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Record W2028518869 · doi:10.1121/1.3238241

Three dimensional finite element modeling of smart foam

2009· article· en· W2028518869 on OpenAlexaff
Pierre Leroy, Noureddine Atalla, Alain Berry, Philippe Herzog

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsPoromechanicsOrthotropic materialFinite element methodActuatorMaterials scienceAcousticsPiezoelectricityAbsorption (acoustics)DissipationElectrical impedanceMechanical impedanceComputer scienceMechanical engineeringStructural engineeringComposite materialPorous mediumPhysicsEngineeringPorosity

Abstract

fetched live from OpenAlex

The "smart foam" concept and design originate from the combination of the passive dissipation capability of foam in the medium and high frequency ranges and the active absorption ability of piezoelectric actuator (generally polyvinylidene fluoride) in the low frequency range. This results into a passive/active absorption control device that can efficiently operate over a broad range of frequencies. In this paper, a full three dimensional finite element model of smart foam is presented including its experimental validation. The modeling tool uses quadratic poroelastic elements, as well as elastic, fluid, and piezoelectric elements. The weak integral formulation of the different media involved is presented with the associated coupling conditions. A simplified orthotropic model of poroelastic media is presented. To validate the developed model, a prototype of a smart foam has been realized and its passive absorption and radiation measured in an impedance tube and compared to predictions. The experimental validation demonstrates the validity of the model. This modeling tool constitutes a general platform to simulate and optimize various configurations of smart foams.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.322

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 integrity0.0000.001
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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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

Citations23
Published2009
Admission routes1
Has abstractyes

Explore more

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