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Uncertainty analysis of the influence of delivery system nozzle structure on fluid-thermal coupling in casting molten Pool

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Abstract

The quality of thin strip products is determined by the flow and heat transfer in a molten pool of twin-roll casting, thus it is of great significance to study the structure parameters of the delivery system, including the angle and height of end face nozzle, the angle and height of side nozzle as well as the taper angle of side nozzle. A methodology for simulation of twin-roll thin strips continuous casting process under uncertainty is presented by a three-dimensional numerical model of the flow field and solidification of liquid steel in the molten pool. A sampling-based stochastic model is developed to elucidate the effects of uncertainty in several nozzle structure parameters on the variability of maximum outlet temperature difference and maximum liquid surface turbulence kinetic energy. The results show that taper angle of side nozzle significantly affects the variability in maximum outlet temperature difference, and the uncertainty in angle and height of end face nozzle as well as the height of the side nozzle affects the variability in maximum liquid surface turbulence difference evidently.

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References

  1. Jiao H, Xu Y, Xiong W, Zhang Y, Cao G, Li C, Niu J, Misra RDK (2017) High-permeability and thin-gauge non-oriented electrical steel through twin-roll strip casting. Mater Design 136:23–33

    Article  Google Scholar 

  2. Chen P, Huang H, Ji C et al (2018) Bonding strength of invar/cu clad strips fabricated by twin-roll casting process. T Nonferr Metal Soc 28:2460–2469

    Article  Google Scholar 

  3. Wang Z, Huang X, Li Y et al (2019) Ultra-fine microstructure and excellent mechanical properties of high borated stainless steel sheet produced by twin-roll strip casting. Mat Sci Eng A-Struct 747:185–196

    Article  Google Scholar 

  4. Ge S, Isac M, Lawrence GRI (2012) Progress of strip casting technology for steel historical developments. Iron Steel I JPN 52(12):2109–2122

    Article  Google Scholar 

  5. Rodrigues CMG, Ludwig A, Wu M et al (2019) A comprehensive analysis of macrosegregation formation during twin-roll casting. Metall Mater Trans B Process Metall Mater Process Sci 50(3):1334–1350

    Article  Google Scholar 

  6. Dong J, Wang N, Chen M et al (2014) Coupled numerical simulation on the flow and temperature fields of molten steel Pool in a twin-roll strip caster. Chin J Pro Eng 14(2):211–216 (in Chinese)

    Google Scholar 

  7. Kim W, Kim D, Kuznetsov AV (2000) Simulation of coupled turbulent flow and heat transfer in the wedge-shaped pool of a twin-roll strip casting process. Int J Heat Mass Tran 43(20):3811–3822

    Article  Google Scholar 

  8. Zhu G, Yin J, Ren Q et al (2019) Optimization on nozzle structure of delivery system based on FEM. J Iron Steel Res 31(1):8–14 (in Chinese)

    Google Scholar 

  9. Zhu G, Zhang C, Xu P et al (2016) Experimental study on the distribution of heat flux along contact arc in twin-roll continuous strip casting. Key Eng Mater 693:761–766

    Article  Google Scholar 

  10. Zhu G, HOU X (2011) Effect of nozzle angle of wedge-shaped device on temperature distribution in casting molten Pool. Foundry Techno 32(1):83–86 (in Chinese)

    Google Scholar 

  11. Wang B, Zhang J, Zhang Y et al (2008) Numerical analysis of fluid flow and heat transfer in pool of twin roll strip caster. Ironmak Steelmak 35(1):75–80

    Article  MathSciNet  Google Scholar 

  12. Li Q, Zhang Y, Liu L et al (2012) Effect of casting parameters on the freezing point position of the 304 stainless steel during twin-roll strip casting process by numerical simulation. J Mater Sci 47(9):3953–3960

    Article  Google Scholar 

  13. Muldrew SI, Lux H, Menon V et al (2019) Uncertainty analysis of an SST-2 fusion reactor design. Fusion Eng Des 146:353–356

    Article  Google Scholar 

  14. Wang C, Peng M, Cong T et al (2019) Uncertainty analysis on natural circulation characteristics under ocean conditions. Ann Nucl Energy 128:300–308

    Article  Google Scholar 

  15. Han T (2015) Development of a sensitivity and uncertainty analysis code for high temperature gas-cooled reactor physics based on the generalized perturbation theory. Ann Nucl Energy 85:501–511

    Article  Google Scholar 

  16. Mawardi A, Pitchumani R (2008) Numerical simulations of an optical Fiber drawing process under uncertainty. J Lightwave Technol 26(5):580–587

    Article  Google Scholar 

  17. Acquah C, Datskov I, Mawardi A et al (2006) Optimization of an optical Fiber drawing process under uncertainty. Ind Eng Chem Res 45(25):8475–8483

    Article  Google Scholar 

  18. Hao P, Zhang Y, Frank PF (2013) Uncertainty analysis of solid-liquid-vapor phase change of a metal particle subject to nanosecond laser heating. Ind Eng Chem Res 135:1–11

