MétaCan
Menu
Back to cohort
Record W2084065062 · doi:10.1115/ajk2011-10027

The Influence of Gas and Liquid Flow Rates on the Transient Bubble Characteristics in a Liquid Cross-Flow

2011· article· en· W2084065062 on OpenAlexaff
Kamran Siddiqui, Wajid A. Chishty

Bibliographic record

VenueASME-JSME-KSME 2011 Joint Fluids Engineering Conference: Volume 1, Symposia – Parts A, B, C, and D · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsNational Research Council CanadaWestern University
Fundersnot available
KeywordsBubbleMechanicsBody orificeFlow (mathematics)Transient (computer programming)Materials scienceVolumetric flow rateOpen-channel flowChannel (broadcasting)Inclination angleVertical planePlane (geometry)OpticsPhysicsGeometryMathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The paper reports on an experimental study conducted to investigate the dynamics of gas bubbles when injected from an orifice that is subjected to a liquid cross-flow. The experiments were conducted over a range of gas and liquid flow rates and at various orientations of the liquid channel. An image processing algorithm was developed for the detection and characterization of the bubbles in both temporal and spatial coordinates. The transient behaviour of bubble dynamics at different channel orientations under different liquid and gas flow conditions are presented in the paper. Results show that the equivalent bubble diameter in the vertical plane decreased with increase in time as well as with increase in the gas-to-liquid velocity ratio. The channel inclination has no appreciable effect on the bubble diameter. The streamwise bubble velocity showed significant transient fluctuations, which diminished with an increase in the channel inclination angle. The fluctuations of the bubble vertical velocity were found to be unaffected by the channel inclination angle. The magnitudes of both streamwise and vertical velocities were influenced by the channel inclination, however, the trends were found to be opposite.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.191
Teacher spread0.179 · 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.

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

Explore more

Same venueASME-JSME-KSME 2011 Joint Fluids Engineering Conference: Volume 1, Symposia – Parts A, B, C, and DSame topicFluid Dynamics and MixingFrench-language works237,207