Systems Integration Framework for Hybrid-Electric Commuter and Regional Aircraft
Bibliographic record
Abstract
System integration is one of the key challenges to bringing future hybrid-electric and all-electric aircraft into the market. In addition, retrofitting and redesigning existing aircraft are potential paths toward achieving hybrid and all-electric flight, which are even more challenging goals from a system integration perspective. Therefore, integration tools that bridge the gap between the aircraft and the subsystem level need to be developed for use in the conceptual design stage to address current system integration challenges, such as the use of space, the share between propulsive and secondary power, required level of electrification, safety, and thermal management. This paper presents a multidisciplinary design analysis (MDA) framework that integrates aircraft and subsystem sizing tools. In addition, this paper includes improved physics-based subsystem sizing methods that are also applicable to 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".