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Record W2905468989 · doi:10.1002/pamm.201800473

Passive Vibration Control by Frictional Energy Dissipation in Refrigerant‐Lubricated Gas Foil Bearing Rotor Systems

2018· article· en· W2905468989 on OpenAlexaboutno aff
Tim Leister, Wolfgang Seemann, Benyebka Bou‐Saïd

Bibliographic record

VenuePAMM · 2018
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsFoil bearingRefrigerantDissipationFOIL methodReynolds equationWork (physics)MechanicsLubricationVibrationRotor (electric)CompressibilitySlip (aerodynamics)Materials scienceStructural engineeringEngineeringMechanical engineeringReynolds numberThermodynamicsPhysicsAcousticsComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Even though the dynamic performance of rotors supported by refrigerant‐lubricated gas foil bearings (GFBs) is very sensitive to the amount of energy dissipated in the foil structure, almost none of the existing computational models really capture dry friction with typical stick–slip transitions. The presented work addresses this shortcoming by incorporating an elasto‐plastic bristle friction law into the structural model, which is coupled in an interconnected solution approach to a Reynolds equation for non‐ideal compressible gases and to a modified Jeffcott–Laval rotor model. Numerical results confirm that properly designed GFBs have the potential to benefit significantly from the foil structure acting as a passive vibration control device.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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Same venuePAMMSame topicTribology and Lubrication EngineeringFrench-language works237,207