Modeling the dynamics of electromagnetically agitated metallurgical flows: levitation, cold crucible, aluminium electrolysis, etc.

We present practical modelling techniques for electromagnetically agitated liquid metal flows involving dynamic change of the fluid volume and shape during melting and the free surface oscillation. Typically the electromagnetic field is strongly coupled to the free surface dynamics and the heat-mass transfer. Accurate pseudo-spectral code and the k-omega turbulence model modified for complex and transitional flows with free surfaces are used for these simulations. The considered examples include magnetic suspension melting, induction scull remelting (cold crucible), levitation and aluminium electrolysis cells. The process control and the energy savings issues are analysed.

Investigation of the cold crucible melting process: experimental and numerical study

The dynamic process of melting different materials in a cold crucible is being studied experimentally with parallel numerical modelling work. The numerical simulation uses a variety of complementing models: finite volume, integral equation and pseudo-spectral methods combined to achieve the accurate description of the dynamic melting process. Results show the temperature history of the melting process with a comparison of the experimental and computed heat losses in the various parts of the equipment. The free surface visual observations are compared to the numerically predicted surface shapes.

Maximising heat transfer efficiency in the cold crucible induction melting process

Induction heating is an efficient method used to melt electrically conductive materials, particularly if melting takes place in a ceramic crucible. This form of melting is particularly good for alloys, as electromagnetic forces set up by the induction coil lead to vigorous stirring of the melt ensuring homogeneity and uniformity in temperature. However, for certain reactive alloys, or where high purity is required, ceramic crucibles cannot be used, but a water-cooled segmented copper crucible is employed instead. Water cooling prevents meltdown or distortion of the metal wall, but much of the energy goes into the coolant. To reduce this loss, the electromagnetic force generated by the coil is used to push the melt away from the walls and so minimise contact with water-cooled surfaces. Even then, heat is lost through the crucible base where contact is inevitable. In a collaborative programme between Greenwich and Birmingham Universities, computer modelling has been used in conjunction with experiments to improve the superheat attainable in the melt for a,number of alloys, especially for y-TiAl intermetallics to cast aeroengine turbine blades. The model solves the discretised form of the turbulent Navier-Stokes, thermal energy conservation and Maxwell equations using a Spectral Collocation technique. The time-varying melt envelope is followed explicitly during the computation using an adaptive mesh. This paper briefly describes the mathematical model used to represent the interaction between the magnetic field, fluid flow, heat transfer and change of phase in the crucible and identifies the proportions of energy used in the melt, lost in the crucible base and in the crucible walls. The role of turbulence is highlighted as important in controlling heat losses and turbulence damping is introduced as a means of improving superheat. Model validation is against experimental results and shows good agreement with measured temperatures and energy losses in the cooling fluid throughout the melting cycle.

Dynamic melting model for small samples in cold crucible

Purpose – A small size cold crucible offers possibilities for melting various electrically conducting materials with a minimal wall contact. Such small samples can be used for express contamination analysis, preparing limited amounts of reactive alloys or experimental material analyses. Aims to present a model to follow the melting process. Design/methodology/approach – The presents a numerical model in which different types of axisymmetric coil configurations are analysed. Findings – The presented numerical model permits dynamically to follow the melting process, the high-frequency magnetic field distribution change, the free surface and the melting front evolution, and the associated turbulent fluid dynamics. The partially solidified skin on the contact to the cold crucible walls and bottom is dynamically predicted. The segmented crucible shape is either cylindrical, hemispherical or arbitrary shaped. Originality/value – The model presented within the paper permits the analysis of melting times, melt shapes, electrical efficiency and particle tracks.

Dynamic model for metal cleanness evaluation by melting in a cold crucible

Melting of metallic samples in a cold crucible causes inclusions to concentrate on the surface owing to the action of the electromagnetic force in the skin layer. This process is dynamic, involving the melting stage, then quasi-stationary particle separation, and finally the solidification in the cold crucible. The proposed modeling technique is based on the pseudospectral solution method for coupled turbulent fluid flow, thermal and electromagnetic fields within the time varying fluid volume contained by the free surface, and partially the solid crucible wall. The model uses two methods for particle tracking: (1) a direct Lagrangian particle path computation and (2) a drifting concentration model. Lagrangian tracking is implemented for arbitrary unsteady flow. A specific numerical time integration scheme is implemented using implicit advancement that permits relatively large time-steps in the Lagrangian model. The drifting concentration model is based on a local equilibrium drift velocity assumption. Both methods are compared and demonstrated to give qualitatively similar results for stationary flow situations. The particular results presented are obtained for iron alloys. Small size particles of the order of 1 μm are shown to be less prone to separation by electromagnetic field action. In contrast, larger particles, 10 to 100 μm, are easily “trapped” by the electromagnetic field and stay on the sample surface at predetermined locations depending on their size and properties. The model allows optimization for melting power, geometry, and solidification rate.