Design Space Exploration and Performance Analysis of Low Temperature PEM Fuel Cell Propulsion Aircraft
Bibliographic record
Abstract
Hydrogen propulsion using low-temperature proton exchange membrane fuel cells (LT-PEMFC) offers a promising alternative to conventional combustion engines by reducing component complexity, lowering operating temperatures, and potentially decreasing operating costs. This paper presents a comprehensive system-level analysis of hydrogen-powered LT-PEMFC propulsion systems for aircraft. Custom-developed models integrate fuel cells with auxiliary subsystems, including thermal management, compression, and power electronics, to size the propulsion system and fuel capacity according to a defined mission profile. Key performance metrics, including range, endurance, and liquid hydrogen volume requirements, are evaluated for a generic general aviation class baseline aircraft sized with an automotive-derived net 150 kW LT-PEMFCs. The analysis considers six key design variables (gross takeoff weight (GTOW), lift-to-drag ratio, propulsion efficiency, propulsion specific power, liquid hydrogen gravimetric index, and GTOW mass fraction) along with three operational parameters (climb rate, service ceiling, and cruise speed). Validation against an established flight mission demonstrates close agreement in liquid hydrogen consumption predictions. Under baseline operational conditions, the aircraft with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1,814 ~\text{kg}$</tex> GTOW is predicted to achieve a range of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3,203 ~ \text{km}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$17.5$</tex> hours of flight. Sensitivity analyses indicate that when subjected to alternative design and operational constraints, incremental improvements in the GTOW mass fraction enable an additional 100 km of range with a minimal adjustment, while enhancements in the lift-to-drag ratio and propulsion efficiency reduce the liquid hydrogen required by 64 % and 50 % per 100 km relative to other design variables, respectively. Advanced technology improvements suggest a maximum performance potential of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15,474 ~\text{km}$</tex> in range and 84.7 hours of flight time. These findings provide critical insights for optimizing fuel cell propulsion systems and establishing a clean-sheet design framework for next-generation hydrogen-powered aircraft.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".