Aerodynamic and aeroacoustic evaluation of slat and slot separation control on a small-scale HAWT: A computational study
Bibliographic record
Abstract
The global push toward clean energy has intensified the search for efficient, innovative technologies to harness renewable resources. Small-scale horizontal-axis wind turbines (HAWTs) offer a promising solution to meet growing urban energy demands with minimal environmental impact. This computational study investigates the aerodynamic and aeroacoustic effects of two passive flow separation control techniques, slat and slot, applied to the blades of a small-scale HAWT. Using the NREL S809 Phase II rotor as a benchmark, simulations are conducted using the steady Moving Reference Frame (MRF) and the unsteady Sliding Mesh Motion (SMM) approaches. The k-ω SST turbulence model is validated against experimental data for multiple inflow conditions. The results demonstrate that flow separation is significantly mitigated by both control methods, particularly at higher wind speeds and at specific locations along the blade span. Quantitatively, the slat and slot configurations yield power output increases of 8.74 % and 21.07 %, respectively, compared to the baseline case. However, aeroacoustic analysis reveals that the slot introduces a sound pressure level (SPL) increase of up to 20 dB near 1 kHz, while providing a more balanced performance in terms of noise and energy gain. These findings highlight the slot configuration as a particularly effective solution for enhancing aerodynamic efficiency, while the slat provides a more balanced aeroacoustic profile. The choice between them involves a trade-off between maximum power gain and noise-control requirements in small-scale wind energy systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".