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Oil–Particle Separation in a Falling Sphere Configuration: Effect of Oil Film Thickness

2016· article· en· W2511505045 on OpenAlexaff
Sasan Mehrabian, Edgar Acosta, Markus Bussmann

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceViscosityOil dropletReynolds numberParticle (ecology)Capillary actionComposite materialAsphaltMechanicsLubricationFalling (accident)RheologyParticle sizeChemistryEmulsionGeologyPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

High-speed videos of oil-coated solid spheres falling through an aqueous solution were analyzed to determine the amount of oil separated and the velocity of the coated sphere during free fall. The oil-coated sphere configuration is relevant to understanding the recovery of oil from oil sands; hence, bitumen was used as the oil phase. A new form of a capillary number based on a low-Reynolds number solution is introduced to characterize the separation process. The proposed particle-based capillary number takes into account the effect of the oil film thickness and the viscosity ratio. In this study, the separation of oil from an oil-coated sphere is examined as a function of the oil film thickness, while keeping the viscosity ratio constant at 0.08. From the experimental results, it was observed that there is a critical oil film thickness beyond which oil separation from a particle is observed. Higher oil removal efficiencies are obtained at higher oil film thicknesses. The velocity of an oil-coated sphere is higher than the velocity of an oil-free sphere due to the lubrication effect of the oil layer.

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 score0.233
Threshold uncertainty score0.365

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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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