Icing Simulation of Wind Turbine Blades
Bibliographic record
Abstract
The wind-energy market is in full growth in Quebec, but technical difficulties due to cold climate conditions, such as wind turbine blade icing issues, have occurred for most of the existing projects. In such a context, 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 aerodynamic lift and drag of the iced profiles. The conditions for simulation in the wind tunnel were based on meteorological data measured at the Murdochville wind farm in the Gaspe Peninsula during in-fog icing. Two different in-fog icing conditions were considered, characterized by liquid water contents of 0.22 and 0.24 g/m3, temperatures of-1.5 and -5.7 °C, and wind speeds of 8.8 and 4.4 m/s respectively, generating wet and dry ice accretions. Scaling was carried out based on the 1.8 MW - Vestas V80 wind turbine technical data, for three different radial positions and the two in-fog icing conditions. In wet regime testing, glaze formed mostly near the leading edge and on the intrados. It accumulated by runoff on the trailing edge for blade profiles located at the centre and blade tip. In dry-regime testing, rime accreted on the leading edge and partially on the intrados of the blade profiles located between the middle and the blade tip. The rime accreted on the leading edge was horn shaped, which considerably increased the surface roughness. In both dry and wet regimes, because of a greater ice amount for high radial positions, lift decreased with an increase in radial position, while drag increased following a power law. Between the centre and the tip, drag increased considerably compared to lift, which seriously decreased rotor blade aerodynamic performances. An ideal horizontal-axis wind-turbine model was finally used to evaluate the impact of the lift reduction and drag increase on the wind turbine blade. For both icing events, the model shows that drag force becomes too great compared to lift, resulting in negative torque and the wind turbine stoppage. Torque reduction is more significant on the last half of the blade. Setting up a de-icing system only on this part of the blade would enable to decrease heating energy costs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".