Gas atomization pulverization analysis optimization method based on analogue simulation

Abstract

The invention relates to an analogue simulation-based gas atomization pulverization analysis optimization method, which comprises the following steps of: constructing a two-dimensional axisymmetric geometric model of gas atomization equipment, and setting boundary conditions; respectively simulating flow field evolution and particle size distribution by combining a single gas phase model, a VOF two-phase flow model and a DPM discrete phase model based on the geometric model; and according to a simulation result, analyzing and optimizing baffle parameters in gas atomization powder preparation. According to the method, details which are difficult to observe in an experiment can be captured, and a simulation result can be visually displayed, so that analysis and understanding are facilitated; and meanwhile, a baffle is added into the two-dimensional geometric model, and parameters of the two-dimensional geometric model are optimized through analogue simulation, so that the particle size, the distribution condition and the atomization efficiency of the powder are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas atomization powder making, and more specifically to an analysis and optimization method for gas atomization powder making based on simulation. Background Art

[0002] Metal powder is an important metal raw material in the fields of thermal spraying, powder metallurgy, 3D printing, etc. The metal powder used in 3D printing has a particle size between 20 and 50 mm, and not only requires high powder purity and few impurity elements, but also requires high sphericity, few satellite powders, good powder fluidity, and high bulk density.

[0003] Currently, the most widely used powder preparation method in industrial production is vacuum gas atomization powder making. The principle of gas atomization powder making technology is that high-temperature molten metal is broken into tiny droplets under the action of atomizing gas, and finally cooled and solidified into fine powder particles.

[0004] However, there are still problems in the current gas atomization powder making process, such as insufficient atomization efficiency and insufficiently small powder particle size. For the preparation of alloy powder, the key lies in the droplet breaking process during atomization. However, it is very difficult to observe the entire breaking process with common experimental means, and it is also very difficult to reproduce the complex process of impact breaking. That is, the physical model and atomization mechanism regarding the melt breaking problem have not been perfected.

[0005] Therefore, how to overcome the above problems and obtain low-cost and high-quality alloy powder is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention first provides a simulation method for gas atomization powder making, and further studies the influence of baffle parameters on the flow field evolution and particle size distribution of gas atomization powder making based on this method, so as to obtain gas atomized powder with a smaller and more concentrated particle size distribution.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An analysis and optimization method for gas atomization powder making based on simulation, comprising:

[0009] Constructing a two-dimensional axisymmetric geometric model of a gas atomization device and setting boundary conditions;

[0010] Based on the geometric model, respectively simulating the flow field evolution and particle size distribution; wherein,

[0011] Using single-phase gas atomization numerical simulation to simulate the flow field evolution, including determining hydrodynamic data through the mass conservation equation, continuity equation and energy equation, and using a turbulence model to simulate the flow field evolution process according to the hydrodynamic data to obtain a single-phase flow field velocity contour map;

[0012] Use VOF two-phase flow numerical simulation and DPM discrete phase numerical simulation to simulate the particle size distribution, including simulating the interfacial behavior between incompatible fluids in the flow field through VOF two-phase flow numerical simulation to obtain the primary atomization metal melt cloud map; simulating the motion information of discrete particles in the flow field through DPM discrete phase numerical simulation to obtain the secondary atomization particle trajectory map; and obtaining the particle size distribution map through the primary atomization metal melt cloud map and the secondary atomization particle trajectory map;

[0013] Analyze and optimize the baffle parameters in gas atomization powder making according to the single-phase flow field velocity cloud map, the primary atomization metal melt cloud map, the secondary atomization particle trajectory map, and the particle size distribution map.

[0014] A method for analyzing and optimizing gas atomization powder making based on simulation disclosed by the present invention, compared with the prior art,

[0015] 1) This application combines the single-gas phase, VOF two-phase flow, and DPM discrete phase models to simulate the atomization process of preparing alloy powder by vacuum gas atomization in multiple levels and from multiple angles. It not only overcomes the limitations of existing experimental means, but also can simulate the flow field evolution and particle size distribution in detail, capturing details that are difficult to observe in experiments. At the same time, through the velocity cloud map, metal melt cloud map, and particle trajectory map, the simulation results can be intuitively displayed, facilitating analysis and understanding;

