Abstract
Droplet splashing behavior caused by the top blowing supersonic jets impacting the liquid metal surface significantly affects the decarburization efficiency and refractory erosion during the basic oxygen furnace (BOF) steelmaking process. However, simulating the mass and size of splashing droplets is challenging because the droplet size differs by multiple orders of magnitude from the molten bath. Herein, a hybrid model (VOF-to-DPM) coupling the volume of fluid model (VOF) and discrete phase model (DPM) was combined with the adaptive mesh refinement (AMR) technique to successfully achieve high-resolution and quantitative capture of splashing droplets. The simulation results are in good agreement with the droplet splashing rate calculated by the theoretical formula based on the Blowing number (NB) within the allowable error range. The generation mechanisms of splashing droplets caused by single-hole and multiple-hole jets impacting the liquid surface were clarified. Furthermore, the effects of oxygen lance height and top blowing flow rate on the total droplet mass, mass and percentage of droplets sprayed on the furnace wall, and the droplet size were also investigated. It was revealed that with the decrease of the oxygen lance height, the total droplet mass increases and then decreases, and the droplet size increases. As the top blowing flow rate increases, the total mass and size of droplets both tend to increase. The proportion of droplets sprayed on the furnace wall increases sequentially when the impact cavities are in the penetrating mode, splashing mode, and quasi-dimpling mode. Moreover, the relationship between the cavity morphology and the droplet splashing was quantitatively characterized. As the modified cavity shape index (Icm) increases, the droplet splashing mass increases then decreases and finally increases. The change in cavity mode is the main factor affecting the droplet splashing behavior.
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Abbreviations
- BOF:
-
Basic oxygen furnace
- VOF:
-
Volume of fluid
- DPM:
-
Discrete phase model
- VOF-to-DPM:
-
Volume of fluid-to-discrete phase model
- AMR:
-
Adaptive mesh refinement
- RRS:
-
Rosin-Rammler sperling
- CFD:
-
Computational fluid dynamics
- RANS:
-
Reynolds averaged Navier-stokes
- CSF:
-
Continuum surface tension
- SST:
-
Shear stress transport
- PISO:
-
Pressure implicit with splitting of operators
- PRESTO!:
-
PREssure STaggering Option
- CICSAM:
-
Compressive interface capturing scheme for arbitrary meshes
- PUMA:
-
Polyhedral unstructured mesh adaption
- HNA:
-
Hanging node adaption
- MRL:
-
Maximum refinement level
- FFT:
-
Fast Fourier transform
- ρ g :
-
Density of gas phase (kg·m-3)
- ρ l :
-
Density of liquid phase (kg·m-3)
- μ g :
-
Dynamic viscosity of gas phase (Pa·s)
- μ l :
-
Dynamic viscosity of liquid phase (Pa·s)
- \(\overrightarrow{u}\) :
-
Velocity vector (m·s-1)
- \({\overline{\overline{\tau }}}\) :
-
Viscous stress term (–)
- g:
-
Gravitational acceleration (m·s-2)
- f σ :
-
Surface tension (N·m-3)
- P :
-
Static pressure (MPa)
- σ :
-
Surface tension coefficient (N·m-1)
- K :
-
Curvature (m-1)
- \(\overrightarrow{\text{n}}\) :
-
Surface normal vector (–)
- \(\widehat{\text{n}}\) :
-
Unit vector normal to the interface (–)
- m p :
-
Particle mass (kg)
- τ r :
-
Relaxation time (s)
- d p :
-
Particle diameter (m)
- Re :
-
Relative Reynolds number (–)
- C d :
-
Drag coefficient between particle and air (–)
- a 1, a 2, a 3 :
-
Parameters varying with Reynolds number [43] (–)
- u p :
-
Velocity of the discrete phase droplet (m·s-1)
- V p :
-
Volume of continuous phase droplet (m3)
- k :
-
Turbulent kinetic energy (m2·s-2)
- ω :
-
Turbulent frequency (s-1)
- σ k :
-
Prandtl numbers for turbulent kinetic energy (–)
- σ ω :
-
Prandtl numbers for turbulent energy dissipation rate (–)
- μ :
-
Dynamic viscosity of the fluid (Pa·s)
- Ω:
-
Vorticity tensor (–)
- y :
-
Distance to the wall (m)
- σ k ,1, σ ω ,1, σ k ,2, σ ω ,2 :
-
Empirical constants, [45] and their respective values are1.176, 2.0, 1.0 and 1.168
- F 1 and F 2 :
-
Mixing functions (–)
- r i :
-
Vector pointing from the gravity center of the lump to every face center (–)
- n i :
-
Face normal vector (–)
- U g :
-
Critical velocity of gas (m·s-1)
- R B :
-
Droplet splashing rate (kg·s-1)
- F G :
-
Top gas flow rate (Nm·s-3)
- N B :
-
Blowing number (–)
- I cm :
-
Modified cavity shape index (–)
References
Q. Liu, W.Y. Chen, L. Hu, H.B. Xie, and X. Fu: Phys. Fluids, 2015, vol. 27(8), p. 082106.
M.A. Mendez, A. Gosset, and J.M. Buchlin: Exp. Therm. Fluid Sci., 2019, vol. 106, pp. 48–67.
