A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation

TL;DR

This paper introduces a novel, computationally efficient data assimilation method for real-time heat flux estimation in continuous casting molds, outperforming traditional techniques in accuracy and noise robustness.

Contribution

The paper presents a new parameterization-based inverse problem solution that enables real-time heat flux estimation with improved performance and computational efficiency.

Findings

Parameterization method outperforms Alifanov's regularization in accuracy

Method is robust against measurement noise

Suitable for real-time industrial applications

Abstract

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.