Influence of Surface Roughness Modeling on the Aerodynamics of an Iced Wind Turbine S809 Airfoil
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Bibliographic record
Abstract
Ice formation on structures like wind turbine blade airfoils significantly reduces their aerodynamic efficiency. The presence of ice on airfoils causes deformation in their geometry and an increase in their surface roughness, enhancing turbulence, particularly on the suction side of the airfoil at high angles of attack. An approach for understanding this phenomenon and assessing its impact on wind turbine operation is modeling and simulation. In this contribution, a computational fluid dynamics (CFD) study is conducted using FENSAP-ICE 2022 R1 software available in the ANSYS package. The objective was to evaluate the influence of surface roughness modeling (Shin et al. and beading models) in combination with different turbulence models (Spalart–Allmaras and k-ω shear stress transport) on the estimation of the aerodynamic performance losses of wind turbine airfoils not only under rime ice conditions but also considering the less studied case of glaze ice. Moreover, the behavior of the commonly less explored pressure and skin friction coefficients is examined in the clean and iced airfoil scenarios. As a result, the iced profile experiences higher drag and lower lift than in the no-ice conditions, which is explained by modifying skin friction and pressure coefficients by ice. Overall, the outcomes of both turbulence models are similar, showing maximum differences not higher than 10% in the simulations for both ice regimes. However, it is demonstrated that the influence of blade roughness was critical and cannot be disregarded in ice accretion simulations on wind turbine blades. In this context, the beading model has demonstrated an excellent ability to manage changes in roughness throughout the ice accretion process. On the other hand, the widely used roughness model of Shin et al. could underestimate the lift and overestimate the drag coefficients of the wind turbine airfoil in icy conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it