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Record W4384753938 · doi:10.1063/5.0158233

Air-bubble entrainment by translating turbulent jets in stagnant water

2023· article· en· W4384753938 on OpenAlexafffund
Mahmud Rashedul Amin, David Z. Zhu, N. Rajaratnam

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBubblePhysicsMechanicsStrouhal numberEntrainment (biomusicology)TurbulenceJet (fluid)VortexAir entrainmentClassical mechanicsReynolds numberAcoustics

Abstract

fetched live from OpenAlex

This paper presents an experimental study on air-bubble entrainment in quiescent water by translating circular turbulent jets. The jet diameters, plunging heights, impact velocities, and translating velocities were varied during the experiments to investigate their relative effect on the bubble characteristics. The experimental observations reveal that the jet translation affects the air bubble entrainment mechanism and the distribution of bubble size. A rotating cavity forms around the plunging jet due to the translation of the jet. Depending on the translating velocity, the air bubble emanates from the cusp of the cavity and the downstream water surface meniscus with the jet. The bubble swarm produced by the translating jet exhibits vortex shedding with Strouhal numbers between 0.22 and 0.27, comparable to a circular cylinder in cross-flow. The peak of the bubble size distribution varies between 0.5 and 1.5 mm, while the Sauter mean diameter varies between 1.8 and 2.8 mm. The maximum penetration depth of bubbles is found to be a function of the jet impact to translating velocity ratio, and the Capillary number of the air–water interface. The spatial distribution of bubbles along the plume cross section exhibits Gaussian distributions. Finally, the terminal rising velocity of the bubbles shows no obvious effect of the jet translation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.510

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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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