Uncertainty based aircraft derivative design for requirement changes
Bibliographic record
Abstract
ABSTRACT Aircraft manufacturers often consider producing multiple derivatives of aircraft to satisfy various market demands and technical changes while keeping development costs and time to a minimum. Many approaches have been proposed for carrying out derivative design. However, these approaches consider both the baseline design and derivatives together at the conceptual design stage using the entire set of design variables with an assumed set of expected requirements. These frozen requirements on derivative design cannot consider new demands from market changes. In this paper, a method is proposed that uses design optimisation for conceptual design of derivatives for existing aircraft that consider requirement changes. Furthermore, the Possibility-Based Design Optimisation (PBDO) method was implemented to consider uncertainty in the aircraft operation phase. The altitude range of aircraft operation was defined as an uncertain parameter to prevent violation of constraints in the entire operating envelope of the aircraft. The PBDO method yields a more conservative design than those obtained with deterministic design optimisation. In this paper, the proposed derivative design process was applied to the Expedition 350, a small piston engine powered aircraft produced by Found Aircraft, Canada. A derivative that changes the normally aspirated engine to a turbocharged engine for high-altitude operation was considered. An optimum configuration with the new engine was obtained while enhancing performance and stability characteristics. The proposed derivative design process can be implemented on the derivative design of other 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".