Ignition Stability Improvement and Emission Reduction via Multiple Ignition Sites Strategy Under Cold Start and Transient Conditions
Bibliographic record
Abstract
Abstract Downsizing, turbocharging, and lean burn strategies offer improved fuel efficiency and engine-out emissions to that of conventional spark ignition engines. However, maintaining engine stability becomes difficult, especially at low load and low speed operation such as cold start conditions. Under cold start operation, the spark timing is retarded to rush catalyst warm-up temperature followed by advancing the spark timing for engine stability. In this sequence, securing ignition while using retarded spark timing is difficult because of the cold cylinder walls and low engine loads. Through previous investigations, the noval multiple ignition sites strategy demonstrated its capability to expend lean burn boundaries beyond traditional single core spark plug and improve cycle to cycle variation. In this work, multisite ignition is tested on a production 4-cylinder direct injection spark ignition engine. A large number of tests are performed on the engine to investigate the impact of ignition strategy on emissions and stability during catalytic converter warm up period as part of the cold-start operation. Results show that the three-core spark igniter shortens the ignition delay thus providing a wider stable spark timing window for stable engine operation. As a result, the concentration of unburnt fuel in the exhaust gas can be reduced before the catalyst reaches the light-off temperature.
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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 | 0.000 | 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) | 0.001 | 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".