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Record W2082667093 · doi:10.1115/pvp2014-28456

Two-Phase Damping in Vertical Pipe Flows: Effect of Void Fraction, Flow Rate and External Excitation

2014· article· en· W2082667093 on OpenAlexafffund
C. Charreton, Cédric Béguin, A. Ross, Stéphane Étienne, M. J. Pettigrew

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsNatural frequencyTransverse planeVibrationMaterials scienceTube (container)ExcitationAdded massTwo-phase flowAmplitudePhysicsFlow (mathematics)AcousticsStructural engineeringOpticsEngineeringComposite material

Abstract

fetched live from OpenAlex

Predicting vibration effects in steam generators requires good knowledge of two-phase damping ζ2φ. The purpose of this work is to correlate two-phase damping in axial flow with tube transversal excitation frequency and magnitude. The test section consists of a stiff square tube subjected to internal axial flow of air-water mixture. The hydraulic diameter is 3 inches. The tube is supported with linear bearings and fitted with flexible tubing on both ends to allow motion in the transverse direction. Compression springs allow setting the natural frequency of the tube oscillation. A motor provides transverse sinusoidal excitation to the tube assembly. ζ2φ is determined from the frequency response function. As a result of this study, ζ2φ is represented as a function of excitation frequency and amplitude, void fraction and flow rate. Specific information is gained through high frame rate videos of the oscillating tube, including bubble transverse velocity and size for low void fraction, and flow pattern transitions. Indeed, it is suspected that two-phase damping is partly caused by the work rate of virtual mass forces of the gas phase. Better knowledge of the physical process involved in two-phase damping will allow better modeling and prediction of tube behavior.

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 score0.580
Threshold uncertainty score0.270

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 head0.244
Teacher spread0.240 · 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
Published2014
Admission routes2
Has abstractyes

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