Wind turbine performance under icing conditions
Bibliographic record
Abstract
Abstract The wind energy market is in full growth in Quebec but technical difficulties due to cold climate conditions have occurred for most of the existing projects. Thus, icing simulations were carried out on a 0.2 m NACA 63 415 blade profile in the refrigerated wind tunnel of the Anti‐icing Materials International Laboratory (AMIL). The shapes and masses of the ice deposits were measured, as well as the lift and drag forces of the iced profiles. Scaling was carried out based on the 1.8 MW–Vestas V80 wind turbine technical data, for three different radial positions and two in‐fog icing conditions measured at the Murdochville wind farm in the Gaspé Peninsula. For both icing events, the mass of ice accumulated on the blade profile increased with an increase in the radial position. In wet regime testing (first icing event), glaze formed mostly near the leading edge and on the pressure side. It also accumulated by run‐off on the trailing edge of the outer half of the blade. In dry‐regime testing (second icing event), rime mostly accreted on the leading edge and formed horns. For both icing events, when glaze or rime accreted on the blade profile, lift decreased and drag increased. A load calculation using the blade element theory shows that drag force on the entire blade becomes too large compared to lift, leading to a negative torque and the stop of the wind turbine. Torque reduction is more significant on the outer third of the blade. Setting up a de‐icing system only on the outer part of the blade would enable significant decrease of heating energy costs. Copyright © 2007 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".