Validation of a CFD Analysis Model for Predicting CANDU-6 Moderator Temperature Against SPEL Experiments
Bibliographic record
Abstract
A validation of a 3D CFD model for predicting local subcooling of the moderator in the vicinity of calandria tubes in a CANDU-6 reactor is performed. The small scale moderator experiments performed at Sheridan Park Experimental Laboratory (SPEL) in Ontario, Canada [1] is used for the validation. Also a comparison is made between previous CFD analyses based on 2DMOTH and PHOENICS, and the current analysis for the same SPEL experiment. For the current model, a set of grid structures for the same geometry as the experimental test section is generated and the momentum, heat and continuity equations are solved by CFX-4.3, a CFD code developed by AEA technology. The matrix of calandria tubes is simplified by the porous media approach. The standard k-ε turbulence model associated with logarithmic wall treatment and SIMPLEC algorithm on the body fitted grid are used. Buoyancy effects are accounted for by the Boussinesq approximation. For the test conditions simulated in this study, the flow pattern identified is the buoyancy-dominated flow, which is generated by the interaction between the dominant buoyancy force by heating and inertial momentum forces by the inlet jets. As a result, the current CFD moderator analysis model predicts the moderator temperature reasonably, and the maximum error against the experimental data is kept at less than 2.0°C over the whole domain. The simulated velocity field matches with the visualization of SPEL experiments quite well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".