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Record W4380882334 · doi:10.1002/srin.202300195

Numerical Simulation of Electric Arc Heating Process in the Refining Ladle

2023· article· en· W4380882334 on OpenAlexaff
Xipeng Guo, Steve Ryan, Nicholas Walla, Chenn Q. Zhou

Bibliographic record

Venuesteel research international · 2023
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsPortage College
Fundersnot available
KeywordsLadleSlag (welding)Shear stressMaterials scienceElectric arc furnaceMechanicsVolumetric flow rateComputer simulationFlow (mathematics)Stress (linguistics)Multiphase flowRefining (metallurgy)Process (computing)Mechanical engineeringFluid dynamicsMetallurgyEngineeringComposite materialComputer science

Abstract

fetched live from OpenAlex

This article presents a comprehensive multiphase computational fluid dynamics model to simulate the arc heating process in the ladle. The results on temperature distribution, slag eye size, flow characteristics, and wall shear stress under three flow rate conditions are discussed. Validation of the results is carried out by comparing them with industrial measurements. The study highlights that when the flow rate increases by 100%, the temperature difference in the ladle decreases by 17.37%, while the slag eye‐opening and wall shear stress increase by 116.38% and 49.86%, respectively. The study emphasizes the details of model development and utilization of models to optimize the process.

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)0.001
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 score0.049
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.084
GPT teacher head0.405
Teacher spread0.321 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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