Numerical Modelling of Inclusion Behaviour in a Gas-stirred Ladle

Felice, Valerio De; Daoud, Ismael Lis Alves; Dussoubs, Bernard; Jardy, Alain; Bellot, Jean‐Pierre

doi:10.2355/isijinternational.52.1273

Cited by 34 publications

(27 citation statements)

References 24 publications

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“…Some researchers [22][23][24] have reported the effect of foam slag and injected gas on the decarburization and melt flow in Electric Arc Furnace. However, others [25][26][27] investigated the bottom gas stirring using numerical and physical models. In particular, Kumar 28) predicted the onset of droplet formation, and the critical gas flow rate required for droplet.…”

Section: Introductionmentioning

confidence: 99%

Computational Simulation Model for Metallurgical Effects during EAF Refining Stage: Waiting and Arcing Time

Elkoumy¹,

El-Anwar

Fathy³

et al. 2018

ISIJ International

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3D computational fluid dynamics models using Fluent were developed to investigate the steel melt flow during waiting and arcing time. Both models were transient that analyzed 60 seconds to investigate the flow characteristics considering variation in steel melt thermo-physical parameters and operating conditions. The velocity of melt movement was high enough to make a turbulent flow (solved with realize k-ε turbulence model). It was found that the steel melt flow velocity increases by a combined effect of the steel melt temperature and composition, and slag pressure. The slag pressure increases by a double effect of slag density and height, and the steel melt fluid flow velocity changes with the slag pressure. The effect of the slag thickness is more significant than the effect of thermo-physical properties of steel melt. Although, the maximum steel melt velocity "during arcing time" may be as large as 0.67 m/s located at steel met outlet, the melt exhibits completely dead zones with minimum flow velocity distribution especially at the bottom and circumference areas. This indicates the importance of combined stirring and large reaction rates to achieve a complete homogeneous melt especially at bottom and circumference areas.

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Section: Introductionmentioning

confidence: 99%

Computational Simulation Model for Metallurgical Effects during EAF Refining Stage: Waiting and Arcing Time

Elkoumy¹,

El-Anwar

Fathy³

et al. 2018

ISIJ International

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“…1 For instance, they are key elements in environmental pollution and particulate contamination control, fouling of heat exchangers, 2,3 oil transport in pipelines, 4,5 sediment transport in rivers, 6 biofilm formation, 7 or liquid metal processing. 8 Depending on the system, the suspended particles may be solid particles or droplets. The continuous phase is either a gas or a liquid: in the former case, the system is called an aerosol and, in the latter case, we call it a hydrosol.…”

Section: Introductionmentioning

confidence: 99%

Analysis of non‐Brownian particle deposition from turbulent liquid‐flow

et al. 2015

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International audienceThe deposition of non-Brownian particles from turbulent liquid-flow onto channel walls is numerically analyzed. Theapproach combines Lagrangian particle tracking with a kinematic model of the near-wall shear layer. For nonbuoyantparticles, direct interception is the main deposition mechanism and the deposition velocity scales as the particle diameter(in wall units) to the power of 1.7. When wall/particle hydrodynamic interactions are taken into account, the depositionvelocity is significantly reduced and the correction factor scales as the cubic root of the wall roughness to particlediameter ratio. For buoyant particles, sedimentation is usually the predominant deposition mechanism and the hydrodynamicinteractions significantly affect the deposition velocity when the drainage characteristic time driven by buoyancyis of the order of the particle residence time close to the wall. Last, a wall-function for the suspended particles is proposed

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“…A fluid-dynamic model within the framework of the CFD code Ansys Fluent V17.1 was developed in order to simulate the two-phase gas-liquid flow inside a steel ladle using a Euler-Euler approach. The model has previously been described and more detailed information is available in references [15,16]. The CFD calculations are coupled with the thermodynamic software CEQCSI (Chemical EQuilibrium Calculation for the Steel Industry) developed at ArcelorMittal Research in order to estimate the nature of the non-metallic inclusions in the steel as a function of the local steel composition.…”

Section: Methodsmentioning

confidence: 99%

“…Concerning the liquid steel phase, a slip condition is applied either at the inlet and outlet surfaces. A large number of user defined functions (UDFs) has been incorporated in order to take into account the specific features of the model [15].…”

Section: Modeling Of the Liquid Steel-gas Mixturementioning

confidence: 99%

Numerical simulation of modification of non-metallic inclusions by calcium treatment in the argon-stirred ladle

Castro-Cedeño

Jardy

Boissière

et al. 2019

Metall. Res. Technol.

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Nowadays, depending on the steel grade, Ca treatment with the aim of modifying the morphology and melting temperature of non-metallic inclusions is performed in the secondary steelmaking process. The addition of calcium to steel melts rises a technological challenge because at steelmaking temperatures Ca has the tendency to vaporize from the ladle. Efforts are actively pursued in developing solutions that increase Ca yield and improve repeatability of results from treatment to treatment. This work presents a two-phase Euler-Euler flow model of a steel ladle with gas stirring through bottom porous plugs. The model considers that before gas exits through the ladle top, some Ca is transferred from the gas to the liquid steel. The yield is thus defined as the ratio between the Ca transferred to the steel and the total calcium injected into the ladle. The fluid-dynamic calculations are coupled with ArcelorMittal thermodynamic software CEQCSI to get the evolution of the local concentration of dissolved species and non-metallic inclusions assuming local thermodynamic equilibrium. Industrial trials have been performed at one of ArcelorMittal’s facilities with the aim of obtaining data to validate the model. Samples of steel were taken before, during, and after the Ca injection treatment. The total Ca content and the inclusion populations in the steel samples can be compared against the results given by the model, as well as the measured and calculated Ca yield.

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Numerical Modelling of Inclusion Behaviour in a Gas-stirred Ladle

Cited by 34 publications

References 24 publications

Computational Simulation Model for Metallurgical Effects during EAF Refining Stage: Waiting and Arcing Time

Computational Simulation Model for Metallurgical Effects during EAF Refining Stage: Waiting and Arcing Time

Analysis of non‐Brownian particle deposition from turbulent liquid‐flow

Numerical simulation of modification of non-metallic inclusions by calcium treatment in the argon-stirred ladle

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