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Record W1995697208 · doi:10.1115/1.2388996

Vibration Excitation Force Measurements in a Rotated Triangular Tube Bundle Subjected to Two-Phase Cross Flow

2006· article· en· W1995697208 on OpenAlexaff
C. Zhang, M. J. Pettigrew, Njuki Mureithi

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsPolytechnique MontréalNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsDragMechanicsLift-to-drag ratioVibrationLift (data mining)BundleVortex-induced vibrationAdded massMaterials scienceCylinderVortex sheddingStructural engineeringPhysicsAcousticsEngineeringReynolds numberTurbulenceComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Two-phase cross flow exists in many shell-and-tube heat exchangers. Flow-induced vibration excitation forces can cause tube motion that will result in long-term fretting-wear or fatigue. Detailed vibration excitation force measurements in tube bundles subjected to two-phase cross flow are required to understand the underlying vibration excitation mechanisms. An experimental program was undertaken with a rotated-triangular array of cylinders subjected to air/water flow to simulate two-phase mixtures over a broad range of void fraction and mass fluxes. Both the dynamic lift and drag forces were measured with strain gage instrumented cylinders. The experiments revealed somewhat unexpected but significant quasi-periodic forces in both the drag and lift directions. The periodic forces appeared well correlated along the cylinder with the drag force somewhat better correlated than the lift forces. The periodic forces are also dependent on the position of the cylinder within the bundle.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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 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

Citations25
Published2006
Admission routes1
Has abstractyes

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