[0016] 2) The present invention changes the atomization flow field by adding baffles in a two-dimensional geometric model; by analyzing the atomization mechanism of the plate-added structure, simulating the primary atomization process and the single droplet breakup process, as well as the influence of the baffle on the particle trajectory in secondary atomization and the particle size of the powder obtained after secondary atomization, the optimization of baffle parameters is realized, thereby improving the particle size, distribution, and atomization efficiency of the powder. Brief Description of the Drawings

[0017] In order 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 only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0018] Figure 1 It is a flow chart of a method for analyzing and optimizing gas atomization powder making based on simulation;

[0019] Figure 2 It is a structural diagram of a two-dimensional axisymmetric model of the atomization flow field;

[0020] Figure 3 It is a schematic diagram of mesh division;

[0021] Figure 4 It is a single-phase flow field velocity cloud map under different baffle lengths;

[0022] Figure 5 are cloud images of the primary atomization of high-temperature molten metal under different baffle lengths;

[0023] Figure 6 are particle trajectory diagrams of the secondary atomization under different baffle lengths;

[0024] Figure 7 are particle size distribution diagrams under different baffle lengths;

[0025] Figure 8 are velocity cloud images of the unidirectional flow field under different baffle angles;

[0026] Figure 9 are cloud images of the primary atomization of high-temperature molten metal under different baffle angles;

[0027] Figure 10 are particle trajectory diagrams of the secondary atomization under different baffle angles;

[0028] Figure 11 are particle size distribution diagrams under different baffle angles;

[0029] Figure 12 are velocity cloud images of the unidirectional flow field under different baffle positions;

[0030] Figure 13 are cloud images of the primary atomization of high-temperature molten metal under different baffle positions;

[0031] Figure 14 are particle trajectory diagrams of the secondary atomization under different baffle positions;

[0032] Figure 15 are particle size distribution diagrams under different baffle positions;

[0033] Figure 16 is a structural diagram of the gas atomization powder making device in the comparative experiment. Specific implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] The embodiments of the present invention disclose an analysis and optimization method for gas atomization powder making based on simulation; the steps are referred to Figure 1 .

[0036] Example 1

[0037] This embodiment first provides a numerical simulation method for the atomization process of preparing alloy powder by vacuum atomization. Compared with the prior art, this method can simulate the flow field evolution and particle size distribution in detail. This multi-scale simulation method can capture the details from the macroscopic flow field to the microscopic particle motion, so as to better reproduce the complex process of impact fragmentation. At the same time, through the velocity contour map, the metal melt contour map and the particle trajectory map, the simulation results can be intuitively displayed, which is convenient for analysis and understanding.

[0038] In this embodiment, the simulation process includes the following steps:

[0039] 1. Construct a two-dimensional axisymmetric geometric model of the gas atomization device and set boundary conditions;

[0040] 1.1 Geometric modeling

[0041] This application constructs a two-dimensional axisymmetric geometric model according to the nozzle shape of the atomization device and the schematic diagram of the entire atomization chamber. The nozzle type is selected as a close-coupled annular slit nozzle, as Figure 2 shown.

[0042] Since the vacuum atomization device in actual production is very large, the metal melt temperature is too high, the atomization gas pressure is high, and the entire atomization process will be particularly complex. Therefore, in order to simplify the simulation process, save calculation time, and save computer costs, a two-dimensional axisymmetric model is established using Solidworks modeling software. The structural parameters of the two-dimensional model are shown in Table 1 below.

[0043] Table 1

[0044] Nozzle orifice diameter / mm Nozzle angle / ° Duct extension / mm Atomization pressure / MPa 4 22 5.5 2.7

[0045] 1.2 Boundary conditions

[0046] The simplified geometric model of the gas atomization device is as Figure 1 shown. There are a pressure inlet, a pressure outlet, a baffle, and a particle injection point, etc. in this model. The metal melt enters the atomization chamber through the diversion tube and then undergoes the atomization process.

[0047] When performing single-phase gas flow field simulation and analysis, since Fluent requires the X-axis of this geometric model to be the rotation axis of the symmetric model, the symmetry axis of the geometric model is defined as the X-axis (Axis); the atomization gas used in the simulation is nitrogen, and its physical properties are shown in Table 2.

