Dynamic Stall on Pitching Cambered Airfoil with Phase Offset Trailing Edge Flap
Bibliographic record
Abstract
Dynamic stall on wind turbine blades often leads to severe fatigue that tends to decrease the lifespan of the blades. To mitigate cyclic loading on the blades, trailing edge flaps (TEFs) may be used to control the energy captured by the blades. In this study the influence of a TEF on a pitching S833 cambered airfoil is investigated at a Reynolds number of 170,000 and reduced frequencies of and 0.1. The lift and moment hysteresis cycles are presented for mean pitch angles of 0 and 10° to represent stall onset and deep stall. The flap, hinged at 0.8 chord, is pitching with different phase lags to study the influence of flap motion. Coefficient-of-pressure contour plots presented here clearly indicate the leading-edge vortex (LEV) formation and convection. It is concluded that even though the TEF was not capable of controlling the formation of the LEV, it was, however, capable of reducing the LEV magnitude and, more important, a reduction in cyclic loading. It is recommended to dynamically pitch the flap out of phase with the airfoil motion to reduce the min/max of the lift and moment cycles and reduce negative damping that can lead to stall flutter.
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) | 0.001 | 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".