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Record W3109393604 · doi:10.1002/cjce.23951

Effects of high oil viscosity on oil‐gas downward flow in deviated pipes. Part 1: Experimental setup and flow pattern transitions

2020· article· en· W3109393604 on OpenAlexvenueno aff
Gabriel Soto‐Cortés, Eduardo Pereyra, Cem Sarica, Fabián Rivera-Trejo, Carlos Torres

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsMechanicsViscosityFlow (mathematics)Materials scienceSlip (aerodynamics)Stratified flowVolumetric flow rateThermodynamicsComposite materialPhysicsTurbulence

Abstract

fetched live from OpenAlex

Abstract This work provides new experimental data on high viscosity oil‐gas flow. A set of 170 downward flow experiments were carried out using a mixture of air and mineral oil (213 mPa · s, 50.8 mm internal diameter, 21.5 m long) for five different elevations: −45°, −60°, −70°, −80°, and −85°. Superficial liquid and gas velocities varied from 0.05 m/s to 0.7 m/s and 0.7 m/s to 7 m/s, respectively. Flow pattern transitions were identified and analyzed. The results helped to evaluate the effects of pipe inclination and liquid viscosity on the stratified‐annular transition, as well as the characterization of subclasses of annular flow pattern, named falling film, liquid slip, and wavy annular. Based on the experimental evidence, the study proposes a constant liquid‐fraction value as a transition between liquid slip and wavy annular flow patterns. A comparison of the present data with the flow pattern predictions of the available models were made. Results show disagreements, which justifies the need for a detailed study of the effects of viscosity and the inclination of the pipe in liquid‐gas downward flow mixtures.

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 score0.149
Threshold uncertainty score0.537

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 head0.154
Teacher spread0.150 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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