Skip to main content
Log in

Determination of Time-Spatial Varying Mold Heat Flux During Continuous Casting from Fast Response Thermocouples

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

Abstract

To ensure accurate estimation of mold heat flux, this study investigated the impacts of the thermocouples placement inserted in the mold wall, the temperature sampling rate (fs), and the noise level of temperature data on the precision of two-dimensional Inverse Heat Conduction Problem (2DIHCP). The results showed the accuracy of heat flux estimations decreases as the distance between the thermocouple and the mold surface increases, and it is recommended that the distance should not exceed 3 mm. The accuracy of the heat flux initially increases as fs increases from 5 to 10 Hz, reaches a relatively stable state as fs increases from 10 to 60 Hz, and eventually decreases as fs increases from 60 to 100 Hz. Additionally, higher temperature measurement errors typically lead to decreased accuracy in inverse analysis. 2DIHCP was employed to compute the heat flux for a mold simulator experiment, and the results demonstrated its effectiveness in reconstructing the mold heat flux at the meniscus level during the time lapse of a mold oscillation cycle.

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
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. R.B. Mahapatra, J.K. Brimacombe, and I.V. Samarasekera: Metall. Mater. Trans. B, 1991, vol. 22B, pp. 875–88.

    CAS  Google Scholar 

  2. X. Zhang, W. Chen, Y. Ren, and L. Zhang: Metall. Mater. Trans. B, 2019, vol. 50B, pp. 1444–60.

    Google Scholar 

  3. H. Mizukami, Y. Shirai, and S. Hiraki: ISIJ Int., 2020, vol. 60(9), pp. 1968–77.

    CAS  Google Scholar 

  4. J. Yang, Z. Cai, D. Chen, and M. Zhu: Metall. Mater. Trans. B, 2019, vol. 50B, pp. 1104–13.

    Google Scholar 

  5. J. Ji, Y. Cui, X. Zhang, Q. Wang, S. He, and Q. Wang: Steel Research International, 2021, vol. 92(10), p. 2100101.

  6. P.E.R. Lopez, K.C. Mills, P.D. Lee, and B. Santillana: Metall. Mater. Trans. B, 2012, vol. 43B(1), pp. 109–22.

    Google Scholar 

  7. J.K. Brimacombe: Can. Metall. Q., 1976, vol. 15(2), pp. 163–75.

    Google Scholar 

  8. J.A. Kromhout, E.R. Dekker, M. Kawamoto, and R. Boom: Ironmak. Steelmak., 2013, vol. 40(3), pp. 206–15.

    CAS  Google Scholar 

  9. R.J. O’Malley: Steelmak. Conf. Proc., 1999, vol. 82, pp. 13–34.

    Google Scholar 

  10. H. Zhang, W. Wang, F. Ma, and L. Zhou: Metall. Mater. Trans. B, 2015, vol. 46B(5), pp. 2361–73.

    Google Scholar 

  11. A. Badri, T.T. Natarajan, C.C. Snyder, K.D. Powers, F.J. Mannion, M. Byrne, and A.W. Cramb: Metall. Mater. Trans. B, 2005, vol. 36B(3), pp. 373–83.

    CAS  Google Scholar 

  12. H. Zhang and W. Wang: Metall. Mater. Trans. B, 2016, vol. 47B(2), pp. 920–31.

    Google Scholar 

  13. C.E. Shannon: Proc. IRE, 1949, vol. 37(1), pp. 10–21.

    Google Scholar 

  14. M. Roman, D. Balogun, C. Zhu, L. Bartlett, R.J. O’Malley, R.E. Gerald, and J. Huang: IEEE Trans. Instrum. Meas., 2021, vol. 70, pp. 1–10.

    Google Scholar 

  15. J.V. Beck, B. Blackwell, and C.R. Clair Jr: Inverse Heat Conduction: Ill-Posed Problems, James Beck, 1985, pp. 1-300.

