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Record W4243944522 · doi:10.32920/ryerson.14656386.v1

A quasi-2D finite element formulation of active constrained-layer functionally graded beam

2021· preprint· en· W4243944522 on OpenAlexaff
Elena Miroshnichenko

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsConstrained-layer dampingBeam (structure)Materials scienceFinite element methodViscoelasticityNewmark-beta methodPiezoelectricityCantileverFunctionally graded materialStructural engineeringComposite materialMathematical analysisMaterial propertiesMathematicsPhysicsAcousticsEngineeringVibrationVibration control

Abstract

fetched live from OpenAlex

A functionally graded (FG) beam with an active constrained-layer damping (ACLD) treatment is modeled and analyzed. ACLD consists of a passive element, in the form of a viscoelastic layer bonded to the host structure, and an active constraining element which is represented by a piezoelectric fiber-reinforced composite (PFRC) laminate. It is assumed in the current formulation that the field variables are expressible as polynomials through the thickness of the beam and are cubically interpolated across the span. Hamilton's principle is used in the derivation of the equations of motion, which are solved using the Newmark time-integration method. The versatility of the formulation is demonstrated using different support mechanisms in the form of analysis of cantilevered, fixed-end partially-constrained and simply-supported beam cases. The effects of ply orientation in PFRC laminate and varying elastic modulus in the FG beam are also examined.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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.

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
Published2021
Admission routes1
Has abstractyes

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