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Record W2008283385 · doi:10.1121/1.4805406

Improving the sound absorbing efficiency of closed-cell foams using shock waves

2013· article· en· W2008283385 on OpenAlexaff
Olivier Doutres, Noureddine Atalla, Martin Brouillette, Christian Hébert, David Begg

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials sciencePolyurethaneComposite materialShock waveShock (circulatory)Acoustic wavePorous mediumAirflowThermal shockAcousticsPorosityMechanicsMechanical engineering

Abstract

fetched live from OpenAlex

Producing closed-cell foams is generally cheaper and simpler than open-cell foams. However, the acoustic efficiency of closed-cell foam materials is poor because it is very difficult for the acoustic waves to penetrate the material. A method to remove the membranes closing the cell pores (known as reticulations) and thus to improve the acoustic behavior of closed-cell foam material is presented. The method is based on the propagation of shock waves inside the foam aggregate where both the shock wave generator and the foam are in air at room conditions. Various shock treatments have been carried out on a polyurethane foams, and the following conclusions were drawn: (1) the reticulation rate increases and thus the airflow resistivity decreases while increasing the amplitude of the shock treatment; (2) the softness of the foam increases; (3) the process is reliable and repeatable; (4) obtained acoustic performance is comparable to classical thermal reticulation; and (5) the process can be used to control the reticulation rate along the thickness.

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: none
Teacher disagreement score0.563
Threshold uncertainty score0.375

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 integrity0.0000.001
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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