Convective heat transfer behavior and AC dielectric breakdown voltage of electric power transformer oil with magnetic colloidal nano-fluid: An experimental study
Bibliographic record
Abstract
This research reports on an experimental investigation into the alternating insulation breakdown voltage (AC) and convective heat transfer behavior of nano-oil for application in electrical transformers. The base fluid of the examined nano-oil is nitro libra type transformer oil, one of the common mineral oils used in transformers. It contains iron oxide magnetic colloidal nanoparticles. Upon fabrication of the experimental apparatus in the lab, an experimental study on convection heat transfer is performed under laminar flow conditions and with a continuous heat flux applied to the wall. BA100 breakdown voltage measurement instrument based on the IEC 60156 standard is also used for alternating breakdown voltage testing. The current findings from the experimental investigation of convective heat transfer are compared with and verified by the experimental results available in the literature. The experimental results demonstrate that the convective heat transfer coefficient of the iron oxide magnetic nanoparticle-prepared nano-oil is on average 4.51% higher than that of base oil. These particles have an average size of 23 nm and a volume concentration of 0.1%. Additionally, compared to base oil, nano-oil with a volume concentration of 0.1% has a 23.8% higher dielectric breakdown voltage.
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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".