Ignition Energy Discharge of Oscillating Plasma Waveforms Under Atmospheric Conditions
Bibliographic record
Abstract
Oscillating plasma ignition is a promising technique to produce larger initial ignition volume. In this study, an energy waveform analysis of oscillating plasma discharge is investigated. To suffice the industrial applications, the challenges of plasma generation and control platforms are first discussed in this work. A flexible modulation for oscillating plasma generation is established, with the measurements of discharge voltage, secondary current, and discharge current. The phase difference between voltage and current is a critical effect on the energy waveform of oscillating plasma. In relevance to the command pulse train, the energy waveforms corresponding to various plasma discharging events are analyzed, which include normal, arc, and void cases. High-speed imaging, simultaneous with the electrical waveform measurements, is applied to record the plasma formation. Under elevated background pressures, the ignition volume of oscillating plasma is suppressed, and fewer plasma streamers can be observed. The prolonged duration and increased voltage consistently demonstrated positive impacts on flame propagation. This research added a foundation for the plasma diagnostics under engine-like conditions with variations of pressure, temperature, gas composition, and flow pattern.
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 | 0.000 |
| 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".