Passive flow control of shock-induced dynamic stall via surface-based trapped vortex generators
Bibliographic record
Abstract
A novel passive approach for controlling the flow in a 2D dynamic stall at variabl freestream is investigated. 2D computational fluid dynamics simulations of an SC1095 airfoil with surface-based trapped vortex generator (STVG) type passive flow control were conducted. The airfoil was exposed to a fluctuating freestream of Mach 0.537 ± 0.205 and Re = 6.1 × 10 6 (based on the mean Mach number) and experienced a 10° ± 10° pitch oscillation with a frequency of 4.25 Hz. These conditions were selected as an approximation to the flow experienced by a UH-60A helicopter rotor airfoil section in an actual fast forward flight test case. The baseline simulations were cautiously validated with experimental data for both transonic flow and dynamic stall under the variable freestream. Then, 20 different local STVGs type geometry modifications were investigated as a means of passive flow control. Modifications were examined on both the airfoil’s upper and lower surfaces. Results showed that the STVGs were able to mitigate the negative effects of shock-induced dynamic stall. The best geometries could reduce the peak negative pitching moment by as much as 9–23% during the transonic phase of a cycle and by as much as 19–71% during the dynamic stall phase. Also, they were able to reduce peak drag by 8–20% in the transonic phase and by 15–44% in the dynamic stall phase. On the other hand, the lift-to-drag ratio was significantly increased by 3–28% per one rotor cycle. All the above advantages came at virtually no penalty in the lift.
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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) | 0.000 | 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 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".