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Record W2945810891 · doi:10.1002/cjce.23564

Discussion on the pressure drop calculation for oil‐water separated flow using a one dimensional two‐fluid model

2019· article· en· W2945810891 on OpenAlexvenueno aff
Nannan Liu, Wei Wang, Ying‐Ying Liu, Liang Ma, Jing Gong

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPressure dropMechanicsLaplace pressureContact angleWork (physics)Laplace's equationMaterials scienceDrop (telecommunication)Shear stressLaplace transformFlow (mathematics)TurbulenceMathematicsThermodynamicsComposite materialPhysicsEngineeringSurface tensionMathematical analysisMechanical engineering

Abstract

fetched live from OpenAlex

The purpose of this work is to seek the key factors influencing the pressure drop calculation for oil‐water separated flow using a one dimensional two‐fluid model. Closure relations published for the two‐fluid model such as interface configuration, wall, and interfacial shear stress correlations are summarized. Interface configurations are established by numerically solving the Young‐Laplace equation, correlated with the Bond number, contact angle, and water holdup. Results show that the interface transforms from concave to convex with the enlargement of the contact angle and becomes flat as the Bond number increases. For the pressure drop calculation, a limited difference of predicted accuracy between the curve and flat interface is found. Discussions of both the wall and interfacial friction factor correlation on the pressure drop calculation are performed. In contrast to the effect of the interfacial friction factor, the correlation of the wall friction factor is found to have more contributions. We validate the prediction accuracy of different wall frictions factors using eight groups of published experiment results, and one correlation is recommended and being further extended.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.299

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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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