Subgrid-scale energy transfer and associated coherent structures in turbulent flow over a forest-like canopy
Bibliographic record
Abstract
Large eddy simulation allows to incorporate the important driving physics of turbulent flow through forest- or vegetation-like canopies. In this paper we investigate the effects of vortex stretching and coherent structures on the subgrid-scale (SGS) turbulence kinetic energy (TKE). We present three simulations (SGS-d/s/w) of turbulence-canopy interactions. SGS-d assumes a local, dynamic balance of SGS production with SGS dissipation. SGS-s averages the SGS contributions of coherent structures over Lagrangian pathlines. SGS-w assumes that an average cascade of TKE from large- to small-scales occurs through the process of vortex stretching. We compare the consequences of considering a forest of the same morphology as immersed solids or an immersed canopy. Our results show clear differences in the characteristics of flow and turbulence, while both the cases exhibit canopy mixing layers. The results also show that the consideration of vortex stretching resolves about $18$% more TKE with respect to classical Deardorff's TKE model. These observations indicate that the aerodynamic response of the forest canopy is linked to the morphology of the forest cover. Sweep- and ejection-events of the spatially intermittent coherent structures in forests as well as their role in transporting momentum, energy, and scalars are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".