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Record W4380629060 · doi:10.3389/fphy.2023.1171267

Fragmentation of a molten metal droplet in an ambient water flow

2023· article· en· W4380629060 on OpenAlexafffund
Mark Cunningham, David L. Frost

Bibliographic record

VenueFrontiers in Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBreakupMechanicsShock waveNozzleMaterials scienceAmbient pressureThermalChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

The influence of an ambient fluid flow on the fragmentation of hot molten tin droplets (initially at 800°C) and cold low melting point alloy droplets (initially at 70°C) in water is investigated with high-speed photography and flash radiography. The water is accelerated using a converging nozzle to a constant speed of up to 30 m/s using a double piston arrangement designed to eliminate the formation of a shock wave that is present in most earlier studies. At low flow velocities, the fragmentation of hot droplets is governed by thermal effects and vapour formation, growth, and collapse. At high flow velocities, vapour formation is suppressed and the droplet fragmentation is determined by hydrodynamic effects in which hydrodynamic instabilities (Rayleigh-Taylor and Kelvin-Helmholtz) and wavecrest stripping all play a role in the droplet breakup. At intermediate flow velocities, both thermal and hydrodynamic effects play a role. Quantitative image analysis of the radiographs is used to determine the spatial distribution of the droplet mass during the fragmentation process. Comparison with earlier work in which the ambient flow is preceded by a strong shock wave indicates that the transition from thermal to hydrodynamic breakup is strongly dependent on the pressure field.

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 score0.029
Threshold uncertainty score0.285

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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