Study of Assisted Compression Ignition in a Direct Injected Natural Gas Engine
Bibliographic record
Abstract
Natural gas direct injection (DI) and glow plug ignition assist technologies were implemented in a single-cylinder, compression-ignition optical research engine. Initial experiments studied the effects of injector and glow plug shield geometry on ignition quality. Injector and shield geometric effects were found to be significant, with only two of 20 tested geometric combinations resulting in reproducible ignition. Of the two successful combinations, the combination with 0 deg injector angle and 60 deg shield angle was found to result in shorter ignition delay and was selected for further testing. Further experiments explored the effects of the overall equivalence ratio (controlled by injection duration) and intake pressure on ignition delay and combustion performance. Ignition delay was measured to be in the range of 1.6–2.0 ms. Equivalence ratio was found to have little to no effect on the ignition delay. Higher intake pressure was shown to increase ignition delay due to the effect of swirl momentum on fuel jet development, air entrainment, and jet deflection away from optimal contact with the glow plug ignition source. Analysis of combustion was carried out by examination of the rate of heat release (ROHR) profiles. ROHR profiles were consistent with two distinct modes of combustion: premixed mode at all test conditions, and a mixing-controlled mode that only appeared at higher equivalence ratios following premixed combustion.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".