Weight Estimation and Architecture Definition of Fuel Systems for Aircraft Conceptual Design
Bibliographic record
Abstract
The design of hybrid-electric, distributed-electric, and unconventional aircraft requires improving existing conceptual design methods. In particular, hybrid-electric aircraft require more integration between the traditional propulsion system and the aircraft systems (i.e., the electrical power system, flight control systems, and fuel system). Traditional conceptual design methods for aircraft systems rely on statistical data using empirical weight estimation. The shift to hybrid-electric aircraft will particularly impact the fuel system; for example, less traditional fuel storage being required but maintaining the same critical components. As state-of-the-art weight estimation methods use fuel capacity as the key parameter, they cannot be applied to hybrid-electric aircraft studies. This paper proposes two new methods: an updated empirical method tailored explicitly to commuter and regional aircraft and a more generic architecture-based approach. This architecture-based approach estimates the fuel system weight based on individual subsystems and major components from information available in conceptual design, using manufacturer data and physical relationships at the subsystem and component level. The validation and the application to a hybrid-electric regional aircraft case study are presented to illustrate the capability of the new method. In summary, the architecture-based approach for the fuel system enables more detailed subsystem analysis as required in the next generation of multidisciplinary optimization frameworks, for the analysis of certifiability, safety, reliability, and thermal analyses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".