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Record W4251594747 · doi:10.1002/9781119975489.ch7

Lubrication Systems

2011· other· en· W4251594747 on OpenAlexaff
Bernie MacIsaac, Roy Langton

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsGastops (Canada)
Fundersnot available
KeywordsLubricationLubricantBearing (navigation)Mechanical engineeringViscosityMaterials scienceEngineeringComputer scienceComposite material

Abstract

fetched live from OpenAlex

The basic principles of lubrication, friction and wear are examined for simple journal and rolling-element bearings where it is recognized that bearing failures will ultimately occur as a result of surface damage and fatigue even in ideal circumstances. The concept of Elasto-Hydro-Dynamics (EHD) as an operating region is described as a means of optimizing the bearing lubrication and thermal properties of a design. This leads logically to a recognition of the importance of lubricant viscosity and its effect on the lubrication efficiency. The major attributes of both mineral and synthetic lubrication oils are described including viscosity, oxidation (coking) and foam-ability. The lubrication system typical of the modern gas turbine engine is described where high pressure oil is supplied to the main engine bearing sumps and accessory gearbox. The oil scavenged from the sumps is then cooled and de-aerated before being re-pressurized and filtered prior to returning to the engine bearings. System design considerations are discussed including monitoring of key parameters such as temperature pressure and filter delta-P. Oil debris capture via chip detectors is described together with the more advanced inductive debris monitoring technology which can detect both ferrous and non-ferrous particles in the oil and distinguish between particle sizes. Finally comments on the challenges of ceramic bearing technology are presented.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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)0.0080.002

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 head0.010
GPT teacher head0.171
Teacher spread0.161 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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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