A 2D Blade Element Study of a Wind Turbine Rotor under Yaw Loads
Bibliographic record
Abstract
Wind turbines operate within an environment of varying wind direction and magnitude leading to misalignment or yaw between the wind and the turbine rotor. Many control techniques have been applied to small horizontal axis wind turbines (HAWTs) to prevent significant yaw loads, but in reality, most of the time the rotor is under some yaw load which may be significant. A blade element of a HAWT rotor under yaw loading is similar to a sinusoidally pitching airfoil. Here a miniaturized pitching S822 airfoil with a reduced frequency k of 0.025 at a Reynolds number ( Re = 105) is considered where the maximum dynamic angle of attack is equal to the static angle where the maximum lift to drag ratio occurs. These dynamic results obtained with laser Doppler anemometry (LDA), particle image velocimetry (PIV) and a numerical simulation with the Transition SST method are compared to a constant wind-loaded blade element. Since direct load measurement for dynamic miniaturized models in wind tunnels is very challenging, the non-intrusive PIV control volume analysis approach has been applied for load determination. Unlike the rotor in an ideal unidirectional freestream flow, a rotor under yaw loads, as presented here, experiences significant unbalanced loading in one cycle and a non-uniform wake. Results may be used to predict the cyclical range of loads a blade element experiences in yaw conditions and effects on the downstream rotor wake.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".