High-Speed Photography of Stratified Combustion in an Optical GDI Engine for Different Triple Injection Strategies
Bibliographic record
Abstract
To contribute to knowledge required to meet new emission requirements, relationships between multiple injection parameters, degrees of fuel stratification, combustion events, work output and flame luminosity (indicative of particulate abundance) were experimentally investigated using a single-cylinder optical GDI engine. A tested hypothesis was that advancing portions of the mass injected would enhance the fuel-air mixing and thus reduce flame luminescence. An outward-opening piezo actuated fuel injector capable of multiple injections was used to inject the fuel using different triple injection strategies, with various combinations of late and earlier injections leading to various degrees of fuel stratification. Sprays and combustion events were captured using two high-speed cameras and cylinder pressure measurements. The data were analyzed to assess effects of fuel stratification on yellow flame luminescence (assumed to be dominated by soot luminescence), flame propagation, jet flames, pool fires and heat release. The combustion phasing, amount of fuel injected and engine speed were kept constant and the engine was unthrottled for all tested cases. Image sectorization was used to analyze events captured in different parts of the cylinder. The results show that the injection strategy influences fuel spray behavior and the combustion in terms of both flame luminescence patterns and work output. Injecting some of the fuel earlier results in increased spray liquid penetration, streakier sprays (due to the lower back-pressure), and less intense yellow flame luminescence, but also reductions in work output. The greater portions of fuel injected close to the ignition results in increased soot luminescence.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".