Optimising wind turbine design for operation in low wind speed environments
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".