Fuel Slip and Greenhouse Gas Emissions in a Hydrogen Blended Ammonia-Diesel Dual Fuel Engine at Different Engine Loads
Bibliographic record
Abstract
Abstract Ammonia offers easier storage and distribution compared to hydrogen. Transitioning from diesel to green ammonia in heavy-duty compression ignition engines presents a promising pathway for reducing greenhouse gas (GHG) emissions. Nevertheless, ammonia combustion is hindered by its low flame speed and the presence of fuel-bound nitrogen, which can lead to ammonia slip and increased emissions of nitrous oxide (N2O) and nitrogen oxides (NOx), thereby offsetting its zero-carbon advantage. This study experimentally investigates the effect of hydrogen blending on ammonia slip and emissions of NO, N2O, and GHG in a heavy-duty ammonia-diesel dual-fuel engine under three typical engine load conditions. The results indicate that hydrogen blending significantly improves ammonia combustion efficiency and reduces ammonia slip across all tested engine loads. Additionally, hydrogen blending lowers N2O emissions at higher engine loads and/or greater gaseous fuel energy fractions - conditions that result in elevated combustion temperatures. However, at lower engine loads or smaller gaseous fuel energy fractions, where combustion temperatures are relatively lower, hydrogen blending does not effectively reduce N2O emissions. Consequently, hydrogen blending decreases overall GHG emissions when combustion temperatures are high but does not provide the same benefit under lower temperature conditions. A notable side effect of hydrogen blending is an increase in NO emissions, as it raises combustion temperatures and enhances fuel-NO formation. These findings provide valuable insights into optimizing hydrogen-ammonia fuel blends to balance emissions reduction and combustion efficiency in heavy-duty ammonia-diesel dual-fuel engines at different load conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".