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Record W4404170512 · doi:10.1016/j.seppur.2024.130340

Shear-induced oil separation from a sand particle moving in water

2024· article· en· W4404170512 on OpenAlexaff
Doston Shayunusov, D. Eskin, Hongbo Zeng, Petr A. Nikrityuk

Bibliographic record

VenueSeparation and Purification Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShear (geology)Separation (statistics)Geotechnical engineeringOil sandsParticle (ecology)Shear stressEnvironmental sciencePetroleum engineeringGeologyMechanicsMaterials scienceComposite materialPhysicsMathematics

Abstract

fetched live from OpenAlex

Removing oil attached to fine solid particles in water purification presents challenging technical, economic, and ecological issues. The present work numerically investigates the shear effect on oil that contains a 100- μ m-diameter sand particle in a water medium. In this research, we employ the coupled level set and volume of fluid method to track interface evolution accurately. An adaptive mesh refinement technique allows us to resolve an interface down to 60 nm. Computations at various particle Reynolds numbers ( R e p = 2 − 200 ) and oil film thicknesses (5%, 10%, 20%, and 40% of the diameter) reveal four coating behaviour scenarios: deformation, tail formation, partial separation, and full separation. The oil film negligibly deforms at R e p ≤ 5 at most film thicknesses. The separation occurs in full or partial regimes at higher R e p and thicker films. A thinner film forms a tail that periodically fluctuates. The analysis shows that these fluctuations uniquely reshape the oil tail at each time period.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.345

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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