d0, and the density is ρ0. (2) Based on the liquid phase fraction fl of the ingot calculated by S1, the effective criterion for grains is set as: fl > 0.9, then the grains are effective; the first grain distribution result is calculated. (3) Based on the first grain distribution result, reset the grain validity criterion: 0.9 < fl < 1.0, then the grain is valid; calculate the second grain distribution result and use it as the first initial condition in S3; In step S3, the grain growth / remelting factors are selected by the spherical solid / liquid interface shift, and the grain state of the floating crystals is determined based on the temperature field and concentration field. The grain state includes growth rate and distribution morphology.

2. The method for simulating the distribution of floating crystals in DC casting of aluminum alloys as described in claim 1, characterized in that, The geometric modeling parameters in S1 are at least selected from size and shape.

3. The method for simulating the distribution of floating crystals in DC casting of aluminum alloys as described in claim 1, characterized in that, The casting parameters in S1 are selected from the pouring method and alloy type.

4. The method for simulating the distribution of floating crystals in DC casting of aluminum alloys as described in claim 1, characterized in that, The thermophysical parameters in S1 are selected from density, viscosity, specific heat capacity, thermal conductivity, latent heat of solidification, solidus temperature, liquidus temperature, eutectic temperature, and solute element diffusion coefficient.

5. The method for simulating the distribution of floating crystals in DC casting of aluminum alloys as described in claim 1, characterized in that, The boundary conditions in S1 are selected from the thermal boundary conditions of the hot top, the velocity boundary conditions of the hot top, the thermal boundary conditions of water cooling, the velocity boundary conditions of water cooling, the thermal boundary conditions of air cooling, and the velocity boundary conditions of air cooling.

Images