Evaluating the Accuracy of RANS Wind Flow Modeling Over Forested Terrain. Part 2: Impact on Capacity Factor for Moderately Complex Topography
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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".