Validated Dynamic Stall Simulation of Pitching Low Reynolds Number Airfoils
Bibliographic record
Abstract
Deep dynamic stall is one of several complex behaviors that result in extreme variation of the aerodynamic loads on small wind turbine (SWT) blades during unsteady wind conditions. In this study, unsteady Reynolds-averaged Navier–Stokes simulations are performed for two low Reynolds number (Re) airfoils where sinusoidal pitching is applied to replicate the dynamic stall that occurs on rotating SWT blades. The SD 7037 airfoil is simulated at [Formula: see text] and a pitching reduced frequency of [Formula: see text], and the S833 airfoil is at [Formula: see text] and [Formula: see text]. The simulated lift coefficient and dynamic stall timing agree with experimental data, which is attributed to the wall-normal resolution of the mesh and is an advancement from the early prediction of stall seen consistently in previous numerical studies. The accurate prediction of dynamic stall is found to be dependent on the correct simulation of the bursting of the laminar separation bubble (LSB), which initiates the complete separation of the boundary layer and the formation of a leading-edge vortex. The [Formula: see text] [Formula: see text] model combined with the use of a fine mesh at the airfoil leading edge results in an accurate simulation of the bursting LSB and the correct prediction of the deep dynamic stall.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".