CFD Simulations of a Loss of Heat Sink Experiment in the McMaster Nuclear Reactor
Bibliographic record
Abstract
The McMaster nuclear reactor (MNR) is an important research facility that not only provides researchers with neutrons for fundamental science, but also supplies the medical industry with isotopes used for cancer treatment. To ensure the safety and performance of the MNR, modeling of the thermal hydraulics during nominal and accidental conditions is required. For such a task, system codes are customarily used. While system codes can assess the safety aspects of complex thermal–hydraulic systems, the question arises whether such systems can be modeled appropriately in a three-dimensional manner, such as computation fluid dynamics (CFD), while capturing the thermal mixing of the coolant. Modeling the thermal hydraulics of nuclear research reactors using CFD is relatively new. Therefore, validating such methods against experimental data is of utmost importance. The validation of CFD is the main focus of this work. More specifically, the influence of the loss of heat sink on the pool temperature is assessed using the CFD code Ansys CFX. The validation basis was provided by an experiment performed at the MNR in March 2023. In this experiment, the secondary heat removal system was intentionally shut down, and the pool temperature was measured in a few positions. The results obtained by modeling the loss of heat sink in the MNR using Ansys CFX agree well with the experiment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".