journal article·2021-07

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

Bowen Yue, Guangming Zhu, Xiaolin Cao, Song Qiao, Nana Guo, Yukun An

Publisher page

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.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Metallurgical Processes and Thermodynamics · Metallurgy and Material Forming · Aluminum Alloy Microstructure Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (37)

Show source-provided 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 136:23–3310.1016/j.matdes.2017.09.051 ↗
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 28:2460–246910.1016/S1003-6326(18)64892-7 ↗
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 747:185–19610.1016/j.msea.2019.01.054 ↗
4. Ge S, Isac M, Lawrence GRI (2012) Progress of strip casting technology for steel historical developments. Iron Steel I JPN 52(12):2109–212210.2355/isijinternational.52.2109 ↗
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):1334–135010.1007/s11663-019-01527-x ↗
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)
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):3811–382210.1016/S0017-9310(00)00013-2 ↗
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)
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 693:761–76610.4028/www.scientific.net/KEM.693.761 ↗
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)
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):75–8010.1179/030192307X231856 ↗
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):3953–396010.1007/s10853-012-6246-0 ↗
13. Muldrew SI, Lux H, Menon V et al (2019) Uncertainty analysis of an SST-2 fusion reactor design. Fusion Eng Des 146:353–35610.1016/j.fusengdes.2018.12.066 ↗
14. Wang C, Peng M, Cong T et al (2019) Uncertainty analysis on natural circulation characteristics under ocean conditions. Ann Nucl Energy 128:300–30810.1016/j.anucene.2019.01.027 ↗
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 85:501–51110.1016/j.anucene.2015.06.005 ↗
16. Mawardi A, Pitchumani R (2008) Numerical simulations of an optical Fiber drawing process under uncertainty. J Lightwave Technol 26(5):580–58710.1109/JLT.2007.912527 ↗
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):8475–848310.1021/ie060045u ↗
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
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):232–24510.1016/j.jpowsour.2006.01.017 ↗
20. Mawardi A, Pitchumani R (2004) Cure cycle Design for Thermosetting-Matrix Composites Fabrication under Uncertainty. Ann Oper Res 132(1–4):19–4510.1023/B:ANOR.0000045275.81357.ca ↗
21. Launder BE, Spalding DB (1974) The numerical computation of turbulent flow. Comput Method Appl M 3:269–28910.1016/0045-7825(74)90029-2 ↗
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)
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):1575–158210.2355/isijinternational.49.1575 ↗
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):39–4810.1007/s11665-013-0749-y ↗
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):251–25510.1016/j.jmatprotec.2007.03.058 ↗
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)
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):1162–117110.1080/10426914.2016.1269918 ↗
28. Miriyala SS, Mittal P, Majumdar S et al (2016) Comparative study of surrogate approaches while optimizing computationally expensive reaction networks. Chem Eng Sci 140:44–6110.1016/j.ces.2015.09.030 ↗
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.2019.10589510.1016/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 169:116–12510.1016/j.cej.2011.02.061 ↗
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):294–30910.1016/j.ejor.2017.05.026 ↗
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 189:1–1410.1016/j.energy.2019.116253 ↗
33. Miriyala SS, Mitra K (2019) Multi-objective optimization of iron ore induration process using optimal neural networks. Mater Manuf Process. https://doi.org/10.1080/10426914.2019.164347610.1080/10426914.2019.1643476 ↗
34. Zhong W, Qiao C, Peng X et al (2019) Operation optimization of hydrocracking process based on Kriging surrogate mod. Control Eng Pract 85:34–4010.1016/j.conengprac.2019.01.001 ↗
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 144:366–38010.1016/j.measurement.2019.05.037 ↗
36. Sheikholeslami R, Razavi S (2017) Progressive Latin hypercube sampling: an efficient approach for robust sampling-based analysis of environmental models. Environ Model Softw 93:109–12610.1016/j.envsoft.2017.03.010 ↗
37. Goda T, Sato K (2014) History matching with iterative Latin hypercube samplings and parameterization of reservoir heterogeneity. J Pet Sci Eng 114:61–7310.1016/j.petrol.2014.01.009 ↗