Performance of Spark Energy Distribution Strategy on a Production Engine under Lean-Burn Conditions
Bibliographic record
Abstract
Stronger ignition sources become more favorable under extreme lean/EGR conditions. Under those conditions, the reduced pumping loss and low combustion temperature can contribute to further engine efficiency improvement for spark ignited engines. Multicoil ignition system can enhance ignition energy as well as modulate discharge profile. The ignition energy can either be deployed through single spark gap to enhance the ignition capability of the plasma channel, or be distributed to multiple ignition sites to establish multiple flame kernels to secure flame kernel initiation. The multiple ignition coils used for energy distribution ignition strategy also consume more power, in order to maintain the stable operation of the engine under lean operation limit. In this paper, efficacy of concentrated and distributed multicoil ignition strategies were investigated on a spark ignited inline 4-cylinder production engine using a three-ignition-coil pack. Both total ignition energy and discharge duration were kept the same. Power consumption of the added ignition coils were compared with the power gain under lean burn conditions. Ignition performance of both strategies was investigated under various levels of engine loads, from idling condition (1.1 bar BMEP) to medium engine load (5.5 bar BMEP). The test results revealed that the multiple ignition sites technique can effectively decrease the ignition delay throughout the testing cases, but its contribution to lower engine cycle to cycle variation was more significant under low load, lean burn conditions, where the ignition process received more challenges.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".