Flow Separation Control and Aeroacoustic Effects of a Leading-Edge Slat over a Wind Turbine Blade
Bibliographic record
Abstract
To enable wind energy to surpass fossil fuels, the power-to-cost ratio of wind turbines must be competitive. Increasing installation capacities and wind turbine sizes indicates a strong trend toward clean energy. However, larger rotor diameters, reaching up to 170 m, introduce stability and aeroelasticity concerns and aerodynamic phenomena that cause noise disturbances. These issues hinder performance enhancement and social acceptance of wind turbines. A critical aerodynamic challenge is flow separation on the blade’s suction side, leading to a loss of lift and increased drag, ultimately stalling the blade and reducing turbine performance. Various active and passive flow control techniques have been studied to address these issues, with passive techniques offering the advantage of no external energy requirement. High-lift devices, such as leading-edge slats, are promising in improving aerodynamic performance by controlling flow separation. This study explores the geometric parameters of slats and their effects on wind turbine blades’ aerodynamic and acoustic performance. Using an adequate turbulence model at Re = 106 for angles of attack from 14° to 24°, 77 slat configurations were evaluated. Symmetric slats showed superior performance at high angles of attack, while slat chord length was inversely proportional to aerodynamic improvement. A hybrid method was employed to predict noise, revealing slat-induced modifications in eddy topology and increased low- and high-frequency noise. This study’s main contribution is correlating slat-induced aerodynamic improvements with their acoustic effects. The directivity reveals a 10–15 dB reduction induced by the slat at 1 kHz, while the slat induces higher noise at higher frequencies.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".