
FOLLOWUS
State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming 650093, China
School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing 400044, China
Analytical and Testing Center, Chongqing University, Chongqing 400044, China
Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
*Corresponding author. E-mail address:liuzuohua@cqu.edu.cn(Z. Liu).
收稿:2025-10-24,
修回:2026-02-21,
录用:2026-02-25,
网络首发:2026-04-20,
纸质出版:2026-07
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Wang Yu, Meng Tong, Qin Shuang, 等. Investigating flow field control mechanisms in a molten bath using POD and chaos theory with asymmetric nozzle arrangements[J]. 中国化学工程学报(英文), 2026,95(7):347-360.
Wang Yu, Meng Tong, Qin Shuang, et al. Investigating flow field control mechanisms in a molten bath using POD and chaos theory with asymmetric nozzle arrangements[J]. Chinese Journal of Chemical Engineering, 2026, 95(7): 347-360.
Wang Yu, Meng Tong, Qin Shuang, 等. Investigating flow field control mechanisms in a molten bath using POD and chaos theory with asymmetric nozzle arrangements[J]. 中国化学工程学报(英文), 2026,95(7):347-360. DOI:
Wang Yu, Meng Tong, Qin Shuang, et al. Investigating flow field control mechanisms in a molten bath using POD and chaos theory with asymmetric nozzle arrangements[J]. Chinese Journal of Chemical Engineering, 2026, 95(7): 347-360. DOI:
The flow regulation mechanism of bottom-blowing stirring is the key to enhancing the reaction efficiency of a molten bath. Herein
proper orthogonal decomposition (POD) is integrated with chaos theory for the first time to investigate the influence of asymmetric nozzle arrangements on flow field regulation and mixing performance during bottom-blowing smelting. The optimization effect of a 10◦asymmetric nozzle configuration (model III) is specifically examined through numerical simulation and analysis. The results indicate that model III effectively suppresses splashing while increasing splash height and optimizing the flow field structure
thereby markedly enhancing mixing efficiency. The Lyapunov exponent in the main flow region is 1.5—2.3 times higher than that of other models
and energy cascades across the full frequency range of 50—300 Hz. POD analysis reveals that approximately 98% of the total system energy is captured by the first 68 modes
while the first two modes together account for 71.36% of the total energy
indicating a highly efficient low-order energy representation and rapid energy transfer across dominant flow structures. The asymmetric vortex structure effectively coordinates large macroscopic vortices and small-scale eddies
thereby enhancing mixing performance. This work provides theoretical support for the optimization of bottom-blowing furnace stirring systems and demonstrates the potential of POD in metallurgical process analysis.
S.S. Wang, X. Xiong, P.Q. Liu, Q.Z. Zhang, Q. Zhang, C.Y. Tao, Y.D. Wang, Z.H. Liu, CFD simulation of hydrodynamics and mixing performance in dual shaft eccentric mixers, Chin. J. Chem. Eng. 62 (2023) 297—309.
J.K. Sun, J.S. Zhang, W.H. Lin, X.M. Feng, Q. Liu, Effect of bottom blowing mode on fluid flow and mixing behavior in converter, Metals 12 (1) (2022) 117.
K.Z. Song, A. Jokilaakso, Transport phenomena in copper bath smelting and converting processes—a review of experimental and modeling studies, Miner. Process. Extr. Metall. Rev. 43 (1) (2022) 107—121.
L. Zhang, K. Yang, M. Li, Q.T. Xiao, H. Wang, Chaotic characterization of macromixing effect in a gas—liquid stirring system using modified 0—1 test, Can. J. Chem. Eng. 100 (2) (2022) 261—275.
S. Xie, C.F. Liao, B.J. Zhao, Kinetic and thermodynamic studies on lead-rich slag reduction at various CaO/SiO 2 ratios, JOM 74 (9) (2022) 3625—3633.
W. Wang, L.Z. Mu, H.L. Zhao, X.Y. Cai, F.Q. Liu, H.Y. Sohn, CFD study on improvement of non-uniform stirring in a large bottom-blown copper smelting furnace, Min. Metall. Explor. 41 (3) (2024) 1421—1435.
T.T. Lu, L.Z. Mu, Y.D. Xiao, H.L. Zhao, F.Q. Liu, CFD study on bottom-blown copper smelting furnace with unsymmetric gas injection, J. Sustain. Metall. 8 (3) (2022) 1235—1244.
S. Ganguly, S. Chakraborty, Numerical modelling studies of flow and mixing phenomena in gas stirred steel ladles, Ironmak. Steelmak. 35 (7) (2008) 524—530.
K.Z. Song, A. Jokilaakso, CFD modeling of multiphase flow in an SKS furnace with new tuyere arrangements, Metall. Mater. Trans. B 53 (1) (2022) 253—272.
