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Record W4404322430 · doi:10.3390/coatings14111444

Flow Field of Supersonic Oxygen Jet Generated by Various Wear Lengths at the Laval Nozzle Exit

2024· article· en· W4404322430 on OpenAlexaboutno aff
Fuhai Liu, Shaobo Lu, Rong Zhu, Guangsheng Wei, Kai Dong

Bibliographic record

VenueCoatings · 2024
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsNozzleJet (fluid)Supersonic speedMechanicsFlow (mathematics)Materials scienceChoked flowAerospace engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

As a main apparatus, the oxygen lance is used to deliver the oxygen element and transfer kinetic energy into the molten bath in the steelmaking process. However, the Laval nozzle exit would be gradually worn out during the service life, which suppresses the performance of the oxygen lance. This paper investigated three different wear length (Lw) conditions at the exit of the Laval nozzle through numerical simulations and high-temperature experiments with various oxygen flow rates. The result showed that the entrainment of the ambient gas was the key factor of the wear phenomenon for the Laval nozzle exit. The maximum total temperature of the gas phase at the Laval nozzle exit formed by the Lw of 0 mm, 2 mm, and 4 mm were 300 K, 959 K, and 1700 K, respectively. Thus, by increasing the Lw value, the total temperature of the gas phase was rapidly improved at the exit of the Laval nozzle, which further accelerated the wear phenomenon at the exit of the Laval nozzle. Besides, axial velocities at the end of the potential core formed by the Lw of 0 mm, 2 mm, and 4 mm were 483.7 m/s, 480.0 m/s, and 478.7 m/s, respectively. As a result, the wear phenomenon reduced the impaction ability of the oxygen jet, which suppressed the impaction depth and radius, resulting in a smaller droplet generation rate.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.371

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 head0.005
GPT teacher head0.206
Teacher spread0.202 · 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
Published2024
Admission routes1
Has abstractyes

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