Quantifying Trailing Edge Flap Control Capability on Wind Turbines in a Controlled Environment
Bibliographic record
Abstract
As wind turbine diameters continue to increase, the cyclic loading experienced by the turbine blade due to wind shear and yaw misalignment increases dramatically hindering the development of larger more efficient turbines. Based on current literature, controlling a section of the trailing edge of the turbine blade was found to reduce cyclic loading. To quantify the load control capability of a trailing edge flap (TEF), an experimental rig was designed to evaluate a TEF on a 3.5-m-diam wind turbine operating under different tip speed ratios, yaw angles, and blade pitch angles inside a large wind generation facility. The TEF was tested at two different spanwise locations [Formula: see text] and 0.82. The instrumented blade was capable of measuring rotor torque, flapwise/edgewise blade root bending moment, and normal force coefficient at two different spanwise locations [Formula: see text] and 0.82. The experimental results show that the use of a TEF was capable of manipulating the normal force coefficient by 40% and the flapwise bending moment by 20%. The relationship between the TEF angle and both blade bending moment and normal force coefficient was linear for all the cases tested. The results also show that when the TEF was centered at [Formula: see text] it could control the turbine loading more effectively. The results presented here prove that the TEF is capable of reducing cyclic loading on wind turbine blades.
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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 | 0.001 |
| 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 | 0.001 |
| 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".