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Record W2993713291

Preliminary acoustic performance investigation of concentric-tube perforated muffler design

2006· article· en· W2993713291 on OpenAlexaffvenue
Jun Zuo, Colin Novak, Helen Ule, Ramani Ramakrishnan, Robert Gaspar

Bibliographic record

VenueCanadian acoustics · 2006
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsToronto Metropolitan UniversityUniversity of Windsor
Fundersnot available
KeywordsMufflerTube (container)AcousticsTransmission lossAttenuationConcentricPerforationAcoustic attenuationExpansion chamberSilencerHelmholtz resonatorMaterials scienceEngineeringResonatorInletMechanical engineeringPhysicsOpticsElectrical engineeringGeometryMathematics
DOInot available

Abstract

fetched live from OpenAlex

Two impendence models based on the theoretical and empirical results are compared using a one dimensional segmentation method to explore the differences in predicted transmission loss (TL) of a concentric-tube perforated muffler with zero mean flow. Simulations demonstrate that perforated tube mufflers have better acoustic attenuation properties than simple expansion chamber mufflers. These perforated tube elements are widely used in resonators and mufflers to attenuate exhaust system noise. The physical segmentation or simplification of the perforated tube includes the effect of perforation in each segment, which is considered as a branch with a solid tube connecting the branches of each adjoining segment. A one dimensional segment model is compared with Ricardo WAVE, a commercial software modeling package. Results show that large discrepancy in prediction of transmission loss is found between the WAVE for a large perforated muffler configuration.

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 categoriesMeta-epidemiology (narrow)
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 score0.119
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.197
Teacher spread0.178 · 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.

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

Citations0
Published2006
Admission routes2
Has abstractyes

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