Systematic Methodology for Aircraft Concept Development with Application to Transitional Aircraft
Bibliographic record
Abstract
Aircraft concept development is the process of generating feasible concepts/configurations and selecting the one that best fulfills the requirements. It is a decisive step as it significantly affects the entire design process and ultimately the aircraft performance. Ideally, all possible configurations are considered during the concept development process. However, due to the enormous varieties of aircraft concepts, the method that considers all concepts and selects the best one is yet a challenge. Therefore, employing designers’ experience/intuition and/or replicating/evolving existing similar configurations remain the predominant methods used. Despite their advantages, such approaches may result in selecting poor configurations or overlooking valuable ones, especially when designing unconventional aircraft. Poor configurations significantly increase the design and manufacturing costs and time, as they typically require rework in the later design phases. This paper presents a systematic concept development methodology to efficiently generate and select the best aircraft configuration, during the conceptual design phase, using structured design methods. The methodology considers the identification and prioritization of all possible alternatives for the aircraft components, an effective generation of the candidate configurations, and selection of the best configuration. The methodology is exemplified via a case study where the best configuration for a highly maneuverable transitional unmanned aerial vehicle is selected.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".