Turbulence Modeling of Iced Wind Turbine Airfoils
Bibliographic record
Abstract
Icing is a severe problem faced by wind turbines operating in cold climates. It is affected by various fluctuating parameters. Due to ice accretion, a significant drop in the aerodynamic performance of the blades’ airfoils leads to productivity loss in wind turbines. When ice accretes on airfoils, it leads to a geometry deformation that seriously increases turbulence, particularly on the airfoil suction side at high angles of attack. Modeling and simulation are indispensable tools to estimate the effect of icing on the operation of wind turbines and gain a better understanding of the phenomenon. This paper presents a numerical study to assess the effect of surface roughness distribution, along with the effect of two turbulence models on estimating wind turbine airfoils’ aerodynamic performance losses in the presence of ice. Aerodynamic parameter estimation was performed using ANSYS FLUENT, while ice accretion was simulated using ANSYS FENSAP-ICE. The results using the adopted modeling approaches and the simulation tools were compared with another numerical study and validated against experimental data. The validation process demonstrated the model’s accuracy when considering roughness distribution via the beading model available in ANSYS FENSAP-ICE. The two turbulence models examined (Spalart–Allmaras and k-ω SST) gave comparable results except for the drag at high angles of attack. The k-ω SST model was more efficient in replicating turbulence at high angles of attack, leading to higher accuracy in aerodynamic loss estimation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".