Prediction of Turbulent Flow Over a Flat Plate With a Step Change From a Smooth to a Rough Surface Using a Near-Wall RANS Model
Bibliographic record
Abstract
Abstract The present paper reports a numerical study of fully developed turbulent flow over a flat plate with a step change from a smooth to a rough surface. The Reynolds number based on momentum thickness for the smooth flow was Reθ=5950. The focus of the study was to investigate the capability of the Reynolds-averaged Navier–Stokes (RANS) equations to predict the internal boundary layer (IBL) created by the flow configuration. The numerical solution used a two-layer k−ε model to implement the effects of surface roughness on the turbulence and mean flow fields via the use of a hydrodynamic roughness length y0. The prediction for the mean velocity field revealed a development zone immediately downstream of the step in which the mean velocity profile included a lower region affected by the surface roughness below and an upper region with the characteristics of the smooth-wall boundary layer above. In this zone, both the turbulence kinetic energy and Reynolds shear stress profiles were characterized by a significant reduction in magnitude in the outer region of the flow that is unaffected by the rough surface. The turbulence kinetic energy profile was used to estimate the thickness of the IBL, and the resulting growth rate closely matched the experimental results. As such, the IBL is a promising test case for assessing the ability of RANS models to predict the discrete roughness configurations often encountered in industrial and environmental applications.
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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) | 0.001 | 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 | 0.000 |
| 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".