Microcylinder and Slot Combination for Flow Separation Control Over a Wind Turbine Airfoil
Bibliographic record
Abstract
ABSTRACT This study explores a novel passive flow control strategy combining a microcylinder and slot configuration to mitigate flow separation over an S809 wind turbine airfoil under stall conditions at a Reynolds number of 10 6 . Numerical simulations are conducted using the Reynolds‐averaged Navier–Stokes (RANS) approach. The primary objectives are to (i) reduce or eliminate the flow separation region, (ii) enhance aerodynamic performance, and (iii) assess the effectiveness of combining two passive control techniques. The study evaluates the effects of microcylinder diameter, relative position to the leading edge, and the gap‐to‐diameter ratio (G/D). Results indicate that placing the microcylinder too close to the suction surface can harm aerodynamic performance. However, an optimized microcylinder position effectively suppresses flow separation and improves aerodynamic coefficients for angles of attack (AoA) between 16° and 24°. When combined with strategically positioned slots, the optimized configuration achieves a 97.47% reduction in the separation region and a 16.83% improvement in lift‐to‐drag ratio compared to the microcylinder alone at 24° AoA. The study also highlights the underlying flow control mechanisms contributing to separation suppression. Although this method proves effective at high AoAs, it incurs a drag penalty at lower AoAs, leading to a reduced lift‐to‐drag ratio compared to the baseline airfoil. These findings demonstrate the potential and limitations of the proposed microcylinder‐slot combination for aerodynamic performance enhancement in wind turbine applications.
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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".