  16. G. Blanc, M. Raynaud, and T.H. Chau: Revue générale de thermique, 1998, vol. 37(1), pp. 17–30.

    CAS  Google Scholar 

  17. Y. Liu, Z. Xu, X. Wang, and D. Zhang: Ironmak. Steelmak., 2021, vol. 48(8), pp. 901–08.

    CAS  Google Scholar 

  18. M.O. Ansari, J. Ghose, S. Chattopadhyaya, D. Ghosh, S. Sharma, P. Sharma, A. Kumar, C. Li, R. Singh, and S.M. Eldin: Micromachines, 2022, vol. 13(12), p. 2148.

  19. A.V.S. Oliveira, A. Avrit, and M. Gradeck: Int. J. Heat Mass Transf., 2022, vol. 185, 122398.

    CAS  Google Scholar 

  20. D. Balogun, M. Roman, R.E. Gerald, L. Bartlett, J. Huang, and R. O’Malley: Metall. Mater. Trans. B, 2023, vol. 54B(3), pp. 1326–41.

    Google Scholar 

  21. M.N. Özisik and H.R. Orlande: Inverse Heat Transfer: Fundamentals and Applications, CRC Press, Boca Raton, 2021, pp. 3–111.

    Google Scholar 

  22. A.S. Vaka, S. Ganguly, and P. Talukdar: Int. J. Therm. Sci., 2021, vol. 160, 106648.

    Google Scholar 

  23. C.A.M. Pinheiro, I.V. Samarasekera, J.K. Brimacomb, and B.N. Walker: Ironmak. Steelmak., 2000, vol. 27(1), pp. 37–54.

    CAS  Google Scholar 

  24. B.G. Thomas, M.A. Wells, and D. Li: Sensors, Sampling, and Simulation for Process Control, 2011, pp. 119-26.

  25. X. Wang, L. Tang, X. Zang, and M. Yao: J. Mater. Process. Technol., 2012, vol. 212, pp. 1811–18.

    Google Scholar 

  26. P. Hu, X. Wang, J. Wei, M. Yao, and Q. Guo: ISIJ Int., 2018, vol. 58, pp. 892–98.

    CAS  Google Scholar 

  27. E.N. Dvorkin, M.A. Cavaliere, and M.B. Goldschmit: Comput. Struct., 2003, vol. 81, pp. 559–73.

    Google Scholar 

  28. M. Gonzalez, M.B. Goldschmit, A.P. Assanelli, E.N. Dvorkin, and E.F. Berdaguer: Metall. Mater. Trans. B, 2003, vol. 34B, pp. 455–73.

    CAS  Google Scholar 

  29. S. Chakraborty, S. Ganguly, E.Z. Chacko, S.K. Ajmani, and P. Talukdar: Int. J. Therm. Sci., 2017, vol. 118, pp. 435–47.