[0048] Table 2

[0049] <![CDATA[Heat capacity / J·(kg·K) -1 > <![CDATA[Thermal conductivity / W·(m·K) -1 > <![CDATA[Viscosity / kg·(m·s) -1 > <![CDATA[Density / kg·m -3 > 1040.67 0.0242 1.663e-5 ideal - gas

[0050] Since a certain pressure is required for the gas to enter the atomization chamber, and the pressure is set to 2.7 MPa in the experiment, the gas inlet is set as the pressure inlet boundary (Pressure inlet); the high-temperature metal melt is 316L stainless steel melt, and its physical properties are shown in Table 3.

[0051] Table 3

[0052] <![CDATA[Heat capacity / J·(kg·K) -1 > Viscosity / mPa·s <![CDATA[Surface tension / N·m -1 > <![CDATA[Density / kg·m -3 > 769.856 0.00457 1.53 6857

[0053] After the high-temperature metal melt enters the atomization chamber through the diversion tube, the melt has a certain velocity under the action of gravity in the gas flow field. Therefore, the inlet of the high-temperature metal melt in the diversion tube is used as the velocity inlet (Velocity inlet); the pressure outlet (Pressure outlet) is set as the upper and right boundaries in the atomization chamber; the other surfaces near the nozzle are defined as the wall (Wall).

[0054] In the process of simulating and emulating with the DPM discrete phase model, the model also includes particle injection points, which are mainly set at the edge below the diversion tube in the geometric structure. The axial distance of the set injection point from the central axis is 1 mm, and the radial distance is 5.5 mm. The whole process is that the gas enters from the nozzle orifice, that is, the pressure inlet, and the expansion wave generated after expansion and compression in the atomization chamber through the slit breaks up the metal melt, and the gas flow pushes the broken melt downstream until the pressure outlet.

[0055] 1.3. To improve the reliability of the simulation results and capture the details of the flow field more accurately; the present application uses unstructured grids for meshing.

[0056] Since Fluent itself cannot perform meshing, ANSYS preprocessing settings need to be selected and meshed. The present application uses ICEM CFD to mesh the atomization model, using unstructured grids, and naming each boundary according to the model of the nozzle atomization flow field. Since the atomization gas enters the atomization chamber through the slit and the gas flow rate is relatively large, grid encryption needs to be carried out at the slit and the gas outlet position when meshing. The grid cell size is set to 0.1 mm, and the growth rate is 1.01, as Figure 3 shown in the mesh division condition.

[0057] 2. Based on the geometric model, simulate the flow field evolution and particle size distribution respectively;

[0058] 2.1. This embodiment numerically simulates the flow field evolution using single-phase gas atomization, including determining the hydrodynamic data through the mass conservation equation, continuity equation and energy equation, and using the turbulence model to simulate the flow field evolution process according to the hydrodynamic data to obtain the velocity contour map of the single-phase flow field.

[0059] This application mainly involves the process of energy transfer during atomization, and at the same time, the selection of the turbulence model needs to be considered. Specifically:

[0060] The expression of the mass conservation equation is:

[0061]

[0062] In the formula, ρ is the density of the fluid; t is the time; is the velocity vector of the fluid; S m is the generation or disappearance rate of mass per unit volume.

[0063] The expression of the continuity equation is:

[0064]

[0065] In the formula, x refers to the axial direction; r refers to the radial direction; represents the change in density due to fluid flow along the x-axis direction; represents the change in density due to fluid flow in the r direction, represents the geometric term for the increase in area with the increase in radius.

[0066] The expression of the energy equation is:

[0067]

[0068] In the formula, represents the rate of change of the total energy per unit volume with respect to time; represents the energy transfer due to fluid flow and heat conduction; k eff ▽T represents the heat conduction effect caused by the temperature gradient; represents the energy change during compression or expansion; S h represents the possible energy including external heat sources and energy released by chemical reactions.

[0069] In the formula, the energy E per unit mass of the fluid is defined as:

[0070]

[0071] In the formula, h is the specific enthalpy; represents the pressure potential energy term, representing the energy due to the pressure and density of the fluid; represents the kinetic energy term, representing the kinetic energy of the fluid due to motion.

[0072] During gas atomization, the gas flow velocity in the flow field is very fast, the uncertainty in the flow field is relatively high, the atomization pressure is large, and the gas flow in the flow field is in a turbulent form. Therefore, selecting a suitable turbulence model can effectively analyze the entire flow field.

[0073] The turbulence model selected in the atomization process of this application is simulated by solving the N - S equations of the fluid velocity field and pressure field. Considering that the flow velocity is relatively high near the wall surface of the actual atomization nozzle structure, the SST k - ω model is selected for simulation in this application. This model uses a blending function to combine the standard k - ω model with the k - ε model, and has higher accuracy in the near - wall free - stream calculation compared to the standard k - ω model and the k - ε model.

