Wall-Modeled and Hybrid Large-Eddy Simulations of the Flow over Roughness Strips
Bibliographic record
Abstract
The flow over alternating roughness strips oriented normally to the mean stream is studied using wall-modeled large-eddy simulations (WMLES) and improved delayed detached-eddy simulations (IDDES) (a hybrid method solving the Reynolds-averaged Navier–Stokes (RANS) equations near the wall and switching to large-eddy simulations (LES) in the core of the flow). The calculations are performed in an open-channel configuration. Various approaches are used to account for roughness by either modifying the wall boundary condition for WMLES or the model itself for IDDES or by adding a drag forcing term to the momentum equations. By comparing the numerical results with the experimental data, both methods with both roughness modifications are shown to reproduce the non-equilibrium effects, but noticeable differences are observed. The WMLES, although affected by the underlying equilibrium assumption, predicts the return to equilibrium of the skin friction in good agreement with the experiments. The velocity predicted by the IDDES does not have memory of the upstream conditions and recovers to the equilibrium conditions faster. Memory of the upstream conditions appears to be a critical factor for the accurate computational modeling of this flow.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".