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Record W2037733484 · doi:10.1121/1.3587748

On the use of a variational boundary element/finite element approach to predict the vibroacoustic response of structures.

2011· article· en· W2037733484 on OpenAlexaff
Franck Sgard, Kamel Amichi, Noureddine Atalla, Hugues Nlisse

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de SherbrookeInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsFinite element methodOrthotropic materialBoundary element methodStructural acousticsAcousticsBaffleSound powerComputationSound transmission classSound pressureAcoustic radiationEnclosurePoint (geometry)Boundary value problemBoundary (topology)Mathematical analysisMathematicsComputer sciencePhysicsGeometryRadiationStructural engineeringSound (geography)VibrationOpticsEngineeringAlgorithm

Abstract

fetched live from OpenAlex

This paper deals with the application of a variational boundary element/finite element approach to predict the vibroacoustic response of structures. First, the theoretical formulation is presented. Then two specific examples are considered and numerical results are presented. The first case concerns the calculation of the sound transmission through a curved orthotropic sandwich panel. The blocked pressure field acting on the structure is first calculated and then used as a loading in the computation of the forced response of the panel. The second case consists of the calculation of the sound radiation of an assembly of plates at different angles excited by uncorrelated point forces. This configuration is of special interest to evaluate the accuracy of analytical Leppington’s radiation efficiency correction factors related to baffle angles to predict the acoustic power radiated by a box-shaped acoustic enclosure.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.041
GPT teacher head0.245
Teacher spread0.204 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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