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Record W4225105018

Comportement vibratoire des plaques microperforées finies

2022· preprint· fr· W4225105018 on OpenAlexaff
Lucie Gallerand, Thomas Dupont, Philippe Leclaire

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2022
Typepreprint
Languagefr
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Les plaques microperforées sont utilisées dans l’industrie pour leurs propriétés d’absorption acoustique. Ces structures légères, de par des échanges dans les couches limites visqueuses et thermiques près de l’interface fluide-structure, permettent l’absorption des ondes acoustiques. Il est montré dans ce travail que ces structures peuvent également induire un amortissement supplémentaire significatif particulièrement pour les basses fréquences. Ces solutions sans ajout de masse peuvent être un complément ou une alternative aux matériaux viscoélastiques couramment utilisés pour amortir les vibrations en moyennes et hautes fréquences.Ainsi il est proposé dans ce projet d’étudier les effets des microperforations sur l’amortissement des plaques finies. Pour cela, un modèle analytique basé sur une approche vibratoire de plaques poreuses finies est proposé. Des études paramétriques mettent en évidence l’existence d’une fréquence caractéristique qui lorsqu’elle coïncide avec un mode de plaque permet d’obtenir un amortissement ajouté maximal autour de ce mode. Cette fréquence est fonction des paramètres géométriques des microperforations. Des mesures vibratoires sur des plaques finies avec et sans microperforations permettent de valider le modèle et de confirmer ainsi l’effet d’amortissement ajouté par les microperforations en basses fréquences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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