Advances and Challenges in Combustion Control of Radio Frequency Oscillating Plasma Discharge
Bibliographic record
Abstract
Article Advances and Challenges in Combustion Control of Radio Frequency Oscillating Plasma Discharge Linyan Wang , Xiao Yu , Graham Reader , and Ming Zheng * Department of Mechanical, Automotive and Materials Engineering, University of Windsor, 401 Sunset Avenue, Windsor, N9B 3P4 Ontario, Canada * Correspondence: mzheng@uwindsor.ca Received: 18 July 2023 Accepted: 13 September 2023 Published: 20 September 2023 Abstract: Oscillating plasma (corona) ignition is a promising technique for producing a larger initial ignition volume for achieving cleaner combustion. The oscillating plasma system provides a quick response to the ignition command compared with the conventional transistor-coil-ignition systems (TCI). Subsequently, the shorter combustion duration contributes to further improvement of engine efficiency under lean/diluted conditions. The challenges of oscillating plasma ignition appear under elevated background pressures, mostly owing to fewer plasma streamers and even void discharge, thus the advantage of oscillating plasma discharge over spark discharge is compromised. To suffice the industrial applications, the challenges of plasma generation and control platforms are investigated and discussed in this work. The challenges include ignition control, background condition impacts, and other external disturbances. A flexible modulation for oscillating plasma generation is established, with the measurements of discharge voltage and current. High-speed imaging, simultaneous with electrical waveform measurements is applied to record the plasma formation and flame propagation. This research provides advances in the ignition control for the oscillating plasma discharge, adding a foundation and reference for the oscillating plasma diagnostics under engine-like conditions with variations of pressure, temperature, gas composition, and flow pattern.
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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) | 0.000 | 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) | 0.000 | 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".