Differential Low-Temperature AC Breakdown Between Synthetic Ester and Mineral Oils: Insights From Both Molecular Dynamics and Quantum Mechanics
Bibliographic record
Abstract
Synthetic ester oil (SEO) is promising for bolstering sustainable development and enhancing operational reliability of transformers at low-temperature. The intention of this study is to acquire understanding of differential low-temperature AC breakdown characteristics exhibited by SEO and mineral oil (MO), while delving into the microscopic influencing factors. The results unveil that the breakdown voltage between -20°C and 20°C illustrates a "V"-shaped trend, with SEO consistently higher than MO. SEO presents its maximum breakdown voltage at -20°C and its minimum at 0°C, whereas MO demonstrates its maximum breakdown voltage at 20°C and its minimum at -10°C. Molecular dynamics (MD) and quantum mechanics (QM) calculations reveal that hydrogen bond and interaction energy associated with the state of water, along with the fraction of free volume, mean square displacement, and diffusion coefficient associated with particle transport property, collectively exert considerable influence on the breakdown voltage. Compared to MO, SEO exhibits a higher number of hydrogen bonds and interaction energies, while displaying lower fraction of free volumes, mean square displacements, and diffusion coefficients. Furthermore, the presence of electron trap, in conjunction with these combined factors, leads to a substantially higher breakdown voltage of SEO (31.4 kV) than that of MO at sub-zero temperatures. The wider energy gap of MO compared to SEO leads to a slightly higher breakdown voltage for MO (19.9 kV) compared to SEO at above-zero temperatures. This study provides experimental data and theoretical guidance for the promotion and stable operation of SEO-immersed transformers in UHVDC systems deployed in cold regions.
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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".