MétaCan
Menu
Back to cohort
Record W1980374652 · doi:10.1121/1.3514502

Experimental study of a smart foam sound absorber

2011· article· en· W1980374652 on OpenAlexaff
Pierre Leroy, Alain Berry, Philippe Herzog, Noureddine Atalla

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsAcousticsMaterials scienceMicrophoneActuatorAdmittancePolyvinylidene fluorideTransfer functionFrequency domainSmart materialSound pressureElectrical impedanceComputer scienceComposite materialEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This article presents the experimental implementation and results of a hybrid passive/active absorber (smart foam) made up from the combination of a passive absorbent (foam) and a curved polyvinylidene fluoride (PVDF) film actuator bonded to the rear surface of the foam. Various smart foam prototypes were built and tested in active absorption experiments conducted in an impedance tube under plane wave propagation condition at frequencies between 100 and 1500 Hz. Three control cases were tested. The first case used a fixed controller derived in the frequency domain from estimations of the primary disturbance at a directive microphone position in the tube and the transfer function between the control PVDF and the directive microphone. The two other cases used an adaptive time-domain feedforward controller to absorb either a single-frequency incident wave or a broadband incident wave. The non-linearity of the smart foams and the causality constraint were identified to be important factors influencing active control performance. The effectiveness of the various smart foam prototypes is discussed in terms of the active and passive absorption coefficients as well as the control voltage of the PVDF actuator normalized by the incident sound pressure.

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 score0.501
Threshold uncertainty score0.324

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 science0.0010.000
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 head0.034
GPT teacher head0.275
Teacher spread0.241 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

Explore more

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