[0074] The empirical models for the transport of turbulent kinetic energy k and specific dissipation rate (ω) expressed in tensor form are as follows:

[0075]

[0076] In the formula, i and j refer to the axial and radial directions respectively, ρ is the density of the atomizing gas, u i and u j represent different components of the fluid velocity, x j and x i represent different coordinates of the spatial position; p is the pressure; G ω are the generation terms of k and ω respectively; Y k and Y ω are the dissipation terms of k and ω respectively; D ω is the cross - diffusion term; S i , S k , S ω are the user - defined source terms of each equation.

[0077] Γ, Γ k , Γ ω are the diffusion coefficients of velocity, kinetic energy, and specific dissipation rate respectively, as follows:

[0078] Γ = μ + μ t

[0079] Γ k = μ + μ t / σ k

[0080] Γ ω = μ + μ t / σ ω

[0081] In the formula, μ is the viscosity coefficient, σ k represents the turbulent kinetic energy per unit mass, and σ ω represents the specific dissipation rate.

[0082] μ t is the turbulent viscosity coefficient, as follows:

[0083]

[0084] In the above formula, represents an empirical constant, which is a dimensionless coefficient used to adjust the calculation of the turbulent viscosity coefficient. a * represents an empirical coefficient related to the low Reynolds number correction, S represents the modulus of the strain rate tensor, characterizing the flow deformation rate, with the unit of s-1, F2 represents the behavior used to smoothly switch between the k-ω and k-ε models in the SST k-ω model, depending on the flow variables, α l represents an empirical constant related to the turbulent transport equation;

[0085] 2.2. Use the VOF two-phase flow numerical method and the DPM discrete phase numerical method to simulate the particle size distribution, including simulating the interfacial behavior between incompatible fluids in the flow field through the VOF two-phase flow numerical method to obtain the primary atomization metal melt cloud map;

[0086] The VOF (Volume of Fluid Method) model is an Eulerian grid applicable to the interface of one or more incompatible fluids. At the same time, it is a surface tracking method. This model uses the reference function F to construct and track the free surface occupied by the cell at any point. For the high-temperature metal melt, the volume function F is 1 in the liquid phase, 0 in the gas phase, and between 0 and 1 at the gas-liquid interface;

[0087] At the same time, the volume function F satisfies the condition:

[0088]

[0089] The relevant control equations are:

[0090]

[0091] In the formula, U is the flow velocity; ρ and μ are the density and viscosity; g is the acceleration due to gravity; F s is the surface tension term; k eff is the effective derivative coefficient; is the rate of change of momentum with time; is the convective momentum transport term (inertial term); -▽P is the pressure gradient term, and P is the static pressure; is the viscous stress term; is the rate of change of the total energy with time; is the convective energy transport term; k eff ▽T is the heat conduction term; is the enthalpy transport caused by component diffusion; S h is the energy source term;

[0092] Gas-liquid interface volume fraction equation:

[0093]

[0094] The calculation of the density and viscosity of the gas-liquid two-phase is uniformly represented by φ(x,t) as follows:

[0095] φ(x,t) = F(x,t)φ t + [1 - F(x,t)]φ g

[0096] Where φ(x,t) represents ρ(x,t), μ(x,t), l, and g represent the liquid phase and the gas phase respectively.

[0097] 2.3. Obtain the secondary atomization particle trajectory diagram by numerically simulating the motion information of discrete particles in the flow field through the DPM discrete phase;

[0098] This application changes from the steady calculation of single-phase gas flow to unsteady calculation, and sets the DPM model after the iterative calculation is stable. The DPM (Discrete Phase Model) model uses the Euler-Lagrange method to calculate trajectories and forces, thereby simulating the spray process. In the DPM model, it is assumed that: the particles are mass points without volume in the computational domain; particles with similar properties such as position, velocity, temperature, particle size, etc. are calculated in the form of particle parcels; the particles are spherical; the particles are affected by the fluid. The motion of a single particle in the Lagrangian reference frame can be described by a simple force balance equation:

[0099]

[0100] Where: τ r is the relaxation time of the particle: represents the acceleration of the particle; A represents the area; represents the difference between the velocity of the fluid and the velocity of the particle; represents the gravitational acceleration; ρ - ρ p represents the difference between the density of the fluid and the density of the particle.

