Passive Flow Control of Dynamic Stall via Surface-based Trapped Vortex Generators
Bibliographic record
Abstract
A novel passive flow control concept - based on the local modification of an airfoil's surface - is proposed and examined via CFD for the mitigation of the negative effects of dynamic stall, i.e. for the reduction of peak negative pitching moment while not deteriorating significantly the original lift and drag characteristics. 2D CFD simulations of a NACA 0012 airfoil exposed to a freestream of Mach 0.3 and Re = 3.76×10⁶ and undergoing a 15°±10° pitch oscillation with a reduced frequency of 0.101 were conducted. The baseline airfoil simulations were carefully verified and validated, showing excellent agreement with wind tunnel data. Twenty-six different local geometry modifications were proposed and examined, all functioning as a trapped-vortex generator. The surface modifications were examined on both the upper and lower surfaces. In case of the upper surface modifications, the best geometries could reduce the peak negative pitching moment by as much as 37-63%, while sacrificing only 2-10% of peak lift and reducing drag by 14-38%. On the other hand, the lower surface modifications demonstrated the ability to increase lift by 4-16% with only minor penalty in pitching moment and drag.
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) | 0.000 | 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".