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Record W2072344213 · doi:10.1121/1.3384520

A semi-analytical method to predict the vibroacoustics response of composite and isotropic stiffened panels.

2010· article· en· W2072344213 on OpenAlexaff
Abderrazak Mejdi, Noureddine Atalla

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsFinite element methodIsotropyVibrationBoundary element methodBoundary value problemStructural engineeringCoupling (piping)ModalMoment (physics)Materials scienceAcousticsPhysicsMathematical analysisMathematicsEngineeringClassical mechanicsComposite material

Abstract

fetched live from OpenAlex

The vibroacoustics behavior of aircraft-type stiffened panels is classically analyzed using deterministic methods such as the finite element and boundary element method at low frequencies or energy based methods at higher frequencies. In the present work, a general semi-analytical method based on modal expansion technique is developed to predict the vibration and acoustics radiation of both metallic and composite flat stiffened panels. Both unidirectional and bidirectional stiffened panels with eccentric stiffeners and various shapes are analyzed using the same matrix formulation. The presented model is also able to predict the response of both regular and irregular stiffened panels. The contributions of the force and moment modal coupling at each beam location are accounted for together with the effect of interaction between ribs in the case of orthogonal stiffened plate. The response to various types of excitations (point force, diffuse acoustic field, and turbulent boundary layer) are presented in terms of their joint acceptance. The model is numerically validated by comparison with the FEM/BEM and hybrid FEM/SEA methods for various configurations and excitations. Excellent agreement is found.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.705
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicComposite Structure Analysis and OptimizationFrench-language works237,207