MétaCan
Menu
Back to cohort
Record W2036595625 · doi:10.1115/1.2349563

A Study of Acoustic Resonance in a Staggered Tube Array

2006· article· en· W2036595625 on OpenAlexaff
Paul Feenstra, D. S. Weaver, Frantisek L. Eisinger

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAcoustic resonanceHeat exchangerTube (container)Resonance (particle physics)AcousticsFlow (mathematics)Materials scienceSound pressureMechanicsPhysicsComposite materialThermodynamicsAtomic physics

Abstract

fetched live from OpenAlex

Experimental laboratory research was performed to study the effect of test section width on the magnitude of acoustic resonance generated in a small pitch ratio staggered tube bank. Three different test section widths were studied: 505mm, 714mm, and 953mm. The results for acoustic resonance were compared to the tube bank data of Blevins and Bressler (1993, J. Sound Vib., 164(3), pp. 503–533), Ziada, Bolleter, and Chen (1984, ASME Symposium on Flow-Induced Vibrations, ASME, New York, Vol. 2, pp. 227–242); Ziada, Oengören, and Buhlmann (1989, J. Fluids and Struct., 3, pp. 293–324), and Fitzpatrick and Donaldson (1977, ASME J. Fluids Eng., 99, pp. 681–686). The present study showed that test-section width may be a significant factor in determining the maximum acoustic pressures generated by the flow. In particular, the simple relationship between maximum acoustic pressure and input energy parameter derived by Blevins and Bressler was not a reliable predictor for the array studied and will likely underpredict the maximum acoustic pressures in the lower modes of practical heat exchangers.

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)0.001
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: 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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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

Citations21
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicFluid Dynamics and Vibration AnalysisFrench-language works237,207