Skip to main content
Log in

Numerical Simulation of Droplet Splashing Behavior in Steelmaking Converter Based on VOF-to-DPM Hybrid Model and AMR Technique

  • Original Research Article
  • Published:
Metallurgical and Materials Transactions B Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

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

  1. Q. Liu, W.Y. Chen, L. Hu, H.B. Xie, and X. Fu: Phys. Fluids, 2015, vol. 27(8), p. 082106.

    Article  ADS  Google Scholar 

  2. M.A. Mendez, A. Gosset, and J.M. Buchlin: Exp. Therm. Fluid Sci., 2019, vol. 106, pp. 48–67.

    Article  Google Scholar 

  3. D.B. Villaverde, A. Gosset, and M.A. Mendez: Phys. Fluids, 2021, vol. 33(6), p. 062114.

    Article  ADS  Google Scholar 

  4. 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.

    Article  ADS  CAS  Google Scholar 

  5. C.W.J. Berendsen, J.C.H. Zeegers, and A.A. Darhuber: J. Colloid Interface Sci., 2013, vol. 407, pp. 505–15.

    Article  ADS  CAS  PubMed  Google Scholar 

  6. L.L. Cao, Y.N. Wang, Q. Liu, and X.M. Feng: ISIJ Int., 2018, vol. 58(4), pp. 573–84.

    Article  CAS  Google Scholar 

  7. M. Lv, S.P. Chen, L.Z. Yang, and G.S. Wei: Metals, 2022, vol. 12(11), p. 1918.

    Article  CAS  Google Scholar 

  8. 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.

    Article  ADS  Google Scholar 

  9. B. Zhang, K. Chen, R.F. Wang, C.J. Liu, and M.F. Jiang: Metals, 2019, vol. 9(4), p. 409.

    Article  Google Scholar 

  10. W. Kleppe and F. Oeters: Archiv für das Eisenhüttenwesen, 1977, vol. 48(3), pp. 139–43.

    Article  CAS  Google Scholar 

  11. B. Deo and R. Boom: Fundamentals of Steelmaking Metallurgy, Prentice Hall International, London, 1993, pp. 45–46.

    Google Scholar 

  12. H.Y. Hwang and G.A. Irons: Metall. Mater. Trans. B, 2012, vol. 43B(2), pp. 302–15.

    Article  ADS  Google Scholar 

  13. Subagyo, G.A. Brooks, K.S. Coley, and G.A. Irons: ISIJ Int., 2003, vol. 43(7), pp. 983–89.

  14. M. Alam, J. Naser, G. Brooks, and A. Fontana: ISIJ Int., 2012, vol. 52(6), pp. 1026–35.

    Article  CAS  Google Scholar 

  15. S. Sabah and G. Brooks: ISIJ Int., 2014, vol. 54(4), pp. 836–44.

    Article  CAS  Google Scholar 

  16. N. Standish and Q.L. He: ISIJ Int., 1989, vol. 29(6), pp. 455–61.

    Article  Google Scholar 

  17. M.M. Li, Q. Li, S.B. Kuang, and Z.S. Zou: Ind. Eng. Chem. Res., 2016, vol. 55(12), pp. 3630–40.

    Article  CAS  Google Scholar 

  18. N.A. Molloy: J. Iron Steel Inst, 1970, vol. 20(8), pp. 943–50.

    Google Scholar 

  19. M.A. Barron, D.Y. Medina, and J. Reyes: World J. Eng. Technol., 2021, vol. 9(4), pp. 793–803.

    Article  Google Scholar 

  20. T. Tanaka and K. Okane: Tetsu-to-Hagané, 1988, vol. 74(8), pp. 1593–1600.

    Article  CAS  Google Scholar 

  21. Q.L. He and N. Standish: ISIJ Int., 1990, vol. 30(4), pp. 305–09.

    Article  CAS  Google Scholar 

  22. S. Sabah and G. Brooks: Metall Mater. Trans. B, 2015, vol. 46B(2), pp. 863–72.

    Article  ADS  Google Scholar 

  23. 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.

    Article  CAS  Google Scholar 

  24. T. Haas, A. Ringel, V.V. Visuri, M. Eickhoff, and H. Pfeifer: Steel Res. Int., 2019, vol. 90(9), p. 1900177.

    Article  Google Scholar 

  25. T. Fabritius, P. Mure, E. Virtanen, P. Hannula, M. Luomala, and J. Härkki: Ironmak. Steelmak., 2002, vol. 29(1), pp. 29–35.

