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Record W3109075746

Low frequency sound absorption in a porous material with periodically distributed dead-end pores

2015· preprint· en· W3109075746 on OpenAlexaff
Philippe Leclaire, Olga Umnova, Thomas Dupont, Raymond Panneton

Bibliographic record

VenueUniversity of Salford Institutional Repository (University of Salford) · 2015
Typepreprint
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials sciencePerforationBiot numberPorosityAbsorption (acoustics)Porous mediumSound transmission classNoise reduction coefficientMechanicsComposite materialAcousticsOpticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

A theoretical and numerical study of the sound propagation in air-saturated porous media with straight pores bearing lateral cavities (dead-ends) is proposed. The straight pores can be considered as the main (Biot) pores. The lateral cavities are located at ”nodes” periodically spaced along each ”main pore” axis. The effect of periodicity in the distribution of the lateral cavities is studied and the low frequency limit valid for the closely spaced dead-ends is considered separately. It is shown that the absorption coefficient and transmission loss are influenced by the viscothermal losses in the main pores as well as their perforation rate. The presence of long or short dead-ends significantly alters the acoustical properties of the material. These depend strongly on the geometry (diameter and length) of the dead-ends, on their number per node and on the periodicity along the propagation axis. These effects are primarily due to low sound velocity in the main pores and on thermal losses occurring in the lateral cavities. The model predictions are compared with experimental results and examples of material design featuring periodically distributed dead-end pores are proposed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.186
Teacher spread0.174 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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