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Record W1539743715 · doi:10.1002/2014wr016019

Microbubble transport in water‐saturated porous media

2015· article· en· W1539743715 on OpenAlexaff
Ye Ma, Xiang‐Zhao Kong, Alexander Scheuermann, S. A. Galindo‐Torres, D. Bringemeier, Ling Li

Bibliographic record

VenueWater Resources Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsGolder Associates (Canada)
FundersAustralian Coal Industry’s Research Program
KeywordsPlumeBubblePorous mediumSpargingFlumeMechanicsScalingMaterials sciencePorosityVolumetric flow rateFlux (metallurgy)Flow (mathematics)Environmental scienceMeteorologyChemistryComposite materialPhysicsGeometry

Abstract

fetched live from OpenAlex

Abstract Laboratory experiments were conducted to investigate flow of discrete microbubbles through a water‐saturated porous medium. During the experiments, bubbles, released from a diffuser, moved upward through a quasi‐2‐D flume filled with transparent water‐based gelbeads and formed a distinct plume that could be well registered by a calibrated camera. Outflowing bubbles were collected on the top of the flume using volumetric burettes for flux measurements. We quantified the scaling behaviors between the gas (bubble) release rates and various characteristic parameters of the bubble plume, including plume tip velocity, plume width, and breakthrough time of the plume front. The experiments also revealed circulations of ambient pore water induced by the bubble flow. Based on a simple momentum exchange model, we showed that the relationship between the mean pore water velocity and gas release rate is consistent with the scaling solution for the bubble plume. These findings have important implications for studies of natural gas emission and air sparging, as well as fundamental research on bubble transport in porous media.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.004

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 head0.056
GPT teacher head0.289
Teacher spread0.233 · 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 designNot applicable
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

Citations27
Published2015
Admission routes1
Has abstractyes

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