D.B. Villaverde, A. Gosset, and M.A. Mendez: Phys. Fluids, 2021, vol. 33(6), p. 062114.
H.M.J.M. Wedershoven, C.W.J. Berendsen, J.C.H. Zeegers, and A.A. Darhuber: Phys. Rev. Appl., 2015, vol. 3(2), p. 024005.
C.W.J. Berendsen, J.C.H. Zeegers, and A.A. Darhuber: J. Colloid Interface Sci., 2013, vol. 407, pp. 505–15.
L.L. Cao, Y.N. Wang, Q. Liu, and X.M. Feng: ISIJ Int., 2018, vol. 58(4), pp. 573–84.
M. Lv, S.P. Chen, L.Z. Yang, and G.S. Wei: Metals, 2022, vol. 12(11), p. 1918.
B.K. Rout, G. Brooks, M.A. Rhamdhani, Z.S. Li, F.N.H. Schrama, and A. Overbosch: Metall. Mater. Trans. B, 2018, vol. 49B, pp. 1022–33.
B. Zhang, K. Chen, R.F. Wang, C.J. Liu, and M.F. Jiang: Metals, 2019, vol. 9(4), p. 409.
W. Kleppe and F. Oeters: Archiv für das Eisenhüttenwesen, 1977, vol. 48(3), pp. 139–43.
B. Deo and R. Boom: Fundamentals of Steelmaking Metallurgy, Prentice Hall International, London, 1993, pp. 45–46.
H.Y. Hwang and G.A. Irons: Metall. Mater. Trans. B, 2012, vol. 43B(2), pp. 302–15.
Subagyo, G.A. Brooks, K.S. Coley, and G.A. Irons: ISIJ Int., 2003, vol. 43(7), pp. 983–89.
M. Alam, J. Naser, G. Brooks, and A. Fontana: ISIJ Int., 2012, vol. 52(6), pp. 1026–35.
S. Sabah and G. Brooks: ISIJ Int., 2014, vol. 54(4), pp. 836–44.
N. Standish and Q.L. He: ISIJ Int., 1989, vol. 29(6), pp. 455–61.
M.M. Li, Q. Li, S.B. Kuang, and Z.S. Zou: Ind. Eng. Chem. Res., 2016, vol. 55(12), pp. 3630–40.
N.A. Molloy: J. Iron Steel Inst, 1970, vol. 20(8), pp. 943–50.
M.A. Barron, D.Y. Medina, and J. Reyes: World J. Eng. Technol., 2021, vol. 9(4), pp. 793–803.
T. Tanaka and K. Okane: Tetsu-to-Hagané, 1988, vol. 74(8), pp. 1593–1600.
Q.L. He and N. Standish: ISIJ Int., 1990, vol. 30(4), pp. 305–09.
S. Sabah and G. Brooks: Metall Mater. Trans. B, 2015, vol. 46B(2), pp. 863–72.
M.J. Luomala, T.M.J. Fabritius, E.O. Virtanen, T.P. Siivola, T.L.J. Fabritius, H. Tenkku, and J.J. Harkki: ISIJ Int., 2002, vol. 42(11), pp. 1219–24.
T. Haas, A. Ringel, V.V. Visuri, M. Eickhoff, and H. Pfeifer: Steel Res. Int., 2019, vol. 90(9), p. 1900177.
T. Fabritius, P. Mure, E. Virtanen, P. Hannula, M. Luomala, and J. Härkki: Ironmak. Steelmak., 2002, vol. 29(1), pp. 29–35.
M.J. Luomala, T.M.J. Fabritius, and J.J. Härkki: ISIJ Int., 2004, vol. 44(5), pp. 809–16.
S. Amano, S. Sato, Y. Takahashi, and N. Kikuchi: Eng. Rep., 2021, vol. 3(12), p. 12406.
S.C. Koria and K.W. Lange: Metall. Trans. B, 1984, vol. 15(1), pp. 109–16.
