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Record W2920806118 · doi:10.1139/cjce-2018-0502

Nonlinear dynamic analysis of a Stockbridge damper

2019· article· en· W2920806118 on OpenAlexvenueno aff
Nilson Barbieri, Marlon Elias Marchi, Marcos José Mannala, Renato Barbieri, Lucas de Sant’Anna Vitor Barbieri, Gabriel de Sant’Anna Vitor Barbieri

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNonlinear systemDamperShakerStructural engineeringFinite element methodBeam (structure)VibrationCantileverCurvatureMathematical modelStiffnessEngineeringMechanicsMathematicsPhysicsAcousticsGeometry

Abstract

fetched live from OpenAlex

The purpose of this work is to validate a nonlinear mathematical model (finite element method) for dynamic simulation of Stockbridge dampers of electric transmission line cables. To obtain the mathematical model, a nonlinear cantilever beam with a tip mass was used. The mathematical model incorporates a nonlinear stiffness matrix of the element due to the nonlinear curvature effect of the beam. To validate the mathematical model, the numerical results were compared with experimental data obtained on a machine adapted from cam test. Five different circular cam profiles with eccentricities of 0.25, 0.5, 0.75, 1.25, and 1.5 mm were used. Vibration data were collected through three accelerometers arranged along the sample. A good concordance was found between the numerical and experimental data. The same behavior was observed in tests of another Stockbridge damper excited by a shaker. The nonlinear behavior of the system was evidenced.

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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.003
GPT teacher head0.176
Teacher spread0.172 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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