[0101]

[0102] Where: ρ p and d p refer to the density and diameter of the particle;

[0103] C d refers to the drag coefficient:

[0104]

[0105] a1, a2, and a3 are all empirical constants.

[0106] In one embodiment, a dynamic drag model can also be used. In this case, the change in the droplet shape is described in the drag coefficient as:

[0107]

[0108] C d = C d0 (1 + 2.632y)

[0109] Where y is the deformation of the droplet. When y = 0, the effective drag coefficient is the drag coefficient of a sphere, and at the maximum distortion y = 1, the drag coefficient of a flat disk is obtained.

[0110] Re refers to the relative particle Reynolds number:

[0111]

[0112] In the DPM discrete phase numerical simulation, the particle breakup model is the KHRT model, which includes the growth of aerodynamic instability and the growth of instability when the droplet is accelerated by the airflow into the free stream, and is used to connect the fluid core near the nozzle region and the daughter droplets emerging from this core. The effective length of the liquid core is approximated by the Levich theory as:

[0113]

[0114] Where: C L refers to the Levich constant;

[0115] d0 is the nozzle diameter;

[0116] ρ l and ρ2 refer to the gas density and the melt density.

[0117] In DPM, first select the material of the injected particles as Steel, then change the material properties to 316L stainless steel in the parameter table, select the surface injection method as the injection method, choose the Rosin-Rammler particle size method to describe the particle size of the metal melt, and then input the particle end time, mass flow rate, particle size velocity, particle size temperature. The functional relationship between the mass fraction and the particle size is:

[0118]

[0119] Where: d refers to the particle size; refers to the average particle size; n refers to the size distribution index.

[0120] Furthermore, a particle size distribution map is obtained through the primary atomization metal melt cloud map and the secondary atomization particle trajectory map.

[0121] In this embodiment, an implicit solution algorithm is adopted, the time step is 1e-6, and the number of iterations per time step is 20, and the velocity-pressure coupling scheme is Coupled.

[0122] Example Two

[0123] 3. Analyze and optimize the baffle parameters in gas atomization powder making according to the simulation results.

[0124] In order to improve the powder particle size, distribution and atomization efficiency, in this application, a baffle is added to the two-dimensional model to change the atomization flow field. At the same time, based on the numerical simulation method provided in the above embodiments, the atomization mechanism of the baffle structure is analyzed. By analyzing the primary atomization process and the single droplet breakup process, as well as the influence of the baffle on the particle trajectory in secondary atomization and the powder particle size obtained after secondary atomization, the baffle parameters are optimized.

[0125] In this embodiment, the baffle parameters include baffle length, baffle angle and baffle position.

[0126] The analysis process includes:

[0127] 1) Under the conditions of keeping the nozzle angle, the extension of the draft tube and the atomization pressure unchanged, change the length of the baffle added in the atomization chamber, and conduct multiple simulation simulations to analyze the single-phase flow field velocity cloud diagram, the primary atomization metal melt cloud diagram, the secondary atomization particle trajectory diagrams under different conditions and the particle size distribution under different baffle lengths, respectively refer to Figures 4 - 7 ; among them, Figures 4 - 6 the baffle length corresponding to (a) in is 21 mm, the baffle length corresponding to (b) is 22 mm, and the baffle length corresponding to (c) is 23 mm;

[0128] It can be seen from Figures 4 - 7 that the average particle size and median particle size of the atomized powder gradually increase with the increase of the baffle length. If the baffle length is too long, it will affect the change of the air flow field recirculation area. If the baffle is too long, it will also cause the air flow to disperse on both sides, weakening the effect of secondary breakup.

[0129] 2) Under the conditions of keeping the nozzle angle, the extension of the draft tube and the atomization pressure unchanged, change the baffle angle of the baffle added in the atomization chamber. Through multiple simulation simulations, analyze the single-phase flow field velocity cloud diagram, the primary atomization metal melt cloud diagram, the secondary atomization particle trajectory diagrams under different conditions and the particle size distribution, refer to Figures 8 - 11 , among them, Figures 8 - 10 in, the baffle angles corresponding to (a), (b), (c) and (d) are 60, 70, 80 and 90 degrees respectively;

[0130] It can be seen from the figure that the average particle size and median particle size of the atomized powder gradually decrease with the increase of the baffle angle. Under the condition of keeping the baffle length unchanged, changing the baffle angle affects the flow field structure in the atomization chamber, increases the air flow velocity, and at the same time increases the aerodynamic force, promoting more complete droplet breakup.

