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Record W4415687882 · doi:10.1080/00084433.2025.2573538

Study of the influence of the composite-type nozzle design on the gas-dynamic characteristics of a gas jet

2025· article· en· W4415687882 on OpenAlexaboutno aff
Т. S. Golub, L. S. Мolchanov, Олександр Мінай, Andrii Koveria

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Sediment Control
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleJet (fluid)Current (fluid)Flow (mathematics)

Abstract

fetched live from OpenAlex

Today, numerous designs of the top-blowing nozzles and lances exist, but the challenge of improving top-blowing performance remains relevant. The paper (for the first time) studies the use of composite-type nozzles (consisting of a central cylindrical and annular outer part with a peripheral area of 25%, 50% and 75% of the total nozzle cross-sectional area) compared to Laval and cylindrical nozzles equivalent in cross-sectional area. It was found that the peripheral part has the greatest influence on the hydro-gas-dynamic characteristics of the experimental gas jets. At 25% of the peripheral part, the central structure of the ‘barrel’ chain and the interaction characteristics with the liquid are similar to jets flowing from Laval nozzles, but the liquid penetration depth is less. An increase in the peripheral fraction to 50% – 75% is characterised by the formation of jets with a complex central part, with even less penetration into the liquid, but with a wider crater than from a cylindrical nozzle, and with the absence of high splashes and the formation of a fine ‘mist’ of liquid.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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