Numerical Simulation of Fluid Flow and Heat Transfer in the Advanced CANDU® Reactor Endshield Using ANSYS-CFX and Porous Media Approach
Bibliographic record
Abstract
A distinguishing design feature of CANDU® nuclear reactors is the use of horizontal fuel channels housed in a horizontal vessel called the calandria vessel, which is made of stainless steel. The calandria vessel has two endshields (each consists of two tubesheets called calandria tubesheet and fuelling tubesheet), which provide supports for the fuel channels among other purposes. The two tubesheets of each endshield are joined by a series of stainless steels tubes called lattice tubes. The space within each endshield between the tubesheets and the outside of lattice tubes is filled with cooling water and carbon steel balls. Thus, the endshields provide shielding to reduce radiation reaching the fuelling machine vaults. Nuclear heat is generated within the endshields. Endshields also receive heat from the primary heat transport system by conduction through the fuel channel bearings, by conduction and radiation through the annular insulating gap for the lattice tubes, and by convection from feeder cabinet. Three finite volume models have been developed to simulate different aspects of the coupling between the fluid flow and thermal energy. In model 1, the whole space inside the endshield is modeled as double porous medium to represent the lattice tubes and the steel balls regions respectively. In model 2, the lattice tubes are modeled in details and a single porosity is used to model the space occupied by the steel balls only. This detailed model also predicts the temperature on the surface of the lattice tubes. The work presented in the paper shows that the results from both models are in good agreement. It also shows that the current design of the ACR® endshield cooling satisfies the design requirements with respect to the heat transfer to the shield cooling system during normal operation. Model 3 is used to predict temperature and flow behaviour under transient load service conditions.
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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 | 0.000 | 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".