    Google Scholar 

  30. H. Zhang, W. Wang, and L. Zhou: Metall. Mater. Trans. B, 2015, vol. 46B(5), pp. 2137–52.

    Google Scholar 

  31. P. Jayakrishna, S. Chakraborty, S. Ganguly, and P. Talukdar: Can. Metall. Q., 2021, vol. 60(4), pp. 320–49.

    CAS  Google Scholar 

  32. T. Rees, H.S. Dollar, and A.J. Wathen: SIAM J. Sci. Comput., 2010, vol. 32(1), pp. 271–98.

    Google Scholar 

  33. Y. Yu and X. Luo: Int. J. Heat Mass Transf., 2015, vol. 90, pp. 645–53.

    Google Scholar 

  34. I. Nowak, J. Smolka, and A.J. Nowak: Appl. Therm. Eng., 2010, vol. 30(10), pp. 1140–51.

    CAS  Google Scholar 

  35. M. Cui, Y. Zhao, B. Xu, and X.W. Gao: Int. J. Heat Mass Transf., 2017, vol. 107, pp. 747–54.

    Google Scholar 

  36. B. Zhang, J. Mei, M. Cui, X.W. Gao, and Y. Zhang: Int. J. Heat Mass Transf., 2019, vol. 140, pp. 909–17.

    Google Scholar 

  37. Y. Yu and X. Luo: Appl. Therm. Eng., 2017, vol. 114, pp. 36–43.

    Google Scholar 

  38. Y. Li, G. Wang, and H. Chen: Appl. Therm. Eng., 2015, vol. 80, pp. 396–403.

    Google Scholar 

  39. B.A. Tourn, J.C. Hostos, and V.D. Fachinotti: Int. Commun. Heat Mass Transf., 2023, vol. 142, 106647.

    Google Scholar 

  40. Y. Zeng, H. Wang, S. Zhang, Y. Cai, and E. Li: Int. J. Heat Mass Transf., 2019, vol. 134, pp. 185–97.

    Google Scholar 

  41. G. Wang, S. Wan, H. Chen, K. Wang, and C. Lv: J. Heat Transf. 2018, vol. 140(12), pp. 122301.

  42. Y. Li, H. Wang, and X. Deng: Int. J. Heat Mass Transf., 2019, vol. 134, pp. 656–67.

    Google Scholar 

  43. F. Zhu, J. Chen, Y. Han, and D. Ren: Int. J. Heat Mass Transf., 2022, vol. 194, 123089.

    Google Scholar 

  44. B.A. Tourn, J.C. Hostos, and V.D. Fachinotti: Int. Commun. Heat Mass Transf., 2021, vol. 127, 105488.

    Google Scholar 

  45. J.V. Beck: Int. J. Heat Mass Transf., 1970, vol. 13, pp. 703–16.

    Google Scholar 

  46. C.U. Ahn, C. Park, D.I. Park, and J.G. Kim: Int. J. Heat Mass Transf., 2022, vol. 183, 122076.

    Google Scholar 

  47. K. Babu and T.S.P. Kumar: Metall. Mater. Trans. B, 2010, vol. 41B, pp. 214–24.

    CAS  Google Scholar 

  48. B.A. Tourn, J.C. Hostos, and V.D. Fachinotti: Int. Commun. Heat Mass Transf., 2021, vol. 125, 105330.

    Google Scholar 

  49. A. Badri, T.T. Natarajan, C.C. Snyder, K.D. Powers, F.J. Mannion, and A.W. Cramb: Metall. Mater. Trans. B, 2005, vol. 36B(3), pp. 355–71.

    CAS  Google Scholar 

  50. X. Xie, D. Chen, H. Long, M. Long, and K. Lv: Metall. Mater. Trans. B, 2014, vol. 45B(6), pp. 2442–52.

    Google Scholar 

  51. P.C. Hansen: Numerical Algorithms, 1994, vol. 6(1), pp. 1–35.

    Google Scholar 

  52. H.R. Orlande, O. Fudym, D. Maillet, and R.M. Cotta: Thermal Measurements and Inverse Techniques, CRC Press, Boca Raton, 2011, pp. 233–619.

    Google Scholar 

  53. L. Olson and R. Throne: Inverse Prob. Eng., 2000, vol. 8(3), pp. 193–227.

    Google Scholar 

  54. R.C. Aster, B. Borchers, and C.H. Thurber: Parameter Estimation and Inverse Problems, Elsevier, New York, 2018, pp. 93–252.

    Google Scholar 

  55. K.A. Woodbury and J.V. Beck: Int. J. Heat Mass Transf., 2013, vol. 62, pp. 31–39.

    Google Scholar 

  56. C.R. Vogel: Computational Methods for Inverse Problems, Society for Industrial and Applied Mathematics, Philadelphia, 2002, pp. 97–124.

    Google Scholar 

  57. M.N. Özisik: Heat Conduction, 2nd ed. Wiley, New York, 1993, pp. 62–75.

    Google Scholar 

  58. S. Khajehpour, M.R. Hematiyan, and L. Marin: Int. J. Heat Mass Transf., 2013, vol. 58(1–2), pp. 125–34.

    Google Scholar 

  59. H. Zhang and W. Wang: Metall. Mater. Trans. B, 2017, vol. 48B(2), pp. 779–93.

    Google Scholar 

  60. W. Wang, X. Long, H. Zhang, and P. Lyu: ISIJ Int., 2018, vol. 58(9), pp. 1695–704.

    CAS  Google Scholar 

  61. A. Kamaraj, S. Tripathy, G. Chalavadi, P.P. Sahoo, and S. Misra: Steel Res. Int., 2022, vol. 93(3), p. 2100121

  62. A.S. Jonayat and B.G. Thomas: Metall. Mater. Trans. B, 2014, vol. 45B, pp. 1842–64.

    Google Scholar 

  63. A. Matsushita, K. Isogami, M. Takeyoshi, T. Ninomi, and K. Tsutsumi: Trans. Iron Steel Inst. Jpn., 1988, vol. 28, pp. 531–34.

