Hydrogen propulsion systems for aircraft, a review on recent advances and ongoing challenges
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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