Design of a scaled wind turbine with a smart rotor for dynamic load control experiments
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Bibliographic record
Abstract
Abstract The ever increasing size of wind turbines poses a number of design issues for the industry, like increasing component mass and fatigue loads. An interesting concept for reducing fatigue loads is the implementation of spanwise distributed devices to control the aerodynamic loading along the span of the blade, thus mitigating fluctuations in loading and adding damping to the blade modes. This is usually referred to as the smart rotor concept. In the design of such a rotor, as compared to a traditional one, the integration of sensors and actuators poses additional design challenges. In the research discussed in this paper, a scaled smart rotor was designed and constructed to study its fatigue load reduction potential. A 1.8 m diameter rotor was manufactured and equipped with trailing‐edge flaps. The flaps were based on piezo electric Thunder actuators that allow for high‐frequent actuation. The dynamic strain behaviour of the blade was analysed for optimal placement of the sensors. Several sensors that record the strains and accelerations at different locations along the blade were implemented, but the controller was based on a piezo electric strain sensor. The rotor blades were mounted on a small turbine in the Delft University's Open Jet Facility wind tunnel and a mathematical state space model was obtained by using dedicated system identification techniques. Single‐Input Single‐Output, Multi‐Input Multi‐Output ℋ︁ ∞ feedback and feedforward controllers were designed, each focusing on different parts of the load spectrum. The rotor was tested at 0 and 5° yaw angles, with and without load control. A significant reduction of the dynamic loads was attained. Copyright © 2010 John Wiley & Sons, Ltd.
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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 it