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Record W3151509198 · doi:10.1063/5.0022294

Method for controlling boundary condition effects on the measurement of acoustic properties of small samples in tubes

2021· article· en· W3151509198 on OpenAlexafffund
Thomas Dupont, Kévin Verdière, Philippe Leclaire, Raymond Panneton

Bibliographic record

VenueReview of Scientific Instruments · 2021
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de SherbrookeÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaConseil régional de Bourgogne-Franche-Comté
KeywordsMaterials scienceTransfer-matrix method (optics)HomogeneousMaterial propertiesAcousticsBoundary value problemBoundary (topology)Boundary element methodSpiral (railway)Frame (networking)Mixing (physics)MechanicsFinite element methodComposite materialComputer scienceStructural engineeringPhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

As standard ASTM E2611 reveals, the normal incidence sound transmission loss measured on a small sample in an acoustic tube is not only a property of the material but also strongly dependent on boundary conditions (generally unknown) and on the way the material is mounted. This article proposes an experimental method to control the effects of the lateral boundary conditions in an acoustic tube. The main objective is to deduce the properties of a "client element" (material sample) from the measured global acoustic properties of a patchwork composed by the "client material" and a known "host support." Three patchwork configurations have to be distinguished: patchworks with and without an impervious and rigid interface between the elements and patchworks composed by elements that cannot be identified as equivalent fluids. For each of these configurations, the use of a specific method based on the Mixing Rule Method (MRM) or on the Parallel Transfer Matrix Methods (P-TMM or dP-TMM) used in reverse way is proposed. Numerical and experimental validations are proposed in acoustic tubes on a convenient configuration: a material sample surrounded by an air ring. This configuration allows reducing the material elastic-frame behavior to leave a limp-frame behavior. The proposed methods allow removing the effect of the lateral air ring host surrounding the material. For homogeneous materials, the two methods based on MRM and dP-TMM give similar good results. For non-homogeneous materials or for materials that cannot be modeled as equivalent fluids, only the method based on dP-TMM gives good results.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.050
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.074
GPT teacher head0.292
Teacher spread0.219 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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