A Combined Hybrid 3-D/2-D Model for Flow and Solidification Prediction during Slab Continuous Casting

Abstract

A combined hybrid 3-D/2-D simulation model was developed to investigate the flow and solidification phenomena in turbulent flow and laminar flow regions during slab continuous casting (CC). The 3-D coupling model and 2-D slicing model were applied to the turbulent flow and laminar flow regions, respectively. In the simulation model, the uneven distribution of cooling water in the width direction of the strand was taken into account according to the nozzle collocation of secondary cooling zones. The results from the 3-D turbulent flow region show that the impact effect of the molten steel jet on the formation of a solidification shell is significant. The impact point is 457 mm below the meniscus, and the plug flow is formed 2442 mm below the meniscus. In the laminar flow region, grid independence tests indicate that the grids with a cell size of 10 × 10 mm 2 are sufficient in simulations to attain the precise temperature distribution and solidification profile. The liquid core of the strand is not entirely uniform, and the solidification profile agrees well with the integrated distribution of cooling water in secondary cooling zones. The final solidification points are at a position of 400-500 mm in the width direction and are 17.66 m away from the meniscus.

Continuous Casting

“…Fourteen research articles have been published in this Special Issue of Metals. Twelve of these [1][2][3][4][5][6][7][8][9][10][11][12] relate to the continuous casting of steel, a general schematic for which is shown in Figure 1. As is evident from this figure, the overall process consists of a ladle and a tundish through which molten steel passes, a cooling mould region where solidification starts and at which electromagnetic stirring (EMS) may be applied, secondary cooling regions where water is sprayed on the solidified steel, a so-called strand electromagnetic stirrer, a further region at which final EMS is applied, and withdrawal rollers, by which point the steel has completely solidified.…”

“…Su et al [9] use machine-learning techniques for mold-level prediction by means of variational mode decomposition and support vector regression (VMD-SVR), whereas Cho and Thomas [1] review the literature on electromagnetic forces in continuous casting of steel slabs. Yin et al [10] consider modelling on inclusion motion and entrapment during full solidification in a curved billet caster, while Long et al [4] develop a combined hybrid 3-D/2-D model for flow and solidification prediction during slab continuous casting. Qin et al [6] perform an analysis of the influence of segmented rollers on slab bulge deformation, while Lei and Su [3] use machine learning in the research and application of a rolling gap prediction model.…”

On Modelling Parasitic Solidification Due to Heat Loss at Submerged Entry Nozzle Region of Continuous Casting Mold

“…A 2D multiphase model was used by Jonayat and Thomas to model thermal behavior and slag consumption in the meniscus region during an oscillation cycle [16]. Recently, a combined 3D and 2D hybrid model, verified by an experiment, was applied to simulate flow, solidification, and centerline segregation during slab CC [75,76]. Mitchell and Vynnycky conducted a detailed study that showed that by using asymptotic methods, key CC quantities can be recovered using a simple heat transfer model that considers turbulence and thermo-mechanics [77].…”

Numerical Development of Heat Transfer Coefficient Correlation for Spray Cooling in Continuous Casting

“…Some of the most recognized parameters that affect HTC include nozzle type, spray water flow rate, spray angle, standoff distance, steel surface temperature, spray direction, and nozzle-to-nozzle distance. In practice, researchers have correlated HTC with the most important operating parameters such as casting speed and spray water flow rate though small scale hot plate experiment (Nozaki et al, 1978;Morales et al, 1990;Horsky et al, 2005;Blazek et al, 2013;Long et al, 2018). The most well-recognized correlation was developed by Nozaki (Nozaki et al, 1978) and is shown in Eq.…”