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Record W4408933611 · doi:10.1002/cjce.25678

Impact of slug length in gas–liquid two‐phase flow on the structural stress characteristics of horizontal rigid pipelines

2025· article· en· W4408933611 on OpenAlexvenueno aff
Abdalellah O. Mohmmed, Hussain H. Al‐Kayiem, Abderraouf Arabi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersUniversiti Teknologi Petronas
KeywordsSlug flowSlugPipeline transportFlow (mathematics)MechanicsGeotechnical engineeringGeologyStress (linguistics)Two-phase flowPetroleum engineeringMaterials scienceStructural engineeringEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The intermittent passage of liquid slugs and gas pockets in slug flow generates substantial cyclic stress damage to piping systems and their supports. This issue poses significant challenges to the various industries in which this flow pattern is present. Despite their critical implications, the structural response to slug‐induced forces has not yet been thoroughly elucidated. This study addresses this gap through a comprehensive experimental investigation of the influence of slug length on the structural integrity of pipes. A non‐invasive image‐processing technique was employed to measure the slug length, while biaxial strain gauges captured the pipe wall strain, accounting for Poisson and friction fluid–structure interaction (FSI) coupling mechanisms. The findings revealed a reduction in the induced stresses with increasing superficial liquid velocity and slug length. Furthermore, a semi‐empirical model was developed by integrating slug length with the superficial gas and liquid velocities based on the slug unit concept to predict the structural stresses. The model provides a robust predictive framework for elucidating the relationship between slug length and induced stresses. However, its accuracy is influenced by the slug formation mechanism and various slug flow sub‐regimes. The model demonstrated exceptional predictive capability, achieving a mean error of 6.2% and coefficient of determination of 93%.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.437

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 head0.218
Teacher spread0.213 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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