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Record W2518966494 · doi:10.1002/cjce.22680

Bubble formation regimes during gas injection into a liquid cross flow in a conduit

2016· article· en· W2518966494 on OpenAlexaffvenue
Miguel Balzan, R. Sean Sanders, Brian A. Fleck

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBubbleDimensionless quantityTurbulenceMechanicsJet (fluid)Materials scienceLiquid bubbleInjectorFlow visualizationInletFlow (mathematics)ThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract A study was performed to characterize the different bubble formation regimes that occur during the process of gas jet injection into a liquid cross flow in a conduit. Air was injected perpendicularly into a turbulent, fully developed water flow circulating through a 12.7 mm square channel. Three different gas injectors, with diameters of 0.27 mm, 0.52 mm, and 1.59 mm were used. The bulk water velocity values ranged between 1.1 and 4.3 m/s. The effects that the gas injection velocity, liquid mean velocity, and injection gas injection diameter have on the process of bubble generation were investigated. A high‐speed visualization technique was used to determine the regimes near the gas inlet region. Four distinct regimes were identified: Single Bubbling ( SB ), Pulse ( P ), Elongated Jetting ( EJ ), and Atomizing Jetting ( AJ ). It was observed that the shift between regimes occurs gradually, producing the need to identify transitional regions: SBP and PTJ . Sets of independent dimensionless variables were used to categorize the proposed regimes using bubble formation maps. It was determined that the injection diameter plays a primary role in jet formation: as the injection diameter increased, the observable number of regimes decreased, indicating a more stable and continuous process of bubble generation. Empirical correlations that delimit the boundaries between ordered and chaotic bubble generation are presented.

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 score0.099
Threshold uncertainty score0.322

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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

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