Negative corona discharge mechanism in C4F7N–CO2 and C4F7N–N2 mixtures
Bibliographic record
Abstract
Due to their good dielectric properties and low global warming potential, C4F7N–CO2 and C4F7N–N2 mixtures have shown promising potential to replace SF6 in high voltage gas insulated equipment. However, during manufacturing, installation, and transportation of power equipment, burs and metal particles can be inevitably left inside, and they can cause corona discharge. Fundamental investigation of the corona discharge mechanism is essential to monitor partial discharge signals in environmentally friendly power equipment. This paper applies the fluid model to investigate the discharge mechanism of C4F7N–CO2 and C4F7N–N2 mixtures in negative point-plane corona discharge. A 2D axisymmetric model combines the drift-diffusion equations for electrons, positive ions, and negative ions and Poisson’s equation to study the process of dynamics. The gas is a mixture of C4F7N (5%, 7%, or 13%) and CO2 or N2 (95%, 93%, or 87%). The rise time of the first discharge pulse in C4F7N–CO2 and C4F7N–N2 mixtures is about 0.1 ns. The interval time between the first and the second pulse in the 5% C4F7N–95%CO2 mixture is about 1.5 times longer than that in the 5% C4F7N–95% N2 mixture. When the C4F7N content is 7% and 13%, the interval time between the first and second pulses in C4F7N–CO2 mixtures is about 2 and 3 times longer than those in C4F7N–N2 mixtures, respectively. The suppression regions in C4F7N–CO2 mixtures are larger than those in corresponding C4F7N–N2 mixtures. The total number of electrons, positive ions, and negative ions in C4F7N–CO2 mixtures is higher than that in C4F7N–N2 mixtures, while the reduced electric field in C4F7N–CO2 mixtures is smaller than that in C4F7N–N2 mixtures.
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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 | 0.001 |
| 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".