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Record W2110230865 · doi:10.1002/ls.172

Pressure viscosity coefficients and traction properties of synthetic lubricants for wind turbine gear systems

2012· article· en· W2110230865 on OpenAlexaff
Martin R. Greaves

Bibliographic record

VenueLubrication Science · 2012
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsLockheed Martin (Canada)
FundersDow Chemical Company
KeywordsLubricantViscometerViscosityMaterials scienceTraction (geology)TurbineComposite materialFriction modifierViscosity indexTribologyDry lubricantMechanical engineeringEngineeringBase oilScanning electron microscope

Abstract

fetched live from OpenAlex

ABSTRACT Synthetic lubricants are increasingly used to provide equipment reliability for wind turbine gear boxes. The majority of synthetic lubricants used today are based on polyalphaolefins. In gear systems where contact pressures are high, the pressure viscosity coefficient and traction values of the lubricant are important fundamental properties. A comparison of these properties for a wind turbine lubricant based on a polyalphaolefin and two lubricants based on polyalkylene glycols has been undertaken. Pressure viscosity coefficients were calculated from viscosity measurements made using an ultra‐high pressure falling needle viscometer at pressures up to 50 000 psi. Significant differences in properties were observed with both polyalkylene glycol lubricants showing lower pressure viscosity coefficients and much lower traction values. A calculation of the film thickness values in the Hertzian contact zone suggests that polyalkylene glycol lubricants may provide elastohydrodynamic films that are approximately 25% thicker than polyalphaolefin lubricants. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.019
GPT teacher head0.230
Teacher spread0.212 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

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