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Record W2054428838 · doi:10.1002/srin.201200050

Numerical Simulation of Fluid Flow in Bath during Combined Top and Bottom Blowing VOD Refining Process of Stainless Steel

2012· article· en· W2054428838 on OpenAlexaboutno aff
Ji‐He Wei, Lin Zeng

Bibliographic record

Venuesteel research international · 2012
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTuyereRefining (metallurgy)TurbulenceNozzleMechanicsTurbulence kinetic energyFlow (mathematics)Materials scienceMixing (physics)DecarburizationWork (physics)Kinetic energyMetallurgyThermodynamicsPhysicsBlast furnaceClassical mechanics

Abstract

fetched live from OpenAlex

Abstract The fluid flow in a bath in combined top and bottom blowing vacuum‐oxygen decarburization (VOD) refining process of stainless steel has numerically been simulated. The three‐dimensional mathematical model used is essentially based on that proposed in our previous work for the flow in combined side and top blowing argon‐oxygen decarburization (AOD) process, but considering the influence of reduced ambient pressure. Applying it to the flow in the bath of a 120 t VOD vessel under the refining conditions, the results present that the model can fairly well simulate and estimate the flow phenomena. The flow pattern of molten steel in the bath with the combined blowing is a composite result under the common action of the jets from a three‐hole Laval top lance and gas bottom blowing streams. The jets have a leading role on it; the molten steel in the whole bath is in vigorous stirring and circulatory motion during the blowing process. The streams do not alter the basic features of the gas agitation and liquid flow, but can evidently change the local flow pattern of the liquid and increase its turbulent kinetic energy to a certain extent. The flow field and turbulent kinetic energy distribution in the combined blowing with three tuyeres are more uniform than those in the blowing with double tuyeres. Increasing properly the tuyere eccentricities is of advantage for improving the velocity and turbulent kinetic energy distributions, the stirring and mixing result in the practical VOD refining 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)0.000
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 score0.020
Threshold uncertainty score0.389

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 science0.0000.000
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 head0.032
GPT teacher head0.339
Teacher spread0.308 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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