Impact of Wind Veer and the Coriolis Force for an Idealized Farm to Farm Interaction Case
Bibliographic record
Abstract
The impact of the Coriolis force on the long distance wake behind wind farms is investigated using Large Eddy Simulations (LES) combined with a Forced Boundary Layer (FBL) technique. When using the FBL technique any mean wind shear and turbulent fluctuations can be added with body forces. The wind shear can also include the mean wind veer due to the Coriolis force. The variation of the Coriolis force due to local deviations from the mean profile, e.g., from wakes, is not taken into account in the FBL. This can be corrected for with an extra source term in the equations, hereon defined as the Coriolis correction. For a row of 4 turbines it is shown that the inclusion of the wind veer turns the wake to the right, while including the Coriolis correction turns it to the left. When including both wind veer and Coriolis correction the impact of wind veer dominates. For an idealized farm to farm interaction case, two farms of 4 ∗ 4 turbines with 6 km in between, it can be seen that when including wind veer and the Coriolis correction a approximately 3% increase in the relative production for a full wake direction can be seen and only a slightly smaller increase can be seen when including only wind veer. The results indicate that FBL can be used for studies of long distance wakes without including a Coriolis correction but efforts need to be taken to use a wind shear with a correct mean wind veer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".