Turbulence Distortion Effect on Leading Edge Noise from Wind Turbine Blades
Bibliographic record
Abstract
Turbulence distortion is a potential source for production of flow induced noise and rely on the turbulence properties of aerodynamic flow field. Particularly in wind energy applications, the broadband leading-edge noise from rotating blades becomes significant when the turbulence intensity (TI) and integral length scale factors vary with mean flow velocity and rms velocity fluctuations. The inflow noise generated at leading edge of blade also varies significantly with turbulence characteristics of velocity spectrum that describes the turbulent kinetic energy envelope. Although previous studies on flow induced noise that includes turbulence distortion have accounted for symmetric and cambered airfoils, it has not been investigated for rotating blades as found in wind turbines. In this work, leading edge noise predicted by modified rapid turbulence distortion (RDT) model proposed by Faria et al (2022) is applied and compared with noise levels predicted by Moriarty model for a 2MW model wind turbine blade. For low frequencies in sound spectra, 50Hz < f < 200Hz the sound power predicted by both models agreed within 2-5% with experiment data of a SWT 2.3MW wind turbine blade which has tip speed of ~83 m/s. However, for mid-band frequencies 250Hz < f < 1kHz, the modified rapid distortion model scales the turbulence properties in velocity spectrum more accurately leading to better estimation of noise levels. The modified RDT model has also been tested for different turbulence intensities and found that as turbulence intensity increased, the standard error for noise predicted by modified RDT model reduced by 1% in low frequency region of sound spectrum. This trend is not observed in case of Moriarty model and on contrary noise levels were found to increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".