Large eddy simulation of wind turbine wakes using adaptative mesh refinement
Bibliographic record
Abstract
Abstract The development of turbulent vortical wakes released downstream of wind turbines is a key physical phenomenon as it presents many technological implications for windfarm design and exploitation. The numerical prediction of these wakes constitutes a challenging problem as they involve the shedding of fine vortical structures, their instabilities, and interactions with an ambient turbulent flow. The capture of these complex, three dimensional, unsteady flow phenomena calls for a Large Eddy Simulation (LES) approach. Yet, the computational cost of a scale resolved LES can be huge and the mesh generation process is not obvious when the zones of interest are not known a-priori. Adaptive mesh refinement (AMR) allows generating Eulerian elements only in the regions of interest of the flow, where an action takes place. The AMR strategy proposed here uses the MMG3D library coupled with the YALES2 unstructured finite volume solver. The method is successfully demonstrated on two test cases, the NTNU blind test case for which experimental data exist and the reference NREL 5MW under dynamic yaw conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".