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Record W4388639846 · doi:10.1121/10.0022378

Series and parallel coupling of 3D printed micro-perforated panels and coiled quarter wavelength tubes

2023· article· en· W4388639846 on OpenAlexaff
Giuseppe Catapane, Giuseppe Petrone, Olivier Robin

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAcousticsTube (container)Series (stratigraphy)Noise controlWavelengthCoupling (piping)Noise (video)Computer scienceSound (geography)Point (geometry)OpticsMaterials scienceMechanical engineeringNoise reductionEngineeringPhysics

Abstract

fetched live from OpenAlex

Micro-perforated panel sound absorbers are widely used in noise control applications in the fields of architectural acoustics and transport acoustics. Combining micro-perforated panels with other resonant or sound absorbing systems may broaden the frequency range in which they absorb sound while ensuring that large sound absorption values are reached. In this work, a hybrid sound absorber that combines a micro-perforated panel and a coiled quarter wavelength tube is proposed. Series and parallel configurations of these two systems are studied from analytical, numerical, and experimental point of views. A comparison of two three-dimensional (3D) printing techniques for the production of samples highlights the main challenges for the practical implementation of the proposed design. The advantages and limitations of series and parallel arrangements are discussed and while the parallel configuration is more complex to setup in practice, it provides an improved sound-absorbing performance compared with the series configuration. Finally, the reproducibility of the hybrid absorber in parallel configuration is confirmed by testing samples that were produced with two different 3D printers and in two different laboratories.

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)0.001
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 score0.722
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207