Combustion Stability Improvement via Multiple Ignition Sites on a Production Engine
Bibliographic record
Abstract
For spark ignition (SI) engines, further improvement of engine efficiency has become the major development trend, and lean burn/EGR technologies, as well as intensified in-cylinder flow, need to be adapted to reach that target. Stronger ignition sources become more favorable under extreme lean/EGR conditions. Among the ignition technologies developed, multiple ignition sites technology has been proved to be an effective way to help with the initial flame kernel development. In this paper, a spark ignited 4-cylinder turbo-charged production engine is employed to investigate the impact of multiple ignition sites technology on engine performance under lean burn conditions. Four in-house designed 3-core sparkplugs are installed on the cylinders to replace traditional stock sparkplugs, in order to generate multiple ignition sites in the cylinders. Under partial load, the pumping loss can be reduced when engine is running under lean conditions, so the brake engine efficiency can be improved. However, combustion stability becomes worse in terms of higher cycle to cycle variation of the engine IMEP. Compared with traditional ignition system, the multiple ignition sites technology can extend engine lean operation limit with shorter ignition delay, which can effectively reduce the cycle to cycle variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".