Direct-Injected Hydrogen-Methane Mixtures in a Heavy-Duty Compression Ignition Engine
Bibliographic record
Abstract
A diesel pilot-ignited, high-pressure direct-injection of natural gas heavy-duty single-cylinder engine was fuelled with both natural gas and blends of 10% and 23% by volume hydrogen in methane. A single operating condition (6 bar GIMEP, 0.5 ϕ, 800 RPM, 40%EGR) was selected, and the combustion phasing was varied from advanced (mid-point of combustion at top-dead-center) to late (mid-point of combustion at 15°ATDC). Replacing the natural gas with hydrogen/methane blend fuels was found to have a significant influence on engine emissions and on combustion stability. The use of 10%hydrogen was found to slightly reduce PM, CO, and tHC emissions, while improving combustion stability. 23%hydrogen was found to substantially reduce CO and tHC emissions, while slightly increasing NOx. The greatest reductions in CO and tHC, along with a significant reduction in PM, were observed at the latest combustion timings, where combustion stability was lowest. The high hydrogen-content fuel was found to reduce the ignition delay of the gaseous jet by approximately 20%, without influencing the ignition delay of the diesel pilot. The results were generally consistent at all combustion timings tested, and were found to be insensitive to injection pressure.
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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) | 0.001 | 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) | 0.001 | 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".