MétaCan
Menu
Back to cohort
Record W2907384750 · doi:10.1002/srin.201800429

Angular Re‐Positioning of a Five Ported SEN versus EMS Braking to Stabilize Flows at the Upper Slag‐Liquid Steel Interface

2018· article· en· W2907384750 on OpenAlexafffund
M. Mahdi Aboutalebi, Chantale Labrecque, Julien D’amours, Mihaiela Isac, R. I. L. Guthrie

Bibliographic record

Venuesteel research international · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsRio Tinto (Canada)McGill University
FundersCMC MicrosystemsMcGill University
KeywordsCasterNozzleMechanical engineeringMoldMaterials scienceFluentBrakeFlow (mathematics)MechanicsBuoyancyComputational fluid dynamicsEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The control of fluid flows within the mold region of continuous casters is a key factor in improving the quality of cast products. The performance of two different flow modifiers in the curved mold region of a typical square billet caster are compared. The flow modifiers investigated in this work are a 5‐ported Submerged Entry Nozzle (SEN) and a Brake‐Electromagnetic Stirring (Brake‐EMS) unit, interacting with a Main‐EMS. Two different commercial software programs, ANSYS Fluent and COMSOL, are used to develop a numerical Magneto‐Hydro‐Dynamic (MHD) model of this system, in order to study the efficiency of the selected flow modifiers. According to the simulated results, the dual‐EMS unit (Brake‐EMS together with Main‐EMS), intensifies the swirling flows in the mid‐region of the mold, but cannot decrease the intensive vertical upward flows that are being generated toward the meniscus corners. The proposed radial angulation of the SEN's exit ports, working in tandem with the Main‐EMS, not only develops swirling flows at the mid‐section of the mold, but also transforms the vertical upward flows to a horizontally rotating flow near to the upper surface of the caster. This slight modification calmed flows at the liquid steel meniscus, thereby reducing mold powder entrainment (MPE).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.360
Teacher spread0.312 · 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.

Study designSimulation or modeling
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 routes2
Has abstractyes

Explore more

Same venuesteel research internationalSame topicMetallurgical Processes and ThermodynamicsFrench-language works237,207