Wind Turbine Sound Propagation Using A Finite-Difference Time-Domain Method
Bibliographic record
Abstract
Energy usage is on the rise in both Canada and the United States. Because of this, there is a growing demand and strain on the current infrastructure. More importantly though, there is a strong demand for the use of renewable energy sources to meet this demand. One of the most popular renewable energy sources at this time is the wind turbine. In Ontario, there are plans to implement a significant number of them throughout the province. There are concerns though from residents in the vicinity of them that they cause too much noise, as well as health issues. However, some argue that these complaints stem from incorrectly calculated setback distances due to the lack of use of a detailed sound propagation model. In this study, a sound propagation model was developed using a Finite-Difference Time-Domain method, for a three dimensional computational domain, and simulated using data for a Siemens SWT-2.3-101 wind turbine. The simulations produced data of the sound propagation characteristics of each emitted wave, for each tested case. The model was developed as a starting point and building block for the eventual use in simulations of large domains and complex flow phenomena.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".