An Experimental Investigation on Combustion and Emissions of a Hydrogen Enriched Ammonia-Diesel Dual Fuel Engine at a Medium Load Condition
Bibliographic record
Abstract
Abstract As a carbon-free hydrogen carrier, ammonia is easy to store, handle and distribute compared to hydrogen itself. Switching from diesel to green ammonia in heavy-duty compression ignition engines dominating the power generation of freight transportation industry has the potential to reduce greenhouse gas (GHG) emissions. However, due to the low flame speed and presence of fuel-bound nitrogen, ammonia combustion may result in certain unburned ammonia slip and nitrous oxide (N2O) emissions, which offsets its zero-carbon advantage in applications. In this paper, an investigation on the influence of hydrogen blending on ammonia slip and emissions of nitrogen oxide (NO), N2O and GHG in a heavy-duty ammonia-diesel dual fuel engine is experimentally conducted at a medium engine load, various hydrogen blending ratios, and different gaseous fuel energy fractions. The results reveal that hydrogen blending does help significantly reduce ammonia slip. However, hydrogen blending does not help reduce N2O emissions at relatively lower gaseous fuel energy fractions that result in lower equivalence ratio for hydrogen/ammonia mixture, but does help reduce N2O emissions at relatively larger gaseous fuel energy fractions. As a result, hydrogen blending does not help reduce GHG emissions at relatively lower gaseous fuel energy fractions, but does help at higher gaseous fuel energy fractions. Blending of a small amount of hydrogen significantly improves engine efficiency, but the effect of further increasing hydrogen blending ratio on engine efficiency is insignificant. A side effect of hydrogen blending is that it increases NO emissions, since it not only increases combustion temperature but also promotes the NO formation via fuel route during ammonia combustion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".