Preliminary Studies On Soluble and Colloidal Decomposition Products In Ester Filled Transformers
Bibliographic record
Abstract
Esters dielectric fluids are found to be prominent replicate for mineral oil based insulation systems. Explicit studies on aging performance of these new insulating oils improve existing knowledge on the actual performance and degradation process. In this work, degradation of synthetic ester and natural ester has been investigated in comparison to mineral oil. Investigation is aimed at understanding the generation rate of soluble and colloidal particles in different oils with aging. Thermal aging is carried out as per ASTM D 1934 with a controlled aging history at different durations in presence of cellulose. Later, separation of colloidal particles is carried out as per ASTM D 1698 to study the comparative degradation rate of oils. Turbidity of oil and Particle counter measurements are performed before and after centrifuge to identify the growth of decay contents with aging in oils. A significant difference in the evolution of decay contents has been identified between esters fluids and mineral oils. The impact of colloidal particles on degradation of ester fluids is almost negligible whereas the degradation in mineral oils majorly governed by colloidal particles.
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 | 0.000 |
| 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".