| Abstract Scope |
Continuous casting revolutionizes steel production, offering an efficient method to meet surging demands in construction, automotive, and infrastructure. However, solute segregation during this process, driven by differences in solubilities and densities, introduces chemical imbalances, compromising steel quality with non-uniform properties and potential cracks. Micro-segregation is inherent, while macro-segregation induces casting defects. Computational fluid dynamics (CFD), coupled with solidification and chemical segregation models (focused on carbon), becomes pivotal in observing and mitigating macro-segregation in diverse steel grades. Using CFD, this study mainly investigates macro-segregation of carbon in different steel compositions. Segregation of three different steel compositions is compared using a dimensionless segregation parameter. The simulation results show that low carbon steel, which has lower partition coefficient, exhibits a higher degree of segregation compared to medium carbon steels. |