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Record W2887407360 · doi:10.1088/1361-665x/aad939

Adaptive Helmholtz resonator based on electroactive polymers: modeling, characterization, and control

2018· article· en· W2887407360 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmart Materials and Structures · 2018
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsHelmholtz resonatorResonatorAcousticsAcoustic impedanceElectrical impedanceMaterials scienceHelmholtz free energyCoupling (piping)EngineeringPhysicsMechanical engineeringOptoelectronicsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract This paper presents a new concept and strategy allowing adaptive control of a membraned Helmholtz resonator (HR) embedded in a melamine foam. The designed system aims to adapt the acoustic absorption performances and transmission loss in low frequencies (<500 Hz). The proposed concept consists in replacing the resonator front wall by an electroactive polymer membrane. The stiffness of the membrane can be controlled by an electric field, resulting in a resonance frequency shift. A 2D axisymmetric numerical model based on the finite elements method is developed to characterize the complex structure-acoustic coupling between the membrane, the HR and the host foam to determine the potential of the concept. Experimental measurements are then performed in an impedance tube and compared to numerical results. A feedforward algorithm based on neural networks allows the adaptivity of the membraned HR to the acoustic excitation variation inside the impedance tube.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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