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Record W2124887413 · doi:10.1002/cjce.20647

Influence of bubbles characteristics on the skin friction and velocity gradient on solid sphere

2011· article· en· W2124887413 on OpenAlexvenueno aff
Abdelhafid Essadki, Iordan Nikov, H. Delmas

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleMechanicsCoalescence (physics)SPHERESVolume (thermodynamics)DistributorDead zoneVelocity gradientTwo-dimensional gasMaterials sciencePhysicsThermodynamicsGeology

Abstract

fetched live from OpenAlex

Abstract A detailed study of the effects of individual bubbles at high gas flow‐rate has shown, that the dominant influence on skin friction over a solid sphere is the bubble volume in compared to bubble frequency. Nevertheless the bubble frequency is very important in case of low gas flow‐rate. Referring to bubbles produced by a gas distributor, statistical and spectral analyses were performed to study the influence of bubbling on exposure time and magnitude of fluctuations. Referring to a calibrated bubble train, the existence of critical frequency is demonstrated. A bubble with larger volume and a mobile, oscillatory surface generates larger velocity gradient. In the case of gas distribution, histograms of the velocity gradient for a 2 mm glass sphere creating bubble coalescence reveal the maximum exceeds 48 000 s −1 in the front zone and 2000 s −1 in the rear zone ( θ = 180°). For 5 mm plastic spheres creating bubble break‐up, the maximum of the velocity gradient is only 8100 s −1 for the front part of the sphere and 2000 s −1 in the rear zone.

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 score0.125
Threshold uncertainty score0.257

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 head0.007
GPT teacher head0.159
Teacher spread0.152 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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