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Record W4206596378 · doi:10.1109/tpwrd.2021.3133812

Numerical and Experimental Investigation of the Efficiency of an Oil-Water Gravity Separator at an Electrical Substation

2021· article· en· W4206596378 on OpenAlexaffabout
Federico Torriano, Jean‐Bernard Dastous, Nathalie Thibeault

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsSeparator (oil production)InletComputational fluid dynamicsPetroleum engineeringComputer simulationMechanicsEnvironmental scienceNuclear engineeringEngineeringMechanical engineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This papers presents a numerical approach based on computational fluid dynamics (CFD) that was developed to ensure that gravity oil-water separators at Hydro-Québec electrical substations comply with the environmental legislation in place. More precisely, multiphase simulations are performed for two operating conditions of an in service separator, and an experimental setup is used to produce an oil spill and to measure the time-varying oil concentration at the separator outlet. The results show that, for the tested conditions, the oil droplet size at a separator inlet generally follows a log-normal distribution and that the droplet diameter varies between 100 and 1000 µm with a mean value of 475 µm. Furthermore, both the experimental and numerical results show that the time required for the oil droplets to reach the separator outlet is quite shorter than the theoretical mean residence time. Finally, this study demonstrates that the numerical model predicts the separator efficiency within 1% of the measured value, and that it can be a valuable tool to investigate existing separators or to design new ones.

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 score0.108
Threshold uncertainty score0.275

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 head0.206
Teacher spread0.199 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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