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Record W4412961339 · doi:10.1115/1.4069182

Experimental Study of Two-Phase Flow-Induced Vibrations in Pipelines Under Varying Flow and Burial Conditions

2025· article· en· W4412961339 on OpenAlex
Zhuoran Dang, Ronald J. Hugo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Fluids Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPipeline transportVibrationFlow (mathematics)SIGNAL (programming language)Dimensionless quantityPipeline (software)Vortex-induced vibrationTwo-phase flowAcousticsFlow conditionsStructural engineeringEngineeringGeotechnical engineeringGeologyMarine engineeringMechanicsComputer sciencePhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The complex nature of two-phase flow increases the difficulty of safety monitoring systems in real-world pipelines. Two-phase flow-induced vibration (FIV), when used as a nonintrusive signal for a novel pipeline structural health monitoring (SHM) technique, requires signal analysis procedures and an experimental database. This paper experimentally characterizes two-phase FIV in pipelines across numerous scenarios, including varying flow conditions and in the presence of soil. The experiment measures structural vibration using tri-axial accelerometers mounted at the top of a pipe. Pipe vibration characteristics are analyzed in both the frequency and time domains. Flow conditions are quantified in terms of various two-phase flow factors, including flow pattern, flow pressure, and dimensionless numbers. Damping effects due to varying soil conditions, including unburied, semiburied, and fully buried, are examined.

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.

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 score0.391
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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