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Record W2088708736 · doi:10.1121/1.3587885

Predicting response of a honeycomb sandwich panel to diffuse acoustic field vs turbulent boundary layer excitation using coupled finite element-boundary element approach.

2011· article· en· W2088708736 on OpenAlexaff
Reza Madjlesi, Noureddine Atalla

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de SherbrookeBombardier (Canada)
Fundersnot available
KeywordsHoneycombBoundary element methodMaterials scienceFinite element methodExcitationStiffnessBoundary layerAcousticsSandwich-structured compositeHoneycomb structureComposite numberPhysicsStructural engineeringComposite materialMechanicsEngineering

Abstract

fetched live from OpenAlex

Composite materials are being extensively used in primary and secondary aerospace structures due to high specific strength and stiffness as well as low weight. In this study finite element (FE) and boundary element (BE) are used to study acoustical performance of honeycomb type structures subjected to common excitation sources in flight. Coupled FE-BE is applied to predict transmission loss (TL) of honeycomb type structure, excited by diffuse acoustic field and turbulent boundary layer. Structural FE model correlation is performed using experimental modal analysis. Transmission loss of honeycomb panel was measured at the GAUS TL Lab using diffuse acoustic field. TL results are used to validate coupled FE-BE vibro-acoustic model of a curved honeycomb panel. Correlated model is used to predict response of structure to TBL source. Effect of noise control treatments in reducing radiated noise from honeycomb panel excited by TBL and diffuse field are studied.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.492
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.268
Teacher spread0.226 · 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 teacher head, 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

Citations1
Published2011
Admission routes1
Has abstractyes

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