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Record W2219264440 · doi:10.1115/omae2015-42257

Experimental and Numerical Investigations of Bubble Dynamics in Porous and Non-Porous Media

2015· article· en· W2219264440 on OpenAlexafffund
Mohammad Azizur Rahman, Stephen Butt, Jahrul Alam, M. Shahwan, M. L. Hunt, Syed Imtiaz

Bibliographic record

VenueVolume 10: Petroleum Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBubblePorosityPorous mediumMechanicsComputational fluid dynamicsCoalescence (physics)BreakupMaterials scienceBubble pointMultiphase flowPhysicsComposite material

Abstract

fetched live from OpenAlex

In this study an experimental work is conducted to investigate the shape and speed of an air bubble in a pipe filled with different viscous fluids and porous media. The experimental results are also compared with the Computational Fluid Dynamics (CFD) simulation. Multiphase flows are complex due to the infinitely deformable nature of interface in gas/liquid flows. If one of the phases is gas acts as dispersed phase in the form of bubble, then the complexity will arise from the non-uniform distribution of bubbles in the pipe cross-section and axial distance. Inclusion of different viscous fluids simulating the industrial scale hydrocarbon properties brings added challenge in understating the bubble rise, coalescence and breakup dynamics. Moreover, bubble rise and change of shape of bubble in porous media will bring additional complexity in the flow dynamics. The pipe used in the experiment and CFD was 11.6 cm ID and a length of 100 cm. Three situations were tested: i) an air bubble rising in stagnant water, ii) an air bubble rising in moving water, and iii) an air bubble rising in a stagnant water but filled with porous media with porosity of 27%. Preliminary CFD results indicate that an air bubble has an average velocity of 0.2468 m/s and 0.2524 m/s in stagnant water and moving water, respectively, which is very close to experimental results.

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 score0.964
Threshold uncertainty score0.664

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 head0.200
Teacher spread0.194 · 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
Published2015
Admission routes2
Has abstractyes

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