Some comments on experimental results of three lift controllers for a wind turbine blade section using an active flow control
Bibliographic record
Abstract
Abstract. Controlling wind turbines is generally performed globally (rotor yaw or blade pitch control) to optimize the energy extraction and minimize rotor's loads for rotor's lifetime extension. This means that no information from the blade aerodynamics is up to now taken into account in the control loop while it is well understood that wind inflow interaction with blade aerodynamics can lead to power loss, load fluctuations and noise generation. This work deals with the development of control algorithms applied at the level of the blade section, considering only local aerodynamic sensing and actuators. The objective is to extract the maximum power from the wind energy by maintaining the aerodynamic lift at its highest value, while limiting load fluctuations using different solutions of control which take into account disturbances from different turbulent inflows. Some control strategies are investigated thanks to an experimental bench in the aerodynamic wind tunnel of LHEEA's laboratory in order to compare the tracking performances with respect to different operating scenarios.
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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) | 0.001 | 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".