Statistical analysis of pulsed spark discharges in water: Effects of gap distance, electrode material, and voltage polarity on discharge characteristics
Bibliographic record
Abstract
Repetitive discharges in dielectric liquid are involved in many technological applications. The relatively poor reproducibility of such discharges, induced by significant modification of experimental conditions (electrode and liquid), hinders the understanding of their fundamental dynamics and optimizing processes. In this paper, we study the electrical characteristics of multiple discharges run in de-ionized water, at low frequency (3 Hz), using pin-to-plate electrode geometry, under varying conditions of gap distance (50–500 μm), electrode composition (Cu and W), and voltage polarity (amplitude of ±20 kV and pulse width of 500 ns). The voltage and current waveforms of each occurring discharge are recorded and then processed to determine the probability of discharge occurrence, breakdown voltage, discharge current, discharge delay, injected charge, and injected energy. The results show that the highest numbers of occurring discharges are achieved at shortest distance, using the Cu electrode, and negative polarity. The data points comprising the electrical characteristics waveforms (e.g., breakdown voltage) are more or less dispersed, depending on the electrode composition and voltage polarity. Moreover, in negative polarity, a reflected positive pulse of ∼5 kV is observed when discharges do not occur in the first pulse. Considering that these pulses may induce discharges, their characteristics are also provided. Finally, the voltage-current plots show appreciable dependence on discharge conditions, and the data are well fitted by linear profiles with slopes, i.e., resistances, that may reflect the ignition conditions of the discharge.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".