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Record W2897303145

Suppression of Acoustic Resonance in Pipelines Using Helmholtz Resonators

2018· article· en· W2897303145 on OpenAlexaff
Karim Sachedina, Atef Mohany, Marwan Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of GuelphOntario Tech University
Fundersnot available
KeywordsAcousticsAttenuationHelmholtz resonatorPipeline transportAcoustic attenuationResonatorHelmholtz free energyPipeline (software)Resonance (particle physics)Sound pressureAcoustic resonanceNode (physics)Materials sciencePhysicsEngineeringOpticsMechanical engineeringOptoelectronics
DOInot available

Abstract

fetched live from OpenAlex

The effectiveness of Helmholtz resonators (HRs) inserted in various configurations along a pipeline system is investigated under conditions of acoustic resonance. It is shown that the acoustic damping achieved by a single, large volume HR can be achieved using multiple, smaller HRs. The attenuation is found to be dependent upon the location of the HR along the standing wave, with maximum attenuation achieved at the acoustic pressure antinode and minimal attenuation at the node. Mean flow introduced into the pipeline is shown to slightly reduce the effectiveness of the HRs. Additionally, the use of multiple HRs placed at strategic intervals along the pipeline is shown to achieve significant damping, eliminating the need for determining the standing wave formation in order to determine damping device placement. The results show the potential for using HRs in industrial systems, where space may be limited and characterizing the acoustics inside the pipelines is impractical.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.422

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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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