Evaluating the Accuracy of RANS Wind Flow Modeling Over Forested Terrain. Part 2: Impact on Capacity Factor for Moderately Complex Topography
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Bibliographic record
Abstract
Abstract This study evaluates the uncertainty in speed-up factors predicted using the Reynolds-Averaged Navier–Stokes equations to model flow over moderately complex forested terrain and considers its effect on the uncertainty in wind energy calculations. All simulations are solved using the open-source software openfoam v.2.4.0 with a modified k–ε turbulence closure. The forest drag effect is calculated with two models: a displacement height model and a canopy model that estimates the pressure loss due to the forest through analogy with porous media. Two years of concurrent wind data from three meteorological masts at a potential wind farm site in Canada are used for validation purposes. In all, these experimental data are compared with the predictions of four wind flow models: (A) a terrain only model, (B) a displacement height model, (C) a uniform forest canopy model, and (D) a non-uniform forest canopy model. Overall, the canopy models provide better agreement with the mean statistical results than the displacement height model. In this case, the 2.76% uncertainty in the speed-up factor associated with the wind flow predictions of the non-uniform forest distribution model leads to an uncertainty in the energy calculation of just 5.94%.
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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 it