REVIEW OF STUDIES ON THE CFD-BEM APPROACH FOR ESTIMATING POWER LOSSES OF ICED-UP WIND TURBINES
Bibliographic record
Abstract
The application of computational fluid dynamics (CFD) in wind turbine design and analysis is becoming increasingly common in research on wind energy, resulting in a better knowledge of the aerodynamic behaviour of rotors. Due to the deformation of blade airfoils on account of icing, a significant drop in aerodynamic performance brings wind turbines to lose considerable portions of their productivity. Estimating power degradation due to icing via 3D simulation, although it is essential to capture the three-dimensional turbulence effects, is very costly in computational resources despite technological development it then becomes unfeasible when it comes to different operation scenarios to estimate icing originated power losses. The Quasi-3D simulation based on the CFD-BEM method is a practical alternative for generating wind turbines power curves. It showed effectiveness in predicting performance up to a certain level. More than few studies in the literature have adopted this approach to generate the power curve for both clean (un-iced) and iced-up wind turbines. However, the methodology was not adequately presented and discussed for wind turbine icing. This paper reviews the results of almost all the up-to-date published papers that approached this method, summarizing the findings and federates the research in that field to conclude with concrete facts and details that advance research in this domain.\n\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".