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Record W2277060029 · doi:10.5539/apr.v8n2p16

The Presence of a Revolution Load and Its Rotational Speed of Half the Speed of the Journal Are Necessary Conditions for the Half-Frequency Whirl of Radial Bearings

2016· article· en· W2277060029 on OpenAlexvenueno aff
Jing Li, Zifeng Li

Bibliographic record

VenueApplied Physics Research · 2016
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsVibrationBearing (navigation)MechanicsDissipationLubricationPhysicsRotational speedCompressibilityWhip (tree)Control theory (sociology)Classical mechanicsComputer scienceAcousticsThermodynamics

Abstract

fetched live from OpenAlex

In the radial sliding bearing lubrication theory, the half-frequency whirl phenomena are commonly introduced. However, the correlation of the half-frequency whirl theory with the measured resonance phenomenon is low. This paper studies the establishment of the half-frequency whirl theory, and finds that while researchers are aware of the half-frequency whirl, they disregard the necessary conditions of its occurrence. If there is no revolution load, then the half-frequency whirl does not comply with the principle of minimum potential energy. If it does not conform to the principle of minimum energy dissipation, then the half-frequency whirl does not exist. Only when the revolution load exists and its speed is half the speed of the journal will the half frequency whirl occur. Engineering oil whip results from the vibration of the rotating system. A precise radial sliding bearing lubrication theory should use cylindrical coordinates, consider the compressibility of the liquid, and consider the surface shear stress in the journal bearing capacity calculations.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.037
GPT teacher head0.294
Teacher spread0.257 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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