Spark resistance under high-speed gas flow in the oscillatory damped regime of discharge
Bibliographic record
Abstract
Applying a high-speed gas flow is an effective method to exclude residual plasma and expedite the insulation recovery of high-power spark gaps and can extend their applications to higher-repetition fields. In this paper, the subject of interest is a spark gap repetitively working in a low-damping oscillatory regime of discharge. The influence of gas flow for performance regulation on spark resistance was investigated based on both electrical and optical diagnoses. A high-speed air flow ranging from 0 to 100 m/s was applied by a Laval nozzle, and a significant increase in the damping ratio of the discharge current, implying an increase in spark resistance, was observed with increasing speed of gas flow. According to the optical diagnosis results, the influence of gas flow on the spark channel had two main aspects: configuration and temperature. Although the electron temperature under gas flow showed a trend of slight increase, indicating an increase in the conductivity of the spark channel, the high-speed gas flow could greatly stretch and thin the discharge channel, which was the dominant factor and resulted in an increase in spark resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".