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Record W2954599151 · doi:10.1115/1.4044082

DynamicInteractions Between the Rolling Element and the Cage in Rolling Bearing Under Rotational SpeedFluctuation Conditions

2019· article· en· W2954599151 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Tribology · 2019
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsQueen's University
FundersJiangxi Provincial Department of Science and TechnologyNatural Science Foundation of Jiangxi Province
KeywordsCageRolling-element bearingLubricationRotational speedBearing (navigation)HertzMechanicsFinite element methodNonlinear systemNonlinear elementPhysicsStructural engineeringMaterials scienceClassical mechanicsEngineeringVibrationAcousticsThermodynamics

Abstract

fetched live from OpenAlex

Abstract A nonlinear dynamic model is proposed to investigate the dynamic interactions between the rolling element and cage under rotational speed fluctuation conditions. Discontinuous Hertz contact between the rolling element and the cage and lubrication and interactions between rolling elements and raceways are considered. The dynamic model is verified by comparing simulation result with the published experimental data. Based on this model, the interaction forces and the contact positions between the rolling element and the cage with and without the rotational speed fluctuation are analyzed. The effects of fluctuation amplitude, fluctuation frequency, and cage pocket clearance on the interaction forces between the rolling element and the cage are also investigated. The results show that the fluctuation of the rotational speed and the cage pocket clearance significantly affects the interaction forces between the rolling element and the cage.

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.

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)0.001
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 score0.223
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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