Effect of Smoke on Corona and Breakdown Characteristics of Air Under Positive and Negative DC Voltages
Bibliographic record
Abstract
Wildfires generate a large amount of smoke which primarily contains fine particles that can participate in gas discharge mechanisms under dc electric fields. This paper presents an experimental study evaluating corona discharge and breakdown characteristics of air under various levels of smoke at positive and negative dc voltages. Discharge pulse repetition rate, average discharge current, and partial discharge inception voltage are investigated as corona discharge characteristics of a needle-plane air gap while the breakdown voltage is measured as breakdown characteristics of air in a uniform electric field. The experimental findings show that the positive and negative dc breakdown voltage of a uniform-field air gap under the considered smoke levels is not significant. Additionally, except for the average discharge current, the impact of smoke on negative dc corona characteristics is negligible. The average discharge current under negative dc voltage shows a decrease for increasing smoke densities. The effect of smoke on positive dc corona is terminal and caused by the particle deposition from smoke on electrodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".