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Record W2068642104 · doi:10.1115/imece2003-43582

On the Vibration of a Rotor-Shaft System With Viscoelastically Supported Bearings

2003· article· en· W2068642104 on OpenAlexafffund
N.H. Shabaneh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsOntario Power Generation
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNonlinear systemViscoelasticityVibrationHelicopter rotorBearing (navigation)StiffnessRotor (electric)Spring (device)MechanicsStructural engineeringControl theory (sociology)PhysicsEngineeringAcousticsMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

This paper investigates the dynamic behaviour of a single rotor-shaft system with nonlinear elastic bearings at the ends mounted on viscoelastic suspensions. A Timoshenko shaft model is utilized to incororate the flexibility of the shaft; the rotor is considered to be rigig and located at the mid-span of the shaft. A nonlinear bearing pedestal model is assumed which has a cubic nonlinear spring and linear damping characteristics. The viscoelastic supports of the bearings are modeled as Kelvin-Voigt model. Free vibration analysis is performed on the linear system including the damping of the bearings. Forced vibration analysis is performed on the nonlinear system. Equations of motion are derived for the nonlinear system based on the direct multiple scale method of one-to-one frequency-to-amplitude relationship using third order perturbation expansion. The effects of stiffness and loss coefficients of the viscoelastic supports on the complex natural frequencies are identified for the linear system. The results show that optimum values of the viscoelastic stiffness and loss coefficient can be achieved for a specific rotating shaft system to reduce vibrations and increase the operating regions. In addition, the frequency response of the nonlinear system indicates that a jump phenomenon takes place for high values of the bearing nonlinear elastic coefficient.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.189

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)0.0000.000

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 head0.005
GPT teacher head0.166
Teacher spread0.161 · 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 designTheoretical or conceptual
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

Citations0
Published2003
Admission routes2
Has abstractyes

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