Assessment of the intensity of engine oil contamination during operation
Bibliographic record
Abstract
Abstract The acceleration of internal combustion engines leads to an increase in thermal and mechanical loads on the most critical parts. To ensure the required resource indicators in conditions of increased loads, it is necessary to use high-quality lubricants, the requirements for the operational properties of which are also steadily increasing. In order to ensure the necessary physical and chemical characteristics of engine oils, additives are introduced into their composition, but during operation the quality of engine oil decreases, the reasons for this are both the destruction of additives, and the accumulation of dirt particles in the oil and the ingress of fuel and coolant into the oil. The conducted studies are devoted to determining the intensity of engine oil contamination under operating conditions in the Wärtsilä 6L20 engine lubrication system and the effectiveness of the dispersing additives contained in the oil. Samples of Petro Canada and TARO oils brands were used as prototypes at the beginning of the operational period, at the time of average operating time and at the time of oil change. The dispersing ability of the oil was determined by the method of assessing the oil stain. According to the results of the research, it is concluded that when the properties of the oil change as a result of contamination, their dispersing ability remains satisfactory. This result can serve as a justification for extending the service life of engine oil, provided that its properties are monitored during operation.
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 | 0.001 | 0.001 |
| 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".