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Record W2260582474

Investigations of an impedance tube technique to determine the transmission loss of materials under angular incidence

2015· preprint· en· W2260582474 on OpenAlexaff
Marie Le Bourlés, Émeline Sadoulet-Reboul, Morvan Ouisse, Olivier Doutres, Kévin Verdière, Raymond Panneton

Bibliographic record

VenueEspace ÉTS (ETS) · 2015
Typepreprint
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de SherbrookeÉcole de Technologie Supérieure
FundersAgence Nationale de la Recherche
KeywordsAcousticsElectrical impedanceTube (container)Plane waveTransmission lossTransmission (telecommunications)Angle of incidence (optics)HarmonicOpticsMaterials scienceComputer sciencePhysicsEngineeringMechanical engineeringElectrical engineeringTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

The most popular technique for transmission loss characterization of porous materials is undoubtedly the impedance tube method based on the transfer matrix method (described in ASTM E2611) which is valid for frequencies of a few kilo hertz, depending on the tube's size.A mono dimensional harmonic plane wave is created at one tube's extremity; this wave propagates inside the tube and is partially transmitted through an absorbing material to the opposite side of the tube.Using proper boundary conditions and measuring the sound pressure yields to the estimation of the transmission loss after a few algebraic manipulations.All measurement systems developed until now are based on a normal sound incidence.Yet, some modeling methods used for these materials are based on excitations with variable incidence angle and thus the validation of the developed models requires to use a facility system for which the characterization can be done using a variable wave incident angle on the material.The work proposed here details the design of such an experimental system.This system is based on a new design of the classical impedance tube: a numerical study is carried out to identify the range of validity of the proposed technique.The possibilities that this new system offers as well as its limits in terms of incident angle or frequency are illustrated on specific samples.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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 designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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