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Record W3104599406 · doi:10.1115/detc2001/vib-21434

Parametric Vibration of Viscoelastic Moving Belts Using Standard Linear Solid Model

2001· article· en· W3104599406 on OpenAlexaff
Zhichao Hou, Jean W. Zu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsViscoelasticityVibrationParametric statisticsStability (learning theory)Mathematical analysisAmplitudeResonance (particle physics)MathematicsPhysicsTension (geology)Classical mechanicsComputer scienceAcousticsThermodynamicsOptics

Abstract

fetched live from OpenAlex

Abstract By using a standard linear solid model to describe the viscoelasticity of the belt material, a vibration analysis of a parametrically excited moving belt is performed. Closed-form solutions at principal resonance and summation resonance are derived at the first order approximation. The existence conditions and stability are discussed for the nontrivial solutions, yielding explicit expressions of the existence and the stability conditions in terms of the detuning parameter. Numerical examples clearly show the effects of tension fluctuations and translating speeds on the amplitudes of dynamic responses, the corresponding existence domains and the stability of the solutions. It is also demonstrated that the stability domains of the nontrivial solutions are different from those corresponding to elastic models.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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