Characterizing Impacts of Atmospheric Turbulence on Wind Farms Through Large Eddy Simulation (LES)
Bibliographic record
Abstract
Abstract Wind farms extract energy from the lowest part of the atmospheric boundary layer (ABL). Thus, characterizing the impacts of atmospheric turbulence — precisely, which aspect of it enhances or hinders the capacity factor of wind farms — is currently the least understood and the most demanding topic of wind energy research. This article demonstrates a Large Eddy Simulation (LES) of atmospheric turbulence around an array of 41 full-scale wind turbines with a rotor diameter of 126 m. A wall-adaptive subgrid-scale (SGS) model for atmospheric turbulence around wind farms has been examined. For a moist-free atmosphere in the afternoon, the spectra of kinetic energy are compared with Kolmogorov’s energy spectrum. The power production is discussed with respect to staggered arrangements of turbines. Results show that the LES model has the potential to account for atmospheric turbulence for optimizing tower placements in wind farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".