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Record W2037325191 · doi:10.1115/gt2011-46761

Comparison of the Steady-State and Dynamic Performance of Two Fixed-Geometry Journal Bearings

2011· article· en· W2037325191 on OpenAlexaff
Martin J. Conlon, Azzedine Dadouche, Waldemar Dmochowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBearing (navigation)Eccentricity (behavior)Steady state (chemistry)Plain bearingStructural engineeringVibrationRotordynamicsDisplacement (psychology)Rotor (electric)Materials scienceMechanicsGeologyEngineeringLubricationPhysicsMechanical engineeringAcousticsChemistry

Abstract

fetched live from OpenAlex

This paper evaluates two different journal bearings: a cylindrical bore plain journal bearing and a tri-lobe taper land bearing. Each bearing has the same nominal diameter (89mm) and aspect ratio (L/D = 0.7). The shaft rotational speed ranged from 6krpm to 14krpm and the bearing specific load from 700kPa to 2800kPa. The bearings’ steady-state performance is evaluated according to relative bearing and shaft displacement, bearing operating temperature and power loss. A frequency-domain analysis is used to determine bearing rotordynamic coefficients — it treats the bearing as a mass-spring-damper system. Excitation frequencies range between 20Hz and 350Hz. Ultimately, the tri-lobe taper land bearing offers better stability whereas the cylindrical bore plain journal bearing has a lower eccentricity and runs cooler for a given operating condition. The dynamic properties of the two bearings are found to be similar, although the higher stability of the tri-lobe taper land bearing is also reflected in the dynamic coefficients.

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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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