H. Hu, L.Z. Yang, Y.F. Guo, F. Chen, S. Wang, F.Q. Zheng, B. Li, Numerical simulation of bottom-blowing stirring in different smelting stages of electric arc furnace steelmaking, Metals 11 (5) (2021) 799.
P. Shao, L.P. Jiang, Flow and mixing behavior in a new bottom blown copper smelting furnace, Int. J. Mol. Sci. 20 (22) (2019) 5757.
L. Shui, Z.X. Cui, X.D. Ma, M.A. Rhamdhani, A. Nguyen, B.J. Zhao, Mixing phenomena in a bottom blown copper smelter: A water model study, Metall. Mater. Trans. B 46 (3) (2015) 1218—1225.
B.C. Jiang, X.Y. Guo, Q.M. Wang, Analysis of melt flow characteristics in large bottom-blowing furnace strengthened by oxygen lance jet at different positions, J. Sustain. Metall. 9 (4) (2023) 1704—1715.
L. Shui, Z.X. Cui, X.D. Ma, M.A. Rhamdhani, A.V. Nguyen, B.J. Zhao, Understanding of bath surface wave in bottom blown copper smelting furnace, Metall. Mater. Trans. B 47 (1) (2016) 135—144.
J.L. Liow, N.B. Gray, Slopping resulting from gas injection in a Peirce—Smith converter: Water modeling, Metall. Trans. B 21 (6) (1990) 987—996.
X.B. Zhou, M. Ersson, L.C. Zhong, P.G. Jönsson, Numerical and physical simulations of a com bined top-bottom-side blown converter, Steel Res. Int. 86 (11) (2015) 1328—1338.
O. Olivares, A. Elias, R. Sánchez, M. Díaz-Cruz, R.D. Morales, Physical and mathematical models of gas—liquid fluid dynamics in LD converters, Steel Res. 73 (2) (2002) 44—51.
S. Sarkar, N. Sen, K.K. Singh, Bubble formation at top-submerged nozzles immersed in a quiescent liquid, Prog. Nucl. Energy 161 (2023) 104709.
D.X. Wang, Y. Liu, Z.M. Zhang, P. Shao, T.A. Zhang, Experimental study of bottom blown oxygen copper smelting process for water model, AIP Conf. Proc. 1542 (1) (2013) 1304—1307.
H.L. Zhao, J.Q. Wang, F.Q. Liu, H.Y. Sohn, Flow zone distribution and mixing time in a Peirce: Smith copper converter, Int. J. Miner. Metall. Mater. 29 (1) (2022) 70—77.
M.M. Li, L. Li, Q. Li, Z.S. Zou, Modeling of mixing behavior in a combined blowing steelmaking converter with a filter-based Euler—Lagrange model, JOM 70 (10) (2018) 2051—2058.
Z.Y. Du, S.L. Yang, G.R. Bao, H. Wang, Numerical study of swirling effect on enhancing the bath dynamics in the top-submerged-lance furnace, Metall. Mater. Trans. B 54 (6) (2023) 3115—3129.
S.C. Du, Modeling related to secondary steel making, Steel Res. Int. 83 (9) (2012) 825—841.
Z.S. Yang, L.Z. Yang, T. Cheng, F. Chen, F.Q. Zheng, S. Wang, Y.F. Guo, Fluid flow characteristic of EAF molten steel with differe nt bottom-blowing gas flow rate distributions, ISIJ Int. 60 (9) (2020) 1957—1967.
X. Liu, A.J. Xu, F. Yuan, C.B. Pang, Optimisation of the bottom blowing process for a 200 t converter, Ironmak. Steelmak. 50 (1) (2023) 1—12.
Z.Y. Lai, Z. Xie, L.C. Zhong, Influence of bottom tuyere configuration on bath stirring in a top and bottom combined blown converter, ISIJ Int. 48 (6) (2008) 793—798.
Q. Guo, C.M. Boyce, Structured bubbling in layered gas-fluidized beds subject to vibration: A CFD-DEMstudy, AIChE J. 68 (7) (2022) e17709.
Q. Guo, Y.X. Zhang, A. Padash, K.N. Xi, T.M. Kovar, C.M. Boyce, Dynamically structured bubbling in vibrated gas-fluidized granular materials, Proc Natl Acad Sci USA. 118 (35) (2021) e2108647118.
Q. Guo, S. Chiu, W. Da, C.M. Boyce, Heat transfer within dynamically structured bubbling fluidized beds subject to vibration: A two-fluid modeling study, AIChE J. 69 (2) (2023) e17970.
B. Begiashvili, N. Groun, J. Garicano-Mena, S. Le Clainche, E. Valero, Datadriven modal decomposition methods as feature detection techniques for flow problems: A critical assessment, Phys. Fluids 35 (4) (2023) 041301.
R. Raju, S. Balachandar, D.F. Hill, R.J. Adrian, Reynolds number scaling of flow in a stirred tank with Rushton turbine. Part II: Eigen decomposition of fluctuation, Chem. Eng. Sci. 60 (12) (2005) 3185—3198.
