Microscale Computational Fluid Dynamics Simulation for Wind Mapping Over Complex Topographic Terrains
Bibliographic record
Abstract
Wind mapping is of utmost importance in various wind energy and wind engineering applications. The available wind atlases usually provide wind data with low spatial resolution relative to the wind turbine height and usually neglect the effect of topographic features with relatively large or sudden changes in elevation. Two benchmark cases are studied for computational fluid dynamics (CFD) model evaluation on smooth two-dimensional (2D) and three-dimensional (3D) hills. Thereafter, a procedure is introduced to build CFD model of a complex terrain with high terrain roughness heights (dense urban area with skyscrapers) starting from existing topography maps in order to properly extend the wind atlas data over complex terrains. CFD simulations are carried out on a 1:3000 scale model of complex topographic area using Reynolds averaged Navier–Stokes (RANS) equations along with shear stress transport (SST) k-ω turbulence model and the results are compared with the wind tunnel measurements on the same model. The study shows that CFD simulations can be successfully used in qualifying and quantifying the flow over complex topography consisting of a wide range of roughness heights, enabling to map the flow structure with very high spatial resolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".