Examination of Separate-Effect and Integral Phenomena Within a Grid Spacer with Mixing Vanes: Results for the MATiS-H OECD/NEA Benchmark Exercise
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Bibliographic record
Abstract
Grid spacers within nuclear fuel assemblies play a critical role in fuel performance and contribute to safety margins by enhancing the margin to the critical heat flux. The Organisation for Economic Co-operation and Development/Nuclear Energy Agency has organized a computational benchmark wherein the prediction of flows and turbulence downstream of a mixing-type grid spacer are examined. Studies performed by McMaster University using STAR-CCM+ for the final submission to this MATiS-H blind benchmark exercise related to inter-subchannel mixing and turbulence are presented in this paper. The rationale behind the choice of the computational scheme along with comparisons of the submitted results to the experiments is reported. The goal at the outset of the study was to obtain a reasonably accurate solution with a minimum number of nodes and appropriate turbulence models such that the results would be relevant for engineering applications that include property variations and heat transfer. As such, advanced modeling methods such as large eddy simulation and unsteady Reynolds-averaged Navier-Stokes (URANS) were not included within the scope of the models tested. However, URANS was used to study some specific separate-effect flow features within the grid spacer, and these tests were compared to their steady counterparts.A comprehensive separate-effect study was performed first in order to finalize the computational scheme for the submission. Several partial geometries were studied for steady and unsteady behavior as well as for mesh sensitivity, turbulence, and wall modeling effects. A series of successively more complex simulations, sometimes involving unsteady modeling, was performed up to and including a study of similar 5 × 5 rod bundle geometry reported in the literature. The final submission results are presented in the paper and are compared with the benchmark data that have recently been released.
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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 it