RT Dissertation/Thesis
T1 Efficient computational strategies for the control process of continuous casting machines
A1 Morelli, Umberto Emil
K1 Inverse Problem
K1 Heat Transfer
K1 Continuous Casting
K1 Real-time
K1 Data Assimilation
K1 Condition Estimation
AB In continuous casting machineries, monitoring the mold is essential for thesafety and quality of the process. Then, the objective of this thesis is to develop mathematical tools for the real-timeestimation of the mold-steel heat flux which is the quantity of interest when controlling the mold behaviour. Weapproach this problem by first considering the mold modelling problem (direct problem). Then, we plant the heatflux estimation problem as the inverse problem of estimating a Neumann boundary condition having as datapointwise temperature measurements in the interior of the mold domain given by the thermocouples that are buriedinside the mold plates. In formulating the inverse problem, we consider both the steady and unsteady-state case.For the numerical solution of these problems, we develop several methodologies. We consider traditional methodssuch as Alifanov's regularization as well as novel methodologies that exploit the parametrization of the sought heatflux. We develop the latter methods to have an offline-online decomposition with a computationally efficient onlinepart. Moreover, in the unsteady-state case, we propose a novel, incremental, data-driven model order reductiontechnique to achieve the real-time performance of the online phase. Finally, we test all discussed methods onacademic and industrial benchmark cases. The results show that the proposed novel numerical tools outclasstraditional methods both in performance and computational cost. Moreover, they prove to be robust with respect tothe measurements noise and confirm that the computational cost is suitable for real-time estimation of the heat flux.
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29428
UL http://hdl.handle.net/10347/29428
LA eng
DS Minerva
RD 29 abr 2026