Physical, Performance, and Chemical Changes in Turbine Oils from Oxidation
Bibliographic record
Abstract
Abstract One of the primary factors in determining the life and performance of turbine oils is its oxidation stability. Oxidation can cause a host of problems for in-service turbine oils, such as sludge, deposits, lacquer, and varnish formation. There is limited published research on the performance of turbine oils once oxidation begins. This paper presents data on the relationship between several turbine oil characteristics during the oxidation process by focusing on the measurable physical and chemical changes that occur. Five turbine oils were chosen, each employing different antioxidants and base oil refining technologies to represent a typical selection of commercially available turbine oils. A 49-day experiment was completed in which the oils were subjected to a controlled and accelerated oxidative process under laboratory conditions. Throughout the testing period, samples were drawn and a range of chemical, physical, and performance tests were performed. The data obtained from this experiment identify several correlations that relate to the performance of each oil. These data are useful in understanding the relationship between turbine oil oxidation and fluid performance and will help end-users in selecting and optimizing their turbine lubricant.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".