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Record W2202946801 · doi:10.1002/cjce.22377

Rheological behaviour of oil and water emulsions and their flow characterization in horizontal pipes

2015· article· en· W2202946801 on OpenAlexvenueno aff
Jian Zhang, Jing‐yu Xu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsRheometerRheologyMaterials scienceShear rateLaminar flowComposite materialSauter mean diameterReynolds numberMechanicsThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

Abstract In this work, the rheological behaviour of oil and water emulsions and their flow characterization were studied using a Haake RS6000 rheometer and a lab scale flow loop. The rheological properties of the synthetic emulsions were investigated at oil volume fractions from 0.1–1.0 L/L and shear rates from 0.001–1000 s −1 at a system temperature of 20 °C. The emulsions flow, formed by the SMV static mixer installed before the test section, was observed in the 2‐m long horizontal pipes with 25 and 50 mm inner diameters at different mixture flow rates from 0.01–7.00 m 3 /h. A comparison between rheological measurements and laminar flow data shows that the values of the Sauter mean droplet diameters formed by the SMV static mixer are approximately one order of magnitude bigger than those measured by the rheometer. For oil‐in‐water emulsions with low oil content, the stress‐strain relationships obtained in the rheometer are more suitable for predicting the transport characteristics of the emulsions with a large pipe diameter than those with a small pipe diameter. In addition, the phase inversion points in the pipe flow are closer to those measured at high shear rates in the rheometer. Therefore, the apparent viscosity obtained by the rheology measurements at high shear rates could be introduced for accurate calculations of the mixture Reynolds number and the friction factor in emulsions flow.

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 score0.720
Threshold uncertainty score0.269

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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

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