Nonintrusive Uncertainty Quantification in the Simulation of Steel Reheating Using Polynomial Chaos Expansion

Abstract

Uncertainty quantification (UQ) is deemed critical in steel reheating simulations due to the significant input uncertainties arising when defining steel surface properties and atmospheric furnace conditions. In order to conduct UQ, the study utilizes polynomial chaos expansion, which has been found to significantly curtail the computational effort needed to obtain reliable convergent statistics for the model of interest. Results from a comprehensive UQ analysis of a walking‐beam reheat furnace simulated using Tata Steel's reheat furnace control model, online slab temperature calculation, are presented. Slab temperature evolution and oxide scale growth are chosen as the study's QoIs. The analysis reveals that at the earlier stages of reheating, the majority of the output variance in slab temperature can be traced back exclusively to the emissivity of the slab surface, and the majority of the output variance in oxide scale growth is traced back to the combination of slab's surface emissivity and the initial scale thickness found on steel products prior to reheating. However, as the steel product advances toward the furnace's discharge end, inputs related to oxide scale growth become increasingly important, ultimately becoming the most influential input parameters, although the dynamics of this transition differ between the QoIs.