Uncertainty Quantification in Modeling Mold Heat Transfer in Steel Continuous Slab Casting with CON1D

Computational models are powerful tools to quantify physical phenomena to gain valuable insights into a manufacturing process. Their accuracy is hindered, however, by uncertainty in the input data. Furthermore, when calibrating models with plant measurements, it helps to understand which variables have greatest effect on the critical model outputs. This work applies uncertainty quantification and sensitivity analysis to determine the most influential input parameters in the CON1D model of heat transfer and solidification in steel continuous casting with slag. Results show that the slag rim greatly affects heat flux near the meniscus, so control of its size is important. Heat flux and temperature down the mold depend greatly on velocity of the solid slag layer, and slag solidification temperature, which control the slag layer thickness, which in turn affects the interfacial resistance that controls heat transfer in the process. Scale formation on the mold coldface greatly increases mold temperatures. Based on the results presented here, models of heat transfer in continuous casting such as CON1D would benefit from plant measurements such as slag rim size and solid slag velocity, and lab measurements such as slag viscosity at lower temperatures, to better characterize this important slag property. The accuracy of computational models depends on uncertainty of the input data. This work conducts a sensitivity analysis of the CON1D model of solidification heat transfer in a steel continuous slab-casting mold. Results identify the parameters which are critical to defect-relevant outputs: mold heat flux, shell thickness, surface temperature, and mold wall temperatures. Uncertainty in these parameters reveal what is important for modelers to improve their predictions and for plant practitioners to improve their process.image (c) 2024 WILEY-VCH GmbH