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Record W2054506988 · doi:10.1121/1.4783505

The transmission loss through curved sandwich composite structures

2004· article· en· W2054506988 on OpenAlexaff
Sebastian Ghinet, Noureddine Atalla, Haisam Osman

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2004
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsOrthotropic materialMaterials scienceShell (structure)InertiaRotary inertiaComposite numberTransmission lossStructural acousticsMechanicsPrincipal axis theoremTransverse planePhysicsFinite element methodGeometryOpticsStructural engineeringAcousticsClassical mechanicsComposite materialVibrationMathematics

Abstract

fetched live from OpenAlex

The principal aim of this work is to present a model for the transmission loss of sandwich composite cylindrical shells. The effects of membrane, bending, and transverse shearing as well as rotational inertia are considered in all of the layers composing the structure. The elastic constants of any layer are related to the orthotropic angle-ply defined as the angle of the principal directions of the layers material to the global axis of the shell. Fundamental relations are expressed using the dynamic equilibrium relations of the unit forces in the structure. The structural impedance, critical frequencies and ring frequency are computed numerically in the general case of symmetrical laminated composite shell. Their expressions are developed in a wave approach context. A general eigenvalue approach to compute the dispersion curves of such structures is presented. Using these curves, the radiation efficiency, the modal density, the group velocity an the resonant and nonresonant transmission loss are computed and used within SEA framework to predict the sound transmission loss of these structures. Comparisons with existing models and experimental data are also discussed.

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 score0.917
Threshold uncertainty score0.395

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 studies0.0000.001
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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
Published2004
Admission routes1
Has abstractyes

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