Multidisciplinary Analysis of a Box-Wing Aircraft Designed for a Regional-Jet Mission
Bibliographic record
Abstract
In order to achieve significant fuel savings for transport missions, unconventional designs such as box-wings are being considered for the next generation of civil transport aircraft. Previous studies have shown that nonplanar wings, such as box-wings can achieve superior aerodynamic performance relative to conventional designs. In addition, the box-wing designs may have some structural advantages. However, previous studies have not identified the magnitude of any structural savings in box-wing designs nor have they considered performance of box-wings in off-cruise mission segments. This study performed a multidisciplinary analysis which examined the aerodynamic performance of a box-wing regional jet aircraft throughout its mission and used a fully stressed beam analysis to examine the structure of the wing in detail. Combining this analysis with a constrained optimization algorithm allowed an optimal design to be identified which met critical design constraints such as trim and longitudinal stability. The optimal design burnt less fuel over the course of its mission than a conventional aircraft and the fuel savings would have been greater if the aircraft were designed for a lower cruise Mach number. These findings indicate that a box-wing is a promising candidate for a next-generation of environmentally friendly regional jet 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".