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Record W2319367848 · doi:10.1115/fedsm2009-78307

3-D Simulations of the Bubble Formation From a Submerged Orifice in Liquid Cross-Flow

2009· article· en· W2319367848 on OpenAlexafffund
Majid Nabavi, Kamran Siddiqui, Wajid A. Chishty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsNational Research Council CanadaWestern UniversityMcGill University
FundersNational Research Council Canada
KeywordsBody orificeBubbleMechanicsVolume of fluid methodDiscretizationInletFlow (mathematics)Momentum (technical analysis)RADIUSUpwind schemePhysicsSimulationMathematicsEngineeringMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

The results are presented from a 3-D simulation of the bubble formation from a submerged orifice in liquid cross-flow. VOF model is used for the simulations. The VOF equation is solved using an explicit time-marching scheme. A second order upwind differencing scheme is applied for the solution of momentum equation. The pressure-implicit with splitting of operators (PISO) scheme is used for the pressure-velocity-coupling scheme. Pressure is discretized with a PRESTO scheme. The computational domain has the dimensions of 100 mm length, 50 mm width and 16 mm height with an orifice of 0.25 mm radius, placed at the bottom of the channel and 10 cm from the water inlet. The water inlet velocity of 0.05 and 0.136 m/s and air inlet mass flow rate of 10−6 and 10−5 kg/s are considered. The simulation results are compared with the experimentally acquired images of the bubbles in the cross-flow stream using a high speed camera (3000 fps). A good agreement with respect to bubble shape and bubble terminal velocity is observed between the experimental and simulation results for both cases. The 3-D numerical model is compared with the 2-D model in order to highlight and emphasize the need for 3-D model to correctly simulate the dynamics of such flow configurations.

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 score0.059
Threshold uncertainty score0.162

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 head0.213
Teacher spread0.206 · 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

Citations5
Published2009
Admission routes2
Has abstractyes

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