CFD-Derived Regression Model to Predict Surface Velocity for a Continuous-Casting Round Billet Mold

Area of Science:

• Materials Science and Engineering
• Fluid Dynamics
• Computational Modeling

Background:

• Molten steel flow velocity at the mold surface critically impacts continuous casting quality, affecting flux melting, slag entrainment, and heat transfer uniformity.
• Accurate prediction of surface flow velocity is essential for minimizing defects and ensuring high-quality continuous casting.

Purpose of the Study:

• To develop a rapid and accurate predictive model for molten steel surface velocity in continuous casting molds.
• To establish a regression model based on Computational Fluid Dynamics (CFD) simulations for predicting maximum surface velocity and high-velocity range.

Main Methods:

• A three-dimensional electromagnetic-fluid dynamic coupled model was established and validated.
• Simulations were performed under various casting speeds, Electromagnetic Stirring (EMS) parameters (current, frequency), and Submerged Entry Nozzle (SEN) immersion depths.
• Regression analysis was used to derive predictive formulas for surface velocity characteristics.

Main Results:

• A CFD-derived regression model predicts maximum mold surface velocity and high-velocity range within milliseconds with <2% relative error.
• Maximum surface velocity increases with casting speed and EMS current, but decreases with EMS frequency.
• SEN immersion depth showed minimal impact on surface velocity and high-velocity range.

Conclusions:

• The developed regression model offers a reliable and fast method for predicting critical molten steel flow parameters in continuous casting.
• This model supports rapid mold design, slag entrainment risk assessment, breakout prediction, and intelligent continuous casting operations.