Influence of ageing on oil degradation and gassing tendency under high‐energy electrical discharge faults for mineral oil and synthetic ester
Bibliographic record
Abstract
In this work, mineral oil and synthetic esters were selected at different ageing factors (based on acidity values). Fresh and aged oils have been subjected to high‐energy discharges (repeated 100 breakdowns) to simulate electric faults of highly vulnerable intensity. The intent of this work is to understand the influence of high‐energy electric faults on oil degradation and gassing tendency at different ageing conditions. In this study, the influence of the high‐energy discharges on degradation and gassing tendency at different ageing factors is reported for mineral oil and synthetic esters. Oil degradation is reported by adopting ultraviolet spectroscopy, turbidity and particle counter as per american society for testing and materials (ASTM) standard test methods. Gassing tendencies and fault gas analysis are understood by dissolved the gas analysis using Duval's triangle and Duval's pentagon methods for mineral and non‐mineral oils. It is found that the influence of high‐energy discharges on oil degradation is higher in mineral oils to that of the synthetic esters. The intensity of the gassing tendency is higher for ester fluids; however, as per the Duval methods, the faulty conditions are at lower levels as compared to mineral oils.
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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".