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Record W2990380412 · doi:10.1115/detc2019-97048

The Effect of Gravity and Rotor Configuration on Dry Friction Whip and Safe Rotor/Stator Clearance

2019· article· en· W2990380412 on OpenAlexaff
Rajiv Kumar Vashisht

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStatorWhip (tree)Rotor (electric)RubbingHelicopter rotorControl theory (sociology)EngineeringVibrationMechanicsPhysicsMechanical engineeringComputer scienceAcoustics

Abstract

fetched live from OpenAlex

Abstract A mathematical model is developed for a real rotor/stator system with high degrees-of-freedoms, multiple disks, flexible bearing supports and couplings. The safe clearance level for coasting up of the rotor is calculated for a general high degree-of-freedom rotor/stator system. The harmful phenomena of dry friction whip, which is generally observable for simple 2 degree-of-freedom Jeffcott rotors in the absence of gravity only, can be proved to exist (in real rotor/stator systems) even in the presence of gravity for a wide range of clearance levels. In case of Jeffcott rotors, by fixing the clearance and increasing the rotor spin frequency, the response of the system follows the pattern: No rub - Forward Annular Rub (FAR) - Partial Forward Whirl (PFW) - Partial Backward Whirl (PBW) - dry whip (WHIP). In case of a real rotor/stator system, at certain frequencies, the system directly jumps to dry whip. The simulated results show a rich variety of system dynamics including FAR, PFW and WHIP in case of vertical rotors where the effect of gravity is neglected. For horizontal rotors, under the effect of gravity, the system response contains multi-harmonics, chaotic responses and multi-period vibrations. Based on these responses, a robust fault diagnosis strategy can be designed to identify the rubbing action in rotating machinery.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.190

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 head0.002
GPT teacher head0.178
Teacher spread0.176 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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