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Record W4405360952 · doi:10.1115/ipc2024-134107

Leak Orientation Effects on Flow-Induced Vibration in Horizontal Multiphase Pipelines

2024· article· en· W4405360952 on OpenAlexaff
Haobin Chen, Zhuoran Dang, Farzaneh Bayati, Simon Park, Ron Hugo

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2024
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsCanadian Nuclear LaboratoriesUniversity of Calgary
Fundersnot available
KeywordsPipeline transportLeakMultiphase flowFlow (mathematics)VibrationOrientation (vector space)MechanicsVortex-induced vibrationPetroleum engineeringMaterials scienceGeologyEnvironmental scienceEngineeringAcousticsMechanical engineeringPhysicsGeometry

Abstract

fetched live from OpenAlex

Abstract Multiphase pipe flow can result in strong structural vibrations. In this paper, an experimental investigation of the effects of leak orientation on flow-induced vibration in horizontal multiphase pipe flow is performed. Two leak orientations are investigated, a leak at the top of the pipe and a leak at the bottom of the pipe. Vibration signals are measured and high-speed videos are recorded. A total of four flow patterns including bubbly flow, plug flow, slug flow and stratified wavy flow are studied. Experimental results show that the leak orientation affects leak flow, leak flow rate, leak percentage and inlet flow rate in differing amounts depending on flow pattern. More pronounced changes are induced by a leak at the bottom of the pipe compared to a leak at the top of the pipe. Variance values of the extracted base functions are computed as a numerical indicator for the presence of a leak. A leak in bubbly flow is the easiest scenario to detect regardless of leak orientation. For a leak at the bottom of a pipe, differences in the computed variance values in plug and slug flows are comparable and detectable. A leak in plug flow induces a larger difference in the variance value than for slug flow if the leak is at the top of the pipe. Both leak orientations show difficulty in determining presence of a leak for stratified wavy flow. It is found that the differences in variance values computed using the x-axis (axial) accelerometer signal are smaller than the other two axes, indicating that more significant changes in FIV are induced in the radial and azimuthal directions during a leak event.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.707

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 communication0.0010.001
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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