Influence of Aging on Oil Degradation and Gassing Tendency for Mineral oil and Synthetic Ester under Low Energy Discharge Electrical Faults
Bibliographic record
Abstract
The intent of this work is to understand the influence of low energy discharge electric faults in mineral oil and synthetic esters on liquid degradation and gassing tendency at different aging conditions (based on acidity values). A low energy discharge electric fault has been created by continuous discharge of 9 kV for five hours on the liquid surface using a suitable laboratory setup. Liquid degradation is reported by adopting UV spectroscopy, turbidity, and particle counter measurements. The gassing tendency is understood by dissolved gas analysis using Duval’s triangle and Duval’s pentagon methods for mineral oil and non-mineral oils accordingly. It is observed that the influence of low energy discharges on liquid degradation is higher in mineral oils than synthetic esters. The fault gasses in mineral oil are involved with electrical and thermal faults accompanied by stray gassing whereas only partial discharge activity is noticed for synthetic esters. Importantly, the existence of low energy discharge faults like corona discharges will involve a generation of excess high molecular weight products as compared to low molecular weight products that are soluble in liquid volume.
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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 | 0.000 |
| 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".