A general empirical formula of current–voltage characteristics for point-to-plane geometry corona discharges
Bibliographic record
Abstract
With a point-to-plane geometry, the experimental investigation of the current–voltage characteristics in corona discharges demonstrated that existing empirical formulae met with some physical difficulties in explaining the results. By mathematically processing the experimental data and applying the updated knowledge of corona inception, a new general formula in characterizing the relationship of corona current–voltage was derived and expressed as I = K(V − V0)n. It was demonstrated that the exponent n falls into a limited scope of 1.5–2.0, and there always exists an optimal exponent n in the scope, which can be determined by maximizing the R-square of regression. Of all the potentially influential factors, it was disclosed that the point radius has the strongest influence on the optimal exponent n, and the effects of ambient conditions and corona polarities are not noticeable. The optimal exponent n holds a fixed value of 2.0 for microscopic points and of 1.5 for large points with a radius in millimetres, but changes decreasingly with the radius for the points of microns. For given experimental conditions, the optimal exponent n almost does not change with the inter-electrode distance. Furthermore, it was demonstrated that the formula is applicable not only for both negative and positive coronas in point-to-plane geometries but also for both polarities in point-to-ring geometries. With the optimal exponent n, the formula can well explain the inconsistencies met by other existing formulae and best represent the characteristics of corona current–voltage with an accuracy of 1 µm.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".