Sensitivity Studies of Shear Stress Transport Turbulence Model Parameters on the Prediction of Seven-Rod Bundle Benchmark Experiments
Bibliographic record
Abstract
This paper reports the findings of a sensitivity study of parameters in the shear stress transport (SST) turbulence model in a commercial computational fluid dynamics (CFD) code to predict an experiment from the Generation IV International Forum Supercritical-Water-Cooled Reactor (GIF SCWR) 2013–2014 seven-rod subchannel benchmark exercise. This study was motivated by the result of the benchmark exercise that all the CFD codes gave similar results to a subchannel code, which does not possess any sophisticated turbulence modeling. Initial findings were that the CFD codes generally underpredicted the wall temperatures on the B2 case in the region where the flow was supercritical. Therefore, it was decided to examine the effect of various turbulence model parameters to determine if a CFD code using the SST turbulence model could do a better job overall in predicting the wall temperatures of the benchmark experiments. A sensitivity study of seven parameters was done, and changes to two parameters were found to make an improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".