Effect of Wind Turbine Wakes on the Performance of a Real Case WRF-LES Simulation
Bibliographic record
Abstract
The main objective of this work is to estimate how much of the discrepancy between measured and modeled flow parameters can be attributed to wake effects. The real case simulations were performed for a period of 15 days with the Weather Research and Forecasting (WRF) model and nested down to a Large-Eddy Simulation (LES) scale of ∼ 100 m. Beyond the coastal escarpment, the site is flat and homogeneous and the study focuses on a meteorological mast and a northern turbine subjected to the wake of a southern turbine. The observational data set collected during the Prince Edward Island Wind Energy Experiment (PEIWEE) includes a sonic anemometer at 60 m mounted onto the mast, and measurements from the two turbines. Wake versus free stream conditions are distinguished based on measured wind direction while assuming constant expansion for the wake of the southern turbine. During the period considered the mast and northern turbine were under the southern turbine wake ∼ 16% and ∼ 11% of the time, respectively. Under these conditions, the model overestimates the wind speed and underestimates the turbulence intensity at the mast but not at the northern turbine, where the effect of wakes on the model error is unclear and other model limitations are likely more important. The wind direction difference between the southern and northern turbines is slightly underestimated by the model regardless of whether free stream or wake conditions are observed, indicating that it may be due to factors unrelated to the wake development such as surface forcings. Finally, coupling an inexpensive wake model to the high-fidelity simulation as a post-processing tool drives the simulated wind speeds at the mast significantly closer to the observed values, but the opposite is true at the coastal turbine which is in the far wake. This indicates that the application of a post-processing wake correction should be performed with caution and may increase the wind speed errors when other important sources of uncertainty in the model and data are not considered.
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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) | 0.000 | 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".