Systems Integration Considerations for Hybrid-electric Commuter Aircraft: Case Study for the DO-228
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1361.vid Hybrid-electric and all-electric aircraft are currently studied extensively at the conceptual level to explore reductions in fuel burn and emissions. Retrofitting and redesigning existing aircraft are potential paths toward achieving hybrid and all-electric flight. New design tools need to be developed to support the study of these new aircraft configurations. In particular, novel design frameworks need to account for system-level aspects, such as the use of space, the required level of electrification, thermal aspects, safety, and maintainability. This paper presents a multidisciplinary design analysis (MDA) framework focusing on aircraft system integration for future aircraft. Specifically, this paper focuses on the integration between aircraft sizing and subsystem sizing tools in an MDA workflow. In addition, the authors include improved physics-based subsystem sizing methods valid for smaller, commuter, or regional aircraft. The capabilities of the developed framework and tools are presented for a case study covering the redesign of the DO-228 with a hybrid-electric propulsion system in combination with the electrification of its systems architecture and different subsystem technologies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".