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Record W3199747115 · doi:10.1002/srin.202100410

A Review on Swirling Flow Casting Technology in Steel Production

2021· review· en· W3199747115 on OpenAlexaff
Qinghua Xie, Muhammad Nabeel, Mikael Ersson, Peiyuan Ni

Bibliographic record

Venuesteel research international · 2021
Typereview
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsCastingFlow (mathematics)Continuous castingMaterials scienceMetallurgySteel castingFluid dynamicsMechanical engineeringProcess (computing)MoldHeat transferMechanicsEngineeringComputer scienceComposite material

Abstract

fetched live from OpenAlex

Casting is a vital process in steel production where solidification process of molten steel occurs in mold coupled with fluid flow, heat transfer, crystal growth, inclusion motion, solute redistribution, segregation, and so on. All these phenomena are closely related to the steel flow behavior. To optimize the flow field in mold at initial moment, the swirling flow casting technology is developed and has received extensive attention in past years. It is believed to be a promising method to further optimize the steel flow and to improve the solidification process. Herein, the swirling flow steel casting is comprehensively reviewed to introduce different realization technologies, new research progress, and the current situation of their application. This aims to promote the development and usage of swirling flow technology in steel casting.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.155
GPT teacher head0.436
Teacher spread0.281 · 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 designOther design
Domainnot available
GenreReview

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

Citations14
Published2021
Admission routes1
Has abstractyes

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