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Record W1966941239 · doi:10.1115/detc2007-34432

Chaos Properties of a Rotor System With Nonlinear Hydro-Static Bearings

2007· article· en· W1966941239 on OpenAlexaff
Changping Chen, Liming Dai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsNonlinear systemBifurcationEccentricity (behavior)Rotor (electric)Control theory (sociology)Hydrostatic equilibriumBearing (navigation)Helicopter rotorPhysicsChaoticMechanicsComputer scienceEngineeringMechanical engineeringLaw

Abstract

fetched live from OpenAlex

General motions of discs and shafts of large rotary machines appear with highly nonlinear behavior. The geometries of the shafts and the supporting systems of this machine can all be treated as nonlinear. This paper aims to investigate the chaos properties of the nonlinear rotating system. The nonlinear dynamic governing equations of the rotating bearing system are derived. The geometric nonlinearity of the shaft, nonlinear hydrostatic forces of the bearings, mass of the shaft and disc, deformation of the shaft and a disc mounted on the shaft, and the viscoelasticity of the supports are all taken into account. Numerical simulations are performed in the research for studying the bifurcation and chaos properties of the specified nonlinear rotating system. The effects of the shaft’s rotating speed and the mass eccentricity of the disc on the nonlinear dynamic properties of the system are investigated in detail. The results show that abundant of bifurcations and chaos exist in this nonlinear rotating system Corresponding to certain parameter values. The bifurcations and chaotic phenomena should be avoided when designing the rotating system by adjusting the design parameters. The results of this research can hereby be used for guiding the design and operation of rotor-bearing systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.269

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 head0.007
GPT teacher head0.168
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 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

Citations1
Published2007
Admission routes1
Has abstractyes

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