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Record W4402749140 · doi:10.1063/5.0217897

Size and velocity of jet drops produced by bursting bubbles at the interface of a water jet

2024· article· en· W4402749140 on OpenAlexafffund
Antonella Succar, Michèle Prévost, Émilie Bédard, Étienne Robert

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsJet (fluid)BurstingMechanicsBubbleClassical mechanics

Abstract

fetched live from OpenAlex

Bursting bubbles at the free surface of aerated faucet water jets may spread pathogens through the released droplets. Many studies focused on the production of jet drops from bursting bubbles at a planar interface, particularly for the first jet drop. The extent to which previous findings apply to bubbles in aerated jets remains unknown. In this study, we produce a wide range of bubble size distributions within different jet diameters and characterize the diameter and velocity of jet drops released from individually bursting bubbles. Several similarities with the planar case are recovered, such as the overall dependence of the jet drop diameter and bursting dynamics on the bubble diameters and the formation of secondary jet drops. However, we observe asymmetries in the ejection of the droplets, and droplets ejected horizontally appear to have the highest ejection velocity among all jet drops. By modeling the evolution of the ejected drops for the different bubble size distributions, we find that for a mean Laplace number Labub=ρwσwRbubμw2≲6×104, a fraction of the drops ejected can become airborne. Droplets deposit within 9 cm for a mean Labub≲2.1×104 and within 33 cm for a mean 2.1×104≲Labub≲1.8×105 from a faucet jet, assuming a countertop situated 20 cm below the faucet outlet. A bubble size distribution with a mean Labub of 6×104 would minimize both the risk of airborne pathogen transmission and that resulting from surface contamination.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.302

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 head0.230
Teacher spread0.222 · 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 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
Published2024
Admission routes2
Has abstractyes

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