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Record W4408231156 · doi:10.1115/1.4068133

Flow-Induced Vibration Analysis of Rigid Horizontal Pipelines Under Two-Phase Flow and Leak Conditions

2025· article· en· W4408231156 on OpenAlexaff
Zhuoran Dang, Haobin Chen, Ronald J. Hugo, Simon S. Park

Bibliographic record

VenueJournal of Fluids Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsCanadian Nuclear LaboratoriesUniversity of Calgary
Fundersnot available
KeywordsFlow (mathematics)LeakMechanicsPipeline transportVibrationTwo-phase flowGeologyEngineeringPhysicsAcousticsMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract This study investigates the vibrational response of horizontal rigid pipelines subjected to internal two-phase flow with simulated leaks. Using spectral-based contour plots and vibrational energy measurements, we analyze the dynamics across various flow velocities and patterns in a 5-m-long, 2-in diameter pipeline. Results indicate that flow patterns and Reynolds numbers significantly influence vibration characteristics. Except for bubbly flow, increasing the mixture Reynolds number amplifies power spectral magnitudes and extends excitation to higher frequencies, independent of leaks. Fluid loss enhances spectral magnitudes at higher liquid Reynolds numbers, with gas Reynolds numbers further intensifying vibration. Leaks modify spectral spikes due to multiphase flow fluctuations, making them more pronounced and persistent. Vibrational augmentation is predominant in the direction of fluid loss, peaking at the leak location and attenuating with increasing distance from the leak location. Slug flow demonstrates the highest increase in vibrational energy. Bubbly flow exhibits maximum leak to no-leak amplification (15–25 dB), followed by slug flow (5–15 dB), and plug flow (<10 dB). Minimal leak-induced effects (<5 dB) occur in stratified wavy and low-velocity intermittent flows. This study establishes a foundation for leak detection and pipeline health monitoring, emphasizing the role of flow-induced vibration analysis in enhancing pipeline safety.

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: none
Teacher disagreement score0.714
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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