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Record W2618125104 · doi:10.1002/cjce.22903

An experimental investigation of high‐viscosity oil‐water flow in a horizontal pipe

2017· article· en· W2618125104 on OpenAlexvenueno aff
Jing Shi, Hameed Al‐Awadi, Hoi Yeung

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsFroude numberTurbulenceMechanicsReynolds numberFlow (mathematics)Flow coefficientPipe flowTwo-phase flowMaterials scienceVolumetric flow ratePressure gradientLubricationViscosityWater flowIsothermal flowOpen-channel flowGeotechnical engineeringGeologyComposite materialPhysics

Abstract

fetched live from OpenAlex

An experimental investigation on high‐viscosity oil‐water flow in a horizontal pipe (I.D. = 26 mm) has been conducted. The oil viscosity investigated varied between 3800 and 16 000 mPa · s. Flow patterns observed in experiments are presented and flow pattern maps are reported. The inversion from oil‐continuous to annular‐water‐continuous is discussed. The average pressure gradient of oil‐continuous two‐phase flow can be reduced before the inversion to annular‐water‐continuous flow due to partial lubrication from discontinuous water streams. The stable water‐lubricated flow develops at a lower input water volume fraction with increase of the superficial oil velocity. An empirical criterion for the formation of stable water‐lubricated flow was proposed in terms of the oil phase Froude number and the input water volume fraction. The friction factor of water‐lubricated flow can be one to two orders of magnitude higher than that of single‐phase water flow and has a faster decrease with increase of the Reynolds number than that of single‐phase turbulent flow. These are linked to the oil fouling on the pipe wall. Models for the prediction of the pressure gradient of core flow proposed by different authors in the literature were evaluated with the experimental data.

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 score0.131
Threshold uncertainty score0.324

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 head0.180
Teacher spread0.174 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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