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Record W1994163614 · doi:10.1017/jfm.2011.166

Pore-scale investigation of immiscible displacement process in porous media under high-frequency sound waves

2011· article· en· W1994163614 on OpenAlexaff
Khosrow Naderi, Tayfun Babadagli

Bibliographic record

VenueJournal of Fluid Mechanics · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials sciencePorous mediumWettingCoalescence (physics)Ultrasonic sensorSaturation (graph theory)PorosityMechanicsDisplacement (psychology)VibrationComposite materialAcousticsPhysics

Abstract

fetched live from OpenAlex

Although experimental and theoretical studies have been performed to identify the effects of elastic waves on multi-phase flow in porous structures, the literature lacks finely tuned experiments at the micro-scale. This paper reports observations and critical analysis of immiscible displacement in micro-scale porous media under ultrasonic energy. A number of experiments are performed on homogeneous and heterogeneous micromodels for varying wave frequency and power, initial water saturation, wettability and injection rates. We show that ultrasonic radiation influences the displacement pattern and yields lower residual non-wetting phase (oil) behind when low injection rates are applied. Higher wave frequency results in faster recovery of oil, but the ultimate recovery is controlled mainly by wave intensity. The presence of initial water saturation has a positive effect on the displacement, especially in an oil-wet medium. Of the possible mechanisms suggested for recovery enhancement under ultrasonic radiation, deformation of pore walls and change in fluid properties due to heating are not an issue in these experiments but other mechanisms including coalescence of oil droplets under oscillation, reduction of wetting films, adherence to grains and the peristaltic movement of fluids due to mechanical vibration were observed to be effective and are discussed in the analysis of the visual observations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.586

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 head0.018
GPT teacher head0.225
Teacher spread0.208 · 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 designBench or experimental
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

Citations29
Published2011
Admission routes1
Has abstractyes

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