Advanced Plasma-Based Ignition Strategy for Future Spark Ignition Engines
Bibliographic record
Abstract
Internal combustion engines will continue to play an important role in transportation for decades to come because of the high onboard energy density. For present passenger vehicles, efforts have been made to reduce the cold start emissions and improve engine efficiency. To reach such goals, lean and diluted mixtures are needed to reduce the chemical reactivity of the mixture, so a higher engine compression ratio can improve thermal efficiency. The decreased flame temperature of the lean/diluted mixtures is also beneficial for NOx reduction. Strong in-cylinder flow is needed to increase flame propagation speed for efficient and complete combustion process. Strong ignition sources are needed to provide robust ignition to support the combustion process. In this paper, the application of advanced plasma-based ignition strategies was reviewed, with special attention to the on-demand plasma energy profiling, which has flexible control over discharge duration and current amplitudes. The ignition performance of multi-core ignition is compared with on-demand energy profiling under cold start and engine idling conditions. For heavy-duty applications burning low and zero carbon renewable fuels with less chemical reactivity, such as ammonia and natural gas, a novel ignition source with remote chamber and detonation tube is also demonstrated for the first time. The air-fuel mixture in the remote ignition chamber can be ignited, and the flame front can propagate and accelerate along the detonation tube to detonation stage, known as the deflagration-to-detonation transition. The high-speed detonation wave has a much stronger ignition capability to improve combustion efficiency of mixture with low chemical reactivities.
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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 | 0.001 |
| 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".