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Record W1999439982 · doi:10.1115/1.1403024

Effect of Flow Regime and Void Fraction on Tube Bundle Vibration

2001· article· en· W1999439982 on OpenAlexaff
Colette E. Taylor, M. J. Pettigrew

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsAtomic Energy (Canada)Canadian Nuclear Laboratories
Fundersnot available
KeywordsBundleHeat exchangerMechanicsPorosityMaterials scienceVibrationTwo-phase flowVortex-induced vibrationTube (container)Void (composites)Fluid dynamicsFlow (mathematics)ThermodynamicsComposite materialPhysicsAcoustics

Abstract

fetched live from OpenAlex

Two-phase cross flow occurs in industrial heat exchangers such as condensers, boilers, and steam generators. Under certain flow regimes and fluid velocities, the fluid forces result in tube vibration and possibly tube damage due to fretting or fatigue. Prediction of these fluid forces requires an understanding of the flow regimes found in heat exchanger tube bundles. Measurements of void fraction within a tube array were taken as an initial step in determining the two-phase flow patterns. The tests were conducted in a Freon 134a test loop at about 1 MPa and 30°C. The measurements were compared against void fraction models commonly used in heat exchanger thermalhydraulic simulation codes and against available flow regime maps. Not surprisingly, the results indicate that a drift-flux model more accurately predicts the void fraction within a tube array. The measurements also confirm the existence of nonuniform void fraction radially around the tube. Based on these measurements and available literature, appropriate void fraction models for use in flow-induced vibration design guidelines are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.003
GPT teacher head0.206
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2001
Admission routes1
Has abstractyes

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