Reducing emissions and fuel consumption in supersonic aviation with ammonia hybrid engines
Bibliographic record
Abstract
Ammonia, emerging as a zero-carbon aviation fuel, presents potential for high-energy hydrogen storage and rapid conversion medium to electricity. Recent electrification efforts in the aviation industry further reinforces its importance as electricity direct storage has challenges especially in terms of their low energy density and maximum attainable airspeed with motor-propellers. This study explores a supersonic hybrid electric engine for medium-haul airlines, combining ammonia-powered turbofan with a proton exchange membrane fuel cell. Mathematical modeling helps generate parametric dataset across different flight phases which is then used for training a physics-informed artificial neural network to identify optimum design points in terms of safety, efficiency and emissions. The hybrid engine outperforms legacy aircraft like the Concorde and subsonic turbofans fueled with either ammonia or fossil kerosene; achieving around 18% reduction in specific fuel consumption and about 31% lower NOx pollutants. Moreover, maintaining high fuel cell power draw-down towards the fan for propulsion also helps achieve greater overall efficiencies than non-hybrids and further ensures that the engine core has enough residual power to operate safely, even after loss of single engine core during flight. Additionally, contrail analysis reveals that the ammonia-fueled PEMFC hybrid forms up to 70% fewer ice crystals than hydrocarbon-based systems, potentially lowering climate forcing of resulting contrails. However, due to higher water vapor emission indices and lower exhaust temperatures, contrails from the hybrid engine can form at ambient temperatures up to 20–30 K warmer than those required by conventional engine configurations.
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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 | 0.000 | 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) | 0.001 | 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".