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Record W3190512373 · doi:10.11159/jffhmt.2018.008

A CFD and Experimental Investigation of Slag Eye in Gas Stirred Ladle

2018· article· en· W3190512373 on OpenAlexvenueno aff
Eshwar Kumar Ramasetti, Ville‐Valtteri Visuri, Petri Sulasalmi, Timo Fabritius

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsLadleComputational fluid dynamicsSlag (welding)Materials scienceMechanicsMetallurgyPhysics

Abstract

fetched live from OpenAlex

In ladle metallurgy, gas stirring and behavior of the slag layer are very important for the quality of the steel. When gas is injected through a nozzle located at the bottom of the ladle into the metal bath, the gas jet exiting the nozzle breaks up into gas bubbles. The rising bubbles break the slag layer and create a slag eye. In this paper, the behavior of the slag eye area for different gas flow rates is been investigated through experimental measurements and CFD simulations. A 1/5-scale water model of 150 ton-ladle was deployed for the experimental measurements and for studying the effect of gas flow rate on the slag eye diameter. The physical modelling results show that the slag eye area changes from 20 to 182 cm 2 when the gas flow rate increases from 1.5 to 15 NL/min. The dimensionless area of the open eye was found to be in agreement with earlier studies. The simulations were carried out in the commercial CFD code ANSYS Fluent with mesh generation in ANSYS Workbench. The numerical model developed is based on the Eulerian Multiphase Volume of Fluid (VOF) approach and employs standard - turbulence model for solving the turbulent liquid flow induced by bubble-liquid interaction. The simulation results of slag eye area showed a good agreement when compared to experimental results measured.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.215
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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