Optimising wind turbine design for operation in low wind speed environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter considers wind turbines at low wind speed and the optimising of blade design to improve performance in these conditions. The key aim is to achieve fast acceleration of the blades when the wind starts blowing because the average wind speed for starting is higher than the cut-in wind speed, which is the conventional measure of low wind performance. A blade element method for starting is described and shown to give good agreement with the measured starting sequences of a three-bladed 500 W turbine. Analytic expressions for starting time appear possible only for simple and unrealistic situations. Using a numerical, differential evolutionary strategy, however, blades can be designed to reduce starting time at the same time as maintaining high efficiency of power extraction. The most efficient blade designs are always very slow to start but, typically, a small trade-off in efficiency is associated with a large decrease in starting time. A practical application of this methodology is described for the blade design of a 5 kW turbine. This chapter shows that reducing starting time is also possible in the context of noise minimisation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it