Effect of Discharge Energy Distribution on Flame Kernel Development
Bibliographic record
Abstract
Abstract The early flame kernel initiation and development are essential to a successful combustion process, especially under lean burn/EGR diluted conditions. Multiple ignition sites strategy has shown promise to secure the flame kernel initiation under extreme engine operating conditions. Two factors are considered to contribute to the enhanced ignition capability, i.e. the higher ignition energy and the multiple initial flame kernels. However, the mechanism why the multiple ignition sites help combustion is less understood. In this work, the impacts of the ignition energy distribution strategy on the flame inception process are investigated in a constant volume combustion chamber. A multi-coil ignition system, along with a sparkplug with three high-voltage electrodes, is used to adjust the discharge energy from 10 mJ to 240 mJ, as well as the energy deposition strategies. Experimental results have shown that the distributed energy strategy with sufficient discharge energy can establish a bigger initial flame kernel, leading to faster flame growth rates, as compared to the concentrated energy strategy.
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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".