    Google Scholar 

  19. Mawardi A, Pitchumani R (2006) Effect of parameter uncertainty on the performance variability of proton exchange membrane (PEM) fuel cells. J Power Sources 160(1):232–245

    Article  Google Scholar 

  20. Mawardi A, Pitchumani R (2004) Cure cycle Design for Thermosetting-Matrix Composites Fabrication under Uncertainty. Ann Oper Res 132(1–4):19–45

    Article  Google Scholar 

  21. Launder BE, Spalding DB (1974) The numerical computation of turbulent flow. Comput Method Appl M 3:269–289

    Article  Google Scholar 

  22. Du F, Lu Z, Wang S et al (2016) Analytical and experimental on the flow rule of molten pool for twin roll strip casting. Iron Steel 51(1):60–64 (in Chinese)

    Google Scholar 

  23. Lei H, Ceng D, He J (2009) A continuum model of solidification and inclusion collision-growth in the slab continuous casting caster. Trans Iron Steel I JPN 49(10):1575–1582

    Article  Google Scholar 

  24. Liu L, Liao B, Guo J et al (2014) 3D numerical simulation on thermal flow coupling field of stainless steel during twin-roll casting. J Mater Eng Perform 23(1):39–48

    Article  Google Scholar 

  25. Bae J, Kang C, Kang S (2007) Mathematical model for the twin roll type strip continuous casting of magnesium alloy considering thermal flow phenomena. J Mater Process Tech 191(1):251–255

    Article  Google Scholar 

  26. Wen G, Zhu M, Wang JY et al (2001) Physical simulation on level fluctuation and Fluidmixing in melt Pool of twin roll strip caster. Res Iron Steel 123:21–26 (in Chinese)

    Google Scholar 

  27. Pantula PD, Miriyala SS, Mitra K (2016) KERNEL: enabler to build smart surrogates for online optimization and knowledge discovery. Mater Manuf Process 32(10):1162–1171

    Article  Google Scholar 

  28. Miriyala SS, Mittal P, Majumdar S et al (2016) Comparative study of surrogate approaches while optimizing computationally expensive reaction networks. Chem Eng Sci 140:44–61

    Article  Google Scholar 

  29. Katocha S, Sehgalb R, Singhc V et al (2019) Improvement of tribological behavior of H-13 steel by optimizing the cryogenic-treatment process using evolutionary algorithms. https://doi.org/10.1016/j.triboint.2019.105895

  30. Ray S, Lalman JA (2011) Using the box–Benkhen design (BBD) to minimize the diameter of electrospun titanium dioxide nanofibers. Chem Eng J 169:116–125

    Article  Google Scholar 

  31. Miriyala SS, Subramanian VR, Mitra K (2018) TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study. Eur J Oper Res 264(1):294–309

    Article  MathSciNet  Google Scholar 

  32. Pantula PD, Mitra K (2019) A data-driven approach towards finding closer estimates of optimal solutions under uncertainty for an energy efficient steel casting process. Energy 189:1–14

    Article  Google Scholar 

  33. Miriyala SS, Mitra K (2019) Multi-objective optimization of iron ore induration process using optimal neural networks. Mater Manuf Process. https://doi.org/10.1080/10426914.2019.1643476

  34. Zhong W, Qiao C, Peng X et al (2019) Operation optimization of hydrocracking process based on Kriging surrogate mod. Control Eng Pract 85:34–40

    Article  Google Scholar 

  35. Baranitharan P, Ramesh K, Sakthivel R (2019) Measurement of performance and emission distinctiveness of Aegle marmelos seed cake pyrolysis oil/diesel/TBHQ opus powered in a DI diesel engine using ANN and RSM. Measurement 144:366–380

    Article  Google Scholar 

  36. Sheikholeslami R, Razavi S (2017) Progressive Latin hypercube sampling: an efficient approach for robust sampling-based analysis of environmental models. Environ Model Softw 93:109–126

    Article  Google Scholar 

  37. Goda T, Sato K (2014) History matching with iterative Latin hypercube samplings and parameterization of reservoir heterogeneity. J Pet Sci Eng 114:61–73

    Article  Google Scholar 

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Funding

This work was supported by the Natural Science Foundation of Shandong Province of China [Grant Numbers ZR2017MEE036 and ZR2017BEM003]; Zibo City School City Integration Development Project [Grant Number 2017ZBXC205]; and National Natural Science Foundation of China [Grant Number 51904179].

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Correspondence to Guangming Zhu.

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Yue, B., Zhu, G., Cao, X. et al. Uncertainty analysis of the influence of delivery system nozzle structure on fluid-thermal coupling in casting molten Pool. Int J Mater Form 14, 593–605 (2021). https://doi.org/10.1007/s12289-020-01549-w

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  • DOI: https://doi.org/10.1007/s12289-020-01549-w

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