<i>k</i> − <i>ε</i> simulations of the neutral atmospheric boundary layer: analysis and correction of discretization errors on practical grids
Bibliographic record
Abstract
SUMMARY The Reynolds‐averaged Navier–Stokes equations/ k −ε approach is the popular and practical choice for carrying out simulations involving the atmospheric boundary layer. However, despite its widespread use, implementation of this approach is not without its challenges—even when considering the simplest case of horizontally homogeneous conditions. Most notably, the distributions of turbulent kinetic energy and its dissipation rate have proved difficult to maintain near solid boundaries, particularly in wind engineering applications where the near‐wall grid is relatively coarse. In this work, the origin of these errors is investigated, and it is shown that by applying appropriate discretization schemes in conjunction with the Richards and Hoxey boundary conditions, truly invariant profiles of all flow properties can be obtained on such grids. Furthermore, with this finding, a wall treatment for coarse grids is proposed that could be implemented for non‐homogeneous conditions. All simulations are carried out using OpenFOAM‐1.6.x. Copyright © 2011 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".