Large Eddy Simulation of the meandering of a wind turbine wake with stochastically generated boundary conditions
Bibliographic record
Abstract
Wind turbine wakes are known to be affected by the large atmospheric turbulent scales, which can cause trajectory variations within a wide frequency band. This phenomenon, called meandering, is suspected to be a cause of premature wear on turbines located inside wind farms. This work proposes a method to generate and apply synthetic turbulent velocity series as boundary conditions in a Large Eddy Simulation of an actuator disk in a flow with realistic turbulence characteristics. The stochastic generation method relies on the inverse Short-Time Fourier Transform (STFT) of a random vector field correlated in Fourier space according to the covariance tensor calculated from the homogeneous isotropic spectral tensor. In contrast with a single Fourier transform, the STFT allows the generation of arbitrarily large velocity fields. The generated series are used as boundary values on the inlet as well as on the lateral boundaries of the domain. This allows for sustained turbulent forcing on the whole length of the domain which is especially useful for a small computational domain relative to the size of the dominant turbulent scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".