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Record W3198128820 · doi:10.3390/ma14175034

The Behavior of Supersonic Jets Generated by Combination Gas in the Steelmaking Process

2021· article· en· W3198128820 on OpenAlexaboutno aff
Binglong Zhang, Fuhai Liu, Rong Zhu

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsSteelmakingNozzleSupersonic speedVolumetric flow rateMechanicsOxygenMaterials scienceMixing (physics)Jet (fluid)Flow (mathematics)Process (computing)ThermodynamicsChemistryMetallurgyPhysics

Abstract

fetched live from OpenAlex

In the duplex steelmaking process, the oxygen flow rate is suppressed to reduce the increasing rate of the temperature in the molten bath, resulting in severe dynamic conditions. To improve the mixing effect of the molten bath, a Laval nozzle structure designed for combination gas has been proposed. In this research, five types of Laval nozzle structure have been built based on the combination gas content, and both numerical simulations and experiments are performed to analyze the flow field of the supersonic jet. The axial velocity and oxygen concentration were measured in the experiment, which agreed well with the numerically simulated data. The results show that both initial axial velocity and potential core length increase with the flow rate of combination gas. Further, applying a higher N2 flow rate could improve the oxygen utilization rate at different ambient temperatures, but this issue increases the oxygen utilization rate; however, the latter can be reduced at higher ambient temperatures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.174

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 designBench or experimental
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

Citations6
Published2021
Admission routes1
Has abstractyes

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