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Record W2993528981

Sound transmission loss improvement by a viscoelastic material used in a constrained layer damping system

2011· article· en· W2993528981 on OpenAlexaffvenue
Ivan Sabourin, Stefan Schoenwald, Jan-Gerrit Richter, Berndt Zeitler

Bibliographic record

VenueNPARC · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsViscoelasticityLoss factorConstrained-layer dampingTransmission lossDamperSound transmission classAcousticsVibrationMaterials scienceStructural engineeringModalStatistical energy analysisSound powerComposite materialVibration controlEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

A constrained layer damper is a viscoelastic material that is embedded between two parallel plates. The vibration energy in the first leaf creates shear strain in the viscoelastic material which dissipates kinetic energy into heat. A CLD is an effective way to maximize transmission loss through a partition by increasing its damping. SEA is a method for estimating the acoustic power flow through a system. The method subdivides the system into smaller elements, the so-called subsystems, that support a group of resonant modes and have a sufficient modal density and modal overlap. The measurement of the loss factors on the wall specimens with and without the viscoelastic material was done as part of a parallel paper by J.G. Richter. The reverberation method was used to measure the total loss factors on one side of the installed wall. The addition of a viscoelastic material between the leaves improves the TL significantly by more than 10 dB in the mid- and high frequency range increasing the STC rating by 8 points from 44 to 52.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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
Published2011
Admission routes2
Has abstractyes

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Same venueNPARCSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207