Novel Image Analysis Method for Blade Aerodynamic Performance on Operational Turbine
Bibliographic record
Abstract
Tuft flow visualisation has been used to study aerodynamic stall on wind turbine blades for several decades. In recent years, advances in the processing power of personal computers have made digital image processing vastly more accessible. In this paper, therefore, a novel method is presented to digitally analyse tuft flow visualisation on the blade of an operating wind turbine. Examination of the outboard 40% of the blade of a 10 m diameter wind turbine revealed stalled flow in wind speeds from 5m/s to 20m/s. The region of stall at those speeds increased from 5% to 40% of the coverage area of the tufts. This increase in the amount of stalled flow is expected for stall-regulated wind turbines. Overall, the results are very promising and demonstrate potential for a wide range of aerodynamics applications including real-time blade stall determination and classical wind tunnel aerodynamics studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 0.001 |
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".