Updated SSG Reynolds stress model in OpenFOAM: The modified code files
Bibliographic record
Abstract
The SSG Reynolds stress model is apparently suitable to obtain the log-layer solution without wall damping functions of the pressure-strain tensor (Wilcox 2006). However, it works only near the no slip solid boundaries with steep velocity gradients. The available SSG model in OpenFOAM does not include the non-linear free surface damping functions and dissipation rate boundary condition, which are needed to simulate turbulence-driven secondary currents. Therefore, these functions were added in the modified SSG model or SSGMod based on the literature and validated for a supercritical uniform case with rectangular cross-section before studying the effect of numerous cross-sectional geometries (archway, horseshoe, and circular) on narrow channel flows comparable to sediment bypass tunnels. The modifications were tested in -Dev version. They should be working in OpenFOAM-10 also. For more details follow the the following manuscript: Kadia, S., Lia, L., Albayrak, I., and Pummer, E. (2024). “The effect of cross-sectional geometry on the high-speed narrow open channel flows: An updated Reynolds stress model study.” Computers & Fluids, 2024, 271, 106184, https://doi.org/10.1016/j.compfluid.2024.106184. Further details and explanations about the code modifications in OpenFOAM are provided in the open-accessed Ph. D. thesis of Dr. Subhojit Kadia titled 'Secondary Currents, Turbulence Characteristics, and Bed Shear Stress Variations in Supercritical Narrow Channel Flows' available at https://hdl.handle.net/11250/3142718 published by NTNU, Norway. References:1. Wilcox, D. C. (2006). Turbulence Modelling for CFD. DCW Industries, Inc., La Canada, California.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.021 |
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".