Coupled Optimization of Aircraft Families and Fleet Allocation for Multiple Markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The requirements for new commercial aircraft can differ significantly for different markets and operators. The economic and environmental sustainability of commercial aviation requires not only the design of efficient new aircraft but also consideration of the operations of these aircraft. This can be achieved by coupling the design optimization of multiple aircraft families with the simultaneous allocation of these aircraft in multiple markets. Including operational allocation of aircraft in the design stage can reduce operational inefficiencies, whereas the design of aircraft families aims at reducing costs through the use of common components and providing increased flexibility for different markets. To investigate the tradeoffs involved in designing efficient, environmentally sustainable aircraft, a coupled design optimization of two aircraft families involving uncertainties in passenger demand over multiple years of operations was conducted. The results obtained show that the coupled design of aircraft families with the allocation of these aircraft to two distinct markets can significantly reduce fuel burn, as well as operating and acquisition costs, when compared to existing aircraft. The optimized aircraft also provide higher operational flexibility, with respect to variations in passenger demand, and improved performance when compared against aircraft optimized individually, taking into consideration routes being flown but decoupled from fleet allocation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it