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Record W2930232721 · doi:10.3390/met9040394

Fluid Flow Control in a Billet Tundish during Steel Filling Operations

2019· article· en· W2930232721 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMetals · 2019
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
FundersSistema Nacional de InvestigadoresComisión de Operación y Fomento de Actividades Académicas, Instituto Politécnico Nacional
KeywordsTundishCasterWater modelSuperheatingVolume of fluid methodContinuous castingMechanicsLiquid steelTurbulenceMaterials scienceFlow (mathematics)CastingMetallurgyBubbleMechanical engineeringEngineeringComposite materialThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

The startup of casting sequences in continuous casting of steel using three different turbulence inhibitors were modeled and simulated through the multiphase volume of fluid model (VOF) in a four-strand tundish. In the actual caster, one of the inhibitors released the liquid steel with a superheat high enough to avoid freezing problems in the outer strands. A second inhibitor improved the flow, yet it yielded steel freezing in these strands. A two-phase air–water system was used to model the liquid steel–air system and the interfaces were tracked by a donor–acceptor principle applied in the computational mesh. These activities led to the design of a third inhibitor. Experimental outcomes and the mathematical simulations agreed remarkably well regarding the velocity of the stream front in the tundish floor and the mass of steel reaching the outer strands. A larger steel mass and a faster stream front helped to completely prevent the steel from freezing in the outer strands. Finally, flow fields during the filling of the tundish using two of these inhibitors were simulated and the results explain the different performances observed experimentally.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.885

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)0.0010.000

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 head0.007
GPT teacher head0.195
Teacher spread0.189 · 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