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Record W2571084440 · doi:10.2355/tetsutohagane.95.33

Development of Swirling Flow Submerged Entry Nozzles for Slab Casting

2009· article· en· W2571084440 on OpenAlexaff
Yuichi Tsukaguchi, Hiroshi Hayashi, Hidenori Kurimoto, Shinichiro Yokoya, Katsukiyo Marukawa, Toshihiro Tanaka

Bibliographic record

VenueTetsu-to-Hagane · 2009
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsKingston Process Metallurgy (Canada)
Fundersnot available
KeywordsNozzleContinuous castingFlow (mathematics)CastingSlabMechanical engineeringMaterials scienceMechanicsEngineeringStructural engineeringMetallurgyPhysics

Abstract

fetched live from OpenAlex

We have started a development of swirling flow submerged entry nozzles in 1997 as a fundamental and effective measure for controlling flow pattern in continuous casting molds.As a first step, we have developed a swirling flow submerged entry nozzle for round billet casting in Wakayama works. Then we started the development of swirling flow submerged entry nozzles for slab casting. A main purpose of our development was to prove the effect of the swirling flow formation in submerged entry nozzles which improve quality of products and productivity of continuous casting processes. We have examined swirling flow submerged entry nozzles with swirl blade in these main bodies, because that was the easiest way to apply swirling flow to submerged entry nozzles in continuous casters without any investment of facilities. We had only to change a submerged entry nozzle for the experiment.Swirling flow submerged entry nozzles for slab casting have been developed and examined in Wakayama and Kashima works. As a result of these examinations, the effect of the swirling flow formation in submerged entry nozzles was evaluated to increase casting speed and improve surface quality of slabs and steel sheets.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.808

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 head0.016
GPT teacher head0.232
Teacher spread0.217 · 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 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

Citations12
Published2009
Admission routes1
Has abstractyes

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