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Record W2046390595 · doi:10.1121/1.3249421

Experimental and numerical comparison of acoustic performance of sound packages with viscoelastic damping or equivalent mass as treatments to flat aluminum panel.

2009· article· en· W2046390595 on OpenAlexaff
Esen Cintosun, Tatjana Stecenko, Noureddine Atalla

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsViscoelasticityMaterials scienceAcousticsFinite element methodNumerical analysisConstrained-layer dampingTransfer-matrix method (optics)Composite materialMechanicsStructural engineeringVibrationPhysicsMathematicsMathematical analysisVibration controlEngineering

Abstract

fetched live from OpenAlex

Acoustic performance parameters of airborne and structure-borne insertion loss were compared experimentally and numerically for sound packages with viscoelastic damping or equivalent mass. Transfer matrix method and finite element method were used to perform the numerical analysis. Viscoelastic material damping and equivalent mass (as part of sound packages) were compared as treatments to an aluminum flat panel. The sound packages were made up of either fiberglass or foam in addition to viscoelastic material damping or equivalent mass. The viscoelastic material damping used in this study is constraining layer damping (CLD). The equivalent mass was a solid material with the same surface weight of CLD. As part of the analysis, aluminum panel with and without sound package was subjected to diffuse acoustic field and point force mechanical excitations. The experimental and numerical results both show the same trends. [Work supported by MTI Polyfab Inc.]

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.304
Teacher spread0.274 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207