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Record W2116908472 · doi:10.1177/1099636213476510

Vibration analysis and design optimization of sandwich beams with constrained viscoelastic core layer

2013· article· en· W2116908472 on OpenAlexaff
Jasrobin Singh Grewal, Ramin Sedaghati, Ebrahim Esmailzadeh

Bibliographic record

VenueJournal of Sandwich Structures & Materials · 2013
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsOntario Tech UniversityConcordia University
Fundersnot available
KeywordsViscoelasticityNonlinear systemParametric statisticsFinite element methodMaterials scienceBeam (structure)Boundary value problemVibrationCore (optical fiber)Loss factorDisplacement (psychology)Structural engineeringMathematical analysisMathematicsComposite materialAcousticsPhysicsEngineering

Abstract

fetched live from OpenAlex

Dynamic properties of sandwich beam-type structure are analyzed using finite element method based on a nonlinear model for displacement field in the viscoelastic core layer of the beam structure. Results obtained for the nonlinear and linear models are compared to the test data available in literature. It is revealed that the nonlinear model provides more accurate results than the linear one. Parametric studies are carried out on the nonlinear model to illustrate the effect of viscoelastic core thickness on the loss factor and natural frequencies of structure. These results are also compared with those obtained for the linear model. Parametric studies on the nonlinear model revealing the sensitivity of the location of untreated and treated patch and the length of treated patch are presented. Finally, an optimization problem is formulated for the nonlinear model to locate the optimal distribution and numbers of partial treatments in order to attain the maximum damping in the sandwich beam for various boundary conditions.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.213
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

Citations33
Published2013
Admission routes1
Has abstractyes

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