Hydrogen propulsion systems for aircraft, a review on recent advances and ongoing challenges
Bibliographic record
Abstract
Air transportation contributes significantly to harmful and greenhouse gas emissions. To combat these issues, there has been a recent emergence of aircraft electrification as a potential solution to mitigate environmental concerns and address fuel shortages. However, current technologies related to batteries, electric machinery, and power systems are still in the developmental phase to meet the requirements for power and energy density, weight, safety, and reliability. In the interim, there is a focus on the more electric and hybrid electric propulsion systems for aircraft. Hydrogen, with its high specific energy and carbon-free characteristics, stands out as a promising alternative fuel for aviation. This paper is centred on the application of hydrogen in aircraft propulsion, mainly fuel cell hybrid electric (FCHE) propulsion systems. Furthermore, application of hydrogen as a fuel for the aircraft propulsion systems is considered. A comprehensive overview of the hydrogen propulsion systems in aviation is presented with an emphasis on the technical aspects crucial for creating a more sustainable and efficient air transportation sector. Additionally, the paper acknowledges the technical and regulatory challenges that must be addressed to attain these goals. • A comprehensive review of hydrogen propulsion systems for aviation is presented. • Key focus on PEMFC and SOFC systems for aircraft propulsion and auxiliary power. • Advances and challenges in hydrogen storage, water management, and fuel cell degradation. • State-of-the-art energy management strategies for fuel cell systems are discussed. • Future perspectives on hydrogen-powered aviation and necessary technological advancements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".