In-blade Load Sensing on 3D Printed Wind Turbine Blades Using Trailing Edge Flaps
Bibliographic record
Abstract
As wind turbines become larger the loading on the blades also increases. Controlling a section of the trailing edge of the turbine airfoil is found to reduce load fluctuations on wind turbine blades. Here a detailed experimental setup is described showing the development of a compact airfoil section capable of measuring the surface pressure, root bending moment, and controlling a TEF simultaneously and in time resolved fashion to quantity the influence of a TEF on a wind turbine. This experimental work includes a trailing edge flap that covers 20% of an S833 airfoil with a chord of 178 mm. Surface pressure and blade root strain are measured for varying angles of attack and flap angle. Coefficient of lift and moment are obtained from the 54 pressure taps. The lift and drag forces are also obtained from the strain gages at the root of the blade. A 3D printed blade section is designed and built to house the actuation and sensing on the airfoil. The trailing edge flap was tested inside a 0.61 m wind tunnel as a baseline case and the results showed how the lift, drag, and root moment on the airfoil can change for different flap angles. The coefficient of lift changed by 30% for flap angle of 20°. The entire blade with the flap will also be installed on a 3.4 diameter wind turbine to study the influence of a flap on load variation.
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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 | 0.001 |
| 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".