Parametric Study of Box-Wing Aerodynamics for Minimum Drag Under Stability and Maneuverability Constraints
Bibliographic record
Abstract
Box-wings are a subset of nonplanar wing designs which have shown promise as a configuration for future transport aircraft. This configuration has been the subject of several studies seeking to determine the optimal wing configuration. However there is uncertainty as to the sensitivity of the wing’s aerodynamic performance, represented as a lift to drag ratio, to changes in some of the its geometric parameters. Specifically, the effects of the stream-wise stagger and the relative area of the two wings are not clear. This study seeks to identify the trends in aerodynamic performance as these parameters are varied while enforcing critical design constraints. Three such constraints are considered: the ability to maintain inherent static stability at cruise, the ability to perform a maneuver without stalling, and the ability to generate sufficient lift to support the aircraft at cruise conditions. A better understanding how the design parameters affect the aerodynamic performance of feasible box-wing designs will provide a better understanding of the sensitivities of such designs and will enable more meaningful analysis of the results from more comprehensive multidisciplinary studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".