Effect of Spark Discharge Duration and Timing on the Combustion Initiation in a Lean Burn SI Engine
Bibliographic record
Abstract
Meeting the increasingly stringent emission and fuel efficiency standards is the primary objective of the modern automotive research. Lean/diluted combustion is a promising avenue to realize high-efficiency combustion and reduce emissions in SI engines. Under diluted conditions, the flame propagation speed is reduced because of the reduced charge reactivity. Enhancing in-cylinder charge motion and turbulence, and thereby increasing the flame speed, is a possible way to harness the combustion process in SI engines. However, charge motion can have a significant effect on the spark ignition process because of the reduced discharge duration and frequent restrikes. A longer discharge duration can aid in the formation of a self-sustained flame kernel and subsequent stable ignition. Therefore, an empirical study is undertaken to investigate the effect of discharge duration and ignition timing on the ignition and early combustion in a port fueled SI engine, operated under lean conditions. Discharge duration is modulated from 1 ms to 8 ms through a continuous discharge strategy. Discharge current and voltage measurements are recorded during the engine operation to characterize the discharge process. In-cylinder charge is diluted using fresh air to achieve lean combustion. In-cylinder pressure measurement and heat release analysis are used to investigate the ignition and combustion characteristics of the engine. Preliminary results indicate that the impact of discharge duration is more pronounced in lean conditions and in the vicinity of the spark advance limit, when ignition process encounters significant challenge. While the discharge duration has a marginal effect on the ignition delay, cyclic variations and stability of the ignition process are notably impacted.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".