Evaluation of Modeling the Effects of Surface Roughness
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-2571.vid On well-designed aerodynamic vehicles, the skin friction contribution to the total vehicle drag can approach 50%. However, after more than a century of study, the prediction of skin friction remains a significant challenge. Further, the effects of surface roughness provide additional challenges. Wind tunnel models are generally machine-finished smooth and tested at Reynolds numbers that do not match flight conditions. The vehicle designer must alter the wind tunnel measured values to account for flight Reynolds numbers and surface roughness. Although several options for these corrections are available, the typical procedure involves the use of semi-empirical formulae for such adjustments. Most Computational Fluid Dynamics (CFD) codes have parameters to account for various degrees of surface roughness. However, more studies are required to evaluate these models over a range of Mach numbers, Reynolds numbers, and surface textures. This work presents the evaluation of the commercially available STAR-CCM+ (Siemens PLM) and CFD++ (Metacomp Technologies) codes against each other and experimental data for several different surface textures, Mach numbers, and Reynolds numbers. The CFD codes were executed for flows over a flat plate with several different sand grain surface roughness levels. To avoid addressing the modeling of flow transition, the experimental data were tripped, and the CFD solutions were run in fully turbulent mode. The results show reasonable agreement in the calculations of the skin friction loading. However, for detailed design (drag needing to be predicted within a few counts), the CFD still showed notable errors.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".