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Mathematical Comparison of Two VOD Nozzle Jets

2011· article· en· W2073675472 on OpenAlexaboutno aff
Zhili Song, Mikael Ersson, Pär G. Jönsson

Bibliographic record

VenueISIJ International · 2011
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
FundersResearch Fund for Coal and Steel
KeywordsNozzleJet (fluid)DecarburizationMechanicsWork (physics)Ambient pressureMaterials scienceOxygenFlow (mathematics)Mechanical engineeringLimiting oxygen concentrationChemistryThermodynamicsMetallurgyPhysicsEngineering

Abstract

fetched live from OpenAlex

Studies of physical phenomena in a jet caused by VOD (Vacuum Oxygen Decarburization) nozzles have been carried out. The VOD process is a metallurgical process where the steel-making route is controlled under vacuum environment with oxygen top blowing. In this work, two VOD nozzle models have been employed for an investigation based on two real De Laval geometries used in industry. Numerical modeling was used to study oxygen blowing states of the nozzles at different temperatures and ambient pressures. The nozzle models were numerically computed with two dimensional domains, where vacuum conditions and temperatures were specifically defined. The modeling results showed that one of the nozzles was more applicably proper for lower pressures, displaying a more stable flow pattern. Furthermore, it was found that a change in ambient pressure has a stronger effect on the jet force than a change in ambient temperature. In addition, it was proved that the profiles of the dynamic pressure at a certain blowing distance fit well to Multi-Gaussian curves.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.294
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations10
Published2011
Admission routes1
Has abstractyes

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