RT Journal Article
T1 A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation
A1 Stabile, Giovanni
A1 Morelli, Umberto Emil
A1 Barral Rodiño, Patricia
A1 Quintela Estévez, Peregrina
A1 Rozza, Gianluigi
A1 Stabile, Giovanni
K1 Boundary condition estimation
K1 Continuous casting
K1 Data assimilation
K1 Heat transfer
K1 Inverse problem
K1 Real time
AB In the present work, we consider the industrial problem of estimating in real-time the mold-steel heat flux in continuous casting mold. We approach this problem by first considering the mold modeling problem (direct problem). Then, we plant the heat flux estimation problem as the inverse problem of estimating a Neumann boundary condition having as data pointwise temperature measurements in the interior of the mold domain. We also consider the case of having a total heat flux measurement together with the temperature measurements. We develop two methodologies for solving this inverse problem. The first one is the traditional Alifanov's regularization, the second one exploits the parameterization of the heat flux. We develop the latter method to have an offline–online decomposition with a computationally efficient online part to be performed in real-time. In the last part of this work, we test these methods on academic and industrial benchmarks. The results show that the parameterization method outclasses Alifanov's regularization both in performance and computational cost. Moreover, it proves to be robust with respect to the measurements noise. Finally, the tests confirm that the computational cost is suitable for real-time estimation of the heat flux
PB Wiley
YR 2021
FD 2021
LK http://hdl.handle.net/10347/26383
UL http://hdl.handle.net/10347/26383
LA eng
NO Morelli, UE, Barral, P, Quintela, P, Rozza, G, Stabile, G. A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation. Int J Numer Methods Eng. 2021; 1– 34. https://doi.org/10.1002/nme.6713
NO The authors would like to acknowledge the financial support of the European Union under the Marie Sklodowska-Curie Grant Agreement No. 765374. The authors also acknowledge the partial support by the Ministry of Economy, Industry and Competitiveness through the Plan Nacional de I+D+i (MTM2015-68275-R), by the Agencia Estatal de Investigacion through project [PID2019-105615RB-I00/ AEI / 10.13039/501100011033], by the European Union Funding for Research and Innovation - Horizon 2020 Program - in the framework of European Research Council Executive Agency: Consolidator Grant H2020 ERC CoG 2015 AROMA-CFD project 681447 “Advanced Reduced Order Methods with Applications in Computational Fluid Dynamics” and INDAM-GNCS project “Advanced intrusive and non-intrusive model order reduction techniques and applications”, 2019
DS Minerva
RD 18 abr 2026