Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.022 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".