Sensitivity Analysis of Wind Turbine Broadband Noise Estimation to Semi-Empirical Models Parameters
Bibliographic record
Abstract
The continuous increase of energy demand and the rising concerns on climate change, are pushing the European Union decarbonization strategies and transition toward renewable based energy systems, with wind energy playing a leading role. It is therefore necessary to have a better understanding of how wind turbines (WTs) impact on their surroundings, including their noise emissions. Among the different methods to compute noise emissions of WTs, semi-empirical models are a valid choice to have a-priori estimations of noise spectra and sound pressure levels. These models are based on correlation laws for different physical mechanisms that contribute to noise generation. Popular models for dominant noise sources include the Amiet approach for inflow turbulence noise and the Lowson model for turbulent boundary layer-trailing edge noise. Determining the parameters involved in these models can be challenging, potentially leading to significant errors in noise prediction. In this study, we conducted a novel sensitivity analysis of the models by varying different parameters such as turbulent intensity and dissipation, boundary layer thickness, and temperature. The selected test case is the reference multi-MW horizontal axis wind turbine Neg-Micon 80. The results of the multilevel-multivariate analysis, involving 63,360 combinations of the input parameters, clearly demonstrate a significant dependence of these models on atmospheric turbulence parameters. Furthermore, these models exhibit an higher sensitivity to input parameters at lower frequencies of the noise spectrum, which are generally associated with higher values of sound pressure level.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".