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Record W3122965287 · doi:10.1115/1.4049709

Flow-Induced Acoustic Resonance of Finned Cylinders With Varying Fin Heights

2021· article· en· W3122965287 on OpenAlexafffund
Md Rashidul Islam, Atef Mohany

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFinResonance (particle physics)ExcitationMaterials scienceAcoustic resonanceCylinderExcited stateSound pressureMechanicsAcousticsGeometryPhysicsAtomic physicsComposite materialMathematics

Abstract

fetched live from OpenAlex

Abstract The flow-excited acoustic resonance phenomenon, which is instigated by periodic flow perturbation, leads to the generation of acute sound pressure. In this work, we investigated the characteristics of the flow-excited acoustic resonance for circular finned cylinders with different fin heights. The fin height is expressed as a normalized form considering the ratio of the fin diameter to the root cylinder diameter. The experiments are performed with finned cylinders having a range of diameter ratios between 1.5<Df/Dr<2.5. The diameter ratios are varied by changing the root diameter and fin diameter separately as well as simultaneously while keeping the fin pitch and the fin thickness constant. The results show that the excitation of acoustic resonance has profound dependence on the diameter ratio. Increasing the diameter ratios of the finned cylinder results in strong acoustic resonance excitation. The lock-in width and the onset of the acoustic resonance excitation also depend on the diameter ratio of the cylinders. Moreover, the results show that using an effective diameter based on the geometrical flow blockage does not take into account the changes occurring in the source of resonance excitation due to the addition of fins.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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