Module discretization and consolidation of a modular morphing wing
Bibliographic record
Abstract
A morphing wing is designed to morph to different wing profiles required for different flight regimes. Such a design is based on modules, and discretization of different wing profiles will yield different sets of modules. In this paper, a novel wing module consolidation methodology is developed to consolidate different sets of modules to a common set of modules for a unified modular morphing wing design. Initial wing profiles are created for different flight regimes using an optimization algorithm coupled with a fluid solver. These wing profiles are then smoothened, and used as reference geometries for module discretization. The results of discretized wings are then consolidated using a sensitivity analysis and a weighting function that allows the designer to assign a higher priority to a specific flight regime. A case study, showing the implementation of this methodology for climb, cruise, and descent flight regimes, reveals that the consolidated wing can be morphed to approximate the original wing profiles within reasonable errors. In climb, maximizing the inverse of drag as a performance index showed a 5.4% decrease by obtaining higher drag values, which is not desirable. In cruise, maximizing lift to drag ratio as a performance index, showed a 2.9% increase. Finally, in descent, maximizing CL3/2/CD as a performance index showed a 1.6% decrease.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".