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Record W4415455402 · doi:10.3397/in_2025_1246419

Vibro-acoustics modeling of lightweight structures with attached noise control materials

2025· article· en· W4415455402 on OpenAlexaff
Noureddine Atalla

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAerospaceNoise (video)Noise controlEmphasis (telecommunications)Range (aeronautics)Control (management)Control systemProcess (computing)

Abstract

fetched live from OpenAlex

Over the last two decades, the author's team has extensively investigated the modeling, characterization, and development of lightweight structures and their integrated sound packages, with particular emphasis on aircraft and aerospace applications. This keynote talk presents a comprehensive review of this body of research. Specifically, the presentation will demonstrate the remarkable effectiveness of the transfer matrix method, together with its numerous extensions, in accurately predicting the vibroacoustic response across a diverse range of lightweight structures and noise control materials. The impressive predictive capability of this methodology, particularly notable given its computational efficiency, will be illustrated through various excitation scenarios and systems featuring strategically positioned structured elements either within the structure or inside the attached noise control treatments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.237
Teacher spread0.225 · 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 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
Published2025
Admission routes1
Has abstractyes

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