Numerical Study on the Turbulent Mixing Coefficient for Supercritical Fluids in Subchannels
Bibliographic record
Abstract
In this study, computational fluid dynamics simulations are carried out to predict the thermal-hydraulic behavior of supercritical fluids in the subchannel of supercritical water-cooled reactor (SCWR) fuel channels. The thermal-hydraulic behavior of supercritical water in triangular array and square array fuel rod bundles is studied numerically. The effects of various parameters including the pitch-to-diameter ratio and Reynolds number on the flow and the heat transfer characteristics are investigated. It is found that the turbulent mixing coefficient of supercritical water in subchannels is strongly dependent on the fluid bulk temperature and pitch-to-diameter ratio in the vicinity of the pseudo-critical point. To have a higher overall turbulent mixing coefficient, a pitch-to-diameter ratio less than 1.2 is recommended for the design of SCWR. The turbulent mixing coefficient correlation for the triangular array rod bundle is developed in this study based on the numerical results. However, the correlation for the mixing coefficient for the square array rod bundle cannot be expressed as a general correlation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".