An Exergy Based Approach to Noise Prevention in Wind Turbines: Concept and Preliminary Assessment
Bibliographic record
Abstract
Unsustainable human activities and practices are polluting water supplies and emitting to the atmosphere greenhouse gases as well as compounds that erode the earth\'s protective ozone layer. The potential impact on human health and economic costs associated with global warming have motivated scientists and engineers to seek sustainable technologies. One such technology is the wind turbine, which harnesses energy from the wind. However, a significant hindrance preventing the widespread use of wind turbines is the noise they produce. This study examines flow over an object and the consequent noise generation produced by this flow-structure interaction. Flow over a cylinder has been chosen as the benchmark. The aim of this study is to correlate three main characteristic parameters of the system, namely, the generated sound pressure level, the exergy destroyed, and the normal flow velocity). The main motivation for this work is to relate the exergy destruction to the noise generated in the flow to improve understanding and to provide a correlation can be utilized to reduce or minimize the noise of wind turbines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".