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Record W3205702922 · doi:10.1115/omae2021-62513

Void Fraction, Pressure Drop, and Flow Pattern Behavior for Two-Phase Non-Newtonian Slug Flow

2021· article· en· W3205702922 on OpenAlexaff
Faraj Ben Rajeb, Syed Imtiaz, Yan Zhang, Amer Aborig, Mohamed M. Awad, Mohammad Azizur Rahman

Bibliographic record

VenueVolume 10: Petroleum Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSlug flowPressure dropMechanicsTwo-phase flowIsothermal flowFlow coefficientFlow (mathematics)Newtonian fluidOpen-channel flowIntermittencyMaterials sciencePipe flowPressure gradientThermodynamicsPhysicsTurbulence

Abstract

fetched live from OpenAlex

Abstract Slug flow is one of the most common flow patterns in non-Newtonian two-phase flow in pipes. It is a very common occurrence in gas-liquid two-phase flow in the pipe. Usually, it is an unfavorable flow pattern due to its unsteady nature, intermittency as well as high pressure drop. The differences between slug flow and elongated bubble flow are not clear because usually these two types of flow combined under one flow category. In general, these two-phase flow regimes are commonly defined as intermittent flow. In the present study, pressure gradient, and wave behavior in slug flow have been investigated depending on experimental work. In addition, void fraction has been estimated regarding available superficial liquid and gas velocities. The experimental records of superficial velocities of gas and liquid for slug flow and other flow patterns is used to create flow regime map for the gas non-Newtonian flow system. The effect of investigated flow regime velocities for non-Newtonian/gas flow on pressure drop and void fraction is reported. Pressure drop has been discovered to be reduced in slug flow more than other flow patterns due to high shear thinning behavior.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.223
Teacher spread0.219 · 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.

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
Published2021
Admission routes1
Has abstractyes

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