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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 OpenAlexafffund
Ali Asgarian, R. D. Morales, Markus Bussmann, Kinnor Chattopadhyay

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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

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
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

Citations2
Published2021
Admission routes2
Has abstractyes

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Same venuePowder MetallurgySame topicFluid Dynamics and Heat TransferFrench-language works237,207