A logarithmic formulation for low-Reynolds number turbulence models with adaptive wall-functions
Bibliographic record
Abstract
This paper presents a logarithmic formulation for low-Reynolds number turbulence models that guarantees the positivity of the turbulence variables. We also propose the use of a new consistent and model-specific wall function approach based on adaptive wall functions. The wall boundary conditions are evaluated from precomputed tables of the solutions of the 1D boundary layer problem at the current conditions and for a given turbulence model. A two-velocity scale wall function is proposed to improve predictions near stagnation, detachment and reattachment points. It is combined with a low Reynolds number turbulence model and the use of the logarithmic formulation to yield a robust and accurate solution procedure that is computationally efficient. Using both model-consistent wall function and a low Reynolds number model largely reduces the limitation of traditional wall functions related to the choice of the wall distance. Furthermore it yields solutions as accurate as when integration is performed down to the wall for a much reduced computational cost. The usual assumption of universality of the profile is investigated to determine the range of validity of the precomputed tables. The performances of the newly developed wall function in presence of pressure gradient is studied on a flat plate with pressure driven separation. Imposing a correctly computed normal derivative for turbulence kinetic energy largely improves results and the universality of the profile while leading to wall distance independent results. The present method is then validated on a complex flow by comparison to experimental results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".