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Record W4402438613 · doi:10.11159/htff24.238

Experimental Investigation of the Transition from the Segregated Flow Regime to the Intermittent Flow Regime in a Horizontal Pipe

2024· article· en· W4402438613 on OpenAlexvenueno aff
Amina Bouderbal, Abderraouf Arabi, Abdellah Arhaliass, Yacine Salhi, El‐Khider Si‐Ahmed, Jack Legrand

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
FundersCampus France
KeywordsMechanicsFlow (mathematics)Pipe flowGeologyMaterials sciencePhysicsTurbulence

Abstract

fetched live from OpenAlex

The importance of two-phase flows in the industry is well established.The prediction of the flow regimes is of paramount importance.For instance, in the transport of hydrocarbon, the flow along the pipe often involves instabilities that can cause transitions between flow regimes, such as the transition from segregated to intermittent flow, resulting in pressure oscillations that can cause significant pipe damage.Understanding and studying the parameters influencing this transition is important for correct regime forecasting and pipe design.This study aims, firstly, providing a classification of sub-regimes in a 40 mm pipe, knowing that flow regimes are essential and useful for modelling hydrodynamic parameters and heat transfer in two-phase gas-liquid flows.Moreover, the present work will be focused on the intermittent regime in which oscillations generated by the passage of liquid slugs may cause pressure oscillations that can lead to pipe leaks.Thus, accurate prediction of the onset of intermittent flow and a better knowledge of the conditions under which it occurs are of great importance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes1
Has abstractyes

Explore more

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