Numerical Investigation of a Dynamically Morphing UAS-S45 Wing Airfoil at moderate Reynolds number
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1585.vid The unsteady flow characteristics and responses of the UAS-S45 airfoil with a morphing trailing edge shape at high angles of attack undergoing deflections are investigated at a Reynolds number of 2.4 × 106. The flexible trailing edge was simulated using a computational fluid dynamics approach using a dynamic mesh and user-defined functions. The goal was to achieve a dynamically deflected trailing edge in an unsymmetrical airfoil and assess the influence of unsteady morphing trailing edge deflection on transient forces and flow field unsteadiness. The steady aerodynamic characteristics of the morphing deflection and the conventional deflection was initially studied. Then, the unsteady aerodynamic characteristics of the morphing wing was investigated as the trailing edge deflects at different rates. The dynamic flow responses to downward deflections are studied using the turbulence model. The time histories of the lift and drag coefficient responses exhibit a proportional relationship between the morphing frequency and the slope of response at which these parameters evolve. Coefficients of lift, drag, and moment of the deflected trailing edge airfoils were compared to those of the reference airfoils for various angles of attack. The numerical results show that the transient lift coefficient in the deflection process was higher than that of the static case at different angles of attack. The transient lift coefficient were higher as the deflection frequency increased. It was also revealed that the trailing edge deflection did not favor the flow reattachment. In addition, the dynamic mesh strategy, cell quality, and the proposed method of deforming the morphing trailing-edge was presented. increased. It was also revealed that the trailing edge deflection did not favor the flow reattachment. In addition, the dynamic mesh strategy, cell quality, and the proposed method of deforming the morphing trailing-edge was presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".