Aerodynamic Flow Control using a Dynamically Morphing Leading Edge (DMLE) for the UAS-S45 Airfoil
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3746.vid The Dynamically Morphing Leading Edge (DMLE) was analyzed for the dynamic stall control over a pitching UAS-S45 airfoil. The unsteady parametrization framework was designed to model the morphing leading edge's motion over time. This approach was integrated into the numerical solver (Fluent) by designing a User-Defined-Function (UDF) for the dynamic deflection of the airfoil boundaries by using the dynamic mesh technique. Unsteady flow analysis over the sinusoidally pitching UAS-S45 airfoil was carried out using the dynamic mesh method. The transition-SST turbulence model captures the dynamic airfoil flow parameters with leading-edge vortex dynamics. In order to analyze the DMLE as a flow control method, the leading edge was morphed at three different chordwise locations of 15 %, 20 % of the chord, and 25 % of the chord with varying deflection frequencies at stall angles of attack. The DMLE was analyzed to obtain the dynamic lift and drag forces for 1 Hz and 2 Hz frequencies. Numerical studies have shown that the new radius of curvature of the DMLE airfoil can minimize the streamwise unfavorable pressure gradient and postpone Dynamic Stall Vortex (DSV) to prevent severe flow separation. The morphing airfoil delayed the stall angle of attack by 14.26% and C_(L,max) increased from 2.49 to 3.04. The Leading Edge Vortex (LEV) generation and separation were slightly impacted by its deflection frequency and location. Additionally, an overshoot in the stable lift coefficient was noted and measured while deflecting the DMLE at stall angles of attack. Further investigations into various parameters, such as reduced frequency, droop angle, and amplitude on the DMLE, could reveal more significant aerodynamic characteristics.
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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".