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Record W3120344475 · doi:10.1115/gt2020-14136

Experimental Investigation of the Effects of Squeeze Film Damper Design on Highspeed Rotor System

2020· article· en· W3120344475 on OpenAlexaff
Vincent Iacobellis, Kamran Behdinan, Dennis Chan, Dave Beamish

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDamperRotor (electric)EngineeringVibrationRotordynamicsCritical speedDisplacement (psychology)Bearing (navigation)Helicopter rotorStructural engineeringParticle displacementAmplitudeMechanical engineeringAcousticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Squeeze film dampers (SFDs) are used in highspeed rotordynamics systems to mitigate vibrations while traversing critical speeds. SFDs are critical in dissipating large amplitude motions and dynamic loading transferred from the rotor to the bearing supports during highspeed operation. Little testing on the effect of SFDs on rotor shafts under highspeed operating conditions is available in the literature. Thus, a SFD-rotor test rig has been designed and built to study the effect of SFD oil supply pressure, oil temperature, oil inlet feed number/orientation, unbalance, and seals on the response of a Jeffcott rotor. In this paper, the test rig is introduced along with its calibration through static and dynamic testing. It was found that the rotor displacement results were improved through the addition of trial masses to provide a runout correction and that using rubber supports for the support structure generated more symmetric results in the vertical and horizontal planes compared to steel supports. Initial results for the test rig are also presented for different unbalances with and without oil supply and with and without SFD end seals. In these cases, increased unbalance produced higher amplitude motion with a corresponding increase in critical speed and decrease in damping.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.218

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 head0.011
GPT teacher head0.180
Teacher spread0.168 · 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 designBench or experimental
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

Citations3
Published2020
Admission routes1
Has abstractyes

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