Seamless morphing trailing edge flaps for UAS-S45 using high-fidelity aerodynamic optimization
Bibliographic record
Abstract
The seamless trailing edge morphing flap is investigated using a high-fidelity steady-state aerodynamic shape optimization to determine its optimum configuration for different flight conditions, including climb, cruise, and gliding descent. A comparative study is also conducted between a wing equipped with morphing flap and a wing with conventional hinged flap. The optimization is performed by specifying a certain objective function and the flight performance goal for each flight condition. Increasing the climb rate, extending the flight range and endurance in cruise, and decreasing the descend rate, are the flight performance goals covered in this study. Various optimum configurations were found for the morphing wing by determining the optimum morphing flap deflection for each flight condition, based on its objective function, each of which performed better than that of the baseline wing. It was shown that by using optimum configuration for the morphing wing in climb condition, the required power could be reduced by up to 3.8% and climb rate increases by 6.13%. The comparative study also revealed that the morphing wing enhances aerodynamic efficiency by up to 17.8% and extends the laminar flow. Finally, the optimum configuration for the gliding descent brought about a 43% reduction in the descent rate.
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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 | 0.000 |
| 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) | 0.001 | 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".