k ‐ ϵ Model for the Atmospheric Boundary Layer Under Various Thermal Stratifications
Bibliographic record
Abstract
This paper presents a numerical method for predicting the atmospheric boundary layer under stable, neutral, or unstable thermal stratifications. The flow field is described by the Reynolds’ averaged Navier-Stokes equations complemented by the k‐ϵ turbulence model. Density variations are introduced into the momentum equation using the Boussinesq approximation, and appropriate buoyancy terms are included in the k and ϵ equations. An original expression for the closure coefficient related to the buoyancy production term is proposed in order to improve the accuracy of the simulations. The resulting mathematical model has been implemented in FLUENT. The results presented in this paper include comparisons with respect to the Monin-Obukhov similarity theory, measurements, and earlier numerical solutions based on k‐ϵ turbulence models available in the literature. It is shown that the proposed version of the k‐ϵ model significantly improves the accuracy of the simulations for the stable atmospheric boundary layer. In neutral and unstable thermal stratifications, it is shown that the version of the k‐ϵ models available in the literature also produce accurate simulations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".