Wall-model response to departure from equilibrium in turbulent flows over rough surfaces
Bibliographic record
Abstract
Most turbulent boundary-layer flows in engineering and natural sciences are out of equilibrium. While direct numerical simulation and wall-resolved large-eddy simulation can accurately account for turbulence response under such conditions, lower-cost approaches like wall-modelled large-eddy simulation often assume equilibrium and struggle to reproduce non-equilibrium effects. The recent ‘Lagrangian relaxation-towards-equilibrium’ (LaRTE) wall model (Fowler et al. 2022 J. Fluid Mech. vol. 934, 137), formulated for smooth walls, applies equilibrium modelling only to the slow dynamics that are more likely to conform to the assumed flow state. In this work, we extend the LaRTE model to account for wall roughness (LaRTE-RW) and apply the new model to turbulent flow over heterogeneous roughness and in accelerating and decelerating flows over rough surfaces. We compare predictions from the new LaRTE-RW model with those from the standard log-law equilibrium wall model (EQWM) and with experimental data to elucidate the turbulence response mechanisms to non-equilibrium conditions. The extended model transitions seamlessly across smooth-wall and fully rough regimes and improves prediction of the skin-friction coefficient, especially in recovering trends at roughness transitions and in early stages of pressure-gradient-driven flow acceleration or deceleration. Results show that LaRTE-RW introduces response delays that are beneficial when EQWMs react too quickly to disturbances, but it is less effective in flows requiring rapid response, such as boundary layers subjected to accelerating–decelerating–accelerating free stream conditions. These findings emphasize the need for further model refinements that incorporate fast turbulent dynamics not currently captured by LaRTE-RW.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".