Evaluating the Accuracy of RANS Wind Flow Modeling Over Forested Terrain—Part 1: Canopy Model Validation
Bibliographic record
Abstract
This study uses the Reynolds-averaged Navier–Stokes (RANS) equations to validate a canopy model by computing a fully developed wind flow within and above a horizontally homogeneous dense forest as in the work of Dalpé and Masson. The model is paired with a modified k–ε turbulence closure. A set of boundary conditions (BCs) that rely on the law of the wall for a sustainable atmospheric boundary layer (ABL) is used. All simulations are conducted in the open source software OpenFOAM v.2.4.0 (OpenCFD Ltd (ESI Group)). Two practical aspects are considered in the validation process. First, an accurate leaf area index (LAI) integration to exactly fit the wind shear is evaluated. Since the physical foliage parameters may not be accessible for all type of forests, a generic leaf area density α distribution is tested. The results of this test show that a generic distribution is sufficient for preliminary analyses to improve accuracy of wind flow predictions over forested terrain. Second, the approach of Dalpé and Masson is limited to cyclic BCs which are not practical for real sites. For cases without cyclic BCs, imposing a proper slope on the inlet velocity profile is of high importance. This condition can be achieved through adjustment of the roughness length at the inlet.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".