A Conceptual Sizing Tool for Regional and Commuter Aircraft with Hybrid-Electric Propulsion
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0213.vid The rapidly growing aviation industry aims to reduce its carbon footprint drastically in the upcoming years. Therefore, there is a need for environmentally friendly aircraft that integrate new configurations and technologies, such as hybrid-electric, distributed-electric, and all-electric propulsion. New conceptual design tools need to be put in place to analyze the potential benefits of these new configurations and technologies. The hybrid-electric sizing tool described in this paper is developed with the objective of evaluating a potential reduction of fuel burn by estimating the fuel mass, battery mass, and overall mass for a given mission profile and level of hybridization and is based on a series of top-level requirements, flight segments' constraints, and aircraft characteristics. The proposed method implements performance constraints for Part 23 and Part 25 certified aircraft, as much promise is seen in the hybridization of regional or commuter aircraft. The validation of the tool focuses particularly on this segment of aircraft. In addition, this paper includes a comparison with other hybrid-aircraft sizing tools in the literature. Overall, this tool, integrated into a multidisciplinary design analysis and optimization framework, enables performing system integration studies for hybrid-electric aircraft.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".