A Comparison of Second-Moment Closure Models in the Prediction of Vortex Shedding From a Square Cylinder Near a Wall
Bibliographic record
Abstract
A computational study is presented that examines the capability of various second-moment closure models in the prediction of two-dimensional, nonstationary flow around a square cylinder in proximity to a wall. The linear return-to-isotropy/isotropization-of-production model RTI+IP and the nonlinear SSG pressure-strain models were combined with the DH and modified LUM diffusion models in the computations. In terms of global activity, the drag is well-predicted in terms of both magnitude and variation with cylinder-to-wall gap width S/D. The Strouhal number St was reasonably well-predicted in terms of magnitude, but the predicted trend with decreasing S/D was incorrect for all model combinations. The lift was not well-predicted in terms of magnitude or trend. Prediction of the detailed flow structure in the vicinity of the cylinder and in the wake was favourable, though the magnitudes of some velocity and Reynolds-stress components were over-predicted. It was argued that the large differences between the results at the intermediate gap width may be due to the difference between the measured and predicted critical gap widths. On the basis of the predicted global and detailed activity, the modified LUM model combined with the nonlinear SSG model was suggested as being the most viable combination for future studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".