Spark Plasma Stretching and Flame Propagation via High Frequency Pulsed Current Management
Bibliographic record
Abstract
Abstract The performance and advancement of ignition systems become more important than ever to further improve the fuel efficiency and exhaust emissions of modern engines. The ignition processes of modern engines are often subjected to a fuel-lean or inert-gas-diluted mixture of considerably high density and strong air motion. Bulk gas movements in the vicinity of the spark gap, such as crossflows, can stretch the plasma channel across the spark gap, which enhances the total discharge energy compared with quiescent conditions. A higher discharge current has been proven to be an effective way to prolong the spark plasma stretching. However, high discharge current (boosted up to 3A) increases the power consumption of the ignition systems, affecting system durability and energy efficiency. Besides, the continuous high current cause rapid spark electrode erosion, which affects the durability of the spark plug. In this work, a novel high-frequency pulsed current management strategy is proposed to improve plasma stretching and energy release, while decreasing the total energy consumption of the ignition system. Within precise control of discharge duration, a high-frequency pulsed discharge strategy is achieved without interfering with the plasma stretching process. The characteristics of the plasma channel are recorded via both electrical and optical measurements.
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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 | 0.000 | 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".