Aerodynamic Optimization of a Novel Synthetic Trailing Edge and Chord Elongation Morphing: Application to the UAS-S45 Airfoil
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1582.vid Conventional chord increase in airfoil trailing edge flaps is typically done by attaching multiple separated fowler flaps in the airfoil trailing edge, however, the airflow is prone to be turbulent near the discontinuities between the junctions of fowler flaps. The novel chord-wise extendable morphing trailing edge design for Unmanned Aerial System UAS-S45 is investigated using the gradient-based optimization approach. The purpose of this study is to investigate the advantages of synthetic trailing edge (vertical deflection) and chord elongation morphing (horizontal deflection) in comparison to conventional UAS-S45 airfoil, and to find the optimum range of deflection of the proposed morphing design for different flight conditions. The results indicate that by increasing the angle of attack, the influence of chord elongation on aerodynamic efficiency becomes considerable and the optimum angle of attack is found at near-zero values. The synthetic morphing of trailing edge and chord elongation conduct to an aerodynamic efficiency increase of up to 25.8%.
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 | 0.000 |
| 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".