Stability of mineral oil and oil–ester mixtures under thermal ageing and electrical discharges
Bibliographic record
Abstract
This study summarises the results of an experimental investigation on the stability of mineral‐based oil mixtures when they are confronted with different ratios of synthetic ester under conditions of electrical discharge, electrical breakdown and a combination of both. The condition of the oil samples was assessed using diagnostic techniques such as gassing tendency, turbidity, dissolved decay products (DDP) and dielectric dissipation factor (DDF), according to ASTM standards. A comparison is made between the performances of fresh and aged samples. The results provide experimental evidence that the chemical composition of hydrocarbon blend is contributing factors to oil gassing. It was observed that aged oils release more gases than new one. It was also observed that the gassing tendency increased with increasing amount of ester for the mixed fluids. The pressure and the absorbance of gases vary proportionally with ester content. Under thermal stress, an increase in pressure is observed especially for the mineral oil sample. The turbidity, DDP and DDF measurements revealed higher values for mineral oil. Importantly, the stability improved with increasing ester content in the blends. Mixed mineral oil/ester therefore offers many advantages with concomitant cost reductions compared with pure synthetic esters.
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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 | 0.001 |
| 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".