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Record W2001884629 · doi:10.1115/fedsm2012-72313

The Effect of Nozzle Shape and Configuration on Bubble Formation in a Liquid Cross Flow

2012· article· en· W2001884629 on OpenAlexafffund
Mona Hassanzadeh Jobehdar, Aly H. Gadallah, Kamran Siddiqui, Wajid A. Chishty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsNational Research Council CanadaWestern University
FundersNational Research Council Canada
KeywordsBubbleShadowgraphyNozzleMechanicsFlow (mathematics)Liquid bubbleCross section (physics)Materials scienceVolumetric flow rateOpticsAcousticsPhysicsLaserThermodynamics

Abstract

fetched live from OpenAlex

Gas injection into a liquid cross flow from a nozzle causes bubble formations which have potential applications in industry such as chemical plants, waste water treatment and bio- and nuclear-reactors. The purpose of this study is to experimentally investigate the effects of nozzle shape and configuration with respect to the liquid cross-flow direction, on the bubbly flow characteristics such as bubble formation, detached bubble size and frequency at different gas and liquid flow rates. The experiments were conducted in a Plexiglas two-dimensional rig using a high speed camera. High speed imaging and an image processing algorithm were used to track each individual bubble and to quantify the bubble growth as well as the detachment frequency and the bubble velocity. Back light shadowgraphy which utilizes a low intensity diffuse light source to illuminate the background was used to image bubbles. Nozzles were mounted in the test section which was designed such that the flow in this section has a two-dimensional profile. The results showed that the bubble size increases with an increase in GLR (gas to liquid flow rates ratio). Furthermore, the bubble formations and detached bubble size were strongly influenced by the nozzle shape and configuration.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.108

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 head0.213
Teacher spread0.209 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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