Statistical analysis of pulsed discharges in dielectric liquid: effects of voltage amplitude, pulse width, electrode configuration, and liquid composition
Bibliographic record
Abstract
Abstract Discharge in dielectric liquid is an active field of research involved in many technological applications. Such multiple discharges are often applied during an experiment and that the electrode and liquid are altered by these stochastically behaving discharges. Therefore, it is important to address the variation of discharge characteristics as a function of discharge occurrence. In this study we analyze the electrical characteristics of multiple discharges run at low repetition rate (5 Hz) in a liquid of known initial composition using an electrode with a specific geometry. The discharges are run continuously until they fail to occur due to the increase in gap distance, and the recorded voltage and current waveforms are processed using an algorithm in order to determine the probability of discharge occurrence, breakdown voltage, discharge current, and discharge delay. The injected charge and energy of each discharge are also calculated. Furthermore, the effects of applied voltage, pulse width, electrode configuration, and liquid composition on the characteristics of the discharge are investigated. The obtained results demonstrate that the highest and lowest numbers of occurred discharges are achieved using the plate-to-plate and pin-to-pin configurations, respectively. Moreover, the composition of the liquid has an appreciable effect on the discharge current, as well as on electrode erosion. For discharges in water and cyclohexane, we measured the lowest and highest current, respectively. As for the erosion rate, it was comparable for the three liquid hydrocarbons but relatively smaller than that in water. The plot of breakdown voltage as a function of discharge current varies depending on the discharge parameters and displays regions that may be linked to the ignition mechanisms. The original data reported herein is of great significance for various applications that utilize repetitive discharges in liquid.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".