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Record W4389140411 · doi:10.1115/pvp2023-106102

Applicability of Non-Uniformly Varying the Fin Density of Tandem Finned Cylinders As a Viable Vortex and Noise Suppression Technique

2023· article· en· W4389140411 on OpenAlexaff
Mohammed Alziadeh, Atef Mohany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsVortex sheddingAcoustic resonanceVortexWakeSound pressureParticle image velocimetryAcousticsResonance (particle physics)Materials scienceMechanicsInstabilityFinFlow visualizationAeroacousticsFlow (mathematics)PhysicsTurbulenceComposite materialReynolds number

Abstract

fetched live from OpenAlex

Abstract This article explores the applicability of using non-uniform finned tubes in tandem arrangement at L/Deq(avg) = 2.5 to suppress flow-induced acoustic resonance. Particle Image Velocimetry (PIV) measurements are performed to visualize the vortex development in the wake before and during flow-induced acoustic resonance. Acoustic pressure measurements are performed to characterize the aeroacoustic response of the tandem finned tubes. It was found that non-uniform finned tubes weaken the vortex shedding process and reduce the sound pressure level (SPL) during flow-induced acoustic resonance associated with the vortex shedding process by 50%. However, non-uniform finned tubes do not hinder the instability of the shear layers within its gap. This makes non-uniform finned tubes susceptible to flow-induced acoustic resonance associated with the instability of the gap shear layers. During this type of acoustic resonance excitation, highly discrete and well-organized vortex cores are formed in the gap and wake of both uniform and nonuniform finned tubes with SPL similar to that generated by the uniform finned tubes. A summary of the results are presented in the paper.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.245

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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