MétaCan
Menu
Back to cohort
Record W4210340206 · doi:10.47363/jeast/2021(3)125

Experimental and Numerical Investigation of Phase Separation with Entrance Mixing

2021· article· en· W4210340206 on OpenAlexafffund
E. Weiwei, Kevin Pope

Bibliographic record

VenueJournal of Engineering and Applied Sciences Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeparator (oil production)Laminar flowMechanicsInletVolume of fluid methodVolume fractionComputer simulationMixing (physics)Multiphase flowMaterials scienceThermodynamicsGeologyPhysicsFlow (mathematics)

Abstract

fetched live from OpenAlex

In this paper, experimental and numerical methods are used to investigate the separation of an oil / water mixture. An American Petroleum Institute (API) gravity-based separator was built to conduct the experimental studies. The numerical simulations were developed with the same geometry as the experimental setup. The effect of inlet velocity and the oil volume fraction on the separation process is investigated with the new numerical predictions. Validations of the simulation model show that the numerical predictions of the multiphase Volume of Fluid (VOF) model with the laminar viscosity model agree well with the experimental results. The results of oil volume fraction and velocity vector distribution in the separator showed that there was a mixing zone located at the entrance, which had a lower relative oil volume fraction and a higher velocity. The study of the inlet velocity effect on the mixing length of the entrance mixing zone shows that when the fluid in the separator is in the laminar range, the mixing length is less than 40% of the total separator length. However, when the inlet velocity was increased until the fluid in the separator reached the transient range, the mixing length occupied 90% of the total separator length.

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 score0.206
Threshold uncertainty score0.204

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 head0.227
Teacher spread0.222 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Engineering and Applied Sciences TechnologySame topicFluid Dynamics and MixingFrench-language works237,207