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Record W2786190023 · doi:10.2495/cmem-v6-n2-326-333

Numerical study of an oil–water flow in a gravitational separator

2017· article· en· W2786190023 on OpenAlexaff
Federico Torriano, M. Lessard, Nathalie Thibeault

Bibliographic record

VenueInternational Journal of Computational Methods and Experimental Measurements · 2017
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsSeparator (oil production)Petroleum engineeringEnvironmental scienceMechanicsWater flowGeologyMarine engineeringEngineeringGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

During their operation or in the event of an accident, power transformer can release a certain amount of oil in the subjacent soil. In order to avoid a fire hazard or any contamination to the environment, it is critical to capture any oil that was accidentally spilled. For this reason, catchment basins are placed below each power transformer and each basin is connected through pipes to a gravitational oil-water separator, which allows the oil droplets carried by the water flow to rise toward the surface and coalesce near the free surface. By doing so, the oil phase is separated from the mixture and it can be properly disposed afterwards. Prior to 1995, gravitational separators at Hydro-Qubec have not been designed according to the American Petroleum Institute (API) standards [1] but this does not necessarily imply that such separators do not comply with the environmental legislation in place. Thus, in order to evaluate if modifications to the existing gravitational separators are required, Hydro-Qubec has launched in 2012 an R&D project aimed at performing separator efficiency studies through a Computational Fluid Dynamics (CFD). In this paper, a numerical simulation of a gravitational oil-water separator in service at Hydro-Qubec using an inhomogeneous multiphase model is presented. Moreover, a new configuration of the existing separator is numerically tested and the results show that its performance is significantly improved.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.035
GPT teacher head0.389
Teacher spread0.354 · 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 designSimulation or modeling
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

Citations1
Published2017
Admission routes1
Has abstractyes

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