    Article  CAS  Google Scholar 

  26. M.J. Luomala, T.M.J. Fabritius, and J.J. Härkki: ISIJ Int., 2004, vol. 44(5), pp. 809–16.

    Article  CAS  Google Scholar 

  27. S. Amano, S. Sato, Y. Takahashi, and N. Kikuchi: Eng. Rep., 2021, vol. 3(12), p. 12406.

    Article  Google Scholar 

  28. S.C. Koria and K.W. Lange: Metall. Trans. B, 1984, vol. 15(1), pp. 109–16.

    Article  Google Scholar 

  29. M.M. Li, Q. Li, Z.S. Zou, and B.K. Li: JOM, 2019, vol. 71(2), pp. 729–36.

    Article  CAS  Google Scholar 

  30. J.K. Sun, J.S. Zhang, R. Jiang, X.M. Feng, and Q. Liu: Steel Res. Int., 2023, vol. 94(1), p. 2200532.

    Article  CAS  Google Scholar 

  31. M. Lv, H. Li, T.C. Lin, K. Xie, and K. Xue: Steel Res. Int., 2021, vol. 92(10), p. 2100103.

    Article  CAS  Google Scholar 

  32. W. Jin, J. Xiao, H.X. Ren, C.H. Li, Q.J. Zheng, and Z.B. Tong: Powder Technol., 2022, vol. 407, p. 117622.

    Article  CAS  Google Scholar 

  33. 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.

    Article  ADS  Google Scholar 

  34. M.D. Martino, D. Ahirwal, and P.L. Maffettone: Phys. Fluids, 2022, vol. 34(9), p. 9318.

    Article  Google Scholar 

  35. 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.

    ADS  Google Scholar 

  36. 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.

    Article  ADS  CAS  Google Scholar 

  37. Y.B. Liu, J. Yang, and Z.Q. Lin: Metall. Mater. Trans. B, 2022, vol. 53B(4), pp. 2030–50.

    Article  ADS  Google Scholar 

  38. D. Stefanitsis, P. Koukouvinis, N. Nikolopoulos, and M. Gavaises: J. Energy Eng., 2021, vol. 147(1), p. 04020077.

    Article  Google Scholar 

  39. S.K. Sharma, J.W. Hlinka, and D.W. Kern: Iron. Steelmak., 1977, vol. 4(7), pp. 7–18.

    Google Scholar 

  40. C.W. Hirt and B.D. Nichols: J. Comput. Phys., 1981, vol. 39(1), pp. 201–25.

    Article  ADS  Google Scholar 

  41. J.U. Brackbill, D.B. Kothe, and C. Zemach: J. Comput. Phys., 1992, vol. 100(2), pp. 335–54.

    Article  ADS  MathSciNet  CAS  Google Scholar 

  42. A.D. Gosman and E. Loannides: J. Energy, 1983, vol. 7(6), pp. 482–90.

    Article  ADS  Google Scholar 

  43. S.A. Morsi and A.J. Alexander: J. Fluid Mech., 1972, vol. 55(2), pp. 193–208.

    Article  ADS  Google Scholar 

  44. Ansys. Ansys Fluent user's guide, Release 2021R1. Southpointe, Canonsburg, ANSYS Inc, 2021.

  45. Ansys. Ansys Fluent Theory Guide, Release 2021R1. Southpointe, Canonsburg, ANSYS Inc, 2021.

  46. Z.L. Li and D.Q. Cang: Steel Res. Int., 2017, vol. 88(4), p. 1600209.

    Article  Google Scholar 

  47. Z.H. Sheng, L.H. Feng, K. Liu, B. Yang, and L.Z. Kong: Metall. Res. Technol., 2021, vol. 118(1), p. 114.

    Article  CAS  Google Scholar 

  48. J.K. Sun, J.S. Zhang, W.H. Lin, X.M. Feng, and Q. Liu: Metals, 2022, vol. 12(1), p. 117.

    Article  Google Scholar 

  49. D.W. Stanton and C.J. Rutland: Int. J. Heat Mass Transfer, 1998, vol. 41(20), pp. 3037–54.

    Article  CAS  Google Scholar 

  50. L.J. Leng and N.B. Gray: Metall. Mater. Trans. B, 1996, vol. 27B, pp. 633–46.

    Article  ADS  Google Scholar 

  51. V. Cullinan, D, Morton, J. Liow, and N. Gray: 21st Australasian Chemical Engineering Conf, Australia, 1993, p. 1.

  52. R.D. Deegan, P. Brunet, and J. Eggers: Nonlinearity, 2008, vol. 21(1), p. C1.

    Article  ADS  Google Scholar 

  53. 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.

    Article  CAS  Google Scholar 

  54. J. Martinsson and D. Sichen: ISIJ Int., 2019, vol. 59(1), pp. 46–50.

    Article  CAS  Google Scholar 

  55. C. Cicutti, M. Valdez, T. Perez, R. Donayo, and J. Petroni: Lat. Am. Appl. Res., 2002, vol. 32(3), pp. 237–40.

    CAS  Google Scholar 

Download references

Acknowledgments

This work is financial support by the National Natural Science Foundation of China (51974023) and Jiangxi Provincial Department of Science and Technology (20171ACE50020).

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qing Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11663-024-03024-2

Navigation