Hydrogen fuel cell integrated turbofan engines offer lower costs when climate impact accounted for aviation purposes
Bibliographic record
Abstract
The aviation sector is projected to account for over a quarter of global greenhouse gas emissions in the coming decades. Current reliance on kerosene-fueled turbofan engines leads to significant fuel losses, low exergy efficiency, and severe environmental impacts. This study proposes and evaluates a hybrid turbofan configuration that decouples the fan-stage from the turbine using a solid oxide fuel cell (SOFC) powered by hydrogen. The system is assessed through a comprehensive thermodynamic, exergoeconomic, and exergoenvironmental framework, benchmarked against conventional engines. Thermodynamic cycle analysis shows that SOFC integration improves overall thermal efficiency by approximately 14–22% in the core, with gains of ∼0.3 in thermal efficiency. Despite a 40% increase in relative system cost due to hydrogen and SOFC complexity, exergoeconomic evaluation indicates long-term savings from lower fuel consumption. Exergoenvironmental analysis reveals an 89% reduction in emission damage cost and a 68% drop in total environmental impact, with hydrogen eliminating CO 2 , SO 2 , and UHC emissions and reducing NO x by 35%. Climate simulations indicate that SOFC hybrids lower the aviation-induced global surface temperature rise by over 75% through 2100. The system achieves over $26 million in avoided environmental damage over its operational lifetime. While the SOFC hybrid engine entails higher initial investment and design complexity, it offers a practical and forward-looking solution for aviation decarbonization. The proposed configuration aligns with international climate targets and presents a viable transition pathway for future aircraft propulsion systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".