Bibliographic record
Abstract
To meet the future requirements of fuel economy and exhaust emissions, high-efficiency gasoline engines tend to employ diluted combustion concepts along with intensified charge motion and stratified mixtures. Securing the ignition of such mixtures over the full engine operation range is challenging, because of the lowered mixture reactivity and increased discrepancy of stoichiometry. In recent years, increasing research efforts have been spending on innovations of ignition technologies to tackle the challenges. In this paper, the directions of ignition improvement are highlighted based on the fundamental understanding of the ignition mechanisms. The working principles of the primary types of advanced ignition systems are introduced; and relevant engine and combustion vessel test results are reviewed. The ignition systems are categorized as: (1) high-energy spark ignition, (2) pulsed nanosecond discharge ignition, (3) radio-frequency plasma ignition, (4) laser-induced plasma ignition, and (5) pre-chamber ignition. The advanced ignition systems are commented, regarding the ignition effectiveness and the implementation challenges, according to the literatures and the extensive empirical work at the authors’ laboratory.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".