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Record W2003773720 · doi:10.1121/1.3508903

A simple method to optimize the sound package in a double wall structure.

2010· article· en· W2003773720 on OpenAlexaff
Olivier Doutres, Noureddine Atalla

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSound transmission classTransmission lossAcousticsSoundproofingAbsorption (acoustics)Transmission (telecommunications)Materials scienceBlanketElectrical impedanceComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

The acoustic contributions to the transmission loss of an unbounded sound package in a double panel structure are investigated. For this purpose, a simple analytic expression of the normal incidence sound transmission loss of the double panel structure is proposed in terms of three main contributions: sound transmission loss of the panels, sound transmission loss of the blanket, and sound absorption due to multiple reflections inside the structure. It is shown that (i) at high frequencies, the transmission loss contribution of the blanket is preponderant compared to the absorption contributions;(ii) at the cavity resonance frequencies, the absorption contribution allows to attenuate the dips of insulation; and (iii) at medium and low frequencies, when the absorption performance of the porous layer is poor, the absorption contributions in the air-gaps can decrease the sound transmission loss performance of the double panel. The proposed methodology can also be used to estimate, from classical impedance tube measurements of the sound package only, the transmission loss of the whole double wall configuration. Testing the influence of various sound packages in a given double panel structure thus becomes quick and much less expensive compared to classical tests.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.002
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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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