Numerical study of fully developed turbulent flow within and above a dense forest
Bibliographic record
Abstract
Abstract Fully developed wind flow predictions within and above a dense forest were obtained using a computational fluid dynamics model. The model used a porous media analogy and a modified k‐ϵ turbulence model where source terms were added to the momentum and turbulence equations. The mathematical model was solved using the software FLUENT 6.2. Experimental measurements from a black spruce forest, a jack pine forest and an aspen forest were used to validate the model. Two different ground boundary conditions were proposed: a full‐slip boundary condition and a boundary condition that takes into account the forest ground roughness. Using these two boundary conditions, the accuracy of the proposed method was tested for forests with low foliage density. The innovative top boundary condition of Dalpé and Masson was validated with experimental measurements from Amiro. A sensitivity analysis was also performed on two important parameters: the drag coefficient and the leaf area density distribution. Results indicate that the proposed method simulated well the characteristics of wind flow within and above a forest. Results also indicate that, to obtain accurate results above the forest, it is necessary to take into account the forest ground roughness for forests with CDLAI < 0.6. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".