Discharge Current Management for Diluted Combustion under Forced Flow Conditions
Bibliographic record
Abstract
Lean burn or EGR diluted combustion with enhanced charge motion is effective in improving the efficiency of spark ignition engines. However, the ignition process under these conditions is getting more challenging due to higher ignition energy required by the lean or diluted mixture, as well as the interactions of the gas flow on the flame kernel. Enhanced spark discharge energy is essential to initiate the combustion under these conditions. Moreover, the discharge process should be more carefully controlled to improve the effectiveness of the spark. In this study, spark ignition systems with boosted discharge energy are used to ignite diluted air-fuel mixture under forced flow conditions. The impacts of the discharge current level, the discharge duration and the discharge current profile on the ignition are investigated in detail using optical diagnosis. It is evident from the results that extended discharge duration helps promote the flame propagation, though the effectiveness is limited when the duration exceeds the required minimum value for a self-sustained flame kernel. A higher discharge current level is favorable for generating a stronger flame kernel. With similar discharge energy delivered to the spark gap, a high-current spark with a shorter duration performs better than a longer duration low-current spark. Then the impacts of discharge current profiles are studied. Three spark discharge strategies, including a low-current continuous discharge, a high-current multi-pulse discharge, and a very high transient current discharge, are used in the comparison. The results reveal that sufficiently long discharge duration is critical for success ignition under the tested flow condition. Among the three ignition strategies, the low-current continuous spark discharge performs the best, even with the lowest discharge energy, due mainly to the continuous nature of the spark discharge.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".