A Comparative Study on the Ignition Mechanism of Multi-site Ignition and Continuous Discharge Strategy
Bibliographic record
Abstract
Advanced combustion engines dominate all automotive applications. Future high efficiency clean combustion engines can contribute significantly to sustainable transportation. Effective ignition strategies are studied to enable lean and diluted combustion under considerably high-density mixture and strong turbulences, for improving the efficiency and emissions of future combustion engines. Continuous discharge and multi-site ignition strategies have been proved to be effective to stabilize the combustion process under lean and EGR diluted conditions. Continuous discharge strategy uses a traditional sparkplug with a single spark gap and multiple ignition coil packs. The ignition coil packs operate under a specific time offset to realize a continuous discharge process with a prolonged discharge duration. Multi-site ignition strategy also uses multiple ignition coil packs. The ignition coil packs are connected with a specially designed multi-core sparkplug, which has multiple high voltage electrodes to generate multiple flame kernels. The ignition performances of these two ignition strategies differ from each other because of the distinguished ignition mechanisms. In this paper, the ignition mechanisms of both ignition strategies are investigated empirically via constant volume combustion vessel and a single-cylinder research engine. Test results indicate that the multi-site ignition strategy outperforms the continuous discharge strategy under quiescent and less intensified flow conditions, because of the larger initial flame kernel size. The efficacy of the multi-site ignition strategy relies highly on the survivability of the multiple initial flame kernels. Under extreme lean conditions with intensified flow field, a continuous discharge with long discharge duration can secure the flame kernel initiation process more effectively.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".