Hydrogen fuel cell integrated turbofan engines offer lower costs when climate impact accounted for aviation purposes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it