Metals Processing Simulation Laboratory

The Metals Processing Simulation Laboratory is the computational home of the Continuous Casting Center, based in the Department of Mechanical Engineering at the Colorado School of Mines and directed by Professor Brian G. Thomas.

Purpose

The laboratory, combined with the expertise of our research group, supports the simulation of fluid flow, heat transfer, solidification, and stress generation across a range of metallurgical processes — and especially the continuous casting of steel. Models are built to be fast and practical enough for plant engineers to use, while remaining rigorous enough to capture the underlying physics of the mold region and the solidifying strand.

Computational facilities

The CCC operates two computational facilities — in Brown Hall (W470-I) at the Colorado School of Mines and in 345 MEB at the University of Illinois — that together house more than 20 computer workstations. The group has dedicated access to the “Mio” high-performance computing system at Mines, along with additional supercomputing resources for the most demanding finite-element and computational fluid dynamics (CFD) simulations.

Day-to-day research combines the Center’s in-house codes with commercial packages, principally ABAQUS (nonlinear thermal-stress and mold distortion, via custom user subroutines) and ANSYS Fluent (multiphase flow and heat transfer in the nozzle and mold, via custom user-defined functions).

In-house software

Much of the Center’s work is carried out with simulation codes developed and maintained in-house over more than three decades. Member companies can obtain these codes from the downloads area.

• CON1D

  1-D transient heat-transfer and solidification model of the shell, the mold/shell interfacial gap, and secondary cooling. Calibrates to plant data and predicts strand temperature, optimal mold taper, crack danger, and defect locations. Variants: ROUND1D (billets) and STRIP1D (thin strip).

• CONONLINE

  Real-time online system that runs hundreds of coupled CON1D instances to track temperature, shell-thickness profile, and metallurgical length live at the caster pulpit, with optional closed-loop spray-cooling control. In production use at Nucor Steel Decatur.

• PFSR

  Steady-state model of pressure and flow from tundish through the nozzle to the mold for stopper-rod flow control; used to study nozzle geometry and clogging effects on flow, aspiration, and reoxidation.

• PFSG

  Companion to PFSR for slide-gate flow control: steady-state nozzle pressure and flow analysis for studying nozzle-geometry and clogging effects.

• EQPRECIP

  Thermodynamics calculator predicting equilibrium precipitate phases and amounts in plain low-carbon steels; editable for calibration and usable as a subroutine within other CCC models.

• MIX1D

  1-D transient model of grade-change intermixing in the tundish and strand; predicts through-thickness and along-strand composition distribution in blooms and slabs.

• CON2D

  2-D transient thermal-stress analysis of the solidifying shell using an elastic-plastic-creep constitutive model, coupled with CON1D output, for taper, strand-shape, and crack-formation studies.

• SCHOPPY

  Heuristic schedule optimizer (in Python) for coil-based casting and rolling schedules; minimizes intermixing, trim-loss, and cobble risk under roll-wear, quality, and efficiency constraints.

Materials evaluation facilities

The Advanced Steel Processing and Products Research Center (ASPPRC) at the Colorado School of Mines, together with the Materials Research Laboratory at the University of Illinois, provide microstructure evaluation equipment — including SEM, EBSD, X-ray diffraction, and TEM — along with machine shop and metallography services for sample preparation.

Experimental validation

A 0.6-scale transparent water model of a continuous casting machine — including tundish, stopper-rod flow control, and inlet nozzle — is operated at the Colorado School of Mines. Physical water models and production-caster testing are also available through CCC member facilities, and the resulting measurements supply input conditions, calibration data, and benchmarks used to validate the models and to test proposed process improvements.