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Record W2593356032 · doi:10.1002/srin.201600436

Tundish Open Eye Formation: A Trivial Event with Dire Consequences

2017· article· en· W2593356032 on OpenAlexaff
S. Chatterjee, Donghui Li, Kinnor Chattopadhyay

Bibliographic record

Venuesteel research international · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTundishSteelmakingHomogenization (climate)LadleVolume of fluid methodCasterMetallurgyMechanicsWater modelMaterials scienceTurbulenceInertContinuous castingComposite materialChemistryFlow (mathematics)

Abstract

fetched live from OpenAlex

Inert gas shrouding is a traditional practice in tundish metallurgy and has several benefits such as, protecting the melt stream from air aspirations, aiding inclusion flotation with argon bubbles, and also possible thermal and chemical homogenization. On the down side, it displaces the protective slag layer on the top of the melt, and exposes the steel to the ambient atmosphere. This region is often referred to as the slag eye or open eye. This exposed area leads to higher radiative heat losses, reoxidation of the liquid steel, nitrogen pickup, and subsequent inclusion formation. Although perceived as a trivial event by many, Tundish Open Eye (TOE) has dire consequences. In the present work, TOE formation and its consequences have been investigated. The mathematical modeling of this turbulent multiphase system is performed using the Volume of Fluid (VOF) method, and discrete phase method (DPM), coupled with the standard k ‐ ϵ turbulence model. The mathematical model is compared with the water model results and plant trials. The main objective is to ensure that the steelmaking tundish acts as a refiner and not as a contaminator.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.419
Teacher spread0.315 · 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 designTheoretical or conceptual
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

Citations24
Published2017
Admission routes1
Has abstractyes

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