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Record W3182925598 · doi:10.1080/00325899.2021.1949801

Water atomisation of molten metals: a mathematical model for a water spray

2021· article· en· W3182925598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePowder Metallurgy · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpray characteristicsMomentum (technical analysis)Materials scienceFlux (metallurgy)MechanicsSpray nozzleMass fluxWork (physics)Flow (mathematics)Momentum transferVolumetric flow rateThermodynamicsMetallurgyNozzleOpticsPhysics

Abstract

fetched live from OpenAlex

In water atomization, a molten metal stream is fragmented by high-pressure water sprays by means of momentum transfer. In this work, a flat fan water spray is considered as a two-phase flow: water and a surrounding gas. An existing mathematical model for predicting the velocities of water droplets and entrained gas in a flat fan spray is improved. The total momentum flux of a spray is calculated for different spray travel distances, spray pressures and spray spreading angles, addressing the dependence of spray momentum flux on these parameters. A new quantity, the ‘effective momentum flux’, is introduced which also accounts for the effect of apex angle.Finally, based on the results of lab-scale water atomization experiments, a correlation is proposed for the powder mass median size versus the effective momentum flux of the water spray, consolidating the influence of spray parameters including pressure, travel distance, spreading angle and apex angle.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score0.589

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)0.0010.000

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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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