Large-Eddy Simulation of Utility-Scale Wind Farm Sited over Complex Terrain
Bibliographic record
Abstract
The realm of wind energy is a rapidly expanding renewable energy technology. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind farm performance. In this article, we report the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind turbine array over various rough topographies. First, we evaluated our large-eddy simulation method through a mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyzed the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results show that the ground roughness element’s flow resistance boosts the power production of large wind farms by almost 68% over an aerodynamically rough surface compared with flat terrain. The dispersive stress analysis revealed that the primary degree of spatial heterogeneity in wind farms is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress to the overall drag may be about 45%. Our observation reveals that the power performance of the wind farm in complex terrain surpasses the drag effect. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.
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 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".