[0131] 3) Under the condition of keeping the nozzle angle, the extension of the diversion tube, and the atomization pressure unchanged, the position of the baffle plate added in the atomization chamber is changed. After multiple simulation runs, the velocity contour map of the single-phase flow field, the primary atomization metal melt contour map, the secondary atomization particle trajectory maps under different conditions, and the particle size distribution are analyzed. Refer to Figures 12 - 15 , Figures 12 - 14 In

[0132] , the distances of the baffle plates corresponding to (a), (b), (c), (d), and (e) from the pressure inlet are 9.5 mm, 18.5 mm, 40 mm, 62 mm, and 79 mm respectively;

[0133] To verify the reliability of the numerical simulation process of this application, the following experiment is conducted for comparison. The experiment proves that this application has good consistency and can ensure the preparation of fine powder.

[0134] The experiment uses an air atomization powder-making device as shown in Figure 16 . The device includes a feeding port atomization crucible 1, an atomization nozzle 2. The atomization nozzle 2 is connected to the upper wall surface 3 of the atomization cylinder. There is a baffle plate 4 inside the atomization cylinder. The baffle plate 4 is connected to the upper part of the atomization cylinder through a screw 5. There are an observation port 6 and a high-speed camera lighting port 7 during the atomization process on the atomization cylinder 8, and a powder collection device 9. The molten metal flows in through the atomization crucible 1, the atomization gas enters from the atomization nozzle 2, the distance between the baffle plate 4 and the upper wall surface 3 of the atomization cylinder is adjusted to the optimal value by adjusting the screw 5, lighting is provided at the high-speed camera lighting port 7 on the atomization cylinder 8, and the high-speed camera takes pictures at the observation port 6 to photograph the fragmentation process of the molten metal flow in the atomization cylinder 8. Subsequently, the powder is collected in the powder collection device 9.

[0135] During the experiment, by adjusting the baffle plate length (Lbaffle = 21, 22, and 23 mm), the baffle plate angle (Abaffle = 60°, 70°, 80°, and 90°), and the baffle plate position (Pbaffle = 9.5, 18.5, 40, 62, and 79 mm), the changes in the baffle plate parameters are recorded and analyzed for their effects on the contact situation between the atomization gas and the superalloy melt, as well as the size and particle size distribution of the finally atomized powder, etc.

[0136] Specifically, under the conditions that the nozzle angle, the extension of the diversion tube, and the atomization pressure remain unchanged, as the baffle length increases, the primary atomization effect gradually weakens with the increase of the baffle length, and the increase of the baffle length has a certain impact on the airflow in the flow field. As the baffle length increases, the average particle size of the powder obtained by the secondary atomization numerical simulation gradually increases;

[0137] Under the conditions that the nozzle angle, the extension of the diversion tube, and the atomization pressure remain unchanged, as the baffle angle increases, the primary atomization effect is enhanced with the increase of the baffle angle. During the atomization process, the thickness of the melt thin disk-shaped liquid film decreases with the increase of the angle, and the average particle size of the powder also decreases with the increase of the baffle angle;

[0138] Under the conditions that the nozzle angle, the extension of the diversion tube, and the atomization pressure remain unchanged, as the baffle position moves backward, the primary atomization effect gradually weakens with the increase of the baffle position. The thin disk-shaped liquid film after primary atomization gradually increases as the baffle length moves backward, and the average particle size of the powder obtained by the secondary atomization numerical simulation gradually increases.

[0139] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description in the method part for the relevant parts.

[0140] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing and optimizing gas atomization powder making based on simulation, characterized in that: include: Construct a two-dimensional axisymmetric geometric model of the aerosol device and set boundary conditions; Based on the geometric model, the flow field evolution and particle size distribution are simulated respectively; wherein, The flow field evolution is numerically simulated using single-phase atomization, including determining the fluid mechanics data through the mass conservation equation, continuity equation and energy equation, and the flow field evolution process is simulated according to the fluid mechanics data using the turbulence model to obtain the single-phase flow field velocity cloud map; The particle size distribution is simulated by using the VOF two-phase flow numerical simulation and the DPM discrete phase numerical simulation, including obtaining the primary atomized metal melt cloud map by simulating the interface behavior between the incompatible fluids in the flow field through the VOF two-phase flow numerical simulation; obtaining the secondary atomized particle trajectory map by simulating the movement information of the discrete particles in the flow field through the DPM discrete phase numerical simulation; and obtaining the particle size distribution map through the primary atomized metal melt cloud map and the secondary atomized particle trajectory map; The baffle parameters in gas atomization powder making are analyzed and optimized based on the single-phase flow field velocity cloud map, the primary atomization metal melt cloud map, the secondary atomization particle trajectory map and the particle size distribution map.