    Google Scholar 

  64. E.Y. Ko, J. Choi, J.Y. Park, and I. Sohn: Met. Mater. Int., 2014, vol. 20, pp. 141–51.

    CAS  Google Scholar 

  65. J.Y. Park, E.Y. Ko, J. Choi, and I. Sohn: Met. Mater. Int., 2014, vol. 20, pp. 1103–14.

    CAS  Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge Mr. Xiong Yan at the School of Metallurgy and Environment, Central South University, China, for his help in interpreting the significance of the variation of heat flux in this study. The financial supports from NSFC (No. 52074135), Jiangxi Provincial Natural Science Foundation (No. 20224ACB214011), and Youth Jinggang Scholars Program in Jiangxi Province (QNJG2020049) are greatly acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pengcheng Xiao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Appendix A: Sensitivity Coefficient Matrix

Appendix A: Sensitivity Coefficient Matrix

\({\mathbf{J}}_{{\text{j}}}\) is called as the M × N sensitivity coefficient matrix at time tj and is defined as follows:

$$ {\mathbf{J}}_{j} = \left[ {\frac{{\partial {\mathbf{T}}({\mathbf{q}}^{{\text{j}}} )}}{{\partial {\mathbf{q}}^{{\text{j}}} }}} \right]^{T} = \left( {\begin{array}{*{20}c} {J_{1,1}^{j} } & {J_{1,2}^{j} } & \cdots & {J_{1,N}^{j} } \\ {J_{2,1}^{j} } & {J_{2,2}^{j} } & \cdots & {J_{2,N}^{j} } \\ \vdots & \vdots & \ddots & \vdots \\ {J_{M,1}^{j} } & {J_{M,2}^{j} } & \cdots & {J_{M,N}^{j} } \\ \end{array} } \right),\;{\text{where}}\;J_{m,n}^{j} = \frac{{\partial T_{m}^{j} }}{{\partial q_{n}^{j} }} $$
(A1)

\(J_{m,n}^{j}\) represents the temperature rise at the sensor location (xm, ym) in response to a unit step change in the heat flux at point (xn, yn) on boundaries Γ1, Γ2, and Γ3, at time tj. To obtain the sensitivity coefficient problem, the partial derivative of Eqs. [2b] through [2e] is taken with respect to a heat flux component \(q_{n}^{j}\), (Eq. [A1]), which yields the governing sensitivity coefficient problem.

$$\frac{\partial J}{\partial t}=\frac{\partial^2J}{\partial x^2}+\frac{\partial^2J}{\partial y^2},0<t\leq t_r\,\text{in}\,\Omega=[0,W]\times[0,H]$$
(A2a)
$$ \left. { - \frac{{\partial J}}{{\partial \varvec{n}}}} \right|_{{\Gamma _{{1,}} \cup \Gamma _{2} \cup \Gamma _{3} }} = \left\{ \begin{gathered} \begin{array}{*{20}l} {1,} & {{\text{(}}x,y) = (x_{n} ,y_{n} {\text{)}}} \\ \end{array} \hfill \\ \begin{array}{*{20}l} {0,} & {{\text{others}}} \\ \end{array} \hfill \\ \end{gathered} \right. $$
(A2b)
$$ \left. J \right|_{{_{{\Gamma_{4} }} }} = 0 $$
(A2c)
$$ J(x,y,0) = 0 $$
(A2d)

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

Zhang, H., Xiao, P. Determination of Time-Spatial Varying Mold Heat Flux During Continuous Casting from Fast Response Thermocouples. Metall Mater Trans B 54, 3462–3484 (2023). https://doi.org/10.1007/s11663-023-02925-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11663-023-02925-y

Navigation