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Record W2288201516 · doi:10.1002/cjce.22451

Oil‐water core‐annular flow in vertical pipes: A CFD study

2016· article· en· W2288201516 on OpenAlexvenueno aff
Raghvendra Gupta, Cary Turangan, Rogério Manica

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsCore (optical fiber)Petroleum engineeringMechanicsFlow (mathematics)GeologyMarine engineeringMaterials scienceEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Oil‐water core‐annular flow has the potential to significantly reduce the pumping power required for heavy oil transportation. We study axisymmetric, laminar‐laminar oil‐water core‐annular flow in a pipe (radius 14.2 mm) under three different channel conditions: vertically upward flow, vertically downward flow, and without considering the gravitational effect. For the ‘no‐gravity’ case, a smooth oil‐water interface is obtained and the results correspond to the analytical solution for a ‘fully‐developed’ annular flow. For the vertically upward flow, ‘saw‐tooth’ waves are observed at the interface. No perturbations are introduced to the flow initially; the interfacial waves take shape from the interplay between buoyancy, viscous force, and interfacial tension. CFD simulations are compared with published experimental observations, and found in qualitative agreement with each other. Both the CFD simulations and experiments show the wave amplitude reducing with increasing oil flow rate while keeping the water flow rate constant for a vertically upward flow. CFD simulation for a vertically downward flow shows the presence of very short waves of small amplitude, and the mean interfacial radius is larger than for the no‐gravity case. Moreover, the maximum velocity in the annular water film occurs between the interface and the wall, unlike the upward and no‐gravity cases where the maximum velocity in the water film occurs at the interface.

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 score0.664
Threshold uncertainty score0.327

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 head0.167
Teacher spread0.162 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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