2. The analysis and optimization method according to claim 1, characterized in that: Baffle parameters include baffle length, baffle angle and baffle position.

3. The analysis and optimization method according to claim 1, characterized in that: The geometric model includes walls, pressure inlet, particle injection point, baffles, and pressure outlet; The boundary conditions are set including the pressure inlet boundary, the pressure outlet boundary, and the velocity boundary at the particle entry point.

4. The analysis and optimization method according to claim 1, characterized in that: Before simulation, the geometric model is meshed, and the mesh is encrypted at the slit and the pressure outlet to accurately capture the flow field details.

5. The analysis and optimization method according to claim 1, characterized in that: The mass conservation equation is expressed as: Where: ρ is the density of the fluid, t is the time, is the velocity vector of the fluid, S m is the rate at which mass is created or lost per unit volume; The continuity equation is expressed as: In the formula: x refers to the axial direction, r refers to the radial direction, represents the change in density due to fluid flow along the x-axis, represents the density change due to fluid flow along the r direction, A geometric term that represents the increase in area as the radius increases; The energy equation is expressed as: Where: It represents the rate of change of total energy per unit volume with time. represents the energy transfer due to fluid flow and heat conduction, represents the heat conduction effect caused by temperature gradient, The energy change during the compression or expansion of the reaction, S h Representation may include external heat sources, energy released by chemical reactions; The energy per unit mass, E, is defined as: Where: h is the specific enthalpy, represents the pressure potential energy term, which represents the energy due to the pressure and density of the fluid, represents the kinetic energy term, which represents the kinetic energy of the fluid due to its motion.

6. The analysis and optimization method according to claim 1, characterized in that: The turbulence model simulates the flow field evolution process by solving the NS equations of the fluid velocity field and pressure field. The expression is: Where: i and j refer to the axial and radial directions respectively, ρ is the density of the atomizing gas, and u i and u j represents the different components of fluid velocity, x j and x i Represents different coordinates of spatial position, p is pressure, G ω are the generation terms of k and ω respectively, and Y k , Y ω are the dissipation terms of k and ω respectively, and D ω is the cross-diffusion phase, S i , S k , S ω are the custom source terms of each equation, Γ, Γ k , Γ ω are the diffusion coefficients of velocity, kinetic energy and specific dissipation rate, respectively.

7. The analysis and optimization method according to claim 1, characterized in that: In the VOF two-phase flow numerical simulation, the control equation of VOF is: Where: U is the flow velocity, ρ, μ are the density and viscosity, g is the gravitational acceleration, F s is the surface tension term, k eff is the effective derivative coefficient, is the rate of change of momentum with time, is the convective momentum transport term (inertia term), is the pressure gradient term, P is the static pressure, is the viscous stress term, is the rate of change of total energy with time, is the convective energy transport term, is the heat conduction term, is the enthalpy transport caused by component diffusion, S h is the energy source term.

8. The analysis and optimization method according to claim 1, characterized in that: In DPM discrete phase numerical simulation, the motion of a single particle is described as: Where: τ r is the relaxation time of the particle, represents the acceleration of the particle, A represents the area, represents the difference between the velocity of the fluid and the velocity of the particles, represents the acceleration due to gravity, ρ-ρ p It represents the difference between the density of the fluid and the density of the particles; Where: p and d p refers to the density and diameter of the particles, m refers to the dynamic viscosity of the gas phase, C d Re refers to the drag coefficient and Re refers to the relative particle Reynolds number.

9. The analysis and optimization method according to claim 1, characterized in that: In the DPM discrete phase numerical simulation, the particle breakup model is the KHRT model, which is used to link the fluid core near the nozzle area and the sub-droplets emerging from the core, where the effective length of the liquid core is approximated by the Levich theory as: Where: C L refers to the Levich constant, d0 is the nozzle diameter, ρ l and ρ g Refers to gas density and melt density.

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