F. Mendoza, A.L. Banãles, E. Cid, C. Xuereb, M. Poux, D.F. Fletcher, J. Aubin, Hydrodynamics in a stirred tank in the transitional flow regime, Chem. Eng. Res. Des. 132 (2018) 865—880.
A. de Lamotte, A. Delafosse, S. Calvo, D. Toye, Analysis of PIV measurements using modal decomposition techniques, POD and DMD, to study flow structures and their dynamics within a stirred-tank reactor, Chem. Eng. Sci. 178 (2018) 348—366.
A. de Lamotte, A. Delafosse, S. Calvo, D. Toye, Identifying dominant spatial and time characteristics of flow dynamics within free-surface baffled stirred-tanks from CFD simulations, Chem. Eng. Sci. 192 (2018) 128—142.
A. Liné, J.C. Gabelle, J. Morchain, D. Anne-Archard, F. Augier, On POD analysis of PIV measurements applied to mixing in a stirred vessel with a shear thinning fluid, Chem. Eng. Res. Des. 91 (11) (2013) 2073—2083.
T. Meng, Y. Wang, S. Qin, P.Q. Liu, Y.D. Wang, C.Y. Tao, Z.H. Liu, Complex flow field analysis in multi-shaft stirred reactors: Dynamics of wave-vortex coupling revealed by POD and DMD methods, Chem. Eng. Sci. 301 (2025) 120753.
T. Meng, A. Paglianti, F. Alberini, G. Montante, Y. Wang, Z.H. Liu, Investigating coupling and intensification mechanisms in multi-shaft digesting stirring reactors using POD, AIChE J. 71 (11) (2025) e70028.
T. Meng, Y. Wang, S.S. Wang, S. Qin, Q. Zhang, Y.D. Wang, C.Y. Tao, Y.Q. Xu, Z. H. Liu, Exploration of multishafts stirred reactors: An investigation on experiments and large eddy simulations for turbulent chaos and mixing characteristics, Ind. Eng. Chem. Res. 63 (5) (2024) 2441—2456.
T. Meng, J. Yang, S.S. Wang, Y. Wang, S. Qin, Y.D. Wang, C.Y. Tao, Q. Zhang, Z. H. Liu, Multi-shaft stirred reactors mixing efficiency: Rapid characterization strategy based on chaotic attractors, AIChE J. 70 (10) (2024) e18510.
T. Meng, Y. Wang, S. Qin, P.Q. Liu, Q. Zhang, Y.D. Wang, C.Y. Tao, Z.H. Liu, From patterns to cocktails: A novel visualization method for turbulent flow fields in stirred reactors, Ind. Eng. Chem. Res. 63 (44) (2024) 19320—19328.
S.L. Brunton, B.R. Noack, P. Koumoutsakos, Machine learning for fluid mechanics, Annu. Rev. Fluid Mech. 52 (2020) 477—508.
H.F. Liu, Y.Z. Yang, Z.H. Dai, Z.H. Yu, The largest Lyapunov exponent of chaotic dynamical system in scale space and its application, Chaos 13 (3) (2003) 839—844.
I. Daubechies, J.F. Lu, H.T. Wu, Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool, Appl. Comput. Harmon. Anal. 30 (2) (2011) 243—261.
J. Lumley, Stochastic Tools in Turbulence, Academic Press, New York and London,2007.
L. Sirovich, Turbulence and the dynamics of coherent structures. I. Coherent structures, Q. Appl. Math. 45 (3) (1987) 561—571.
K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd, Elsevier, Amsterdam,1990.
Y.N. Wang, L.L. Cao, Z.F. Cheng, B. Blanpain, M.X. Guo, Mathematical methodology and metallurgical application of turbulence modelling: A review, Metals 11 (8) (2021) 1297.
ANSYS, ANSYS Fluent Theory Guide 19.1, ANSYS, Canonsburg, PA, 2019.
M.A. Ramírez-Argáez, Numerical simulation of fluid flow and mixing in gas-stirred ladles, Mater. Manuf. Process. 23 (1) (2007) 59—68.
Y. Liu, M. Ersson, H.P. Liu, P.G. Jönsson, Y. Gan, A review of physical and numerical approaches for the study of gas stirring in ladle metallurgy, Metall. Mater. Trans. B 50 (1) (2019) 555—577.
B.C. Jiang, X.Y. Guo, Q.M. Wang, Two-dimensional analysis of melt pneumatic stirring in large bottom blowing furnace based on CFD, Nonferrous Met. Extr. Metall. (6) (2023) 39 — 50.
H.J. Yan, F.K. Liu, Z.Y. Zhang, Q. Gao, L. Liu, Z.X. Cui, D.B. Shen, Influence of lance arrangement on bottom-blowing bath smelting process, Chin. J. Nonferrous Metals 22 (8) (2012) 2393—2400.
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