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Record W2171791218

A practical impedance tube method to estimate the normal incidence sound transmission loss of double wall structure

2011· article· en· W2171791218 on OpenAlexafffund
Olivier Doutres, Noureddine Atalla

Bibliographic record

VenueEspace ÉTS (ETS) · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSound transmission classAcousticsElectrical impedanceSound (geography)Tube (container)Transmission lossMaterials sciencePhysicsEngineeringElectrical engineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

The objective of this paper is to propose a practical impedance tube method to optimize the
\nsound transmission loss of double wall structure by concentrating on the sound package
\nplaced inside the structure. In a previous work, the authors derived an expression that
\nbreaks down the transmission loss of a double wall structure containing a sound absorbing
\nblanket separated from the panels by air layers in terms of three main contributions; (i)
\nsound transmission loss of the panels, (ii) sound transmission loss of the blanket and (iii)
\nsound absorption due to multiple reflections inside the cavity. The sound transmission loss
\ncontributions of the blanket can thus be estimated from three acoustic measurements using
\nimpedance tube techniques: two reflection coefficients at the front face and the rear face of
\nthe blanket placed in specific positions characteristic of its position inside the double wall
\nstructure and its sound transmission coefficient. The method is first validated in the case of
\nan aeronautic-type double wall structure filled with a 3.5 inch fiberglass. Next, it is applied
\nto a multilayer sound package with a particular focus on the interlayer-interface conditions.

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: none
Teacher disagreement score0.463
Threshold uncertainty score0.659

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 studies0.0000.000
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 head0.032
GPT teacher head0.349
Teacher spread0.318 · 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
Published2011
Admission routes2
Has abstractyes

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