M.M. Li, Q. Li, Z.S. Zou, and B.K. Li: JOM, 2019, vol. 71(2), pp. 729–36.
J.K. Sun, J.S. Zhang, R. Jiang, X.M. Feng, and Q. Liu: Steel Res. Int., 2023, vol. 94(1), p. 2200532.
M. Lv, H. Li, T.C. Lin, K. Xie, and K. Xue: Steel Res. Int., 2021, vol. 92(10), p. 2100103.
W. Jin, J. Xiao, H.X. Ren, C.H. Li, Q.J. Zheng, and Z.B. Tong: Powder Technol., 2022, vol. 407, p. 117622.
J.F. Zhao, W. Lin, P.B. Li, W. Chu, Y.H. Tong, and W.S. Nie: Acta Astronaut., 2021, vol. 183, pp. 23–28.
M.D. Martino, D. Ahirwal, and P.L. Maffettone: Phys. Fluids, 2022, vol. 34(9), p. 9318.
C. Lvoll, M.H. Sun, X.X. Chen, H.L. Zhao, Y.L. Liu, and H.X. Yin: Metall Mater. Trans. B, 2023, vol. 54B(2), pp. 807–22.
L.M. Li, W.S. Xu, X.J. Li, X. Sun, G.J. Yang, and Z.C. Zhu: JOM, 2023, vol. 75(5), pp. 1357–70.
Y.B. Liu, J. Yang, and Z.Q. Lin: Metall. Mater. Trans. B, 2022, vol. 53B(4), pp. 2030–50.
D. Stefanitsis, P. Koukouvinis, N. Nikolopoulos, and M. Gavaises: J. Energy Eng., 2021, vol. 147(1), p. 04020077.
S.K. Sharma, J.W. Hlinka, and D.W. Kern: Iron. Steelmak., 1977, vol. 4(7), pp. 7–18.
C.W. Hirt and B.D. Nichols: J. Comput. Phys., 1981, vol. 39(1), pp. 201–25.
J.U. Brackbill, D.B. Kothe, and C. Zemach: J. Comput. Phys., 1992, vol. 100(2), pp. 335–54.
A.D. Gosman and E. Loannides: J. Energy, 1983, vol. 7(6), pp. 482–90.
S.A. Morsi and A.J. Alexander: J. Fluid Mech., 1972, vol. 55(2), pp. 193–208.
Ansys. Ansys Fluent user's guide, Release 2021R1. Southpointe, Canonsburg, ANSYS Inc, 2021.
Ansys. Ansys Fluent Theory Guide, Release 2021R1. Southpointe, Canonsburg, ANSYS Inc, 2021.
Z.L. Li and D.Q. Cang: Steel Res. Int., 2017, vol. 88(4), p. 1600209.
Z.H. Sheng, L.H. Feng, K. Liu, B. Yang, and L.Z. Kong: Metall. Res. Technol., 2021, vol. 118(1), p. 114.
J.K. Sun, J.S. Zhang, W.H. Lin, X.M. Feng, and Q. Liu: Metals, 2022, vol. 12(1), p. 117.
D.W. Stanton and C.J. Rutland: Int. J. Heat Mass Transfer, 1998, vol. 41(20), pp. 3037–54.
L.J. Leng and N.B. Gray: Metall. Mater. Trans. B, 1996, vol. 27B, pp. 633–46.
V. Cullinan, D, Morton, J. Liow, and N. Gray: 21st Australasian Chemical Engineering Conf, Australia, 1993, p. 1.
R.D. Deegan, P. Brunet, and J. Eggers: Nonlinearity, 2008, vol. 21(1), p. C1.
J.K. Sun, J.S. Zhang, W.H. Lin, L.L. Cao, X.M. Feng, and Q. Liu: Steel Res. Int., 2021, vol. 92(9), p. 2100179.
J. Martinsson and D. Sichen: ISIJ Int., 2019, vol. 59(1), pp. 46–50.
C. Cicutti, M. Valdez, T. Perez, R. Donayo, and J. Petroni: Lat. Am. Appl. Res., 2002, vol. 32(3), pp. 237–40.
Acknowledgments
This work is financial support by the National Natural Science Foundation of China (51974023) and Jiangxi Provincial Department of Science and Technology (20171ACE50020).
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Sun, J., Zhang, J., Jiang, R. et al. Numerical Simulation of Droplet Splashing Behavior in Steelmaking Converter Based on VOF-to-DPM Hybrid Model and AMR Technique. Metall Mater Trans B 55, 1098–1116 (2024). https://doi.org/10.1007/s11663-024-03024-2
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DOI: https://doi.org/10.1007/s11663-024-03024-2